UKRAINE
BUILDING CLIMATE RESILIENCE
IN AGRICULTURE AND FORESTRY




           December 2021
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Table of Contents
ABBREVIATIONS AND ACRONYMS�������������������������������������������������������������������������������������������������������XII

ACKNOWLEDGMENTS ��������������������������������������������������������������������������������������������������������������������������������XIII

EXECUTIVE SUMMARY������������������������������������������������������������������������������������������������������������������������������ XIV


CHAPTER 1: INTRODUCTION�������������������������������������������������������������������������������������������������������������������������1
                      1.1 The Analytical Framework��������������������������������������������������������������������������� 2

CHAPTER 2: HOW WILL UKRAINE’S CLIMATE CHANGE IN THIS CENTURY? �������������������� 7
                      2.1 Summary ���������������������������������������������������������������������������������������������������� 7
                           ecent Climatic Changes in Ukraine���������������������������������������������������������� 8
                      2.2 R
                          Projections at the Oblast Level����������������������������������������������������������������� 15
                      2.4 
                          Projections at the City Level���������������������������������������������������������������������� 15
                      2.5 
                          Other Climate and Vulnerability Indicators������������������������������������������������ 20
                      2.6 

           MPACT OF CLIMATE CHANGE ON AGRICULTURE����������������������������������������������24
CHAPTER 3: I
                      3.1 Summary of Key Findings������������������������������������������������������������������������� 24
                      3.2 Yield Projections���������������������������������������������������������������������������������������� 25
                      3.3 Impact of Water Availability on Crop Yields ���������������������������������������������� 32
                          Agricultural Production Projections����������������������������������������������������������� 32
                      3.4 
                          Agricultural Value Projections�������������������������������������������������������������������� 35
                      3.5 
                          Effects of Changes in the Growing Season���������������������������������������������� 35
                      3.6 
                           imitations of the Analysis of Climate Change Impact on Agriculture������� 35
                      3.7 L

           THE DISTRIBUTIONAL EFFECT OF CLIMATE CHANGE
CHAPTER 4: 
           ON AGRICULTURE��������������������������������������������������������������������������������������������������������������� 40
                      4.1 Summary of Key Findings������������������������������������������������������������������������� 40
                      4.2 The Share of Agriculture in the National and Oblast GDP����������������������� 41
                          Impact of Climate Change on Agriculture
                      4.3 
                          and Household Income and Expenditure�������������������������������������������������� 42
                          Impact of Climate Change on Agriculture and Poverty����������������������������� 46
                      4.4 




                                      Ukraine. Building Climate Resilience in Agriculture and Forestry                                           iii
CHAPTER 5: IMPACT OF CLIMATE CHANGE ON FORESTS�������������������������������������������������������� 50
                        5.1 Summary��������������������������������������������������������������������������������������������������� 50
                            Climate Vulnerability Indices for Forests��������������������������������������������������� 51
                        5.2 
                             ffect of Climate Change on Key Forest Species������������������������������������� 55
                        5.3 E
                        5.4 Impact on Forest Fires ���������������������������������������������������������������������������� 58
                        5.5 Analysis Limitations ���������������������������������������������������������������������������������� 60

           THE SPATIAL DISTRIBUTION OF AGRICULTURAL IMPACTS:
CHAPTER 6: 
           OBLAST-LEVEL ANALYSIS����������������������������������������������������������������������������������������������� 61
                        6.1. Spatial Distribution of Potential Benefits for Agriculture �������������������������� 61
                             patial Distribution of Potential Risks
                        6.2 S
                            from Climate Change for Agriculture��������������������������������������������������������� 65

            CTIONS TO BUILD CLIMATE RESILIENCE
CHAPTER 7: A
           IN AGRICULTURE AND FORESTRY������������������������������������������������������������������������������� 71
                            Strengthen Institutions, Policy and Planning��������������������������������������������� 72
                        7.1 
                             romote Transition to Climate-Smart Agriculture and Forestry ���������������� 74
                        7.3 P

REFERENCES������������������������������������������������������������������������������������������������������������������������������������������������������76

ANNEX 1. METHODOLOGY�������������������������������������������������������������������������������������������������������������������������� 85

ANNEX 2. PROJECTED SEASONAL CHANGES���������������������������������������������������������������������������������112

          ATA FOR AGRICULTURAL ASSESSMENT
ANNEX 3. D
         & DISTRIBUTIONAL ANALYSIS�����������������������������������������������������������������������������������������116

ANNEX 4. DATA FOR FORESTRY ASSESSMENT��������������������������������������������������������������������������� 133

ANNEX 5. BENEFITS OF ADAPTION MEASURES��������������������������������������������������������������������������� 143




iv                  Ukraine. Building Climate Resilience in Agriculture and Forestry
FIGURES
Figure 1: Map of Ukraine..................................................................................................... 1
Figure 2: Methodology......................................................................................................... 3
Figure 3: Climatic Zones in Ukraine, 1980-2016................................................................. 9
Figure 4: Projected Annual Mean Temperature Increases.................................................. 10
Figure 5: Annual Temperature Change............................................................................... 12
Figure 6: Annual Temperature Change............................................................................... 12
Figure 7: Annual Precipitation Change (map)..................................................................... 13
           onthly Precipitation Change in RCP 4.5............................................................ 14
Figure 8: M
           onthly Precipitation Change in RCP 8.5 – End of Century
Figure 9: M
          Compared to Baseline ........................................................................................ 14
Figure 10: Projected Changes in Temperatures and Precipitation by Oblast....................... 16
           Multi-Year Mean Monthly Temperature and Temperature Change
Figure 11: 
           for Different Climatic Periods and the Baseline................................................... 18
           
Figure 12: Multi-Year Mean Monthly Precipitation Sums (lines)
           and Projected Changes (histograms).................................................................. 19
           Temperature in the Hottest and Coldest Months
Figure 13: 
           for the Baseline period (1991-2010) and at the End of the 21st
           Century Under RCP 4.5 and RCP 8.5................................................................. 21
Figure 14: Changes in Frost Nights...................................................................................... 22
Figure 15: Changes in Tropical Nights................................................................................. 22
Figure 16: Seasonal Precipitation in RCP 4.5 and RCP 8.5................................................. 27
Figure 17: C
            rop Yields [tons/ha] in 2010 and Changes in Yields [%]
           in 2030 and 2050 for Selected Crops ................................................................. 29
Figure 18: Impact of Optimal Water Availability in 2030....................................................... 33
Figure 19: Difference Between RCP 4.5 and RCP 8.5 for 2030........................................... 34
Figure 20: Changes in Total Agricultural Production by Oblast, RCP 8.5............................ 36
Figure 21: Changes in Total Value by Oblast, RCP 8.5........................................................ 37
Figure 22: Agriculture as a Share of GDP ($US) in 2010, by Oblast................................... 41




                                 Ukraine. Building Climate Resilience in Agriculture and Forestry                                  v
           Changes in Income by Oblast for 2030 Due to Price Increases......................... 43
Figure 23: 
           Increase in Income from the Change in Value of Agricultural
Figure 24: 
           Output due to Climate Change, Mean Projection (2030).................................... 44
           Changes in Income Under the Three Projections
Figure 25: 
           by Oblast for 2030
           (both Income and Price Effects)......................................................................... 45
           Changes in Expenditure by Oblast for 2030
Figure 26: 
           (Effect of Price Increases only) in the Mean Scenario........................................ 46
            eadcount Poverty, Poverty Gap and Severity of Poverty:
Figure 27: H
           Values for the Baseline Period [%] and Changes in 2030
           Relative to the Baseline [%], Low Projection...................................................... 48
           Gini Сoefficient Сhanges in 2030 Relative to the Baseline [%].......................... 49
Figure 28: 
Figure 29: Forestland Across Ukraine’s Oblast.................................................................... 51
            elative Changes of Vorobjov’s Heat Availability
Figure 30: R
           Index to Climate, 1991-2010.............................................................................. 53
           Relative Changes of Vorobjov’s Moisture
Figure 31: 
           Availability Index to Climate, 1991-2010............................................................. 54
           Impact of Climate Change on Areas with Growing Potential
Figure 32: 
           for English Oak and European Beech, 2100...................................................... 56
           Impact of Climate Change on Areas with Growing Potential
Figure 33: 
           for Selected Forest Species, in 2050.................................................................. 57
Figure 34: Total Burned Areas and Numbers of Forest Fires in Ukraine............................. 59
Figure 35: Relative Changes in Wheat Productivity, Through 2030.................................... 61
            hange in Value of Agricultural Output in 2030 Relative
Figure 36: C
           to 2010 for the Mean Projection Scenario:
           Optimal Water Availability vs. Water Scarcity Projection Scenario..................... 64
           Difference in the Value of Agricultural Production
Figure 37: 
           Between Optimal Water Availability and Water Scarcity Projections
           in US$ million/year.............................................................................................. 66
Figure 38: Share of Agriculture in National and Local GDP, by Oblast................................ 69
Figure 39: Reduction in Agriculture Production Values, by Oblast, Through 2030.............. 70
           Combined Changes in Household Income,
Figure 40: 
           Poverty, and Inequality, Through 2030............................................................... 70




vi              Ukraine. Building Climate Resilience in Agriculture and Forestry
Figure 41: Effect of Use of Multi Model Ensembles for Temperature and Precipitation........ 90
           Mean Annual Air Temperature Change (left) and Values for Percentiles
Figure 42: 
           over the RCM Ensembles (right) for Three Periods and Two RCPs................... 91
            ean Annual Precipitation Change (left) and Values for Percentiles
Figure 43: M
           over the RCM Ensembles (right) for Three Periods and Two RCPs................... 92
            imulations Prepared for RCP 4.5 for CORDEX-Adjust Output (left)
Figure 44: S
           and for Euro-CORDEX Output (right)................................................................. 95
           Simulations Prepared for RCP 8.5 for CORDEX-Adjust Output (left)
Figure 45: 
           and for Euro-CORDEX Output (right)................................................................. 95
           The Continental Climate Ivanov Index for Historic Periods (E-OBS)
Figure 46: 
           and Ensembles of the RCMs by Periods of the 21st Century............................. 97
Figure 47: De Martonne Aridity Index................................................................................... 98
Figure 48: Crop Growth Processes in the WOFOST Model...............................................100
Figure 49: Simulation Model (WOFOST) for Crop Yield Assessment.................................103
Figure 50: The IMPACT Model System by IFPRI...............................................................104
Figure 51: Workflow for Forests Vulnerability Assessment to Climate Change................... 105
Figure 52: Distributional Analysis Workflow........................................................................ 109
            hanges in Warm-Season Length in the Recent Period
Figure 53: C
           1991-2010 (E-Obs), Near-Future (RCP 8.5) and the End
           of the Century (RCP 4.5 and RCP 8.5)..............................................................112
            hanges in Growing Season Length in the Recent Period
Figure 54: C
           1991-2010 (E-Obs), Near-Future (RCP 8.5) and the End
           of the Century (RCP4.5 and RCP 8.5)...............................................................113
            hanges in the Active-Vegetation Season Length
Figure 55: C
           in the Recent Period 1991-2010 (E-Obs), Near-Future (RCP8.5)
           and the End of the Century (RCP4.5 and RC8.5).............................................114
           Changes in the Summer Season Length
Figure 56: 
           in the Recent Period 1991-2010 (E-Obs), Near-Future (RCP8.5)
           and the End of the Century (RCP4.5 and RC8.5).............................................115
           ncrease in Expenditure Needed to Keep Wellbeing Constant
Figure 57: I
           with Food Price Increases..................................................................................117
            ange of Change in Income for all Deciles between Low
Figure 58: R
           and High Scenario, RCP 8.5, 2030....................................................................125




                               Ukraine. Building Climate Resilience in Agriculture and Forestry                        vii
            patial-Temporal Dynamics of Vorobjov’s
Figure 59: S
           Moisture Availability Index for Forests...............................................................137
           Spatial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 60: 
           of Climate for Scots Pine (Pinus sylvestris L.)...................................................138
           Spatial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 61: 
           of Climate for English Oak (Quercus robur L.)................................................... 139
           Spatial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 62: 
           of Climate for European Beech (Fagus sylvatica L.).........................................140
           Spatial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 63: 
           of Climate for Norway Spruce (Picea abias L.)..................................................141
            ensity of Forest Fires in Ukraine by Oblast in Forests Subordinated
Figure 64: D
           to the State Forest Resources Agency of Ukraine, 2007–2020.........................142




viii          Ukraine. Building Climate Resilience in Agriculture and Forestry
TABLES
Table 1: Increases in Average Annual Temperature and Precipitation................................ 10
Table 2: Changes in Yields Across Oblasts for Major Crops Due to Climate Change........ 26
          hange in Total Production (Millions of Tons) for Major Crops
Table 3: C
         as Compared to the Baseline (with Change in Land Area Allocation
         for Each Crop)....................................................................................................... 34
          haracteristics of Climatic Seasons in Ukraine in Two Past Periods
Table 4: C
         (E-OBS data) and Three Future Periods Under the RCP4.5 Scenario
         (Ensemble of 34 RCMs from Euro-CORDEX Data).............................................. 38
          haracteristics of Climatic Seasons in Ukraine in Two Past Periods
Table 5: C
         (E-OBS data) and Three Future Periods Under the RCP8.5 Scenario
         (Ensemble of 34 RCMs from Euro-CORDEX Data............................................... 38
          ffect of Measures to Maintain Optimal Water Balance
Table 6: E
         on Change in the Value of Agricultural Output for Selected Crops
         (for the mean yield projection) .............................................................................. 63
          blasts Most Affected by the Impacts of Climate Change
Table 7: O
         on Agriculture, by Category................................................................................... 68
          umber of CORDEX Datasets Processed by Combination of RCMs
Table 8: N
         and Overarching GCMs......................................................................................... 88
          ist of CORDEX-Adjusted Outputs Based on Combinations
Table 9: L
         of GCM-RCM-Ensemble-Adjustment.................................................................... 93
Table 10: Minimum Input Weather Data Required for WOFOST........................................ 101
Table 11: Criteria Used in the Integrated Assessment Tables............................................. 109
          ntegrated Criteria Assessment of Oblasts with the Highest Share
Table 12: I
          of Agriculture in Their GDP in the Near Future.................................................... 111
Table 13: Weight of Agriculture in Relation to GDP (US Dollars) in 2010, per Oblast......... 116
Table 14: Agricultural Production by Type of Unit in Ukraine, 2019.................................... 118
Table 15: Percent Changes in Value of Selected Crops in Ukraine, 2010-2030................. 118
          Poverty Consequences of Agricultural Impacts of Climate Change
Table 16: 
          (Only Price Effects Considered) RCP 8.5, 2030.................................................. 120
          Poverty Consequences of Agricultural Impacts of Climate Change
Table 17: 
          (Low Scenario) RCP 8.5, 2030............................................................................ 121
          Poverty Consequences of Agricultural Impacts of Climate Change
Table 18: 
          (Mean Scenario) RCP 8.5, 2030......................................................................... 122
           overty Consequences of Agricultural Impacts of Climate Change
Table 19: P
          (High Scenario) RCP 8.5, 2030........................................................................... 123




                                 Ukraine. Building Climate Resilience in Agriculture and Forestry                              ix
          Base Values of the Gini Coefficient and Changes
Table 20: 
          in the Coefficient RCP 8.5, 2030......................................................................... 124
Table 21: Rating of Oblasts with the Highest Share of Agriculture in GDP......................... 127
Table 22: Rating of Oblasts by the Highest Change in Agriculture Production................... 129
Table 23: Rating of Oblasts by the Combined Social Changes.......................................... 131
Table 24: Average Annual Air Temperature in Forest Regions of Ukraine.......................... 133
          Changes in the Area of Vorobjov’s Heat Availability Index (T)
Table 25: 
          for Forests of Ukraine, %.................................................................................... 135
           hanges in Area of Climatic Zones for Vorobjov’s
Table 26: C
          Humidity Index (W) for Forests, %...................................................................... 136
Table 27: Distribution of Forest Areas by Classes of Natural Fire Hazard.......................... 142
          Effect of Adaptation Measures to Maintain the Optimal Water
Table 28: 
          Availability on Change in the Value of Agricultural Output
          for Selected Crops (mean projection)................................................................. 143
          Effect of Adaptation Measures to Maintain the Optimal Water
Table 29: 
          Availability on Change in the Value of Agricultural Output
          for Selected Crops (low projection)..................................................................... 144
          Effect of Adaptation Measures to Maintain the Optimal Water
Table 30: 
          Availability on Change in the Value of Agricultural Output
          for Selected Crops (high projection)................................................................... 145
          Change in Value of Agricultural Output Relative to 2010 (Maize):
Table 31: 
          Water Optimal vs Water Scarce Projection......................................................... 146
          Change in Value of Agricultural Output Relative to 2010 (Soybean):
Table 32: 
          Water Optimal vs Water Scarce Projection......................................................... 148
          Change in Value of Agricultural Output Relative to 2010 (Sunflower):
Table 33: 
          Water Optimal vs Water Scarce Projection......................................................... 150




x              Ukraine. Building Climate Resilience in Agriculture and Forestry
BOXES
Box 1: Description of Methodologies.................................................................................... 5
Box 2: Urban Vulnerability to Temperature Extremes........................................................... 20
Box 3: Impact of Water Shocks on Agricultural Yields.......................................................... 39
Box 4: Impact of Climate Change on Forests and Decline of Ecosystem Services............. 58
Box 5: National Climate Policy and Coordination: A Variety of Approaches......................... 73
Box 6: Examples of Climate-Smart Agriculture.................................................................... 75




                               Ukraine. Building Climate Resilience in Agriculture and Forestry                             xi
ABBREVIATIONS AND ACRONYMS
ATR	                      Annual temperature range

CSA	                      Climate-smart agriculture

EU	                       European Union

EURO-CORDEX	European branch of Coordinated Regional
             Downscaling Experiment

FPIC	                     Free prior and informed consent

GDP	                      Gross domestic product

GIS	                      Geographic information system

ha	Hectare

IFPRI	                    International Food Policy Research Institute

IMPACT	International Model for Policy Analysis of Agricultural Commodities
        and Trade

IPCC	                     Intergovernmental Panel on Climate Change

LTA	                      Long-term average

NCRC	                     National climate resource center

RCM	                      Regional climate model

RCP	                      Representative concentration pathway

TPM	                      Third-party monitoring

UHMI	                     Ukrainian Hydrometeorological Institute

URIFFM	                   Ukrainian Research Institute of Forestry and Forest Melioration

WOFOST	                   World Food Studies Crop Simulation Model




xii       Ukraine. Building Climate Resilience in Agriculture and Forestry
ACKNOWLEDGMENTS
The team thanks the World Bank’s Management in Europe and Central Asia, especially Arup
Banerji, Director, Eastern Europe; Steven N. Schonberger, Regional Director for Sustainable
Development, Europe and Central Asia; and Asli Demirguc-Kunt, Chief Economist. The team
is especially grateful for the unceasing support and guidance of Kseniya Lvovsky, Practice
Manager, Environment, Natural Resources and Blue Economy Practice for Europe and Cen-
tral Asia.
The cooperation, information and insights provided by officials of the Government of Ukraine,
especially Ms. Iryna Stavchuk, Deputy Minister, Ministry of Environment and Natural Resourc-
es, is gratefully acknowledged. The team appreciates the time and feedback provided by all
stakeholders in Ukraine who participated in consultations during the study.
The team is grateful to the following peer reviewers at the World Bank for their valuable con-
tributions at various stages of the study: Erick C.M. Fernandes, Stephane Hallegatte, Rich-
ard Damania, Urvashi Narain, Tamer Samah Rabie, Sergiy Zorya, and Will Martin. The team
benefited from the insights and feedback of Baher El-Hifnawi, Kanta Kumari Rigaud, Philippe
Ambrosi, Gayane Minasyan, Ana Bucher, Daniel Besley, and the Agricultural Practice team.
This report was prepared by a World Bank team which included Madhavi M. Pillai, Elena
Strukova Golub, Michael M. Lokshin, Oksana Rakovych, and Thanh Phuong Ha. Valenti-
na Fomenko and Sara Feinstein Held provided editorial input and Nadia Kislova, Linh Van
Nguyen and Grace Aguilar provided administrative support throughout the process. The report
benefited from input on climate adaptation policy and institutional issues by Oksana Davis and
gratefully acknowledges the additional resources received from the NDC Partnership’s Just-
in-Time program for this work.
The background technical studies were led by Anil Markandya of Metroeconomica, Spain;
Svitlana Krakovska and Oleksii Kryvobok at the Ukrainian Hydrometeorological Institute
(UHMI); Ihor Buksha at the Ukrainian Research Institute of Forestry and Forest Melioration
named after G. M. Vysotsky (URIFFM); Kristina Govorukha at Technische Universität Berga-
kademie Freiberg, Germany; Francisco Greño, Elena Paglialung, Itziar Ruizgauna, and An-
doni Txapartegi (Metroeconomica, Spain) and a team of experts from the Basque Centre for
Climate Change, Spain. Thanks to Vira Balabukh, Anastasiia Chyhareva and Tetiana Shpytal,
all from UHMI, for their contribution to the technical reports. Special thanks are due to Claas
Teichman, Scientist, Climate Service Centre, Hamburg, Germany for valuable guidance and
review of the methodology for climate projections, and to Anatoly Shvidenko, Emeritus Re-
search Scholar, International Institute for Applied Systems Analysis, Austria, for feedback on
the impact of climate change on forests.




                        Ukraine. Building Climate Resilience in Agriculture and Forestry   xiii
EXECUTIVE SUMMARY
Ukraine has made impressive progress on key reforms and restored macro-financial
stability, but weak growth and poverty remain a concern. The Maidan Revolution of 2013–
14, the events in Crimea in 2014, and the ongoing armed conflict in the eastern region since
2014 have all played an important role in undermining economic growth. A weak recovery
since 2015 reflects both lower potential growth and the severity of the 2014-15 economic
crisis. While poverty has declined relative to its peak during the crisis, it remains higher than
during the pre-crisis period: In 2019, 23% of the population lived below the national poverty
line, versus 8% in 2013 (World Bank 2021d).
Despite these economic challenges, Ukraine recognizes climate change as the most
consequential factor this century, affecting the economy and future generations.
The country updated its Nationally Determined Contribution (NDC) in 2021 and recently af-
firmed its commitment to the European Green Deal. However, in the absence of dedicated
analyses, the nature of climate impacts on Ukraine’s economy are not yet fully understood.
The present study is the first detailed assessment of the potential impacts of climate
change on Ukraine, with a focus on agriculture, a key driver of the economy and jobs.
It was designed as a bottom-up study, based on detailed climate projections for over 7,400
grid points covering the country — which together with biophysical modeling, were used to
estimate the impact on key crops and forest timber species. This analysis provides an insight
into the spatial dimension of climate change — how these changes would be experienced in
different oblasts in the country. The results point in the direction of actions to avoid negative
impacts, and reveal potential to tap into new opportunities. The study focused mainly on two
scenarios, RCP 4.5 and RCP 8.5, which are compatible with a global 2.4°C and 4.3°C warm-
ing limit by 2100, respectively (IPCC 2021).
This report is supported by four background technical reports on climate projections,
impact on agriculture, impact on forests and distributional analysis. In addition, climate
datasets of over two terabytes generated for this assessment are housed at the Ukrainian
Hydrometeorological Institute, Kyiv. The results of this study are expected to inform Ukraine’s
national adaptation strategy, which is now being finalized. This study also paves the way for
the development of sub-national and sectoral adaptation strategies with the spatially disaggre-
gated information that has been generated for all oblasts. It will also inform the World Bank’s
programs in Ukraine — the Climate Change and Development Report in particular.



Key Findings: Climate
Ukraine’s climate has changed significantly over the last 60 years, with accelerating
warming since the 1980s resulting in the rates of 0.4-0.6°C per decade that exceed the
mean value in Europe and are higher than the global rate by a few times. This caus-
es changes in the precipitation regime: While total annual precipitation has not changed




xiv         Ukraine. Building Climate Resilience in Agriculture and Forestry
significantly in recent decades, greater precipitation was observed in the autumn and less
precipitation in other seasons, with the most decreases occuring in summer. Rising air tem-
peratures causing increased evaporative demand with uneven precipitation have resulted
in lower accumulations of moisture in the soil, leading to an increase in the frequency and
intensity of droughts in the last decade.
The strongest annual temperature increases of over 4°C are projected for RCP 8.5 at the
end of the century with the largest effect on the east and northeast of Ukraine (Kharkivs-
ka, Luhanska, Sumska oblasts) and the smallest in the west (Ivano-Frankivska, Lvivs-
ka, Volynska oblasts). In the scenario with lower GHG concentrations (RCP 4.5), estimated
warming is projected to be approximately twice as small. Cities could experience intense tem-
perature increases by the end of the century (over +5.0°C in summer in Luhansk and in winter
in Kyiv), aggravated by the urban heat island effect. These impacts will need to be further ana-
lyzed for their effect on the heating and cooling needs of the population, especially the health
considerations of vulnerable groups, and for their effect on urban infrastructure.
Annual temperature cycles are expected to be altered during the century due to higher
projected monthly temperature increases in summer months in warmer regions, and in
winter months in colder regions. These temperature increases will likely result in continuing
reductions in the annual temperature ranges already observed and the decreasing continen-
tality of the climate. These changes will have significant implications for ecosystem dynamics
and vegetation growth. Rising temperatures in summer could result in heatwaves and increas-
es in aridity in Ukraine’s south and east.
Over the course of the year, minimum temperatures at night are expected to rise
most sharply in the cold season, while daily maximum temperatures will increase
the most in the summer season. It will result in a decrease in the number of days and
nights with negative temperatures while the number of tropical nights with temperatures
over 20°C and summer days with mean daily temperatures over 15°C will increase. More
than 100 tropical nights and up to 135 summer days per year are projected for the south-
ern steppe by the end of the century under RCP 8.5.
In all scenarios, annual precipitation in Ukraine is projected to increase, with larger
increases  towards the end of the century, especially under RCP 8.5. Precipitation is
projected to increase significantly in the winter months for almost the entire country. Larger
precipitation increases are expected in northern oblasts (especially in the northwest, e.g.,
Rivnenska, Volynska). The summer months are projected to have a relative decline getting
larger over time under RCP 4.5 and RCP 8.5.
By the end of the century, changes under RCP 8.5 show not only twice higher warming
but broader ranges of precipitation variability across oblasts, suggesting strong spatial
differences. The southern and central areas are characterized by the lowest increase in precip-
itation, with a significant decrease in warmer months exacerbating with temperature rise. Over-
all, the southern and central oblasts are projected to become drier, and northern and western
oblasts wetter with rising uncertainty of the delineation between these two opposite tendencies
under RCP 8.5.




                        Ukraine. Building Climate Resilience in Agriculture and Forestry    xv
The frequency and intensity of extreme weather and climate events, including heat-
waves, thunderstorms, heavy precipitation, pluvial and river flooding, droughts, hail-
storms, squalls, tornadoes, heavy snowfalls, freezing rains, accumulation of wet snow,
icing, etc., are expected to rise with higher warming. Extreme events especially, those
known as “low-likelihood, high-impact events,” (IPCC 2021) could have additional and signif-
icant consequences on all sectors and ecosystems, resulting in a significant number of lost
jobs and livelihoods. Most losses would be concentrated in sectors of middle and lower-in-
come workers – manufacturing, utilities, retail, and tourism. The Inter-governmental Panel on
Climate Change Sixth Assessment Report (IPCC AR6) assigns low confidence levels to the
occurrence of these events, which does not exclude the possibility of their occurrence, but
instead, is a reflection of the limits of predictability of these events. The potential impacts of
such events on Ukraine need to be analyzed through a separate study.



Key Findings: Agriculture and Forests
With no adaptation interventions, the range of possible yield outcomes is large
as is the risk of outcomes below expectations in any given year. Yields of selected
crops (winter wheat, barley, maize, soybean, and sunflower) were modeled with a probability
distribution for low and high projection: i.e., the 5th percentile of the distribution and the 95th
percentile, respectively. Under RCP 8.5 yields of all crops, except wheat and soybean, face
significant decline in 2030 and in 2050. In percentage terms, the decline is greater for barley
followed by maize. However, the projected decline in maize yield is more important, since it is
a critical export commodity.
While climatic conditions become favorable for higher productivity of winter wheat in
the near future period and up to the mid-century under both RCP 4.5 and 8.5, the unpre-
dictability of precipitation patterns make oblast-level adaption planning very essential
to prepare the agriculture sector for this climatic shift. Based on the projected changes
in precipitation (autumn, winter), increased CO2 concentration, and decrease in the number of
frost nights, yields are projected to increase 20-40% by 2050 as compared to the 2010 base-
line period in the north and northwestern parts of the country first. This result is also in line
with projections for the EU states in the recent PESETA IV study (Feyen 2020). Conditions for
increased productivity of winter wheat also become favorable by mid-century for more areas
of the country under RCP 8.5, based on the increase in autumn and winter precipitations pro-
jected under this scenario. However, the unpredictability of precipitation patterns especially
for the latter part of the century under RCP 8.5 makes it essential to pay greater attention to
projected changes at the local level. The detailed projections from this study could be used to
develop regional or local adaptation plans.
The productivity of maize, sunflower and barley could also see an increase by mid-cen-
tury, provided that climate-smart water management interventions are deployed for
their production. Climate-smart strategies for water management could increase overall
yields by 20-40%, and up to 80% for maize and 40-80% for sunflower.




xvi         Ukraine. Building Climate Resilience in Agriculture and Forestry
With optimal water availability, benefits for maize, soybean and sunflower crops could
reach US$112 million per year over the 10-year period from 2026- 2035 under the mean
projection.1 Simulations of low and high yield projections show that the annual benefits of
maintaining optimal water balance could amount to US$264-504 million or 2-4% of Ukraine’s
GDP for agriculture in 2019. The highest benefit of better water management in relative terms
is expected for soybean output that could increase by 26-40%. The highest impact is estimat-
ed at US$92.7 million for maize.
To benefit from higher agricultural value, it is essential to carry out an assessment of
the feasibility of different water management options. While an assessment of water re-
sources was not part of this study, carrying out such assessments as part of oblast adaptation
planning would be imperative to understanding the costs and suitability of different options for
water management and water availability. Water management strategies adopted to offset cli-
mate impacts could vary by crop and by oblast, and could include planting of drought-resistant
varieties, use of cover crops, conservation agriculture, and drip-irrigation, among others. In
addition, for winter crops, sowing dates may need to be shifted to later times (October-Novem-
ber), when increases in temperature and precipitation are predicted. For spring crops, sowing
dates would need to be earlier, with harvests before the dry weather conditions at the end of
July and August, especially in the south of Ukraine.
Based on the temperature and humidity conditions projected under both RCPs, a significant
reduction is expected in the area suitable for the growth of spruce, beech, pine and oak. Less
than 3% of the country’s forest areas would have optimal conditions for Norway spruce, Scots
pine and beech under RCP 8.5 projections and just 8% of the territory will have optimal condi-
tions for English oak. By mid-century, under both RCP 4.5 and RCP 8.5, only the Carpathians
will remain a suitable zone for Norway spruce. In the Carpathians, the forest boundary is ex-
pected to move to a higher altitude.
The projected changes are likely to exacerbate disturbances and stressors such as
wildfires and insects. During prolonged droughts, a significant proportion of forest biomass
becomes combustible, increasing the fuel load of the forest. In addition, pest infestations
which have been documented with warming conditions can result in the deterioration of forest
health and increased tree mortality. These will, in turn, enlarge the fuel load available for com-
bustion in wildfire events. Pine forests in the southern and northern steppe and forest-steppe
areas will also be at high risk due to the drier conditions expected there under both RCPs.



Impact of Climate Change on Agriculture
and Inequality by Oblast
Climate change will have a greater impact on some oblasts than others based on
its impact on agricultural production, and the resultant impact on poverty indica-
tors. The top five oblasts with the highest impact in absolute terms by 2030 are Cherkaska,
Khersonska, Kirovohradska, Poltavska, and Vinnytska. Kirovohradska oblast has the highest
agricultural GDP in Ukraine and the value of its agricultural production will also be considera-
1
    	   The low projection considered for this analysis reflects the lowest production potential of the selected crops under climate
        change. These values describe the worst-case scenario, in which the potential reduction in the agricultural production values
        will be the most significant.




                                     Ukraine. Building Climate Resilience in Agriculture and Forestry                         xvii
bly impacted by the changing climatic conditions in this century. By mid-century Kyivska and
Zhytomyrska oblasts will undergo significant changes in climatic conditions. With a consistent
rise in dry and hot conditions, Kyivska and Chernivetska oblasts will be exposed to extremely
high temperatures, as indicated by the increasing number of tropical nights that may result in
increase of extreme weather events.
The most significant loss in household incomes and the highest increase in poverty and
inequality due to lower agricultural production values is projected to be in Kharkivska,
Kirovohradska, Lvivska, Luhanska, and Zhytomyrska. Although the agricultural sector ac-
counts for a relatively minor share in the GDP of most of these oblasts, the projected chang-
es in agricultural production values will have significant implications for inequality measures.
These oblasts would be most susceptible to the rise in food prices and reduction of income
from agricultural production caused by the warming climate. Among the five oblasts, Lvivska
and Zhytomyrska oblasts will be most exposed to the reduction of projected precipitation in
spring and summer in relative terms, with potentially significant losses of agricultural produc-
tion value in the near future period.


Opportunities and Priorities for Climate Action
Ukraine must take action to address the potential risks and opportunities that climate
change will present for agriculture and forestry, and in turn livelihoods and poverty
levels, across the country. Based on the analysis presented in this report, as well as inter-
national experience, actions are recommended along three broad streams:
    •	   Strengthen Institutions, Policy and Planning
    •	   Increase Scientific Capacity and Research
    •	   Promote Transition to Climate-Smart Agriculture and Forestry



Strengthen Institutions, Policy, and Planning
Establish a national level institutional mechanism to coordinate climate change poli-
cy and actions across all line ministries. Enabling fiscal risk assessment of climate ac-
tions, policy and planning and climate budget tagging will be necessary to prepare critical
sectors such as energy, infrastructure, health, and agriculture to address climate impacts.
Establish a mechanism to integrate climate change action within the Ministry of Agrari-
an Policy and Food (MAPF). Strengthening climate expertise and functions will equip MAPF
with the necessary knowledge and technical capabilities to support effective and coherent
climate policies and programs for farmers. It will also be important for MAPF to regularly carry
out agriculture sector climate vulnerability assessments and develop action plans (every five
years).
Include climate change risk assessment in oblast development planning. Carrying out
more comprehensive impact assessment reviews at the oblast level will be important to iden-
tifying specific climate risk considerations for development planning and tailoring action to the
sectors that face highest risk in the oblast.




xviii       Ukraine. Building Climate Resilience in Agriculture and Forestry
Increase Scientific Capacity and Research
Enhance institutional capacity for collecting, maintaining, analyzing, and disseminat-
ing climate data through a National Climate Resource Center. Strengthen the Ukraine
Hydrometeorological Institute (UHMI) and the Ukrainian Hydrometeorological Center (UHMC)
as a National Climate Resource Center (NCRC). Both institutions fall under the jurisdiction
of the State Emergency Service of Ukraine, and combining them under the umbrella of an
NCRC can ensure systematic research on hydrometeorology, agrometeorology, and climate
science, including up-to-date climate projections, assessment of risks and impacts at the sec-
toral, national, and regional levels. This will help strengthen the capacity and resources of
the UHMI and UHMC to analyze and manage big data for climate planning. This study filled
an important data gap by generating over two terabytes of highly granular data on a range of
climate indicators for Ukraine using the latest available global and regional climate models.
It will be necessary to continue analyzing and updating this data for sub-national adap-
tation planning, which will require significant hardware and software capacity as well as
trained personnel within these institutions. It will also help Ukraine participate in and take
advantage of the EURO-CORDEX2 experiment and develop highly disaggregated climate
projections that could be used to estimate climate risks in different sectors of the national
economy and on the sub-national level.


Promote Transition to Climate-Smart Agriculture and Forestry
Promote climate-smart agriculture including, better soil and water management (e.g.,
through contour ploughing, contour bunding, conservation tillage, surface mulching,
and revegetation and reforestation of areas around farmlands), agroforestry (planting
combinations of trees and crops), drought-resistant varieties of key crops and cov-
er crops, and expand landscape diversity and connectivity to increase the ability of
ecosystems to adapt to changing climate conditions and stresses. Maintain or restore
riparian areas, wetlands, peatlands and floodplains to help regulate water balance and reduce
soil erosion; give incentives to farmers through agro-tourism and eco-tourism programs to
manage non-arable lands to maintain biodiversity and natural habitats. These approaches
have been shown to protect agriculture from environmental and climate stresses.
Promote Farmer Information Systems and Precision Agriculture Technologies. Provide
farmers with reliable and accessible knowledge about climate-smart agriculture and enhance
their capacity for adaptation. An information system for farmers through mobile, online and
in-person extension services will be key to raising awareness and initiating action on the
ground. Promoting the use of precision agriculture (including Variable Rate Technology, or VRT,
remote sensing and drones) would help move Ukraine towards more climate-friendly technol-
ogies by reducing wastage of water and other inputs. To develop and maintain such systems,
Ukraine can leverage its significant capacity and large pool of talent in information technologies.




2
    	   EURO-CORDEX is the European branch of the international CORDEX initiative, a program sponsored by the World Climate
        Research Program (WRCP) to organize an internationally coordinated framework to produce improved regional climate
        change projections for all land regions world-wide. See https://euro-cordex.net/.




                                  Ukraine. Building Climate Resilience in Agriculture and Forestry                    xix
Improve targeting of subsidy programs and develop insurance products for climate
risks. Agricultural loans and subsidies could be redesigned and better targeted to incentivize
the adoption of climate-smart technologies by farmers. Another approach would be to in-
crease farmers’ resilience to climate change via the coverage of residual risks not addressed
by adaptation actions. Products such as parametric crop insurance would help in areas where
adverse weather events such as droughts and long-lasting heatwaves are expected and there
is limited capacity for adaptation. As a part of the wider adaptation strategy, index insurance
protects farmers’ investments from weather volatility and climate uncertainty.
As the forest sector requires sustainable management with long-range climate risk
planning, it is especially important to include climate risk management in the forthcom-
ing Forest Strategy 2030 and the country’s associated plans for reforestation/affores-
tation. A regularly updated national forest inventory will be key, in addition to field trials, to
monitor growth and plan the planting of timber. Increasing capacity in geospatial technologies
is essential for the management of forest fires. It is crucial to plan for this sector as it impacts
the hydrological balance and soil conditions for agriculture.




xx          Ukraine. Building Climate Resilience in Agriculture and Forestry
CHAPTER 1:
INTRODUCTION
Despite its economic challenges and the COVID-19 pandemic, Ukraine is making efforts to-
wards a green transition. There has been impressive progress on key reforms and the res-
toration of macro-financial stability, but weak growth and poverty remain concerns. However,
Ukraine has recently affirmed its commitment to the European Green Deal and updated its
Nationally Determined Contribution (NDC) in 2021.
Recognizing climate change as the defining factor of global development in this century,
Ukraine has begun focusing on climate risks and planning for adaptation. Ukraine’s climate
policies have traditionally focused on mitigation, given its status as one of most carbon-inten-
sive economies in Europe, but there is increasing urgency to understand the impact climate
change could have on key sectors such as agriculture and forestry.


     Figure 1: Map of Eco-regions of Ukraine




     Source: Buksha et al. 2020




                        Ukraine. Building Climate Resilience in Agriculture and Forestry      1
Agriculture is a key driver of the economy, contributing about 10% to the national GDP and
employing 17% of the labor force. The sector accounted for about 44% of total exports in
2018 (World Bank 2021d). Agriculture also contributes significantly to the subsistence, food
security, and livelihoods of the rural population, with about four million farmers farming 15
million hectares. However, Ukraine’s agriculture exports are of low value (€436 per hectare
compared to Poland €2030, Germany €630/hectare (UN 2021; FAO 2021b). Farmers face
high input costs, particularly for fertilizers, and lack access to financing due to fragmented and
poorly designed subsidies (World Bank 2021d). Despite very high potential, agriculture could
face risks due to climate change.
The ongoing decentralization reforms and the establishment of an agricultural land market of-
fer an important opportunity to address climate change. Territorial communities are expected to
take charge of local development budgets and the management of (some) natural resources,
including environmentally critical lands in Ukraine. This, combined with the opening of agricul-
tural land markets, is expected to give communities and farmers greater control over land and
resources, leading to sustainable land management. Empowering local level decision-making
is recognized internationally as a good practice for better development outcomes. However, it
is critical to ensure that the newly empowered decision-makers have the resources and infor-
mation to make the right decisions, including on impending climate change risks.
The Government has started taking steps toward adaptation but there is a need to enhance
the knowledge base. The President of Ukraine’s Decree of March 23, 2021 (№ 111/2021) in-
dicates that ecological security is being linked to national security and emphasizes the need
to address climate change and adaptation. The Government has drafted two documents em-
phasizing the importance of increasing the resilience of forest ecosystems to climate change:
National Strategy on Environmental Security and Adaptation to Climate Change through 2030
and State Strategy of Forest Management of Ukraine until 2035. However, there remains in-
sufficient information to underpin policies and action plans.
This study lays the foundation for developing detailed adaptation planning at the national
and sub-national levels. It presents a comprehensive assessment of the impacts of climate
change, with a deep dive into the agriculture sector and a limited analysis of climate impacts
on forests. The approach and data generated can be used for deep dives in other sectors and
for developing oblast-level adaptation plans.



1.1 The Analytical Framework
The study was conducted in four stages, starting with climate projections and biophysical
and economic impact assessments, followed by distributional analysis and identification of
hotspot oblasts which are most likely to be affected by poverty and inequality. (Box 1, and
Annex I for more details). The first stage involved projections of key climate variables for three
future periods: the near future (2021-2040), mid-century (2041-2060), and end of the century
(2081-2100) under RCP 2.6 (compatible with a 2°C global warming limit by 2100), RCP 4.5,
(compatible with a 2.4°C global warming limit), and RCP 8.5 (compatible with a 4.3°C global
warming limit). Seven key climate variables were simulated: min, max, and mean air tempera-
ture; precipitation; surface wind speed; and relative humidity. Additional climate indexes were
also calculated for sectoral analysis.




2           Ukraine. Building Climate Resilience in Agriculture and Forestry
The second stage assesses the biophysical impacts of climate change on forestry and the
agricultural sector. Specifically, the forestry assessment studies changes in the ecological
amplitude (zones of tolerance) for eight main forest-forming species in three future periods
under RCP 4.5 and RCP 8.5. These species include Norway spruce, European beech, com-
mon hornbeam, Scots pine, English oak, black alder, silver birch, and black locust. The as-
sessment under RCP 2.6 was not conducted due to limited climate data for this scenario. The
agricultural analysis simulates changes in yields and production for five prominent crops in
the near future (2026-2035) and mid-century (2046-2055) under RCP 4.5 and RCP 8.5. These
are barley, maize, soybean, sunflower, and winter wheat, which together accounted for 61%
of production volume in 2018 (Ukraine Statistics 2021).



     Figure 2: Methodology

       Climate protection            Biophysical Impact            Economic impacts              Distributional impact


      For: Near Future               On: Agriculture and           On: Agriculture               Impact analysis of the
      (2021-2040).                   Forestry                                                    effects on agriculture by
      Mid-Century                                                  For: Near Future,             income group, impact
      (2041-2060),                   For: Near Future,             Mid-Century                   on poverty and Gini
      End of century                 Mid-Century                                                 coefficient
                                                                   Scenario:
      (2081-2100)                    Scenario:                     SSP2 & RCP 8.5                For: Near Future
      Scenario: RCP 2.6,             RCP 4.5, RCP 8.5
      RCP 4.5, RCP 8.5
      Resolution: ~ 10 x 10 km




      Euro CORDEX                    Agriculture: WOFOST           Agriculture; IMPACT Model     Local data on household
      (GCMs & RCMs)                  Model (change in yield) for   (change in production &       and expenditures
                                     five major crops              value) for five major crops
      Core climate indicators:
      min, max and mean
      temperature, precipitation,
      surface wind speed,            Forestry: assessment of                                     Impact of increases in
      relative humidity              eight key forest species                                    consumer/producer prices
                                     for ecological amplitude                                    and agricultural income
                                     using local forest typology                                 change on poverty and Gini
      Climate indicators for         models                                                      coefficient
      sectoral analysis, and
      vulnerability indicators




                                 Ukraine. Building Climate Resilience in Agriculture and Forestry                            3
During stage three, economic impacts of climate change on agriculture were estimated by
assessing changes in production values for the same three future periods under RCP 8.5 (fu-
ture crop prices are not available under RCP 4.5). During stage four, utilizing the agricultural
assessment results, the distributional analysis assessed the impacts of climate change on
households’ real incomes through its impacts on food prices and agricultural income. Based
on existing socio-economic data and all analysis results, “hotspot” oblasts were then identified
and highlighted for prioritizing adaptation actions. Similar analysis for the impact of climate
change on forests was not conducted due to the lack of socio-economic data at subnational
levels and the onset of the COVID-19 pandemic.
The analysis was conducted at a highly granular level, but most of the results are reported
at the oblast level to present spatially meaningful results. Most of the analysis was conduct-
ed at the individual grid level, covering more than 7,400 grid cells over the entire territory of
Ukraine. Then the results were aggregated at the oblast level to facilitate appropriate and
tailored decision-making and planning at the local administrative level. Reporting results at
the oblast levels also allow inter-regional comparisons and prioritization of locations that need
adaptation solutions.


    Caveats and Limitations of the Methodology
1.2 
The climate projections presented in this report have uncertainty ranges and should not be
interpreted as forecasts. The uncertainties in constructing and running climate models are in-
herent and manifold. Climate models cannot fully capture the complexities of climate systems.
When constructing climate models, simplifications, assumptions, and choices of parametriza-
tions are made, resulting in model and projection errors. Although certain methods have been
applied to reduce these systematic and inherent errors, climate projection results are reported
in ranges with upper and lower limits of confidence intervals. Since the projected climate varia-
bles are used as inputs for agricultural assessments, the results for agricultural projections are
also presented in a probabilistic distribution.
The study did not consider extreme events due to the complexity in data analysis on this spe-
cific aspect of climate change and higher uncertainty of their projections. The analysis focused
on the long-term impacts of climate change on specific sectors. The frequency and intensity
of extreme weather and climate events, including heatwaves, thunderstorms, heavy precipita-
tion, hailstorms, squalls, tornadoes, heavy snowfalls, freezing rains, accumulation of wet snow,
icing, etc., could have significant impact on the yields and value of agricultural production but
are not modeled in this study. Estimation of mean changes is justified for this study due to
a higher uncertainty of extreme projections, especially for granular approaches such as the
CORDEX experiment (Seneviratne 2012). However, extreme events, especially those known
as “low-likelihood, high-impact events,” (IPCC 2021) could have additional and significant con-
sequences on all sectors and ecosystems, resulting in a significant number of lost jobs and
livelihoods. Most losses would be concentrated in sectors of middle and lower-income work-
ers – manufacturing, utilities, retail, and tourism. The AR6 report assigns low confidence levels
to the occurrence of these events, which does not exclude their possibility, but instead reflects
the limits of these events’ predictability. Potential impacts of such events on Ukraine need to be
analyzed through a separate study.




4           Ukraine. Building Climate Resilience in Agriculture and Forestry
Box 1: Description of Methodologies

A detailed description of the methodology and models used is given in Annex I.
Climate projections. Datasets from the European branch of the International Coordinated Regional Down-
scaling Experiment (EURO-CORDEX) initiative at a resolution of 0.11 degrees (~12.5 km) are obtained to pro-
duce daily climate projections over approximately 7,400 grid points for the entire territory of Ukraine for more
than 100 years. Over 300 datasets for seven climate variables under three RCPs are obtained for the projec-
tions from various combinations of multiple Regional (RCM) and Global (GCM) climate models. Historical and
baseline data are obtained from the E-OBS v20.0e gridded dataset with the same spatial resolution. The three
RCPs are selected based on the availability of data and include RCP 2.6 (compatible with a 2°C global warm-
ing limit by 2100), RCP 4.5 (compatible with a 2.4°C global warming limit), and RCP 8.5 compatible with
a 4.3°C global warming limit).
Systematic errors inherent in climate modeling are reduced through the utilization of multi-model ensembles
for climate projections and bias-correction of climate data, resulting in probabilistic projections (i.e., climate
variables are projected in ensemble ranges with upper and lower limits at 95% confidence interval). The
means of the ensembles are reported as they represent the most probable values. The results are report-
ed as future changes in climate variables compared to the base period (1991-2010). The historical period
(1961-1990) is also used to compare the results with older studies and the changes that have already taken
place between this period and the baseline.
Forestry impact assessment. The forestry assessment was conducted using Vorobjov’s climate-related
forestry typology model and Didukh’s model of suitable environmental condition for plants. Vorobjov’s for-
estry typology model consists of three climate indexes with the most significant effects on forest growth,
condition, productivity, and biodiversity: humidity (the ombro-regime), continentality, and frostiness (the
cryo-regime). Based on these three indexes, the lower critical (minimum) and upper critical (maximum)
limits and the interval between them (referred to as “zone of ecological amplitude”) are established for each
of the eight forest-forming species, using the methodology developed by Didukh.
Key climate variables are used to calculate Vorobjov’s three indexes to determine changes in the zones of
ecological amplitude for these species in three future periods compared to the baseline of 1991-2010. An
open-source geographic information system application (Q-GIS) was used to perform spatial analysis and
visualize the results.
Agricultural impact assessment. The World Food Studies Crop Simulation (WOFOST) model is used
to assess the biophysical impacts of climate change on yield potentials of five key crops in the near future
and mid-century, relative to the baseline period (2006 – 2015), using meteorological inputs from climate
projections. The WOFOST was calibrated and adapted for Ukraine by the Ukrainian Hydrometeorological
Institute (UHMI). The yield projections were then combined with changes in land areas under each crop in
2030 and 2050 and changes in prices for those years under the combined SSP2 – RCP 8.5 scenario from
the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) developed by
the International Food Policy Research Institute (IFPRI) to estimate changes in production and production
values relative to the baseline period. The reported results are centralized values for 2010, 2030 and 2050
in three sets of projections: low, mean, and high, which reflect an uncertainty range associated with the
uncertainties in climate projection.
Distributional analysis. The analysis utilizes comprehensive data collected for 250- 500 individual house-
holds for each oblast, which allows for identification of variations in income distribution due to climate-in-
duced changes in the agricultural sector. It provides two key outputs: increases in the prices of key food
products as a result of climate change, which allows for estimates of 2030 price increases for key agricultur-
al commodities under RCP 8.5 and RCP 4.5; and changes in agricultural incomes as a result of the effects
on yields, production, and production values.
Identification of “hotspot” oblasts. Using the results from climate impacts on agriculture, “hotspot” oblasts
are grouped based on the: i) change in oblast GDP due to the projected changes in agricultural production;
ii) change in agricultural production values; and iii) change in household incomes, poverty, and inequality.




                         Ukraine. Building Climate Resilience in Agriculture and Forestry                            5
The study also does not consider the effects of pests and diseases on agriculture and forestry
and analysis of more climate change scenarios. Damage caused by pests and diseases is
triggered by a warmer and drier climate, which could be more relevant for Ukraine’s south
and east, were not analyzed. (See Figure 31.) The Coupled Model Intercomparison Project
(CMIP6) is expected during the latter part of 2021, and further analysis using CMIP6 data can
provide information on these parameters. It should be also noted that climate projections were
available from only three regional climate models (RCMs) at the time of the study to estimate
precipitation for RCP 2.6 scenario, while for RCP 4.5 and RCP 8.5, the full ensembles consist
of 43 and 34 RCMs, respectively. Therefore, results for RCP 2.6 are only indicative and are
not used in further agriculture and forest vulnerability assessments.




6          Ukraine. Building Climate Resilience in Agriculture and Forestry
CHAPTER 2:
HOW WILL UKRAINE’S CLIMATE
CHANGE IN THIS CENTURY?
2.1 Summary
Winters are expected to be warmer, and summers hotter; a consistent trend of increases
in annual average temperatures is expected across the country with progressively higher
increases towards the end of the century. Over the course of the year, daily minimum
temperatures rise most sharply in the cold season, while daily maximum temperatures
increase the most in the summer season. The projected ranges of average annual temper-
ature increase for the three periods [the near future (2021-2040); mid-century (2041-2060);
and end of the century (2081-2100)] and under RCP  4.5 already exceed the observed
historical range of changes during the 1991-2010 baseline period. The highest increase in
average annual temperature for the entire country – by nearly 4.3°C – is projected under
RCP 8.5 at the end of the century.
In all scenarios, monthly precipitation will increase by 2100. Precipitation also follows a
complex trend in all three future periods, with its pattern changing in different ways in cold-
er and hotter seasons. In the period at end of the century, wetter weather is expected in
colder months and drier weather in warmer months, particularly in the south and east, but
this pattern is not consistent and there are significant variations across regions. By the end
of the century, the projected precipitation changes spread increases, with higher ranges
anticipated under RCP 8.5. The precipitation pattern is characterized by major increases
in winter months for most of Ukraine. The ranges of changes are much lower in summer
months. The projected mean increase rises to almost 10 mm in December in the far future.
The projections made under RCP 4.5 show comparatively smaller precipitation ranges.
Annual seasonal cycles will be altered. In particular, the projected monthly temperature in-
creases are generally higher in all three periods during the summer months in warmer regions
and during the winter months in colder regions. These temperature increases will likely result
in continuing reductions in the annual temperature ranges already observed. Additionally, the
number of ice days and frost nights are expected to decrease while the number of tropical
nights will increase. These changes will have significant implications for ecosystem dynamics
and vegetation growth.
The southern and central oblasts will become drier, and northern oblasts will become wetter.
At the end of the 21st century, the southern regions will experience an average daily maximum
July temperature above 34°С, a level never before observed in Ukraine, with the southern
steppe remaining the hottest area until the end of the century. Rising temperatures in sum-
mers will result in heatwaves and increased aridity in the south and east. Under RCP 4.5,
summer days will start earlier in the year and end later; and under RCP 8.5, the number of
summer days will increase by an average of 42 days by the end of the century. The largest
temperature increases are expected in the east and northeast of Ukraine (Sumska, Kharkivs-
ka, Luhanska) and the smallest in the west (Volynska, Lvivska, Ivano-Frankivska). Higher




                        Ukraine. Building Climate Resilience in Agriculture and Forestry     7
increases in average daily minimum temperatures indicate warmer nighttime temperatures,
which could increase the need for indoor cooling for longer periods each year.
The largest precipitation increases are projected for the northern oblasts (Rivnenska and
Volynska), while the lowest precipitation increase (and even a decrease in the warm months)
are expected for the southern and central areas. Changes under RCP 8.5 show broader rang-
es of precipitation variability across oblasts, suggesting strong spatial differences.
Cities are projected to experience intense temperature increases toward the end of the centu-
ry (over +5.0°C in summer in Luhansk and winter in Kyiv), aggravated by the urban heat island
effect. The highest warming in summers is expected for Kyiv in July. During the end of century
period, warming will reach +5.0°C in cities in almost every part of the country in every month.


    Recent Climatic Changes in Ukraine
2.2 
Ukraine’s current climate reflects significant changes that the country has been experiencing
as the result of climate change. The current climate of most of the country (85%) is temperate
continental, or “cold,” as classified according to the Koppen-Geiger climate classification (see
Figure 3). The country consists of several climate zones. The cold zone with no dry season and
warm summer (Dfb) covers over 70% of the territory in the west, north, and central parts of the
country, as well as the Crimean Mountains, and corresponds to the forest and forest-steppe
eco-regions. The zone with hot summer humid continental climate (Dfa) includes over 14% of
the country, across the southeast and the northern steppe. The cold semi-arid climate zone
(BSk) corresponds to the southern steppe and covers over 14% of the south including most
of the Crimean Peninsula. The subarctic climate zone (Dfc) covers the Carpathian Mountains,
where tundra climate (ET) is found at the highest altitudes. The humid subtropical climate
zone (Cfa) and temperate oceanic climate zone (Cfb) cover the southern coast and northern
part of the Crimean Mountains. Each of these four climate types account for less than 1% of
the country’s territory.
Ukraine’s climate has changed significantly over the last 60 years, with temperatures rising
at an increasing rate. Since the late 1990s, the mean annual air temperature has been con-
sistently higher than that between 1961 and 1990. Since 2007, it has exceeded the norm by
1.5o C. The last decade, especially the years since 2015, were the warmest ever in Ukraine,
and in the Northern Hemisphere in general. In some years, the increase in mean annual air
temperature surpassed 2.0 °С (2.2°С in 2007, 2.3 °С in 2015, and 2.7 °С in 2019). The daily
minimum temperature rise is largest in the cold seasons, while maximum daily temperature in-
creases the most in summer. Such changes have led to a decrease in the duration of the cold
season, the number of frost days, and the severity of winters. At the same time, the changes
have resulted in a longer and hotter growing season, an increased number of summer days,
and, accordingly, a longer recreation season. Consequently, the number of hot days and the
duration of the hot spells, heat load, and heat stress on the human body are also increasing.
The precipitation regime in Ukraine has also changed: While total annual precipitation has
not changed, there has been a redistribution of precipitation levels among different seasons.
Increases in precipitation levels are observed in autumn, and decreases in winter, with even
greater decreases in the summers. Furthermore, the unevenness of precipitation and its in-
tensity have increased, causing an extension in the duration of the dry periods. Rising air




8           Ukraine. Building Climate Resilience in Agriculture and Forestry
Figure 3: Climatic Zones in Ukraine, 1980-2016




                                                                Chernihivska
                Volynska
                            Rivnenska                                             Sumska

                                        Zhytomyrska
                                                       Kyiv
                                                      Kyivska
          Lviv
        Lvivska                                                                Poltavska
                                                                                                 Kharkivska
                            Khmelnytska
                   Ternopilska                            Cherkaska
                                          Vinnytska                                                                 Luhanska
        Ivano-Frankivska                                         Kropyvnytskyi
                                                                                                                     Luhansk
  Zakarpatska                                                   Kirovohradska
                     Chernivetska                                                  Dnipropetrovska
                                                                                                         Donetska


                                                               Mykolaivska
                                                                                             Zaporizka

                                                      Odeska                 Khersonska
                                                        Odesa




                                                                                    Crimea




       Köppen-Geiger
       climate classification
       (1980–2016)
                BSk Arid, steppe, cold (14.3 %)

                Cfa Temperate, no dry season, hot summer (0.3 %)

                Cfb Temperate, no dry season, warm summer (<< 0.1 %)

                Dfa Cold, no dry season, hot summer (14.1 %)

                Dfb Cold, no dry season, warm summer (70.4 %)

                Dfc Cold, no dry season, cold summer (0.9 %)

                ET Polar, tundra (<< 0.1 %)

                           Source: Beck et al. 2018.




                             Ukraine. Building Climate Resilience in Agriculture and Forestry                                  9
temperatures and uneven precipitation have resulted in lower accumulations of moisture in
the soil, leading to an increase in the frequency and intensity of droughts. Drought episodes
have almost doubled in the last twenty years with the dangerous tendency of increasing the
recurrence of arid conditions in the Polissya eco-region, which was previously sufficiently wet,
and also causing aridity in the northern regions of the forest-steppe.


    Annual Temperature and Precipitation Projections
2.3 
The temperature and precipitation trends show greater changes toward the end of the century.
The expected increases in average annual temperature and precipitation during this century
are presented in Table 1. Projected average annual temperature change by the end of the
century for RCP 4.5 and RCP 8.5 scenarios, compared to the base period and the differences
between the two scenarios, are presented Figures 4a, 4b, and 4c.


Table 1: Increases in Average Annual Temperature and Precipitation


            2021-2040                     2041-2060                     2081-2100
            temperature / precipitation   temperature / precipitation   temperature / precipitation


 RCP 2.6    0.8±1.4°C / 3 %               1.0±1.7°C / 2 %               0.9±1.8°C / 6 %

 RCP 4.5    0.9±1.4°C / 6 %               1.5±1.7°C / 5 %               2.1±1.8°C / 6 %

 RCP 8.5    1.1±1.5°C / 4 %               2.0±1.7°C / 5 %               4.3±2.1°C / 8 %




Figure 4 : Projected Annual Mean Temperature Increases
            rojected Annual Mean Temperature Increase
Figure 4a: P
           (compared to baseline 1991-2010) for RCP 4.5 at the End of the Century




10          Ukraine. Building Climate Resilience in Agriculture and Forestry
            rojected Annual Mean Temperature Increase (compared to base-
Figure 4b: P
           line 1991-2010) RCP 8.5 at the End of the Century




           Temperature Differences Between the Two Scenarios at the End
Figure 4c: 
           of the Century




                Ukraine. Building Climate Resilience in Agriculture and Forestry   11
                                                                                  The RCP 4.5 emission scenario (which as-
                                                                                  sumes some climate policies) causes a mi-
Figure 5: Annual Temperature Change
                                                                                  nor difference in temperature increase in
                                                                                  the near future but has a greater impact in
RCP 8.5 End of century: 2081-2100                                                 mid-century and even more so at the end
        RCP 8.5 Mid-century: 2041-2060                                            of the century. The range of mean changes
        RCP 8.5 Near future: 2021-2040                                            in annual air temperature is approximate-
RCP 4.5 End of century: 2081-2100                                                 ly +2.0±0.2°C under RCP 4.5 and is much
        RCP 4.5 Mid-century: 2041-2060                                            more pronounced (4.2±0.2°C) under RCP
        RCP 4.5 Near future: 2021-2040
                                                                                  8.5. The difference between the projected
            Historical period: 1991-2010
                                                                                  temperature changes under RCP 4.5 and
                                           1      2         3        4       5    RCP 8.5 rises sharply from 0.5°C in 2041-
                                                      Temperature change [°C]
                                                                                  2060 to 2.2°C in 2081-2100. The spatial
                                                                                  distributions of temperature rise under both
                                                                                  RCP 4.5 and RCP 8.5 are similar over time,
                                                                                  with the highest temperature increases in
                                                                                  the northeast and the lowest in the west and
                                                                                  northwest and near the Black Sea coast. The
                                                                                  highlighted territories are most exposed to
                                                                                  warming under the highest emission scenar-
                                                                                  io (RCP 8.5) without mitigation measures.
Figure 6: Annual Temperature Change                                               Small increases in the annual precipitation
                                                                                  totals are projected for all periods across all
RCP 8.5 End of century: 2081-2100                                                 RCP scenarios. The ranges for the project-
        RCP 8.5 Mid-century: 2041-2060                                            ed changes in precipitation and tempera-
        RCP 8.5 Near future: 2021-2040                                            ture and historical data for the base period
RCP 4.5 End of century: 2081-2100                                                 (1991-2010) are given in Figure 5 and Figure
        RCP 4.5 Mid-century: 2041-2060                                            6 on the box-whisker-plots.3 These figures
        RCP 4.5 Near future: 2021-2040                                            present the spreads of changes in average
            Historical period: 1991-2010                                          annual temperature and precipitation over
                                            -50   0        50      100     150    the territory of Ukraine, showing the mean,
                                                      Precipitation change [mm]   minimum, and maximum values under each
                                                                                  RCP in every period.
                                                                                  In all three future periods, precipitation fol-
                                                                                  lows a complex trend, diverging between
                                                                                  colder and hotter seasons. The pattern of
                                                                                  monthly changes in precipitation is expected
                                                                                  to be wetter in colder months and dryer in
                                                                                  warmer months, particularly in the southern
                                                                                  and eastern regions. However, significant
                                                                                  differences are expected across regions,
                                                                                  and this pattern is not observed everywhere.
                                                                                  In general, monthly precipitation projections
                                                                                  confirm the previous findings of precipitation

3
    	    This figure and later the box and whisker diagrams: the lower and upper hinges of boxes correspond to the first and third
         quartiles (the 25th and 75th percentiles). The upper whisker extends from the hinge to the largest value no further than 1.5o
         (inter-quartile range) from the hinge. Inter-quartile range is the distance between the first and third quartiles of data distribution.




12                     Ukraine. Building Climate Resilience in Agriculture and Forestry
Figure 7: Annual Precipitation Change (mapped)
                                     RCP 4.5 Near future: 2021-2040                                                                                                                  RCP 4.5 Mid-century: 2041-2060



                                                                           Chernihivska                                                                                                                                       Chernihivska
                  Volynska                                                                                                                                         Volynska
                               Rivnenska                                                       Sumska                                                                            Rivnenska                                                        Sumska

                                              Zhytomyrska                                                                                                                                        Zhytomyrska


                                                                Kyivska                                                                                                                                            Kyivska

        Lvivska                                                                             Poltavska                                                     Lvivska                                                                              Poltavska
                                                                                                                Kharkivska                                                                                                                                          Kharkivska
                                  Khmelnytska                                                                                                                                       Khmelnytska
                      Ternopilska                                       Cherkaska                                                                                       Ternopilska                                        Cherkaska
                                                  Vinnytska                                                                          Luhanska                                                       Vinnytska                                                                             Luhanska
         Ivano-Frankivska                                                                                                                                     Ivano-Frankivska
  Zakarpatska                                                              Kirovohradska                                                           Zakarpatska                                                                Kirovohradska
                           Chernivetska                                                          Dnipropetrovska                                                            Chernivetska                                                            Dnipropetrovska
                                                                                                                          Donetska                                                                                                                                             Donetska


                                                                          Mykolaivska                                                                                                                                        Mykolaivska
                                                                                                          Zaporizka                                                                                                                                           Zaporizka


                                                               Odeska                                                                                                                                             Odeska
                                                                                          Khersonska                                                                                                                                         Khersonska




                                                                                                AR Krym                                                                                                                                             AR Krym




                                 RCP 4.5 End of century: 2081-2100                                                                                                                    RCP 8.5 Near future: 2021-2040



                                                                         Chernihivska                                                                                                                                      Chernihivska
                Volynska                                                                                                                                        Volynska
                             Rivnenska                                                       Sumska                                                                          Rivnenska                                                         Sumska

                                           Zhytomyrska                                                                                                                                       Zhytomyrska


                                                              Kyivska                                                                                                                                           Kyivska

      Lvivska                                                                             Poltavska                                                   Lvivska                                                                               Poltavska
                                                                                                             Kharkivska                                                                                                                                         Kharkivska
                                Khmelnytska                                                                                                                                      Khmelnytska
                    Ternopilska                                    Cherkaska                                                                                         Ternopilska                                     Cherkaska
                                                Vinnytska                                                                         Luhanska                                                      Vinnytska                                                                               Luhanska
       Ivano-Frankivska                                                                                                                                Ivano-Frankivska
Zakarpatska                                                              Kirovohradska                                                          Zakarpatska                                                                Kirovohradska
                      Chernivetska                                                            Dnipropetrovska                                                          Chernivetska                                                             Dnipropetrovska
                                                                                                                       Donetska                                                                                                                                           Donetska


                                                                        Mykolaivska                                                                                                                                       Mykolaivska
                                                                                                        Zaporizka                                                                                                                                          Zaporizka


                                                              Odeska                                                                                                                                            Odeska
                                                                                        Khersonska                                                                                                                                        Khersonska




                                                                                              AR Krym                                                                                                                                           AR Krym




                                    RCP 8.5 Mid-century: 2041-2060                                                                                                                RCP 8.5 End of century: 2081-2100



                                                                         Chernihivska                                                                                                                                       Chernihivska
                Volynska                                                                                                                                         Volynska
                             Rivnenska                                                       Sumska                                                                           Rivnenska                                                         Sumska

                                           Zhytomyrska                                                                                                                                         Zhytomyrska


                                                              Kyivska                                                                                                                                            Kyivska

      Lvivska                                                                             Poltavska                                                    Lvivska                                                                               Poltavska
                                                                                                             Kharkivska                                                                                                                                           Kharkivska
                                Khmelnytska                                                                                                                                       Khmelnytska
                    Ternopilska                                    Cherkaska                                                                                          Ternopilska                                        Cherkaska
                                                Vinnytska                                                                         Luhanska                                                        Vinnytska                                                                              Luhanska
       Ivano-Frankivska                                                                                                                                 Ivano-Frankivska
Zakarpatska                                                              Kirovohradska                                                           Zakarpatska                                                                Kirovohradska
                      Chernivetska                                                            Dnipropetrovska                                                           Chernivetska                                                              Dnipropetrovska
                                                                                                                       Donetska                                                                                                                                              Donetska


                                                                        Mykolaivska                                                                                                                                        Mykolaivska
                                                                                                        Zaporizka                                                                                                                                           Zaporizka


                                                              Odeska                                                                                                                                            Odeska
                                                                                        Khersonska                                                                                                                                         Khersonska




                                                                                              AR Krym                                                                                                                                            AR Krym




                                                                        Annual precipitation change to base period 1991-2010 [%]
                                     -10% — -5%                               -5% — 0%                                0% — 5%                   5% — 10%                                   10% — 15%                                    15% — 20%




                                                                Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                                                                                               13
                                                                                                                                        redistribution in the annual cycle with more
                                                                                                                                        noticeable changes under RCP  8.5, par-
Figure 8: M
           onthly Precipitation Change                                                                                                 ticularly during summers (Figure 8). Monthly
          in RCP 4.5 – End of Century                                                                                                   precipitation is projected to increase under
          Compared to Baseline                                                                                                          both RCPs by 2100 (Krakovksa et al. 2017),
                                                                                                                                        by which time the impacts become more pro-
                                                                                                                                        nounced, especially during winters.
     20

                                                                                                                                        The projected monthly temperature increas-
     10                                                                                                                                 es in all three future periods are generally
                                                                                                                                        higher in the summer months in hotter re-
     0
                                                                                                                                        gions and in the winter months in colder
  -10
                                                                                                                                        regions. Monthly temperature changes, par-
                                                                                                                                        ticularly changes in the annual temperature
  -20                                                                                                                                   range (ATR), are of special interest for future
                                                                                                                                        climate impact assessments. The ATR signi-
                                                                                                                                        fies the difference between the average tem-
                                                                                              September
              December




                                                                                                                    November
                                     February
                          January




                                                                                                          October
                                                                                     August




                                                                                                                               Annual
                                                 March




                                                                       June
                                                         April

                                                                 May



                                                                              July




                                                                                                                                        peratures of the warmest and coldest months
                                                                                              Precipitation change [mm]                 in a year. Historical data show that ATR has
                                                                                                                                        generally been declining in Ukraine (Balabu-
                                                                                                                                        kh and Malitskaya 2017), and the projections
                                                                                                                                        indicate that this decline is likely to continue.
                                                                                                                                        The main reason for this decline is the rela-
                                                                                                                                        tively higher increase in temperature during
           onthly Precipitation Change
Figure 9: M                                                                                                                             the coldest months.
          in RCP 8.5 – End of Century
          Compared to Baseline


      20




      10




          0




     -10




     -20
                                                                                              September
               December




                                                                                                                    November
                                      February
                           January




                                                                                                          October
                                                                                     August




                                                                                                                               Annual
                                                 March




                                                                       June
                                                         April

                                                                 May



                                                                              July




                                                                                              Precipitation change [mm]




14                                      Ukraine. Building Climate Resilience in Agriculture and Forestry
    Projections at the Oblast Level
2.4 
Regional temperature changes show a consistent increase until the end of the century under
RCP  4.5 and RCP  8.5, with increases higher in average daily minimum temperatures than
in daily maximum temperatures. Figure 10 shows both precipitation and temperature projec-
tions for each oblast for both scenarios. For each projected period, the increase in minimum,
maximum, and mean temperatures [°C] is compared to the base period. Under RCP 8.5, tem-
peratures are higher than under RCP 4.5, but most notable is the increase in minimum tem-
peratures (blue bars) in both scenarios. It is much more intense than the increase in maximum
temperatures, indicating increasing aridity and warming in the summer months and fewer cool
days in the winter. The width of the bands describing the range of precipitation changes in
the scenarios and projection periods shows a significant increase in regional variability, which
becomes more pronounced at the end of the period. The projected warming temperatures and
shift in precipitation patterns caused by global warming will, in turn, lead to increased water
demand due to higher evapotranspiration rates.
These climate trends are of critical importance for regions with a higher proportion of ru-
ral population which is dependent on agricultural income, as distributional effects of climate
change on household incomes are expected to be stronger in oblasts where households rely
on agricultural production. These oblasts are Chernivetska, Dnipropetrovska, Ivano-Frankivs-
ka, Kirovohradska, Rivnenska, Ternopilska, Vinnytska, Volynska, and Zhytomyrska.
The regional variations in precipitation become stronger by the end of the century. For precipi-
tation, projected changes are more heterogeneous across RCPs and time horizons. In general,
the southern and central areas are characterized by the lowest increase in precipitation, with
decreases even in warm months. In case of RCP 4.5, in both near future and middle-of-centu-
ry periods, a low increase in the average annual precipitation level, with the highest decrease
in summers, is registered for the southeastern (Khersonska, Zaporizka, Donetska, Luhanska,
Mykolaivska, and Odeska) and western oblasts (Zakarpatska). In contrast, larger precipitation
increases are recorded in the northern oblasts (especially in Rivnenska, and Volynska in the
northwest).



    Projections at the City Level
2.5 
Cities are projected to experience intense temperature increases through the end of the cen-
tury (over +5.0°C in summer in Luhansk and in winter in Kyiv), aggravated by the urban heat
island effect (see Box 2). Monthly mean temperatures and precipitation — as annual cycles
with their projected changes over the base period (1991-2010) — are presented and analyz-
ed for five representative cities in different geographical regions of Ukraine, including Kyiv
(north), Lviv (west), Kropyvnytskyi (center), Luhansk (east), and Odesa (south). They are also
aggregated for the entire country. For all periods, the annual cycle of air temperature has the
same pattern, with July as the hottest month and January as the coldest. Historical monthly
temperature data from E-OBS (1991-2010 period vs. 1961-1990) shows that the most warm-
ing has taken place in winter and summer months, with slight cooling in December in all cities
except Lviv; Luhansk (-0.3°C in May) and Kropyvnytskyi (-0.1°C in May). Warming during the
other winter months (from historical to the baseline period) is comparable with temperature
increases projected throughout the mid-century period under RCP 8.5 and increases in max-




                        Ukraine. Building Climate Resilience in Agriculture and Forestry    15
                                                                   Figure 10: Projected Changes in Temperatures and Precipitation by Oblast




16
                                                                                      Temperature           change     Precipitation   change                    Temperature       change                      Precipitation   change                        Temperature          change                  Precipitation   change
                                                                                        2021-2040                        2021-2040                                 2041-2060                                     2041-2060                                     2081-2100                                    2081-2100
                                                                   Cherkaska
                                                                   Chernihivska
                                                                   Chernivetska
                                                                   Crimea
                                                                   Dnipropetrovska
                                                                   Donetska
                                                                   Ivano-Frankivska
                                                                   Kharkivska
                                                                   Khersonska
                                                                   Khmelnytska
                                                                   Kyivska
                                                                   Kirovohradska
                                                                   Lvivska
                                                                   Luhanska
                                                                   Mykolaivska
                                                                   Odeska
                                                                   P ol tav s ka
                                                                   Rivnenska
                                                                   Sumska
                                                                   Sevastopol
                                                                   Ternopilska
                                                                   Vi nnyts ka
                                                                   Volynska
                                                                   Zakarpatska
                                                                   Zaporizka
                                                                   Zhytomyrska
                                                                                      0 °C 1°C 2 °C 3°C 4 °C 5 °C     0%      5%       10%      15%   20%     0 °C   1 °C   2 °C     3 °C    4 °C   5 °C      0%      5%       10%      15%       20%     0 °C      1 °C   2 °C     3 °C   4 °C   5 °C   0%      5%       10%      15%   20%


                                                                   RCP 4.5
                                                                                      Temperature           change     Precipitation   change                    Temperature       change                      Precipitation   change                        Temperature          change                  Precipitation   change
                                                                                        2021-2040                        2021-2040                                 2041-2060                                     2041-2060                                     2081-2100                                    2081-2100
                                                                   Cherkaska
                                                                   Chernihivska
                                                                   Chernivetska
                                                                   Crimea
                                                                   Dnipropetrovska
                                                                   Donetska
                                                                   Ivano-Frankivska
                                                                   Kharkivska
                                                                   Khersonska
                                                                   Khmelnytska
                                                                   Kyivska
                                                                   Kirovohradska
                                                                   Lvivska




Ukraine. Building Climate Resilience in Agriculture and Forestry
                                                                   Luhanska
                                                                   Mykolaivska
                                                                   Odeska
                                                                   P ol tav s ka
                                                                   Rivnenska
                                                                   Sumska
                                                                   Sevastopol
                                                                   Ternopilska
                                                                   Vi nnyts ka
                                                                   Volynska
                                                                   Zakarpatska
                                                                   Zaporizka
                                                                   Zhytomyrska
                                                                                      0 °C 1 °C 2 °C 3 °C 4 °C 5 °C   0%      5%       10%      15%   20%     0 °C   1 °C   2 °C     3 °C    4°C    5 °C      0%      5%       10%      15%       20%     0 °C      1 °C   2°C      3 °C   4 °C   5 °C   0%      5%       10%      15%   20%



                                                                                                                       Absolute change in regional temperatures to base period              Min temperature          Max temperature          Average temperature
                                                                   RCP 8.5
imum temperatures are projected in the cities of Kyiv (+2.4°C) and Luhansk (+2.3°C), where
lower winter monthly temperatures are typically observed. The highest summer temperature
increases have been recorded in Kyiv in July (+1.8°C/ from historical to baseline period),
caused by the urban heat island effect.
In the near future projections, the highest temperature rises are in March for all cities under
both RCP scenarios. And the warming is over the 5-95% range only in March for RCP 4.5
in the ensemble of RCMs. In other months and scenarios, uncertainties of the ensemble of
RCMs are significant, resulting in low confidence of the estimates.
During the middle of the century period between 2041-2060, the differences between the
two scenarios become apparent, and in Ukraine’s cities, strong temperature signals are
observed for almost all months, giving rise to high confidence in the warming projections.
Warming in other winter months is comparable to the temperatures projected – through the
middle of the century under RCP 8.5 and through the end of the century under RCP 4.5,
with the maximum increases projected for Kyiv (+2.4°C) and Luhansk (+2.3°C), where lower
winter monthly temperatures are typically observed.
During the end of the century period (2081-2100), the tendency for increased warming in
colder regions in both the winter and summer months is even more pronounced than previ-
ously understood, and the difference between scenarios is the largest (between +2.0°C to
+2.7°C). During the 21st century’s last 20-year period, warming will reach +5.0°C in almost
for every month in cities in every region.
With further warming, annual precipitation will increase for all regions during all periods, with
redistributions occurring during different months throughout the year.
•	   Kyiv (north): The highest warming has been recently recorded during winter in January
     (+2.4°C) and during summer in July (+1.8°C), caused by the urban heat island effect.
     Further warming is expected through the end of the century under the highest emission
     scenario, RCP 8.5, which can result in higher monthly temperatures each year; this means
     an absence of winter seasons, even in the north of the country, and mean monthly sum-
     mer temperatures over +25°C. We see the month with maximum precipitation shift from
     July to June. This shift has already begun, and is visible in a comparison of historic data
     (1961-1990) to the baseline period (1991-2010). It continues for most periods under both
     RCPs (with the exception of the warmest period 2081-2100 under RCP 8.5, during which
     the same precipitation amounts are projected for both June and July).
•	   Lviv (west): Precipitation in the annual cycle reaches its maximum in June-July in the
     historic period (1961-1990). The annual distribution of precipitation changes in the base
     period, with more precipitation in May than June. This trend continues in all three future
     periods. Increased warming is likely to result in a completely new shape of the annual
     precipitation cycle, with maximum precipitation amounts in May and July through all three
     future time periods, except under RCP  4.5, when the annual cycle will likely be similar
     to the shape of the historic period (1961-1990) at the end of the century, with one sharp
     maximum in July.




                         Ukraine. Building Climate Resilience in Agriculture and Forestry    17
            ulti-Year Mean Monthly Temperature and Temperature Change
Figure 11. M
           for Different Climatic Periods and the Baseline4

                                                            KYI V (NORT H)                                                       ODESA (SOUT H)

                             30                                                                                 30
                             25                                                                                 25
                             20
                                                                                                                20




                                                                                                Temper atur e
             Temper atur e




                             15
                                                                                                                15
                             10
                                                                                                                10
                              5
                                                                                                                 5
                              0

                              -5                                                                                 0

                             -10                                                                                -5
                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
                                                        M onth                                                                             M onth

                                                          L UHANSK (EAST )                                                         L VI V (WEST )

                             30                                                                                 30

                             25                                                                                 25
                             20                                                                                 20
             Temper atur e




                                                                                                Temper atur e



                             15                                                                                 15

                             10                                                                                 10
                              5                                                                                  5

                              0                                                                                  0

                              -5                                                                                 -5
                             -10                                                                                -10
                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
                                                                   M onth                                                                     M onth


                                                    KROPYVNYST SKYI (CENT ER)                                                        UKRAI NE

                             30                                                                                 30
                             25                                                                                 25
                             20                                                                                 20
             Temper atur e




                                                                                                Temper atur e




                             15                                                                                 15
                             10                                                                                 10
                                    Temper atur e




                              5                                                                                  5
                              0                                                                                  0
                              -5
                                                                                                                 -5
                             -10
                                                                                                                -10
                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
                                                                                                                      Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
                                                        M onth
                                                                                                                                           M onth


                                                     I RCP4.5               I RCP8.5          II RCP4.5                           II RCP8.5             III RCP4.5
                                                     III RCP8.5             E-Obs 2000-1980   I RCP4.5                            I RCP8.5              II RCP4.5
                                                     II RCP8.5              III RCP4.5        III RCP8.5                          E-Obs 1961-1990       E-Obs 1991-2010




4
    	   In these graphs, lines indicate absolute values, while bars show incremental value compared to the baseline (1990 – 2010).




18                                 Ukraine. Building Climate Resilience in Agriculture and Forestry
            ulti-Year Mean Monthly Precipitation Amounts (lines) and Projected
Figure 12. M
           Changes (histograms) for Different Climatic Periods, Observations,
           and Scenario Datasets for Cities in Different Regions and Ukraine
           as a Whole

                                                       KYI V (NORT H)                                                                                ODESA (SOUT H)

                                 90                                                                                              60
                                 80                                                                                              50
                                 70
           Pr ecipitation, mm




                                                                                                           Pr ecipitation, mm
                                                                                                                                 40
                                 60
                                 50                                                                                              30
                                 40                                                                                              20
                                 30                                                                                              10
                                 20
                                                                                                                                     0
                                 10
                                  0                                                                                             -10
                                -10                                                                                             -20

                                       Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec                                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
                                                            M onth                                                                                             M onth

                                                  L UHANSK (EAST )                                                                                    L VI V (WEST )

                                 70                                                                                             120
                                 60                                                                                             100
                                 50
           Pr ecipitation, mm




                                                                                                           Pr ecipitation, mm




                                                                                                                                 80
                                 40
                                                                                                                                 60
                                 30
                                                                                                                                 40
                                 20
                                                                                                                                 20
                                 10
                                  0                                                                                                  0
                                -10                                                                                             -20
                                -20                                                                                             -40

                                       Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec                                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
                                                            M onth                                                                                             M onth

                                         KROPYVNYT SKYI (CENT ER)                                                                                       UKRAI NE

                                70                                                                                              90
                                60                                                                                              80
                                                                                                                                70
                                                                                                 Pr ecipitation, mm
 Pr ecipitation, mm




                                50
                                                                                                                                60
                                40                                                                                              50
                                30                                                                                              40
                                20                                                                                              30
                                                                                                                                20
                                10                                                                                              10
                                0                                                                                           0
                          -10                                                                                             -10

                                      Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec                                                   Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
                                                           M onth                                                                                             M onth



                                          I RCP4.5                I RCP8.5                II RCP4.5                                              II RCP8.5              III RCP4.5
                                          III RCP8.5              I RCP4.5                I RCP8.5                                               II RCP4.5              II RCP8.5
                                          III RCP4.5              III RCP8.5              E-Obs 1961-1990                                        E-Obs 1991-2010




                                                          Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                   19
 Box 2: Urban Vulnerability to Temperature Extremes

  With climate change, high temperature extremes will become more frequent and more severe, while the
  intensity and frequency of extreme low temperatures will gradually decrease (Naumann et al. 2020).
  A heat wave is a period during which the maximum daily air temperature over five consecutive days
  exceeds the mean maximum historical air temperatures by 5°C (Shevchenko et al. 2014a). Ukraine had
  the highest incidence of heat waves during 2001-2010, with the longest being the 24-day heatwave in
  Luhansk (Shevchenko et al. 2014b). Heatwaves are likely to become more frequent, intense, and long-
  lasting following a 20% increase in projected average daily maximum temperatures (see Figure 13) in
  the south of the country compared to the period 1990-2001. If global warming reaches 2°C and 3°C, the
  occurrence probability of heat waves will increase by a factor of 10 to 20, respectively, compared to the
  1981-2010 period (Naumann et al. 2020). With a disappearing winter season and mean monthly summer
  temperatures over +25°C, heat waves are likely to increase in frequency. This is particularly important for
  Kyiv, Kharkiv and Luhansk, which are already affected by heat waves (Shevchenko et al. 2014b).
  Impact on health, infrastructure, and the economy. Heat stress affects quality of life, especially in cities
  due to the urban heat island effect — increased air temperature in the central part of a city compared
  to its suburbs. With an increasing number of hot days (Figure 15) heatstroke and cardiovascular,
  cerebrovascular, and respiratory diseases could become more prevalent (Naumann et al. 2020). Extended
  high temperatures can damage concrete infrastructure and public transportation, and adversely affect the
  operation of thermal and nuclear power plants (Platts 2018).




    Other Climate and Vulnerability Indicators
2.6 

Significant increases in the regional average daily maximum temperatures in the hottest month
are projected under both RCP 4.5 and RCP 8.5. Monthly means of daily maximum tempera-
tures over 30°С in the hottest month were observed mainly in the southern steppe of Ukraine
in the baseline period (1991-2010). This region will remain the hottest until the end of the cen-
tury under both the RCP 4.5 and RCP 8.5 scenarios, but projections show that the area with
daily maximum temperatures above 30°C will expand to the entire southern and central parts
of Ukraine (Figure 13). In the south of the country, the average daily maximum temperature in
July will exceed 34°С. Such a temperature has never been observed in Ukraine in the recent
past (Balabukh and Malitskaya 2017).
The number of ice days and frost nights will decrease dramatically—by 22 days—in the south
of Ukraine under RCP 4.5, as shown in Figure 14. An increase in air temperature, especially
minimum temperature during the cold season, will cause a significant reduction in the number
of frost nights by the end of the century for both scenarios. Under RCP 4.5, by the end of the
century Polissya may experience an additional decrease of 34 (or more) frost nights.
More than 100 tropical nights and up to 135 summer days per year are projected for the south-
ern steppe during the end of century period under RCP 8.5. Rising summer temperatures will
result in heatwaves and increased aridity in the south and east.




20           Ukraine. Building Climate Resilience in Agriculture and Forestry
            emperature in the Hottest and the Coldest Months for the Baseline
Figure 13: T
           Period (1991-2010) and at the End of the 21st Century Under RCP 4.5
           and RCP 8.5

                      Mean Maximum Near-Surface Air Temperature E-Obs v20.0                            Bias-Adjusted Mean Maximum Near-Surface Air Temperature RCP4.5
                               in the hottest month (July) 1991-2010                                                  in the hottest month (July) 2081-2100
 Lattitude




                                                                                    Lattitude
                                            Longitude                                                                              Longitude

                                    Maximum temperature (Celcius)                                                                    Deg. C




                  Bias-Adjusted Mean Maximum Near-Surface Air Temperature RCP8.5                                Mean Minimum Near-Surface Air Temperature E-Obs v20.0
                                 in the hottest month (July) 2081-2100                                                 in the coldest month (January) 1991-2010

                                                                                        Lattitude
      Lattitude




                                              Longitude                                                                            Longitude

                                               Deg. C                                                                       Minimum temperature (Celcius)




                   Bias-Adjusted Mean Minimum Near-Surface Air Temperature RCP4.5                         Bias-Adjusted Mean Minimum Near-Surface Air Temperature RCP8.5
                                in the coldest month (January) 2081-2100                                               in the coldest month (January) 2081-2100
      Lattitude




                                                                                           Lattitude




                                               Longitude                                                                             Longitude

                                               Deg. C                                                                                 Deg. C




                                            Ukraine. Building Climate Resilience in Agriculture and Forestry                                                               21
Figure 14: Changes in Frost Nights                                                     Figure 15: Changes in Tropical Nights
                     Change in Frost Days 1991-2010 vs 1961-1990 E-Obs v20.0                               Change in Tropical Nights 1991-2010 vs 1961-1990 E-Obs v20.0
                  Number of days where minimum temperature is below 0 degree Celcius                     Number of days where minimum temperature is above 20 degrees Celcius




                                                                                         Lattitude
  Lattitude




                                              Longitude                                                                            Longitude
                                           change in days                                                                        change in days




                            Change in Frost Days in 2081-2100 RCP4.5                                               Change in Frost Days in 2081-2100 RCP4.5
                  Number of days where minimum temperature is below 0 degree Celcius                   Number of days where minimum temperature is above 20 degrees Celcius




                                                                                           Lattitude
      Lattitude




                                               Longitude                                                                            Longitude

                                             change in days                                                                      change in days




                            Change in Frost Days in 2081-2100 RCP8.5                                               Change in Frost Days in 2081-2100 RCP8.5
                  Number of days where minimum temperature is below 0 degree Celcius                   Number of days where minimum temperature is above 20 degrees Celcius
                                                                                         Lattitude
  Lattitude




                                                Longitude                                                                          Longitude

                                             change in days                                                                       change in days




Note: The scale shows a decrease in number                                             Note: The scale shows how tropical nights
of frost days from the baseline period (top) to                                        increase in a range between 8 to 72 trop-
mid-century (middle) to far future (bottom).                                           ical nights a year from the baseline (top)
Shades of blue represent a range from 2 to                                             to mid-century (middle) to end of century
30 fewer frost days in a year compared to the                                          (bottom). Lighter colors indicate a smaller
baseline. Shades of red mark a more dramatic                                           increase in tropical nights over the year
decrease in the number of frost days predict-                                          compared to darker shades.
ed for RCP 8.5: from 34 to 70 fewer frost days
in a year compared to the baseline




22                              Ukraine. Building Climate Resilience in Agriculture and Forestry
The highest warming in summers is forecast for July in Kyiv, the capital of Ukraine, demon-
strating the effects of urbanization. Under the RCP 4.5 scenario, in the southern steppe, the
number of summer days will significantly expand as compared to the base period, with Odes-
ka and Luhanska oblasts reaching more than 100 summer days and rising by an additional 19
days (in the range from 5-32 days) in the end of century period (Figure 15). These changes
will be more than twice as high under RCP 8.5, with summer days projected to increase by
an average of 42 days (the full range being 19 to 62 days) by the end of the century, exceed-
ing 90 days in almost all of Ukraine, except the Carpathians, Prykarpattia, and Polissya, and
reaching a maximum of over 135 days in the southern steppe.




                       Ukraine. Building Climate Resilience in Agriculture and Forestry   23
CHAPTER 3:
IMPACT OF CLIMATE CHANGE
ON AGRICULTURE
The impact of climate change on agriculture is assessed by modeling change in yields
of key crops. The World Food Studies Crop Simulation (WOFOST) Model5 was used to
assess the biophysical impacts of climate change on yield potentials of five key crops for
the near future and mid-century periods, relative to the baseline period (2006 – 2015), with
the latest climate projections. The yield projections were combined with changes in land
areas under each crop in 2030 and 2050, and changes in prices for those years under the
combined SSP2 – RCP 8.5 scenario from the International Model for Policy Analysis of
Agricultural Commodities and Trade (IMPACT) by the International Food Policy Research
Institute (IFPRI) to estimate changes in production and production values relative to the
baseline period.
Variability and uncertainty in the projections of future yields and production due to climate
change is reflected in the “low,” “mean,” and “high” agriculture projections for each RCP
scenario.6 The specific uncertainty ranges (+/- values) allow for systematic interpretation
of the modeling results. The mean projection represents the mean value of the modeled
yield potential throughout the oblast. Low and high projections are the lower and upper
limits of the modeled yield potential, as determined by the confidence interval. This high-
lights the uncertainty associated with the variations of local soil and climatic conditions
within an oblast territory; such variations can be significant and are critical for estimating
potential production and values of agricultural outputs.


3.1 Summary of Key Findings
An increase in crop yields for almost all oblasts is expected under the “high” projection scenar-
io, and in the mean projections scenario for soybean and wheat in both 2030 and 2050 under
both RCPs. Under a low projection scenario, yields of barley, maize, and sunflower would
decrease in almost all oblasts. As the range of projected yield changes across the oblasts is
large, so is the risk of outcomes below expectations in any given year. In the mean projections,
the yields of barley under RCP 4.5 range from negative to positive in 2030, all negative values
in 2050, and all negative for both periods under RCP 8.5. The yields of maize and sunflower
vary from negative to positive under both RCP 4.5 and RCP 8.5, but the decreases will be

5
    	   Elevated atmospheric CO2 concentrations can increase yields at lower temperature increases. The WOFOST model ac-
        counts for CO2 fertilization. Higher levels of CO2 can significantly increase photosynthesis causing an increase in the total
        biomass generation and yield for wheat, barley, sunflower, and soybean and are less relevant for maize.
6
    	   Mean scenario can be interpreted as the most likely realization of climate conditions that affect the crops’ yield and productiv-
        ity. Respectively, low and high projections resemble the most unfavorable and most favorable realization of climate conditions
        affecting the crop yield and productivity. High projections promote higher crops’ yield and productivity with strong regional
        differences due to volatility of local climate conditions.




24                  Ukraine. Building Climate Resilience in Agriculture and Forestry
more pronounced for both crops in all oblasts under RCP 8.5 in 2050. The simulations show
a consistently negative trend for barley and sunflower production and a clear positive trend
for wheat and soybean under both RCPs. However, these trends should be interpreted as
indicative, with considerable uncertainty ranges in the production of each crop in each region.
The projected higher prices for wheat and maize make it especially attractive to increase land
areas under these crops, especially in oblasts where the yield gains through the mid-century
period are significant. The increase in productivity, combined with a growing trend in price
of wheat, is expected to make it a very advantageous crop in the future. The value of wheat
production goes up by 29%- 59% in 2030, and by 57%- 120% in 2050. The price of maize is
expected to increase sharply by 2030 and to almost double by 2050, making it attractive to
grow even if yields decline. The price of soybean is also expected to increase, but less so than
maize (by 32%- 48% relative to 2010, respectively). All oblasts are expected to see increases
in the value of their soybean crops by 2030, and even more so by 2050, as compared to 2010.
The value of barley production in 2030 and 2050 decreases in all oblasts despite the increase
in prices due to the drop in yield. Therefore, the changes in the values of barley production
both in 2030 and 2050 are not significantly different from the baseline.
In the mean projection scenario, the value of production goes up in all oblasts but more so
in the eastern and central-eastern oblasts. Under the low projection, all oblasts experience
declines in the values of production in 2030, but twelve out of the 25 oblasts will see increas-
es by 2050. Under the high projection, all oblasts experience increases in production values,
with even larger increases in 2050 than in 2030, assuming the stated adaptation measures
will take place.
Comparisons of results without adaptation7 and with simulated adaptation measures clearly
indicate the benefits of adaptation for all oblasts, especially those with higher reliance on
agriculture (Cherkaska, Dnipropetrovska, Kirovohradska, and Poltavska). With adaptation
measures the total production value of the five crops is projected to increase by 29% (with
an uncertainty range of -32% to +91%) in 2030 and 56% (with an uncertainty range of -1%
to +112%) in 2050, compared to 2010. The uncertainty range of climate change impacts on
production values is large in both sets of projections (with and without adaptation), but the
uncertainty range for simulation with adaptation shifts towards the possible increase in value,
signaling strong confidence in risk reduction potentials of adaptation measures.



3.2 Yield Projections
Changes in seasonal precipitation and temperature are the primary drivers of projected yield
[tons8/ha] changes in Ukraine.9 The results on yields show a complex set of projections for
the different crops under two scenarios in the two time periods, 2030 and 2050. The projec-
tions also have a certain degree of uncertainty, reflected in a probability distribution. Table
2 below shows the ranges of changes in yields for the different oblasts. Under RCP 8.5, all

7
    	   Change in land allocation, availability of water and shift in sowing times.
8
    	   Ton is used throughout this document to indicate Metric Ton equal to 1000 kg.
9
    	   The De Martonne Aridity Index was also used for this analysis. See Annex I.




                                      Ukraine. Building Climate Resilience in Agriculture and Forestry   25
oblasts will face a decline in yields in the range of -12% to -15% in 2030, with the negative
trend continuing until 2050. The decreases in yields for maize and sunflower become more
pronounced for both crops in all oblasts under RCP 8.5 in 2050, with a range between -23%
and +3% for maize and -21% to +8% for sunflower. Additional measures aimed at maintain-
ing optimal water balance are needed to ensure sunflower and maize yields until 2050. For
wheat, yields show an increase for all oblasts in both 2030 and 2050 under both RCP sce-
narios. An expected high return is partially offset by a relatively high risk of outcomes below
expectations in any given year.
While projected precipitation changes are complex, the upward trend in temperature coupled
with an increasing CO2 concentration in the atmosphere are crucial factors for estimating crop
yields. As detailed in Chapter 2 of this report, the temperature trend is increasing overall for all
seasons. In general, winters are becoming wetter and summers dryer.
The range of possible yield changes across the oblasts is large, as is the risk of outcomes
below expectations in any specific year. The modeling of yields gives a probability distribution
for the ranges of values (see Table 2). The modeling also provides a low- and a high-projection
scenario based on the distribution: the 5th percentile of the distribution (low) and the 95th per-
centile (high). These data points show the risk ranges at the two ends of the distribution. The
changes in high projection are positive and often more than double the mean scenario, while
the low scenario changes are often negative. The high yield projection shows an increase in
crop yields for almost all oblasts in the country, and the low projection indicates a decrease in
yields of barley, maize, and sunflower for almost all oblasts. Given the high resolution of the
analyzed data (7,400 grid cells), yield ranges reflect regional differences in climatic conditions.
The detailed map representation of yields is provided in Figure 17.


Table 2: Changes in Yields Across Oblasts for Major Crops Due to Climate Change10



                     2030                                                   2050

                     RCP 4.5                    RCP 8.5                     RCP 4.5                  RCP 8.5

 Barley              -2.3% to +7.5%             -15.1 to -11.5%             -11.0% to -0.3%          -15.8% to -5.2%

 Maize               -17.2% to +14.1% -22.0% to -2.3%                       -18.8% to +4.3%          -22.9% to +3.0%

 Soybean             +8.6% to +27.9%            +8.8% to +31.7%             +18.3% to +30.4% +21.1% to +46.7%

 Sunflower -25.1% to +8.1%                      -9.4% to +6.1%              -10.6% to +16.0% -20.9% to +7.6%

 Wheat               +8.6% to +44.1%            +13.9% to +40.7% +11.9% to +49.1% +20.8% to +63.5%



10
     	 Figures are for the mean projection and changes in yield [tons/ha] relative to 2010 levels.




26                 Ukraine. Building Climate Resilience in Agriculture and Forestry
Figure 16: Seasonal Precipitation in RCP 4.5 and RCP 8.5

                 RCP 4.5 Near future: 2021-2040                                                                       RCP 4.5 Mid-century: 2041-2060


                                                 Chernihivska                                                                                          Chernihivska
          Volynska                                               Sumska                                         Volynska                                               Sumska
                 Rivnenska                                                                                             Rivnenska
                             Zhytomyrska                                                                                           Zhytomyrska

                                            Kyivska                                                                                               Kyivska
      Lvivska                                                 Poltavska      Kharkivska                     Lvivska                                                 Poltavska      Kharkivska
                   Khmelnytska                                                                                           Khmelnytska
             Ternopilska                       Cherkaska                                                           Ternopilska                       Cherkaska
                             Vinnytska                                                      Luhanska                               Vinnytska                                                      Luhanska
     Ivano-Frankivska                                                                                      Ivano-Frankivska




                                                                                                                                                                                                             Winter
 Zakarpatska                                     Kirovohradska                                         Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                    Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                              Donetska

                                                Mykolaivska                                                                                           Mykolaivska
                                                                          Zaporizka                                                                                             Zaporizka

                                            Odeska         Khersonska                                                                             Odeska         Khersonska




                                                                  Crimea                                                                                                Crimea




                                                 Chernihivska                                                                                          Chernihivska
          Volynska                                               Sumska                                         Volynska                                               Sumska
                 Rivnenska                                                                                             Rivnenska
                             Zhytomyrska                                                                                           Zhytomyrska

                                            Kyivska                                                                                               Kyivska
      Lvivska                                                 Poltavska      Kharkivska                     Lvivska                                                 Poltavska      Kharkivska
                   Khmelnytska                                                                                           Khmelnytska
             Ternopilska                       Cherkaska                                                           Ternopilska                       Cherkaska
                             Vinnytska                                                      Luhanska                               Vinnytska                                                      Luhanska
     Ivano-Frankivska                                                                                      Ivano-Frankivska




                                                                                                                                                                                                             Spring
 Zakarpatska                                     Kirovohradska                                         Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                    Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                              Donetska

                                                Mykolaivska                                                                                           Mykolaivska
                                                                          Zaporizka                                                                                             Zaporizka

                                            Odeska         Khersonska                                                                             Odeska         Khersonska




                                                                  Crimea                                                                                                Crimea




                                                 Chernihivska                                                                                          Chernihivska
          Volynska                                               Sumska                                         Volynska                                               Sumska
                 Rivnenska                                                                                             Rivnenska
                             Zhytomyrska                                                                                           Zhytomyrska

                                            Kyivska                                                                                               Kyivska
      Lvivska                                                 Poltavska      Kharkivska                     Lvivska                                                 Poltavska      Kharkivska
                      Khmelnytska                                                                                           Khmelnytska
                Ternopilska                    Cherkaska                                                              Ternopilska                    Cherkaska
                                Vinnytska                                                   Luhanska                                  Vinnytska                                                   Luhanska




                                                                                                                                                                                                             Summer
     Ivano-Frankivska                                                                                      Ivano-Frankivska
 Zakarpatska                                     Kirovohradska                                         Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                    Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                              Donetska

                                                Mykolaivska                                                                                           Mykolaivska
                                                                          Zaporizka                                                                                             Zaporizka

                                            Odeska         Khersonska                                                                             Odeska         Khersonska




                                                                  Crimea                                                                                                Crimea




                                                 Chernihivska                                                                                          Chernihivska
          Volynska                                               Sumska                                         Volynska                                               Sumska
                 Rivnenska                                                                                             Rivnenska
                             Zhytomyrska                                                                                           Zhytomyrska

                                            Kyivska                                                                                               Kyivska
      Lvivska                                                 Poltavska      Kharkivska                     Lvivska                                                 Poltavska      Kharkivska
                   Khmelnytska                                                                                              Khmelnytska
             Ternopilska                       Cherkaska                                                              Ternopilska                    Cherkaska
                             Vinnytska                                                      Luhanska                                  Vinnytska                                                   Luhanska
     Ivano-Frankivska                                                                                      Ivano-Frankivska
                                                                                                                                                                                                             Autumn




 Zakarpatska                                     Kirovohradska                                         Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                    Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                              Donetska

                                                Mykolaivska                                                                                           Mykolaivska
                                                                          Zaporizka                                                                                             Zaporizka

                                            Odeska         Khersonska                                                                             Odeska         Khersonska




                                                                  Crimea                                                                                                Crimea




                                                Change in bias-adjusted precipitation to baseline [%]
                  -25% – -15%                                    -15% – -5%                            -5% – 5%                                   5% – 15%                                       15% – 25%




                                                      Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                                27
                 RCP 8.5 Near future: 2021-2040                                                                          RCP 8.5 Mid-century: 2041-2060


                                                 Chernihivska                                                                                             Chernihivska
          Volynska                                               Sumska                                            Volynska                                               Sumska
                 Rivnenska                                                                                                Rivnenska
                             Zhytomyrska                                                                                              Zhytomyrska

                                            Kyivska                                                                                                  Kyivska
      Lvivska                                                 Poltavska      Kharkivska                        Lvivska                                                 Poltavska      Kharkivska
                   Khmelnytska                                                                                              Khmelnytska
             Ternopilska                       Cherkaska                                                              Ternopilska                       Cherkaska
                             Vinnytska                                                      Luhanska                                  Vinnytska                                                      Luhanska




                                                                                                                                                                                                                Winter
     Ivano-Frankivska                                                                                         Ivano-Frankivska
 Zakarpatska                                     Kirovohradska                                            Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                       Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                                 Donetska

                                                Mykolaivska                                                                                              Mykolaivska
                                                                          Zaporizka                                                                                                Zaporizka

                                            Odeska         Khersonska                                                                                Odeska         Khersonska




                                                                  Crimea                                                                                                   Crimea




                                                 Chernihivska                                                                                             Chernihivska
          Volynska                                               Sumska                                            Volynska                                               Sumska
                 Rivnenska                                                                                                Rivnenska
                             Zhytomyrska                                                                                              Zhytomyrska

                                            Kyivska                                                                                                  Kyivska
      Lvivska                                                 Poltavska      Kharkivska                        Lvivska                                                 Poltavska      Kharkivska
                      Khmelnytska                                                                                              Khmelnytska
                Ternopilska                    Cherkaska                                                                 Ternopilska                    Cherkaska
                                Vinnytska                                                   Luhanska                                     Vinnytska                                                   Luhanska




                                                                                                                                                                                                                Spring
     Ivano-Frankivska                                                                                         Ivano-Frankivska
 Zakarpatska                                     Kirovohradska                                            Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                       Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                                 Donetska

                                                Mykolaivska                                                                                              Mykolaivska
                                                                          Zaporizka                                                                                                Zaporizka

                                            Odeska         Khersonska                                                                                Odeska         Khersonska




                                                                  Crimea                                                                                                   Crimea




                                                 Chernihivska                                                                                             Chernihivska
          Volynska                                               Sumska                                            Volynska                                               Sumska
                 Rivnenska                                                                                                Rivnenska
                             Zhytomyrska                                                                                              Zhytomyrska

                                            Kyivska                                                                                                  Kyivska
      Lvivska                                                 Poltavska      Kharkivska                        Lvivska                                                 Poltavska      Kharkivska
                   Khmelnytska                                                                                              Khmelnytska
             Ternopilska                       Cherkaska                                                              Ternopilska                       Cherkaska




                                                                                                                                                                                                                Summer
                             Vinnytska                                                      Luhanska                                  Vinnytska                                                      Luhanska
     Ivano-Frankivska                                                                                         Ivano-Frankivska
 Zakarpatska                                     Kirovohradska                                            Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                       Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                                 Donetska

                                                Mykolaivska                                                                                              Mykolaivska
                                                                          Zaporizka                                                                                                Zaporizka

                                            Odeska         Khersonska                                                                                Odeska         Khersonska




                                                                  Crimea                                                                                                   Crimea




                                                 Chernihivska                                                                                             Chernihivska
          Volynska                                               Sumska                                            Volynska                                               Sumska
                 Rivnenska                                                                                                Rivnenska
                             Zhytomyrska                                                                                              Zhytomyrska

                                            Kyivska                                                                                                  Kyivska
      Lvivska                                                 Poltavska      Kharkivska                        Lvivska                                                 Poltavska      Kharkivska
                      Khmelnytska                                                                                              Khmelnytska
                Ternopilska                    Cherkaska                                                                 Ternopilska                    Cherkaska
                                                                                            Luhanska                                                                                                 Luhanska
                                                                                                                                                                                                                Autumn




                                Vinnytska                                                                                                Vinnytska
     Ivano-Frankivska                                                                                         Ivano-Frankivska
 Zakarpatska                                     Kirovohradska                                            Zakarpatska                                     Kirovohradska
              Chernivetska                                       Dnipropetrovska                                       Chernivetska                                       Dnipropetrovska
                                                                                      Donetska                                                                                                 Donetska

                                                Mykolaivska                                                                                              Mykolaivska
                                                                          Zaporizka                                                                                                Zaporizka

                                            Odeska         Khersonska                                                                                Odeska         Khersonska




                                                                  Crimea                                                                                                   Crimea




                                        Change in bias-adjusted precipitation to baseline [%]

         -25% – -15%                                    -15% – -5%                                     -5% – 5%                              5% – 15%                                          15% – 25%




28                       Ukraine. Building Climate Resilience in Agriculture and Forestry
            rop Yields [tons/ha] in 2010 and Changes in Yields [%]
Figure 17: C
           in 2030 and 2050 for Selected Crops


                                                     Base period ~2010
                                                 Productivity of crops


                                                                                  Chernihivska
                                 Volynska
                                            Rivnenska                                                Sumska

                                                        Zhytomyrska

                                                                        Kyivska
                         Lvivska                                                                  Poltavska
                                                                                                                     Kharkivska
                                              Khmelnytska




                                                                                                                                                             Wheat
                                     Ternopilska                            Cherkaska
                                                            Vinnytska                                                                   Luhanska
                          Ivano-Frankivska
                   Zakarpatska                                                    Kirovohradska
                                      Chernivetska                                                    Dnipropetrovska
                                                                                                                             Donetska


                                                                                 Mykolaivska
                                                                                                                Zaporizka

                                                                        Odeska                 Khersonska




                                                                                                       Crimea




                                                                                  Chernihivska
                                 Volynska
                                            Rivnenska                                                Sumska

                                                        Zhytomyrska

                                                                        Kyivska
                         Lvivska                                                                  Poltavska




                                                                                                                                                             Sunflower
                                                                                                                    Kharkivska
                                              Khmelnytska
                                     Ternopilska                            Cherkaska
                                                            Vinnytska                                                                   Luhanska
                          Ivano-Frankivska
                   Zakarpatska                                                    Kirovohradska
                                      Chernivetska                                                   Dnipropetrovska
                                                                                                                             Donetska


                                                                                 Mykolaivska
                                                                                                                Zaporizka

                                                                        Odeska                 Khersonska




                                                                                                       Crimea




                                                                                  Chernihivska
                                 Volynska
                                            Rivnenska                                                Sumska

                                                        Zhytomyrska

                                                                        Kyivska
                         Lvivska                                                                  Poltavska
                                                                                                                     Kharkivska
                                             Khmelnytska
                                    Ternopilska                             Cherkaska
                                                            Vinnytska                                                                   Luhanska
                                                                                                                                                             Maize




                         Ivano-Frankivska
                   Zakarpatska                                                    Kirovohradska
                                      Chernivetska                                                    Dnipropetrovska
                                                                                                                             Donetska


                                                                                 Mykolaivska
                                                                                                                Zaporizka

                                                                        Odeska                 Khersonska




                                                                                                       Crimea




                     0—3                         3—6                     6—9                          9 — 12                      12 — 15          15 — 18


                                                        Productivity in base year [t/ha]




                 Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                        29
30   Ukraine. Building Climate Resilience in Agriculture and Forestry
Ukraine. Building Climate Resilience in Agriculture and Forestry   31
3.3 Impact of Water Availability on Crop Yields
For this analysis, the WOFOST model estimates the water-scarce crop yields by oblast in 2030
under RCP 4.5 and RCP 8.5 scenarios in the absence of adaptation measures. To estimate
the potential benefits of adaptation measures, water-scarce crop yields can be compared
with yield projections under optimum water availability (i.e., when the water limitations are
overcome) for two crops, maize and sunflower, for which the impact is the highest (Figure 11).
In the WOFOST model, crop productivity is defined by the planting date, CO2 concentration,
radiation, and temperature. In the case of optimal water availability yield, the WOFOST mod-
el assumes that there is no water supply constraint — i.e., that water supply is optimal. The
model does not have specific assumptions on measures to maintain optimal water availability.
The simulations show a considerable increase in yield assuming optimal water availability
for maize (about 20%- 40%) and sunflower (about 60%- 80%) in both RCP 4.5 and RCP 8.5
scenarios (Figure 18). Moreover, irrespective of the RCP scenario until mid-century, the im-
pacts of climate change will depend on other factors, such as solar radiation, if water supply
is sufficient. For some oblasts, these other factors are likely to play minor roles in the overall
climate change impacts on agriculture (Figure 19).

    Agricultural Production Projections
3.4 
Simulations of agricultural value at the oblast level are based on projected yields and assump-
tions of changes in agricultural land areas driven by relative changes in yields and relative
prices for relevant crops in future years. Higher prices for wheat and maize make it more at-
tractive to increase land areas under these crops, especially in oblasts where the yield gains
from climate change are significant. The changes in allocation of crop lands were based on the
analysis carried out by IFPRI (see IFPRI 2016, 2019). These changes are further discussed
in are further discussed in Chapter 7 in the discussion on the benefits of adjusting land areas
as a form of adaptation. The projected changes in areas allocated for each crop for the entire
country are as follows: barley: +2% in 2030 and -6% in 2050; maize: +12% in 2030 and +29%
in 2050; soybean: 0% in 2030 and -6% in 2050; wheat: +10% in 2030 and +15% in 2050. No
changes are projected for sunflower, as it was not modeled by IFPRI.
Wheat and soybean show a clear positive trend to mid-century in both scenarios, whereas
barley and sunflower show a consistently negative trend. The changing climate conditions un-
til mid-century will become beneficial for maize. However, these trends should be interpreted
as indicative, with considerable uncertainty ranges for the production of each crop in each
region. It is important to note that the crop production simulation assumes the reallocation of
land for each crop. The estimated changes in yields, combined with simulated changes in land
areas for each crop, based on the IFPRI model, were used to estimate changes in production
under RCP 4.5 and RCP 8.5, with and without shifting of land allocation for each crop, respec-
tively (Figure 20).




11
     	 Impact of water availability was estimated for soybean, but the impact is significantly lower.




32                  Ukraine. Building Climate Resilience in Agriculture and Forestry
Figure 18: Impact of Optimal Water Availability in 2030
          RCP 4.5: Crop productivity with                                                                 RCP 8.5: Crop productivity with
            optimal water availability                                                                      optimal water availability


                                            Chernihivska                                                                                    Chernihivska
         Volynska                                          Sumska                                        Volynska                                          Sumska
                Rivnenska                                                                                       Rivnenska




                                                                                                                                                                                                   [% to base ~2010]
                                                                                                                                                                                                   Near future ~2030
                        Zhytomyrska                                                                                     Zhytomyrska

                                       Kyivska                                                                                         Kyivska
     Lvivska                                           Poltavska      Kharkivska                     Lvivska                                            Poltavska      Kharkivska
                  Khmelnytska                                                                                     Khmelnytska
            Ternopilska                   Cherkaska                                                         Ternopilska                   Cherkaska




                                                                                                                                                                                                                         Maize
                           Vinnytska                                                 Luhanska                              Vinnytska                                                  Luhanska
    Ivano-Frankivska                                                                                Ivano-Frankivska
Zakarpatska                                Kirovohradska                                        Zakarpatska                                Kirovohradska
             Chernivetska                              Dnipropetrovska                                       Chernivetska                              Dnipropetrovska
                                                                               Donetska                                                                                         Donetska

                                           Mykolaivska                                                                                     Mykolaivska
                                                                   Zaporizka                                                                                        Zaporizka
                                       Odeska         Khersonska                                                                       Odeska         Khersonska




                                                            Crimea                                                                                          Crimea




                                            Chernihivska                                                                                    Chernihivska
         Volynska                                          Sumska                                        Volynska                                          Sumska
                Rivnenska                                                                                       Rivnenska




                                                                                                                                                                                                    [% to base ~2010]
                                                                                                                                                                                                    Mid-century ~2050
                        Zhytomyrska                                                                                     Zhytomyrska

                                       Kyivska                                                                                         Kyivska
     Lvivska                                           Poltavska      Kharkivska                     Lvivska                                            Poltavska      Kharkivska
                  Khmelnytska                                                                                     Khmelnytska
            Ternopilska                   Cherkaska                                                         Ternopilska                   Cherkaska




                                                                                                                                                                                                           Maize
                           Vinnytska                                                 Luhanska                              Vinnytska                                                  Luhanska
    Ivano-Frankivska                                                                                Ivano-Frankivska
Zakarpatska                                Kirovohradska                                        Zakarpatska                                Kirovohradska
             Chernivetska                              Dnipropetrovska                                       Chernivetska                              Dnipropetrovska
                                                                               Donetska                                                                                         Donetska

                                           Mykolaivska                                                                                     Mykolaivska
                                                                   Zaporizka                                                                                        Zaporizka
                                       Odeska         Khersonska                                                                       Odeska         Khersonska




                                                            Crimea                                                                                          Crimea




                                            Chernihivska                                                                                    Chernihivska
         Volynska                                          Sumska                                        Volynska                                          Sumska
                Rivnenska                                                                                       Rivnenska
                        Zhytomyrska                                                                                     Zhytomyrska




                                                                                                                                                                                                 [% to base ~2010]
                                                                                                                                                                                                 Near future ~2030
                                       Kyivska                                                                                         Kyivska
     Lvivska                                           Poltavska      Kharkivska                     Lvivska                                            Poltavska      Kharkivska
                  Khmelnytska                                                                                     Khmelnytska




                                                                                                                                                                                                                       Sunflower
            Ternopilska                   Cherkaska                                                         Ternopilska                   Cherkaska
                           Vinnytska                                                 Luhanska                              Vinnytska                                                  Luhanska
    Ivano-Frankivska                                                                                Ivano-Frankivska
Zakarpatska                                Kirovohradska                                        Zakarpatska                                Kirovohradska
             Chernivetska                              Dnipropetrovska                                       Chernivetska                              Dnipropetrovska
                                                                               Donetska                                                                                         Donetska

                                           Mykolaivska                                                                                     Mykolaivska
                                                                   Zaporizka                                                                                        Zaporizka
                                       Odeska         Khersonska                                                                       Odeska         Khersonska




                                                            Crimea                                                                                          Crimea




                                            Chernihivska                                                                                    Chernihivska
         Volynska                                          Sumska                                        Volynska                                          Sumska
                Rivnenska                                                                                       Rivnenska
                        Zhytomyrska                                                                                     Zhytomyrska
                                                                                                                                                                                                  Mid-century ~2050
                                                                                                                                                                                                  [% to base ~2010]




                                       Kyivska                                                                                         Kyivska
                                                                                                                                                                                                                       Sunflower




     Lvivska                                           Poltavska      Kharkivska                     Lvivska                                            Poltavska      Kharkivska
                  Khmelnytska                                                                                     Khmelnytska
            Ternopilska                   Cherkaska                                                         Ternopilska                   Cherkaska
                           Vinnytska                                                 Luhanska                              Vinnytska                                                  Luhanska
    Ivano-Frankivska                                                                                Ivano-Frankivska
Zakarpatska                                Kirovohradska                                        Zakarpatska                                Kirovohradska
             Chernivetska                              Dnipropetrovska                                       Chernivetska                              Dnipropetrovska
                                                                               Donetska                                                                                         Donetska

                                           Mykolaivska                                                                                     Mykolaivska
                                                                   Zaporizka                                                                                        Zaporizka
                                       Odeska         Khersonska                                                                       Odeska         Khersonska




                                                            Crimea                                                                                          Crimea




                  no change                      up to 20%                        20% - 40%          40% - 60%                   60% - 80%                          80% - 100%




                                                 Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                                                  33
Figure 19: Difference Between RCP 4.5 and RCP 8.5 for 2030
                Difference between RCP 4.5 and RCP 8.5                                                                                                     Difference between RCP 4.5 and RCP 8.5


                                                                Chernihivska                                                                                                                               Chernihivska
                 Volynska                                                                                                                                   Volynska
                            Rivnenska                                            Sumska                                                                                Rivnenska                                            Sumska

                                        Zhytomyrska                                                                                                                                Zhytomyrska




                                                                                                                                                                                                                                                                     Near future ~2030
                                                                                                                           Near future ~2030
                                                       Kyivska                                                                                                                                    Kyivska

           Lvivska                                                             Poltavska                                                              Lvivska                                                             Poltavska
                                                                                                Kharkivska                                                                                                                                 Kharkivska
                                                                                                                                                                        Khmelnytska




                                                                                                                                                                                                                                                                        Sunflower
                             Khmelnytska
                     Ternopilska                           Cherkaska                                                                                            Ternopilska                           Cherkaska
                                                                                                               Luhanska                                                                                                                                   Luhanska




                                                                                                                                 Maize
                                           Vinnytska                                                                                                                                  Vinnytska
            Ivano-Frankivska                                                                                                                           Ivano-Frankivska
      Zakarpatska                                               Kirovohradska                                                                    Zakarpatska                                               Kirovohradska
                      Chernivetska                                                Dnipropetrovska                                                                Chernivetska                                                Dnipropetrovska
                                                                                                        Donetska                                                                                                                                   Donetska


                                                            Mykolaivska                                                                                                                                Mykolaivska
                                                                                            Zaporizka                                                                                                                                  Zaporizka

                                                       Odeska                                                                                                                                     Odeska             Khersonska
                                                                          Khersonska




                                                                                   Crimea                                                                                                                                     Crimea




                                                                Chernihivska                                                                                                                               Chernihivska
                 Volynska                                                                                                                                   Volynska
                            Rivnenska                                            Sumska                                                                                                                                     Sumska
                                                                                                                                                                       Rivnenska
                                        Zhytomyrska                                                                                                                                Zhytomyrska
                                                                                                                           Mid-century ~2050




                                                                                                                                                                                                                                                                     Mid-century ~2050
                                                       Kyivska                                                                                                                                    Kyivska
           Lvivska                                                             Poltavska                                                              Lvivska                                                             Poltavska
                                                                                                Kharkivska                                                                                                                                 Kharkivska
                             Khmelnytska                                                                                                                                Khmelnytska




                                                                                                                                                                                                                                                                         Sunflower
                     Ternopilska                           Cherkaska                                                                                            Ternopilska                           Cherkaska
                                                                                                               Luhanska
                                                                                                                                 Maize

                                           Vinnytska                                                                                                                                  Vinnytska                                                           Luhanska
            Ivano-Frankivska                                                                                                                           Ivano-Frankivska
      Zakarpatska                                               Kirovohradska                                                                                                                              Kirovohradska
                      Chernivetska                                                Dnipropetrovska                                                Zakarpatska
                                                                                                                                                                 Chernivetska                                                Dnipropetrovska
                                                                                                        Donetska                                                                                                                                   Donetska

                                                            Mykolaivska                                                                                                                                Mykolaivska
                                                                                            Zaporizka                                                                                                                                  Zaporizka

                                                       Odeska             Khersonska                                                                                                              Odeska             Khersonska




                                                                                   Crimea                                                                                                                                     Crimea




                                                                                           irrigated                      ∆ ~ 0%               ∆ 5% - 10%                           ∆ 15% - 20%

                                                                                           not irrigated                  ∆ < 5%               ∆ 10% - 15%                          ∆ 20% - 25%

                                                                                                         ∆ Betweeen RCP 4.5 and RCP 8.5 [%]




          hange in Total Production (Millions of Tons) for Major Crops as Compared
Table 3: C
         to the Baseline (with Change in Land Area Allocation for Each Crop)12

                                                          2030                                                                                                                  2050

                                                          RCP 4.5                                                          RCP 8.5                                              RCP 4.5                                                            RCP 8.5

 Barley                                                   - 3%                                                             - 11%                                                - 14%                                                              - 15%

 Maize                                                    - 12%                                                            - 3%                                                     12%                                                                 15%

 Soybean                                                         20%                                                        22%                                                     13%                                                                 19%

 Sunflower                                                - 11%                                                            - 3%                                                 - 8%                                                               - 5%

 Wheat                                                           32%                                                        34%                                                     43%                                                                 55%



12
     	 Figures are based on estimated production [millions of tons] for the mean projection, changes in crop yields [tons/ha]
       multiplied by the crop areas [ha] in each oblast are relative to 2010 levels.




34                                      Ukraine. Building Climate Resilience in Agriculture and Forestry
    Agricultural Value Projections
3.5 
The total production value of the five crops is projected to increase by 29% (with an uncer-
tainty range of -32% to +91%) in 2030 and 56% (with an uncertainty range of -1% to +112%)
in 2050, compared to 2010 (Figure 21). The uncertainty range is clearly large in both sets of
projections, with and without changes in land allocation. However, with changes in land allo-
cation the uncertainty range is on the positive side, while without changes in land allocation,
negative impacts are quite possible. For example, in 2050 in Kirovohradska oblast, shifting
of land allocation has a stronger positive impact on mean agricultural value. The range shifts
from between -31% to 56%, to between 0% to 107%. All oblasts follow the same trend indi-
cating that a shift in land allocation helps avoid losses in the value of agricultural production.
Figure 21 provides information on how to identify oblasts where additional support and adjust-
ment measures can be most helpful. The oblasts in bold currently have a large share of the
total value of the agricultural sector in Ukraine’s GDP; in these oblasts, the agricultural sector
has a significant share in the domestic GDP of the oblasts. Adaptation measures can signifi-
cantly, though not completely, reduce the potential negative impact on the value of the sector
through the mid-century period. Under the mean projection, the value of production goes up
in all oblasts, with larger increases in the eastern and central-eastern oblasts. Under the high
projection, all oblasts experience increases in production values, with even larger increases in
2050 than in 2030, assuming the stated adaptation measures take place.

    Effects of Changes in the Growing Season
3.6 
As shown in Table 4 and Table 5, climate change will result in changes to climatic seasons
across the board, most notably to the growing season (t>50C). While the near future changes
result in a 7% increase in the length of the growing season under both RCP4.5 and RCP8.5
scenarios compared to the baseline period 1961-90, longer-term projections diverge, with a
middle-of-the-century increase of 10% under the RCP4.5 scenario and a 13% increase under
the RCP8.5 scenario. By the end of the century, growing seasons are expected to become
13% longer in the RCP4.5 scenario and 27% longer in the RCP 8.5 scenario. The the growing
season start day will shift by 13 days under both RCP 4.5 and RCP 8.5 scenarios compared to
the baseline period 1961-90. By mid-century, the change will be 17 and 20 days, respectively,
and by the end of the century, the growing season will shift by 22 days under RCP 4.5 or 41
days under RCP 8.5. Additional information is presented in Annex 2.



    Limitations of the Analysis of Climate Change Impact on
3.7 
    Agriculture
Increases in temperature and precipitation changes have a twofold effect depending on the
crop type: contributing to increased productivity of certain crops, but also increasing the risk
of extreme weather events which can negatively affect crop production. Temperature increase
has a positive effect on winter crops during cold periods of vegetation, reduces the risk of frost
damage on spring crops and the time to maturity of certain crops. Together with CO2, fertiliza-
tion, and increased precipitation in vegetation periods, this leads to an increase in productivity
of winter wheat (Figure 17), and an increase in the value of agricultural output for soybean and
sunflower (Figure 34).




                         Ukraine. Building Climate Resilience in Agriculture and Forestry     35
                                                                   Figure 20: Changes in Total Agricultural Production by Oblast, RCP 8.513




36
                                                                                                  With change in crop                       With change in crop                     Without change in crop                  Without change in crop
                                                                                                  land allocation                           land allocation                          land allocation                         land allocation

                                                                                                        2030                                      2050                                     2030                                      2050
                                                                   Cherkaska                     -32%                      44%             -15%                     47%             -36%                      31%             -18%                     27%
                                                                   Chernihivska                  -41%                     108%             -6%                      90%             -45%                      91%             -16%                     68%
                                                                   Chernivetska                  -44%                      61%             -4%                      52%             -48%                      46%             -20%                     26%
                                                                   Crimea                        -53%                     135%             -28%                     162%            -53%                     112%             -35%                     129%
                                                                   Dnipropetrovska               -38%                      63%             -11%                     68%             -36%                      47%             -16%                     48%
                                                                   Donetska                      -37%                      59%             -8%                      63%             -31%                      42%             -11%                     44%
                                                                   Ivano-Frankivska              -39%                      74%             -3%                      71%             -44%                      59%             -18%                     46%
                                                                   Kharkivska                    -46%                      53%             -13%                     43%             -43%                      40%             -17%                     28%
                                                                   Khersonska                    -37%                      78%             -10%                     89%             -37%                      61%             -16%                     67%
                                                                   Khmelnytska                   -37%                      59%             -12%                     50%             -42%                      46%             -24%                     30%
                                                                   Kyivska                       -38%                      51%             -17%                     53%             -43%                      38%             -29%                     30%
                                                                   Kirovohradska                 -38%                      55%             -14%                     56%             -39%                      40%             -21%                     35%
                                                                   Lvivska                       -35%                      76%             -15%                     85%             -41%                      61%             -27%                     60%
                                                                   Luhanska                      -46%                      69%             -13%                     71%             -39%                      50%             -15%                     49%
                                                                   Mykolaivska                   -31%                      66%             -13%                     72%             -29%                      48%             -16%                     50%
                                                                   Odeska                        -34%                      77%             -8%                      84%             -35%                      60%             -15%                     60%
                                                                   Poltavska                     -37%                      47%             -5%                      45%             -40%                      33%             -18%                     22%
                                                                   Rivnenska                     -41%                      58%             -24%                     45%             -45%                      47%             -30%                     33%
                                                                   Sumska                        -42%                      67%             -5%                      58%             -46%                      52%             -18%                     34%
                                                                   Ternopilska                   -33%                      64%             -6%                      59%             -39%                      50%             -19%                     37%
                                                                   Vinnytska                     -32%                      52%             -8%                      55%             -37%                      39%             -20%                     33%




Ukraine. Building Climate Resilience in Agriculture and Forestry
                                                                   Volynska                      -37%                      87%             -13%                     76%             -42%                      72%             -24%                     54%
                                                                   Zakarpatska                   -38%                      80%              5%                      89%             -44%                      63%             -15%                     54%
                                                                   Zaporizka                     -42%                      70%             -13%                     76%             -36%                      51%             -16%                     55%
                                                                   Zhytomyrska                   -41%                      51%             -26%                     45%             -47%                      38%             -39%                     21%
                                                                                                -100%     0%      100%    200%           -100%     0%      100%     200%           -100%     0%      100%    200%           -100%      0%     100%     200%


                                                                                                                                     Low                           High                      Mean


                                                                   13
                                                                        	Changes in production [millions of tons] are estimated as yields [tons/ha] multiplied by the crop areas [ha] allocated for each crop in each oblast relative to baseline 2010 level
                                                                         of production.
                                                                   Figure 21: Changes in Total Value by Oblast, RCP 8.5



                                                                                        With change in crop                 With change in crop              Without change in crop          Without change in crop
                                                                                        land allocation                     land allocation                   land allocation                 land allocation

                                                                                              2030                               2050                              2030                            2050

                                                                   Cherkaska           -32%               64%               2%               91%            -44%               42%          -28%               46%
                                                                   Chernihivska        -45%              130%           -3%                 129%            -52%              108%          -19%               96%
                                                                   Chernivetska        -52%               83%           -3%                  93%            -59%               62%          -32%               46%
                                                                   Crimea              -55%              168%           -32%                238%            -61%              136%          -48%              184%
                                                                   Dnipropetrovska     -21%               97%           10%                 125%            -41%               58%          -23%               72%
                                                                   Donetska            -13%               99%           21%                 122%            -36%               53%          -15%               67%
                                                                   Ivano-Frankivska    -45%               96%           -2%                  118%           -51%               78%          -25%               77%
                                                                   Kharkivska          -30%               87%               9%               92%            -50%               50%          -23%               45%
                                                                   Khersonska          -29%              103%               2%              141%            -40%               74%          -19%               99%
                                                                   Khmelnytska         -41%               76%           -15%                 82%            -48%               60%          -35%               49%
                                                                   Kyivska             -44%               70%           -22%                 90%            -52%               51%          -44%               49%
                                                                   Kirovohradska       -26%               85%               0%              107%            -44%               51%          -31%               56%
                                                                   Lvivska             -40%               94%           -21%                129%            -47%               76%          -38%               91%
                                                                   Luhanska            -25%              109%           14%                 133%            -45%               63%          -19%               75%
                                                                   Mykolaivska         -9%               105%           12%                 133%            -33%               61%          -22%               76%
                                                                   Odeska              -21%              109%               8%              142%            -39%               75%          -20%               91%
                                                                   Poltavska           -34%               73%               7%               90%            -50%               44%          -29%               38%
                                                                   Rivnenska           -46%               73%           -29%                 71%            -50%               60%          -38%               51%
                                                                   Sumska              -46%               91%               0%               98%            -55%               68%          -25%               56%
                                                                   Ternopilska         -37%               81%           -7%                  94%            -44%               64%          -28%               59%
                                                                   Vinnytska           -32%               70%           -7%                  92%            -42%               50%          -30%               53%
                                                                   Volynska            -42%              107%           -16%                 112%           -48%               88%          -32%               79%
                                                                   Zakarpatska         -47%              109%           10%                 154%            -54%               88%          -23%               97%
                                                                   Zaporizka           -22%              108%           10%                 137%            -41%               64%          -22%               82%
                                                                   Zhytomyrska         -53%               71%           -41%                 77%            -59%               54%          -62%               37%

                                                                                      -100%   0%     100% 200% 300%    -100%     0%     100% 200% 300%     -100%   0%     100% 200% 300%   -100%   0%     100% 200% 300%




Ukraine. Building Climate Resilience in Agriculture and Forestry
                                                                                                                      Low                           High             Mean




37
          haracteristics of Climatic Seasons in Ukraine in Two Past Periods
Table 4. C
         (E-OBS data) and Three Future Periods Under the RCP4.5 Scenario
         (Ensemble of 34 RCMs from Euro-CORDEX Data)
              Length of seasons, days                                                                                                                                 Season start day                                                                                                                                          Season end day




                                                                                  Active vegetation season




                                                                                                                                                                                                                                  Active vegetation season




                                                                                                                                                                                                                                                                                                                                                                Active vegetation season
                                                                                                                                   Summer season




                                                                                                                                                                                                                                                                                          Summer season




                                                                                                                                                                                                                                                                                                                              Summer season
                                            Growing season




                                                                                                                                                                                                 Growing season




                                                                                                                                                                                                                                                                                                                                                                                                                 Growing season
            Warm season




                                                                                                                                                                    Warm season




                                                                                                                                                                                                                                                                                                                                                                                                                                                    Warm season
                                                                                                                      (t > 10oC)




                                                                                                                                                       (t > 15oC)




                                                                                                                                                                                                                                                                           (t > 10oC)




                                                                                                                                                                                                                                                                                                                (t > 15oC)




                                                                                                                                                                                                                                                                                                                                                   (t > 15oC)




                                                                                                                                                                                                                                                                                                                                                                                                    (t > 10oC)
                               (t > 0oC)




                                                                      (t > 5oC)




                                                                                                                                                                                     (t > 0oC)




                                                                                                                                                                                                                      (t > 5oC)




                                                                                                                                                                                                                                                                                                                                                                                                                                        (t > 5oC)




                                                                                                                                                                                                                                                                                                                                                                                                                                                                     (t > 0oC)
1961-1990     283                           219                                        172                                           117                              56                         96                                    116                                                  142                                 260                                  288                                         314                                  339

1991-2010     301                           223                                        173                                           121                              39                         90                                    114                                                  143                                 264                                  288                                         314                                  341

2021-2040     305                           235                                        181                                           129                              37                         83                                    111                                                  137                                 267                                  292                                         318                                  342

2041-2060     309                           241                                        188                                           137                              35                         79                                    108                                                  134                                 269                                  295                                         318                                  342

2081-2100     318                           247                                        193                                           143                              29                         74                                    104                                                  130                                 271                                  297                                         320                                  345

Season start and end days: numbers in the table indicate the day of the year (i.e., 56th day
of the year).


          haracteristics of Climatic Seasons in Ukraine in Two Past Periods
Table 5: C
         (E-OBS data) and Three Future Periods Under the RCP8.5 Scenario
         (Ensemble of 34 RCMs from Euro-CORDEX Data
                    Length of seasons, days                                                                                                                             Season start day                                                                                                                                              Season end day
                                                                                           Active vegetation season




                                                                                                                                                                                                                                                Active vegetation season




                                                                                                                                                                                                                                                                                                                                                                         Active vegetation season
                                                     Growing Season




                                                                                                                                       Summer season




                                                                                                                                                                                                                                                                                                Summer season




                                                                                                                                                                                                                                                                                                                                   Summer season
                                                                                                                                                                                                    Growing season




                                                                                                                                                                                                                                                                                                                                                                                                                       Growing season
                 Warm season




                                                                                                                                                                      Warm season




                                                                                                                                                                                                                                                                                                                                                                                                                                                      Warm season
                                                                                                                      (t > 10oC)




                                                                                                                                                       (t > 15oC)




                                                                                                                                                                                                                                                                             (t > 10oC)




                                                                                                                                                                                                                                                                                                                 (t > 15oC)




                                                                                                                                                                                                                                                                                                                                                   (t > 15oC)




                                                                                                                                                                                                                                                                                                                                                                                                    (t > 10oC)
                                (t > 0oC)




                                                                      (t > 5oC)




                                                                                                                                                                                    (t > 0oC)




                                                                                                                                                                                                                     (t > 5oC)




                                                                                                                                                                                                                                                                                                                                                                                                                                        (t > 5oC)




                                                                                                                                                                                                                                                                                                                                                                                                                                                                    (t > 0oC)




1961-1990           283                              219                                        172                                      117                            56                          96                                               116                                           142                                260                                     288                                     314                               339

1991-2010           301                             223                                         173                                      121                            39                          90                                               114                                           143                                264                                    288                                      314                               341

2021-2040           304                             235                                         185                                      133                            40                          83                                               109                                           135                                268                                    294                                      318                               344

2041-2060           315                             247                                         194                                      141                            30                          76                                               105                                                  132                         273                                    299                                      322                                  346

2081-2100           340                             279                                         213                                      160                            16                          55                                               94                                            122                                283                                    307                                      333                                  356




38              Ukraine. Building Climate Resilience in Agriculture and Forestry
This could potentially increase the competitiveness of Ukraine’s agricultural products in the inter-
national market. However, extreme temperature increases combined with insufficient precipitation
can lead to droughts and a decrease in the productivity of crops and provoke natural disturbances
such as pests and diseases. Thus, the comparative advantage projected with these temperature
increases for Ukraine’s agriculture may be affected by extreme weather events which were not
covered by this study.
Modeling the impact of water resources is limited by the capabilities of the integrated assessment
model (Figure 10). The WOFOST model can produce water-limited simulation results when soil
moisture determines whether the crop growth is limited by drought stress. In the water availabil-
ity simulation, the effect of soil moisture on crop growth is optimal. Optimal soil moisture can be
achieved through measures such as irrigation and other water balance management approaches.
Uncertainty in food prices and food security under climate extremes is not accounted for in the
modeling approach. The food prices projected by the IMPACT model for 2030 did not consider
price peaks such as those that occurred in 2010 due to the drought in Russia, which reduced wheat
yields by about one third. This drought also had significant long-term effects: in 2011 the lowest
income decile spent 17% more on food supplies than in 2007. The distributional effects of extreme
events on changes in food prices and food security directly through changes in yields and through
disruption of transport and markets remain a challenge for further analysis, as extreme events are
likely to become more frequent in the near future.


         Box 3: Impact of Water Shocks on Agricultural Yields

         Water shocks include both dry shocks and wet shocks, defined as an occurrence of rainfall that is at least one
         standard deviation below or above the long-term average (LTA) level in the region (Damania et al. 2017).
         Dry shocks. The driest regions are most sensitive to rainfall variability. This is particularly important for
         Ukraine with dry climate types projected to account for about 63.2 to 69.6% of the country’s territory in the
         middle of the century under RCP 4.5 and RCP 8.5, respectively (see Figure 31). Global data indicates that
         dry shocks can reduce agricultural productivity by approximately 14%, while wet shocks increase agricul-
         tural productivity by approximately 17% (Damania et al. 2017). Drought like the one Ukraine experienced in
         2010 (Shevchenko et al. 2014b) is likely to return every two-three years when global warming reaches 2°C,
         or every year when global warming reaches 3°C.
         In 2019, a heat wave which led to a strong rainfall deficit was recorded in Ukraine, with substantially dri-
         er-than usual conditions in some regions with rainfall accumulations below 5 mm. However, cumulative rain-
         fall was within the limits of the LTA in most of the western, southern, and eastern parts of Ukraine, while the
         north (Zhytomyrska, Kyivska, Cherkaska, Chernihivska, Sumska, Kharkivska, Donetska and Luhanska) ex-
         perienced a rain deficit of around 40% relative to LTA (EC 2019a). Those rainfall events slowed the progress
         of harvesting of summer crops, with maize and soybean experiencing 7.4% and 2.2% lower yields than in
         2018, and they also delayed cropping activities and hampered the emergence of winter crops (EC 2019b).
         Wet shocks.14 Historical analysis (1986–2010) shows that heavy rain is the most common climate extreme
         in Ukraine, accounting for 53% of all occurrences of extreme events in the period (Balabuch et al., 2018).
         Extreme rains are most common in the western region, specifically in Lvivska, Ternopilska, and Chernivets-
         ka oblasts, and the Crimean Mountains and highlands. Extreme rain events have become more common in
         Ukraine, increasing with a probability of 99% over the 1971–2010 period. Along with the projected increase
         in annual precipitation (see Figure 7), extreme wet shocks will also increase. Increased annual precipitation
         is likely in almost the entire country, but most pronounced in Ivano-Frankivska, Chernivetska, Lvivska, Rivn-
         enska, Khersonska, and Zhytomyrska oblasts.


   14
        	 The paragraph is based on Balabukh et al., 2018.




                                  Ukraine. Building Climate Resilience in Agriculture and Forestry                    39
CHAPTER 4:
THE DISTRIBUTIONAL EFFECT OF
CLIMATE CHANGE ON AGRICULTURE
4.1 Summary of Key Findings
The distributional analysis of the impact of climate change on households’ real incomes is
assessed through its impacts on the price of foods and agricultural incomes. The increase in
food prices is expected to increase household expenditures by 0.7% to nearly 3% across all
households, depending on the oblast. The effects are regressive, as households in the lower
income quintiles face larger increases in real expenditures. The changes in the values of farm
outputs increase household incomes for all oblasts and all household deciles under the mean
projection, in a range between 0.2% and 1.6%. The combined effects of the changes in food
prices and incomes depend on the projection and share of agricultural income in the house-
holds’ income structure in each oblast. In the mean projection, the changes range between -1
and +1%. In the high projection, household income gains between 0.5 to 3% for all oblasts. In
the low projection, the projected loss of income is between -1 and -3%. The five oblasts with
the largest predicted decreases in income are Zhytomyrska, Sumska, Chernivetska, Rivnen-
ska, and Volynska.
Both the increase in food prices and changes in farm outputs will impact poverty headcounts,
however the impact is not significant under any of the projections (low, mean, and high). The
poverty gap, however, does not increase in all cases: in seven oblasts, it decreases slightly
while in the others, it increases. The severity of poverty also slightly declines in six oblasts but
increases in the rest, with the biggest decrease in Chernihivska (1.3%) and highest increase
(0.8%) in Khemelnytska.
When considering only food price increase, the Gini coefficient results indicate an increase in
inequality in all oblasts, except Ivano-Frankivska. The effect on inequality is tracked through
changes in real income per household. The combined effects of price increase and changes
in agricultural outputs result in a decrease in inequality for six oblasts in the mean projection
scenario but in most cases the decrease is very small. With the low projection scenario, the
inequality measure increases by small amounts in all oblasts (i.e., 1.4% in Vinnytska and
1.07% in Sumska), except for Ivano-Frankivska. In the high projection, all oblasts see an
increase in inequality, with the most significant increase in Donetska (8.3% increase in the
Gini coefficient) and Ivano-Frankivska (3.5%). However, this analysis has not investigated all
possible effects of climate change on welfare. Further work should be carried out to examine
other factors that influence household incomes, including climate-related morbidities and un-
employment, which are not covered in this analysis.




40          Ukraine. Building Climate Resilience in Agriculture and Forestry
4.2 The Share of Agriculture in the National
    and Oblast GDP
The share of the agricultural sector in Ukraine’s GDP has been declining over time, but the
importance of the sector for the GDP of some oblasts is particularly high. The latest data (for
2019) estimates that agriculture, forestry, and fishery account for 9% of GDP, or $13.8 billion.
The size of this sector as a percentage of GDP varies considerably across oblasts (Figure
22).15 For example, agriculture contributes significantly to the GDP (2010 data) in Kirovohrads-
ka (29.25%), Vinnytska (23.45%), Khersonska (21.52%), Cherkaska (20.72%), Khemelnytska
(17.19%), Sumska (15.97%), and Ternopilska (15.77%), which means that any negative im-
pacts of climate change on agricultural production are likely to have significant impact on their
economies. At the same time, agriculture in Kirovohradska, Vinnytska, Cherkaska, Postavska,
and Dnipropetrovska oblasts constitutes relatively large shares of the country’s GDP, and the
climate risks to agriculture in those areas are more likely to impact the national economy. The
full set of data is presented in Annex 3.




          Figure 22: Agriculture as a Share of GDP ($US) in 2010, by Oblast

                                              30.00%                                                                            Kirovohradska



                                                                                                                                                  Vinnytska
                                                                                                           Khersonska
           Share [%] of agricultural sector




                                                                                                                                      Cherkaska
            in oblast GDP [$US] in 2010




                                                       90th percentile

                                              20.00%

                                                                                                      Khmelnytska
                                                                                 Ternopilska
                                                                                                      Sumska
                                                                                                   Mykolaivska                                  Poltavska
                                                                                 Chernihivska
                                              10.00%   Chernivetska                                                   Zaporizka       Odeska
                                                                            Rivnenska
                                                           Volynska                         Crimea
                                                                                                                              Kharkivska
                                                                    Zhytomyrska
                                                                                                                                           Dnipropetrovska
                                                                                                                                                    90th percentile




                                                        Ivano-Frankivska                   Luhanska              Donetska
                                                       Zakarpatska Lvivska
                                                                                                                 Kyivska
                                                                         0.10%             0.20%              0.30%               0.40%            0.50%



                                                                                  Share [%] of oblast's agricultural sector
                                                                                      in Ukraine GDP [$US] in 2010




15
     	 Annex 3 shows the GDP and agricultural value in 2010 per oblast. In this case, the values for 2010 are presented, as the value
       of agriculture by oblast is only available for that year. It is also the baseline year used in the report, as explained below.




                                                                   Ukraine. Building Climate Resilience in Agriculture and Forestry                                   41
The distributional analysis assesses the impact of climate change on households’ real in-
comes through its impacts on the price of foods and agricultural incomes. The agricultural
impacts assessment in Chapter 3 provides two key outputs: i) increases in the prices of key
food products due to climate change and estimates of price increases in 2030 for key agricul-
tural commodities under RCP 8.5 and RCP 4.5 (based on the IFPRI model); and ii) changes
in agricultural incomes due to the climate change effects on yields, production, and production
values. These data were inputs for the distributional analysis of the impacts on households.
The analysis of income considers three sets of projections: low, mean, and high. They reflect
the results of the WOFOST model projecting the impacts of climate change on agriculture, in
which, for a given date and climate projection, the model provides a distribution of likely out-
comes for changes in yields and production for the selected crops (i.e., barley, wheat, maize,
sunflower and soybean) between 2010 and 2030. The resulting changes by oblast are provid-
ed in Annex 5 for the selected crops. The low and high projection scenarios represent the 5th
and 95th percentile of the distribution of yield changes provided, at a very fine scale for each
oblast. Changes in real incomes and indicators of poverty and inequality are estimated for
RCP 8.5 in 2030.16 The analysis is limited to 2030 because by 2050, the baseline expenditure
data cannot be considered as a reasonable point of comparison.



    Impact of Climate Change on Agriculture and Household
4.3 
    Income and Expenditure
The increase in food prices is expected to increase household expenditures by between 0.7%
and nearly 3% across all households, depending on the oblast. The effects are regressive as
households in the lower income quintiles face larger increases of real expenditures up to a
maximum of nearly 3 percent (see Annex 5). The increase in food prices is expected to reduce
incomes by 0.7% to 1.2% across all households, depending on the oblast (shown in Figure 23).
The changes in the values of farm outputs increase household incomes for all oblasts, and
all household deciles, in the range between 0.2 percent and 1.6% under the mean projection
(shown in Figure 24). The households in lower (first) income deciles experience an increase
of up to 1.6% in Luhanska oblast, where the bottom decile’s income rises by 1.6% and the
top decile’s income by 0.8%. The smallest gain is for Zhytomyrska, with an increase for the
bottom decile of only 0.2% and for the top decile of 0.1%. So, for example, in Cherkassy
oblast, the bottom decile has a gain in income of between UAH 54 and 265 per month, with
the average gain being UAH 132. That is 0.4% of average income for that decile. In the low
projection, households in all oblasts experience a decline in income from agriculture, with the
highest changes in the lowest three, ranging from -0.9% to -2.8%. The largest losses are in
Chernivetska, Sumska, Ternopilska, Volynska and Zaporizka, where households in the lowest
decile lose about 2% of income. The smallest losses are in Dnipropetrovska, Donetska, and
Mykolaivska (around 0.1% to 0.2% across all deciles). In almost all cases, the changes in in-
come become smaller as we go up the deciles, meaning that the changes in income (whether
positive or negative) can be considered progressive.


16
     	 The distributional effects required price projections, which were taken from IFPRI. These were only made for RCP8.5. Addi-
       tional information is available in Chapter 2.




42                 Ukraine. Building Climate Resilience in Agriculture and Forestry
                 hanges in Income by Oblast for 2030 Due to Price Increases
     Figure 23: C

                 Ternopilska           -0.7 %
                 Sumska               -0.72 %
                 Chernivetska         -0.77 %
                 Chernihivska         -0.78 %
                 Zaporizka            -0.79 %
                 Volynska             -0.79 %
                 Poltavska             -0.8 %
                 Rivnenska            -0.83  %
                 Ivano-Frankivska     -0.83 %
                 Luhanska             -0.84 %
                 Zakarpatska          -0.84 %
                 Kharkivska           -0.86 %
                 Kirovohradska        -0.88 %
                 Zhytomyrska           -0.9 %
                 Khmelnytska          -0.91  %
                 Vinnytska            -0.92  %
                 Lvivska              -0.92 %
                 Cherkaska            -0.93 %
                 Kyiv City            -0.93 %
                 Dnipropetrovska      -0.93 %
                 Kyivska              -0.96 %
                 Mykolaivska          -0.96 %
                 Khersonska             -1 %
                 Odeska                -1.1 %
                 Donetska              -1.2 %
                                 -1.2% -1.0% -0.8% -0.5% -0.3%         0.0%




The range between the low and high projections for all income deciles indicates that for some
oblasts, the low-income deciles tend to experience a wider range between the low and high
projections. As shown in Annex 3, these oblasts include Chernivetska, Lvivska, Ternopilska,
and Volynska in the west; Poltavska and Chernihivska in the central north; and Luhanska in
the east. This suggests that climate change and associated impacts on agricultural production
may have a significant impact on low-income households, more so than on the households in
the upper-income deciles in these oblasts.
The combined effects of the changes in food prices and incomes vary by scenario and the
share of agricultural income in household income structure in each oblast, as illustrated in Fig-
ure 25. In the low projection, there is a 1% to 3% loss of income, with Zhytomyrska, Sumska,
Chernivetska, Rivnenska, and Volynska oblasts affected the most. In the mean projection, the
changes range between -1% and +1%. In the high projection, almost all oblasts experience
gains of 0.5% to 3%. The exceptions are Donetska oblast and Kyiv city, which lose even in the
case of the high projection.




                        Ukraine. Building Climate Resilience in Agriculture and Forestry     43
                                     ncrease in Income from the Change in Value of Agricultural Output
                          Figure 24: I
                                     due to Climate Change, Mean Projection (2030)

                                                                                      Group 1                                                                                                                                                       Group 2



                         1.5%                                                                                                                                                            1.5%
Increase in income [%]




                                                                                                                                                                Increase in income [%]
                                                                                      Group 1                                                                                                                                                       Group 2
                         1.0%                                                                                                                                                            1.0%


                         1.5%                                                                                                                                                            1.5%

                         0.5%                                                                                                                                                            0.5%
Increase in income [%]




                                                                                                                                                                Increase in income [%]
                         1.0%                                                                                                                                                            1.0%

                         0.0%                                                                                                                                                            0.0%




                                                                                                                                                    Decile 10




                                                                                                                                                                                                                                                                                                                      Decile 10
                                Decile 1



                                           Decile 2



                                                        Decile 3



                                                                        Decile 4



                                                                                     Decile 5



                                                                                                     Decile 6



                                                                                                                Decile 7



                                                                                                                              Decile 8



                                                                                                                                         Decile 9




                                                                                                                                                                                                Decile 1



                                                                                                                                                                                                           Decile 2



                                                                                                                                                                                                                      Decile 3



                                                                                                                                                                                                                                      Decile 4



                                                                                                                                                                                                                                                   Decile 5



                                                                                                                                                                                                                                                                    Decile 6



                                                                                                                                                                                                                                                                               Decile 7



                                                                                                                                                                                                                                                                                                Decile 8



                                                                                                                                                                                                                                                                                                           Decile 9
                         0.5%                                                                                                                                                            0.5%
                                                                                   Income Deciles                                                                                                                                                Income Deciles


                                                                   Cherkaska                    Kyivska               Zakarpatska                                                                                                Donetska                     Khmelnytska                 Odeska
                         0.0%                                                                                                                                                            0.0%
                                                                   Chernihivska                 Vinnytska             Zhytomyrska                                                                                                Khersonska                   Lvivska                     Rivnenska
                                                                                                                                                    Decile 10




                                                                                                                                                                                                                                                                                                                      Decile 10
                                Decile 1



                                           Decile 2



                                                        Decile 3



                                                                        Decile 4



                                                                                     Decile 5



                                                                                                     Decile 6



                                                                                                                Decile 7



                                                                                                                              Decile 8



                                                                                                                                         Decile 9




                                                                                                                                                                                                Decile 1



                                                                                                                                                                                                           Decile 2



                                                                                                                                                                                                                      Decile 3



                                                                                                                                                                                                                                      Decile 4



                                                                                                                                                                                                                                                   Decile 5



                                                                                                                                                                                                                                                                    Decile 6



                                                                                                                                                                                                                                                                               Decile 7



                                                                                                                                                                                                                                                                                                Decile 8



                                                                                                                                                                                                                                                                                                           Decile 9
                                                                                      Group 3                                                                                                                                                       Group 4
                                                                                   Income Deciles                                                                                                                                                Income Deciles


                         1.5%                                      Cherkaska                    Kyivska               Zakarpatska                                                        1.5%                                    Donetska                     Khmelnytska                 Odeska

                                                                   Chernihivska                 Vinnytska             Zhytomyrska                                                                                                Khersonska                   Lvivska                     Rivnenska
Increase in income [%]




                                                                                                                                                                Increase in income [%]




                                                                                      Group 3                                                                                                                                                       Group 4
                         1.0%                                                                                                                                                            1.0%


                         1.5%                                                                                                                                                            1.5%

                         0.5%                                                                                                                                                            0.5%
Increase in income [%]




                                                                                                                                                                Increase in income [%]




                         1.0%                                                                                                                                                            1.0%

                         0.0%                                                                                                                                                            0.0%
                                                                                                                                                    Decile 10




                                                                                                                                                                                                                                                                                                                      Decile 10
                                Decile 1



                                           Decile 2



                                                        Decile 3



                                                                        Decile 4



                                                                                     Decile 5



                                                                                                     Decile 6



                                                                                                                Decile 7



                                                                                                                              Decile 8



                                                                                                                                         Decile 9




                                                                                                                                                                                                Decile 1



                                                                                                                                                                                                           Decile 2



                                                                                                                                                                                                                      Decile 3



                                                                                                                                                                                                                                      Decile 4



                                                                                                                                                                                                                                                   Decile 5



                                                                                                                                                                                                                                                                    Decile 6



                                                                                                                                                                                                                                                                               Decile 7



                                                                                                                                                                                                                                                                                                Decile 8



                                                                                                                                                                                                                                                                                                           Decile 9
                         0.5%                                                                                                                                                            0.5%
                                                                                   Income Deciles                                                                                                                                                Income Deciles


                                                      Chernivetska                        Ivano-Frankivska                 Kirovohradska                                                                                         Mykolaivska                   Sumska                     Volynska
                         0.0%                                                                                                                                                            0.0%
                                                      Dnipropetrovska                     Kharkivska                       Luhanska                                                                                              Poltavska                     Ternopilska                Zaporizka
                                                                                                                                                    Decile 10




                                                                                                                                                                                                                                                                                                                      Decile 10
                                Decile 1



                                           Decile 2



                                                        Decile 3



                                                                        Decile 4



                                                                                     Decile 5



                                                                                                     Decile 6



                                                                                                                Decile 7



                                                                                                                              Decile 8



                                                                                                                                         Decile 9




                                                                                                                                                                                                Decile 1



                                                                                                                                                                                                           Decile 2



                                                                                                                                                                                                                      Decile 3



                                                                                                                                                                                                                                      Decile 4



                                                                                                                                                                                                                                                   Decile 5



                                                                                                                                                                                                                                                                    Decile 6



                                                                                                                                                                                                                                                                               Decile 7



                                                                                                                                                                                                                                                                                                Decile 8



                                                                                                                                                                                                                                                                                                           Decile 9




                                                                                   Income Deciles                                                                                                                                                Income Deciles


                                                      Chernivetska                        Ivano-Frankivska                 Kirovohradska                                                                                         Mykolaivska                   Sumska                     Volynska

                                                      Dnipropetrovska                     Kharkivska                       Luhanska                                                                                              Poltavska                     Ternopilska                Zaporizka




                          44                          Ukraine. Building Climate Resilience in Agriculture and Forestry
The range between the low and high projections for all income deciles indicates that for some
oblasts, the low-income deciles tend to experience a wider range between the low and high
projections. As shown in Annex 3, these oblasts include Chernivetska, Lvivska, Ternopilska,
and Volynska in the west; Poltavska and Chernihivska in the central north; and Luhanska in
the east. This suggests that climate change and associated impacts on agricultural production
may have a significant impact on low-income households, more so than on the households in
the upper-income deciles in these oblasts.
The combined effects of the changes in food prices and incomes vary by scenario and the
share of agricultural income in household income structure in each oblast, as illustrated in Fig-
ure 25. In the low projection, there is a 1% to 3% loss of income, with Zhytomyrska, Sumska,
Chernivetska, Rivnenska, and Volynska oblasts affected the most. In the mean projection, the
changes range between -1% and +1%. In the high projection, almost all oblasts experience
gains of 0.5% to 3%. The exceptions are Donetska oblast and Kyiv city, which lose even in the
case of the high projection. Figure 26 shows the effect of only price increases on these and
other regions which have little income from agriculture and are thus particularly vulnerable to
increases in food prices.



                 hanges in Income Under the Three Projections by Oblast for 2030
     Figure 25: C
                (both Income and Price Effects)

     Mykolaivska        -1.2 %                                                                               1.2 %
     Zaporizka          -1.3 %                                                                               1.2 %
     Donetska           -1.3 %                                                                             -0.068 %
     Dnipropetrovska    -1.3 %                                                                              0.37 %
     Kharkivska         -1.4 %                                                                              0.56 %
     Kyiv City          -1.5 %                                                                              -0.17 %
     Khersonska         -1.6 %                                                                              0.88 %
     Ivano-Frankivska   -1.7 %                                                                              0.73 %
     Cherkaska          -1.7 %                                                                              0.31 %
     Vinnytska          -1.8 %                                                                              0.78 %
     Odeska             -1.8 %                                                                               1.7 %
     Lvivska            -1.8 %                                                                              0.67 %
     Zakarpatska        -1.9 %                                                                              0.98 %
     Luhanska           -1.9 %                                                                               2.8 %
     Kirovohradska       -2 %                                                                                2.2 %
     Poltavska          -2.1 %                                                                               1.6 %
     Kyivska            -2.2 %                                                                              0.61 %
     Chernihivska       -2.4 %                                                                               2.9 %
     Ternopilska        -2.6 %                                                                               2.8 %
     Khmelnytska        -2.6 %                                                                               1.6 %
     Volynska           -2.7 %                                                                                3%
     Rivnenska          -2.8 %                                                                               1.7 %
     Chernivetska       -2.9 %                                                                               1.9 %
     Sumska             -3.1 %                                                                                3%
                                         25%


                                               50%
                                                     75%




     Zhytomyrska        -3.2 %                                                                               1.5 %

                        -4%                    -2%                 0%                 2%                    4%


           Relative change in Household Income [%]:         Low                High                 Mean




                                 Ukraine. Building Climate Resilience in Agriculture and Forestry            45
            hanges in Expenditure by Oblast for 2030 (Effect of Price Increases
Figure 26: C
           only) in the Mean Scenario



                                                                                 Chernihivska
                       Volynska
                                   Rivnenska                                                           Sumska


                                                    Zhytomyrska


                                                                    Kyivska

             Lvivska                                                                               Poltavska
                                                                                                                           Kharkivska
                                      Khmelnytska
                            Ternopilska                                      Cherkaska

                                                       Vinnytska                                                                                   Luhanska
               Ivano-Frankivska

      Zakarpatska                                                                Kirovohradska
                              Chernivetska                                                               Dnipropetrovska
                                                                                                                                        Donetska



                                                                               Mykolaivska
                                                                                                                   Zaporizka


                                                                    Odeska
                                                                                                 Khersonska




                                                                                                         Crimea




                       Change in income [%]: only effect of price increase in the mean scenario


                                  -1.1%                     -1.0%                    -0.9%                        -0.8%




    Impact of Climate Change on Agriculture and Poverty
4.4 
Both the increase in food prices and changes in farm outputs will impact poverty head-
counts. The extent of the impact depends on the low, mean, or high projections but the
changes are not significant under any of the three.
An increase in food prices alone results in an increase in the headcount poverty ratio by
between 0% and 1.6% (see Annex 3). The poverty headcount declines in eight of the 25
oblasts, remains unchanged in four, and increases in the others. The declines are small,
between 0.3 and 1.3%, as are the increases: the highest is 1.3%. Under the high projection,
poverty headcount declines in all oblasts except five (Cherkaska, Ivano-Frankivska, Kyivska
(excluding the city), Vinnytska, and Zakarpatska). The Kyivska oblast (excluding the city)
will see an increase, while the remaining four oblasts will not experience any changes to the
poverty headcount, (see Figure 27).




46                  Ukraine. Building Climate Resilience in Agriculture and Forestry
The poverty gap,17 however, does not increase in all cases in the low projection; it declines
slightly in seven oblasts while increasing in the remaining oblasts, (see Figure 27). The se-
verity of poverty also slightly declines in six oblasts but increases in the rest. The highest in-
crease is 0.8% (Khemelnytska). The poverty gap does not always increase, because as more
households are added to the poverty group, the gap for them is smaller than the average for
the group prior to the change. The poverty gap declines in seven oblasts and increases in the
rest. The severity of poverty declines in nine oblasts and increases in the others. The highest
decline is by 1-3% (Chernihivska) and the highest increase is by 0.95% (Vinnytska). Given the
uncertainties regarding the impacts of change on household incomes, this separate analysis
is a valuable indicator of the broader effects of climate change on consumers.



     limate Change Impact on Agriculture
4.5 C
    and the Gini Coefficient18 of Inequality
An increase in food prices alone leads to an increase in inequality. The combined effects
of price increase and changes in agricultural outputs result in a decrease in inequality in
the mean projection. The effect on inequality is tracked through changes in real income per
household. With the low projection, the inequality measure increases by a small amount in
all oblasts, except for Ivano-Frankivska, where the decline is more substantial at 2.75% (see
Annex 3). Other significant increases are in Vinnytska (1.4%) and Sumska (1.07%). Under the
mean projection, six of the 25 oblasts experience a decrease in equality, but the decrease is
very small in most cases, (see Figure 28). In the high projection, all oblasts see an increase
in inequality, with the largest being in Donetska (8.3% increase in the Gini coefficient) and
Ivano-Frankivska (3.5% increase). When considering only food price increase, the Gini coef-
ficient results indicate an increase in inequality in all oblasts, except Ivano-Frankivska. The
increase is in the range of 0.1 to 1.4%. However, this analysis has not investigated all possible
effects of climate change on welfare. Further work should be carried out to examine the other
factors that influence household incomes, including climate-related morbidities and unemploy-
ment, which are not covered in this analysis.




17
     	 The poverty gap is an estimate of the amount by which income must increase across all poor households to take them above
       the poverty line. It is the sum of the difference between the income level of each household below the poverty line and the
       poverty line, reported as a percent of the poverty line.
18
     	 Gini index measures the extent to which income distribution among individuals or households within an economy deviates
       from a perfectly equal distribution (World Bank 2021c).




                                   Ukraine. Building Climate Resilience in Agriculture and Forestry                          47
                                                                    eadcount Poverty, Poverty Gap and Severity of Poverty: Values for the
                                                        Figure 27: H
                                                                   Baseline Period [%] and Changes in 2030 Relative to the Baseline [%], Low
                                                                   Projection


                                                                                                                      Chernihivska                                                                                                                          Chernihivska
                                                                     Volynska                                                              Sumska                                                                        Volynska                                      Chernihivska
                                                                                                                                                                                                                                                                         Sumska                                                                                                              Volynska
                                                                            Rivnenska                                                                                                                                           Rivnenska
                                                                                                                                                                                                                                    Volynska                                        Sumska
                                                                                                                                                                                                                                                                                                                                                                                                    Rivnenska
                                                                                                                                                                                                                                                                                                                                                                                                      Volynska
                                                                                                                                                                                                                                           Rivnenska                                                                                                                                                         Rivnenska
                                                                                             Zhytomyrska                                                                                                                                  Zhytomyrska                                                                                                                                                          Zhytomyrska
                                                                                                                                                                                                                                                     Zhytomyrska                                                                                                                                                       Zhytomy
                                                                                                           Kyivska                                                                                                                                       Kyivska                                                                                                                                                                                  Kyiv
                                                                                                                                                                                                                                                                     Kyivska
                                                                 Lvivska                                                                Poltavska              Kharkivska                                           Lvivska       Lvivska                                  Poltavska Poltavska
                                                                                                                                                                                                                                                                                          Kharkivska Kharkivska                                                                         Lvivska    Lvivska
                                                                            Khmelnytska                                                                                                                                             KhmelnytskaKhmelnytska                                                                                                                                              Khmelnytska
                                                                                                                                                                                                                                                                                                                                                                                                                 Khmelnytska
                                                                      Ternopilska                                   Cherkaska                                                                                                Ternopilska Ternopilska         Cherkaska Cherkaska                                                                                                                 Ternopilska
                                                                                                                                                                                                                                                                                                                                                                                                           Ternopilska
                                                                                      Vinnytska                                                                                    Luhanska                                                    Vinnytska Vinnytska                                       Luhanska Luhanska                                                                                          VinnytskaVinnyt
                                                             Ivano-Frankivska                                                                                                                                       Ivano-Frankivska
                                                                                                                                                                                                                                 Ivano-Frankivska                                                                                                                                       Ivano-Frankivska
                                                                                                                                                                                                                                                                                                                                                                                                  Ivano-Frankivska
                                                        Zakarpatska                                                  Kirovohradska                                                                             Zakarpatska Zakarpatska                         Kirovohradska
                                                                                                                                                                                                                                                                           Kirovohradska                                                                                           Zakarpatska
                                                                                                                                                                                                                                                                                                                                                                                            Zakarpatska
                                                                       Chernivetska                                                        Dnipropetrovska                                                                    ChernivetskaChernivetska                        Dnipropetrovska
                                                                                                                                                                                                                                                                                         Dnipropetrovska                                                                                          Chernivetska
                                                                                                                                                                                                                                                                                                                                                                                                            Chernivetska
                                                                                                                                                                      Donetska                                                                                                                   Donetska Donetska

                                                                                                                     Mykolaivska                                                                                                                                           MykolaivskaMykolaivska
                                                                                                                                                        Zaporizka                                                                                                                                                    Zaporizka Zaporizka

                                                                                                           Odeska                     Khersonska                                                                                                                   Odeska             Odeska
                                                                                                                                                                                                                                                                                         KhersonskaKhersonska                                                                                                                                     Ode




                                                                                                                                            Crimea                                                                                                                                                        Crimea               Crimea




                                                                                    Headcount Poverty: Base Period [%]                                                                                                                       Headcount
                                                                                                                                                                                                                                            Poverty Gap:Poverty: Change
                                                                                                                                                                                                                                                        Base Period  [%] to Base [%]                                                                                                                                    Poverty
                                                                                                                                                                                                                                                                                                                                                                                                                 Severity       G
                                                                                                                                                                                                                                                                                                                                                                                                                          of Pove




                                                                                                                                                                                                                                                                                                                                                                                                -0.50%




                                                                                                                                                                                                                                                                                                                                                                                                                               0.00%
                                                                                                                                                                                                                          0.0%




                                                                                                                                                                                                                                                                          1.0%




                                                                                                                                                                                                                                                                                                                                      2.0%
                                                         10.0%




                                                                                                   15.0%




                                                                                                                                                       20.0%




                                                                                                                                                                                                                                  15.0%




                                                                                                                                                                                                                                                                 17.5%




                                                                                                                                                                                                                                                                                                          20.0%




                                                                                                                                                                                                                                                                                                                                                   22.5%




                                                                                                                                                                                                                                                                                                                                                                                                         4.00%




                                                                                                                                                                                                                                                                                                                                                                                                                                       5.00%
                                                                                                                        Chernihivska                                                                                                                               Chernihivska
ska                                                                    Volynska                                                             Sumska                                                                          Volynska                                              Sumska
                                                                              Rivnenska                                                    Chernihivska                                                                             Rivnenska                                Chernihivska
                                                                                   Volynska                                                                        Sumska                                                              Volynska                                            Sumska                                                                                                                Volynska
                                                                                           Rivnenska
                                                                                        Zhytomyrska                                                                                                                                           Rivnenska
                                                                                                                                                                                                                                               Zhytomyrska                                                                                                                                                              Rivnenska
                                                                                                              Zhytomyrska                                                                                                                                Zhytomyrska                                                                                                                                                                           Zhytomy
                                                                                                              Kyivska                                                                                                                                        Kyivska
ka                                                                 Lvivska                                                            Kyivska
                                                                                                                                           Poltavska                                                                   Lvivska                                         Kyivska Poltavska
              Kharkivska                                                           Khmelnytska                                                                   Kharkivska                                                           Khmelnytska                                             Kharkivska
                                                                                   Lvivska                                                                     Poltavska    Kharkivska                                            Lvivska                                                Poltavska     Kharkivska                                                                                        Lvivska
                                                                             Ternopilska     Khmelnytska Cherkaska                                                                                                             Ternopilska      Khmelnytska      Cherkaska                                                                                                                                               Khmelnytska
                                  Luhanska                                             Ternopilska
                                                                                           Vinnytska                 Cherkaska                                                Luhanska                                                    Ternopilska
                                                                                                                                                                                                                                                  Vinnytska                Cherkaska                         Luhanska                                                                                              Ternopilska
                                                                Ivano-Frankivska                       Vinnytska                                                                                  Luhanska            Ivano-Frankivska                      Vinnytska                                                Luhanska                                                                                                      Vinny
                                                           Zakarpatska        Ivano-Frankivska             Kirovohradska                                                                                         Zakarpatska     Ivano-Frankivska                  Kirovohradska                                                                                                                  Ivano-Frankivska
ropetrovska                                                               Chernivetska
                                                                         Zakarpatska                                     Dnipropetrovska
                                                                                                                       Kirovohradska                                                                                            Chernivetska
                                                                                                                                                                                                                            Zakarpatska                                           Dnipropetrovska
                                                                                                                                                                                                                                                                             Kirovohradska                                                                                                   Zakarpatska
                     Donetska                                                           Chernivetska                                 Dnipropetrovska
                                                                                                                                            Donetska                                                                                       Chernivetska                                    Dnipropetrovska
                                                                                                                                                                                                                                                                                                     Donetska                                                                                              Chernivetska
                                                                                                                                                                                            Donetska                                                                                                          Donetska
                                                                                                                       Mykolaivska                                                                                                                                               Mykolaivska
      Zaporizka                                                                                                                            Mykolaivska
                                                                                                                                                     Zaporizka                                                                                                                            Mykolaivska                       Zaporizka
                                                                                                                                                                            Zaporizka                                                                                                                                                Zaporizka
ka                                                                                                            Odeska                    Khersonska                                                                                                                       Odeska             Khersonska
                                                                                                                                      Odeska       Khersonska                                                                                                                          Odeska       Khersonska



mea                                                                                                                                            Crimea               Crimea                                                                                                                                        Crimea        Crimea




eriod [%]                                                                                   Poverty Gap: Base
                                                                                              Headcount       Period
                                                                                                         Poverty:    [%] to Base [%]
                                                                                                                  Change                                                                                                                         Poverty
                                                                                                                                                                                                                                          Severity       Gap:Base
                                                                                                                                                                                                                                                   of Poverty: Change to Base[%]
                                                                                                                                                                                                                                                                   Period [%]                                                                                                                                        Severity of P
                                                                                                                                                                                                                                 -0.50%
     20.0%




                                                                                    15.0%




                                                                                                            17.5%




                                                                                                                                               20.0%




                                                                                                                                                                                    22.5%




                                                                                                                                                                                                                                  4.00%




                                                                                                                                                                                                                                                        5.00%




                                                                                                                                                                                                                                                                                             6.00%




                                                                                                                                                                                                                                                                                                                              7.00%




                                                                                                                                                                                                                                                                                                                                                             8.00%
                                                                                                                                                                                                                                                        0.00%




                                                                                                                                                                                                                                                                                   0.50%




                                                                                                                                                                                                                                                                                                                    1.00%




                                                                                                                                                                                                                                                                                                                                                  1.50%




                                                                                                                                                                                                                                                                                                                                                                           2.00%




                                                                                                                                                                                                                                                                                                                                                                                                                       0.00%
                                                                             0.0%




                                                                                                                       1.0%




                                                                                                                                                                            2.0%




                                                                                                                      Chernihivska
a                                                                     Volynska                                                             Sumska
nihivska                                                                     Rivnenska                                                    Chernihivska                                                                                                                                               Chernihivska
                Sumska                                                             Volynska                                                                       Sumska                                                                  Volynska                                                                          Sumska
                                                                                          Rivnenska                                                                                                                                              Rivnenska
                                                                                       Zhytomyrska
                                                                                                           Zhytomyrska                                                                                                                                          Zhytomyrska
                                                                                                           Kyivska
                                                                                                                                  Kyivska                                                                                                                                           Kyivska
             Kharkivska                                          Lvivska                                                              Poltavska
                                                                                                                                    Kharkivska
              Poltavska       Kharkivska                                     Lvivska
                                                                             Khmelnytska                                          Poltavska      Kharkivska                                                                       Lvivska                                                                           Poltavska                  Kharkivska
                                                                      Ternopilska           Khmelnytska Cherkaska                                                                                                                                 Khmelnytska
ska                          Luhanska                                                Ternopilska                    Cherkaska                      Luhanska                                                                                 Ternopilska                                       Cherkaska
                                             Luhanska                                    Vinnytska                                                             Luhanska                                                                                                                                                                                              Luhanska
                                                             Ivano-Frankivska                         Vinnytska                                                                                                                                             Vinnytska
                                                                            Ivano-Frankivska              Kirovohradska                                                                                                        Ivano-Frankivska
ohradska                                                Zakarpatska                                                   Kirovohradska                                                                                                                                                                  Kirovohradska
petrovska                                                              Chernivetska
                                                                       Zakarpatska                                      Dnipropetrovska                                                                                   Zakarpatska
                Dnipropetrovska
                   Donetska                                                           Chernivetska                                   Dnipropetrovska
                                                                                                                                           Donetska                                                                                     Chernivetska                                                                        Dnipropetrovska
                                       Donetska                                                                                                         Donetska                                                                                                                                                                                           Donetska

aivska                                                                                                               Mykolaivska Mykolaivska                                                                                                                                                       Mykolaivska
  Zaporizka               Zaporizka                                                                                                        Zaporizka                       Zaporizka                                                                                                                                                  Zaporizka
                                                                                                           Odeska                Odeska
                                                                                                                                   Khersonska Khersonska                                                                                                                            Odeska
        Khersonska                                                                                                                                                                                                                                                                                                Khersonska




ea                Crimea                                                                                                                     Crimea                Crimea                                                                                                                                                    Crimea




[%] to Base [%]
hange                                                                                         Poverty
                                                                                    Severity of       Gap:
                                                                                                Poverty:   Change
                                                                                                         Base      to[%]
                                                                                                              Period  Base[%]                                                                                                                 Severity of Poverty: Change to Base [%]
                                                                              -0.50%




                                                                                                   0.00%




                                                                                                                              0.50%




                                                                                                                                                          1.00%




                                                                                                                                                                                   1.50%




                                                                                                                                                                                                       2.00%




                                                                                                                                                                                                                                                0.00%




                                                                                                                                                                                                                                                                                           0.30%




                                                                                                                                                                                                                                                                                                                                       0.60%
                          22.5%




                                                                              4.00%




                                                                                                   5.00%




                                                                                                                              6.00%




                                                                                                                                                          7.00%




                                                                                                                                                                                   8.00%
                           2.0%




                                                        48                                   Ukraine. Building Climate Resilience in Agriculture and Forestry
Limitations of the analysis
The study is based on the partial equilibrium                                           Gini Сoefficient Сhanges
                                                                             Figure 28: 
approach which gives a good approximation                                               in 2030 Relative to the Base-
of the likely loss of wellbeing or change in real                                       line [%]
expenditure from the increase in prices of key
agricultural commodities. Thus, future stud-                                                                                     Low
ies can benefit from wider use of input-output
tables or microeconomic simulation models                                                    Volynska
                                                                                                    Rivnenska
                                                                                                                                   Chernihivska
                                                                                                                                                     Sumska

which were not available at the time of the                                                                     Zhytomyrska

                                                                                                                              Kyivska

study. The impact of changes in consumer                                                 Lvivska
                                                                                                      Khmelnytska
                                                                                                                                                   Poltavska      Kharkivska


prices on food demand was taken from var-
                                                                                                Ternopilska                      Cherkaska
                                                                                                                Vinnytska                                                        Luhanska
                                                                                        Ivano-Frankivska
                                                                                                                                   Kirovohradska

ious studies across European countries as
                                                                                    Zakarpatska
                                                                                                 Chernivetska                                   Dnipropetrovska
                                                                                                                                                                           Donetska


similar studies were not available for Ukraine                                                                                    Mykolaivska
                                                                                                                                                               Zaporizka


specifically (Femenia 2019). Additionally,
                                                                                                                              Odeska         Khersonska



Ukraine is in the process of expanding and                                                                                                             Crimea

improving its household income and expend-
iture surveys using the methodology for the                                                                                     Mean
EU Household Final Consumption Expendi-
ture Surveys.19 The harmonization process it                                                 Volynska
                                                                                                                                   Chernihivska


not yet complete and information on several
                                                                                                    Rivnenska                                        Sumska
                                                                                                                Zhytomyrska


cross-sectional variables have not yet been                                              Lvivska
                                                                                                      Khmelnytska
                                                                                                                              Kyivska
                                                                                                                                                   Poltavska      Kharkivska

collected in Ukraine, which complicates data                                                    Ternopilska

                                                                                        Ivano-Frankivska
                                                                                                                Vinnytska
                                                                                                                                 Cherkaska
                                                                                                                                                                                 Luhanska


analysis.                                                                           Zakarpatska
                                                                                                 Chernivetska
                                                                                                                                   Kirovohradska
                                                                                                                                                Dnipropetrovska
                                                                                                                                                                           Donetska

                                                                                                                                  Mykolaivska
                                                                                                                                                               Zaporizka

                                                                                                                              Odeska         Khersonska




                                                                                                                                                       Crimea




                                                                                                                                 High


                                                                                                                                   Chernihivska
                                                                                             Volynska                                                Sumska
                                                                                                    Rivnenska
                                                                                                                Zhytomyrska

                                                                                                                              Kyivska
                                                                                         Lvivska                                                   Poltavska      Kharkivska
                                                                                                      Khmelnytska
                                                                                                Ternopilska                      Cherkaska
                                                                                                                Vinnytska                                                        Luhanska
                                                                                        Ivano-Frankivska
                                                                                    Zakarpatska                                    Kirovohradska
                                                                                                 Chernivetska                                   Dnipropetrovska
                                                                                                                                                                           Donetska

                                                                                                                                  Mykolaivska
                                                                                                                                                               Zaporizka

                                                                                                                              Odeska         Khersonska




                                                                                                                                                       Crimea




                                                                                                                       Change in Gini
                                                                           -8.30%




                                                                                                                                          -2.70%




                                                                                                                                                                             0.00%
                                                                                                                                                                             0.30%
                                                                                                                                                                             0.60%


                                                                                                                                                                                            1.50%




19
     	 See https://ec.europa.eu/eurostat/cache/metadata/en/hbs_esms.htm.




                                   Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                   49
CHAPTER 5:
IMPACT OF CLIMATE CHANGE
ON FORESTS
5.1 Summary
The projections show a significant shrinking of zones for optimal growth, in term of climate
humidity, for most species during the second half of the twenty-first century, especially
during the end of century period. The projected changes in climatic conditions, especial-
ly under RCP 8.5, will particularly impact adult tree species, as they have low adaptive
capacity. This will lead to a deterioration in the condition, productivity, and biodiversity of
forest species.
Based on the temperature and humidity conditions projected under both RCPs, a signif-
icant reduction is expected in the area on the suitability scale for the growth of spruce,
beech, pine and oak. Less than 3% of the country’s forest areas would have optimal con-
ditions for Norway spruce, Scots pine, and beech under RCP 8.5 projections. Only 8% of
the territory will have optimal conditions for English oak under the same scenario.
Under RCP 4.5 projections, conditions suitable for forest growth will remain only in the
Carpathians, western forest-steppe, western part of Polissya (in the form of a new cli-
mate type), and parts of the north of Chernihivska and Sumska oblasts. The steppe and
Polissya are expected to undergo significant changes in the hydrological regime. These
changes will lead to the deterioration of forests and a possible reduction in total forest
areas, particularly in the left bank forest-steppe, steppe, and Polissya. In the Carpathians,
the forest boundary is expected to move to a higher altitude.
The projected changes are likely to exacerbate disturbances and stressors such as wildfires
and insects. During prolonged droughts, a significant proportion of forest biomass becomes
combustible, increasing the fuel load of the forest. In addition, pest infestations which have
been documented with warming conditions, can result in the deterioration of forest health and
increased tree mortality. These will, in turn, enlarge the fuel load available for combustion in
wildfire events. Forest fires may increase due to the occurrence of forest diseases and pro-
longed droughts. According to the PESETA study, forests in the Polissya region with a high
concentration of pine trees have a high risk of fire due to the increase of temperature and
dry spells expected in most of Europe. In Ukraine, pine forests in the southern and northern
steppe and forest-steppe areas will also be at high risk due to the drier conditions expected
there under both RCPs.




50          Ukraine. Building Climate Resilience in Agriculture and Forestry
    Climate Vulnerability Indices for Forests
5.2 
Ukraine ranks 36th among 46 European countries for forest cover. Forest covers about 15.9%
of Ukraine’s territory, about 9.6 million hectares (see Figure 29). Forest cover in Ukraine is di-
vided almost equally between coniferous forests (about 42%) and hardwood broadleaved for-
ests (43%). The most common species are Scots pine, oak, Norway spruce, European beech,
silver birch, black alder, European ash, European hornbeam, and silver fir. Pine accounts for
about 35% of the forest cover; oak (Quercus spp.) 28%; beech (Fagus silvatica) 9%; spruce
(Picea spp.) 8%; and birch (Betula pendula) 7% (World Bank 2020).
The beginning of this century was marked by several strong waves of decline of forests over
nearly the entire country. This decline has had a particularly negative impact on ecosystem
functions and services in the country’ east and south. Projected trends in key climate indica-
tors such as temperature and precipitation, as illustrated in Figures 30 and 31, indicate further
degradation and endangerment of Ukraine’s forests. Climate variability and, in particular, fre-
quency and severity of climatic extremes, have the potential to significantly exacerbate future
projections.
The longer annual warm period projected under both RCP 4.5 and RCP 8.5 would result in
a significantly shorter annual frost period. The increase in the duration of the warm period
(t>5 °C) will prolong the growing season for trees throughout Ukraine relative to the baseline
period by an average of 20-30 days in the mid-century period, and by 30-50 days (depending
on the projections) during the end of century period. The period with a stable temperature
above 5 °C will occur earlier in the spring and later in the fall.


     Figure 29: Forestland Across Ukraine’s Oblast




                         Ukraine. Building Climate Resilience in Agriculture and Forestry     51
Based on the projections for both RCP scenarios, the boundaries of climatic zones will shift
toward the north. This change happens in terms of heat supply according to Vorobjov’s Heat
Availability Index20 for forests. In 1961-1990, Ukraine had four heat zones. These range from
relatively moderate (c) in the Carpathians to warm (f) in the southern steppe forest region, pre-
dominantly temperate in the plains (d) in Polissya and part of the forest-steppe, and relatively
warm (e) in the remainder of the forest-steppe and in the northern steppe. The baseline period
(1990-2010) has already seen an extension in the relatively warm (e) (up to 70% of Ukraine’s
territory) and warm (f) (up to 17.7%). If this trend continues, we will see new types of heat
zones (g/very warm, and h/hot), which were not described by Vorobjov in the baseline period
(See Annex 4). Heat Availability Index projections are presented in Figure 30. Projections
under both RCPs project an increase in annual precipitation relative to the baseline period
(1991-2010) in all forested regions except for the Carpathians which will experience a drier cli-
mate. These changes are reflected by the changes in Vorobjov’s humidity index, accompanied
by changes in the hydrological regime, groundwater levels, etc. Climatic conditions cause the
formation of respective zonal hydrological conditions (hygrotopes) and intrazonal types under
the influence of local landscape, soil type, and moisture availability. For this reason, changes
need to be analyzed in high spatial resolution. Vorobjov considers 2 – 6 types of climate hu-
midity as favorable for forest growth. The changes of Vorobjov’s humidity index are presented
in Annex 4; this change is shown in Figure 31.
Both RCP 4.5 and RCP 8.5 project a further increase in aridity and a shift in the humidity limits
to the north. A new type of climate, extremely dry, which was not described by the Vorobjov
Index, is expected to appear in the south (see Annex 4). Water scarcity causes forest degra-
dation, as forests are especially sensitive to droughts and other climate extremes that cause
changes in hydrological conditions as drop in groundwater levels. Ukraine has a poor water
resource endowment and very unstable water flow. In recent years, water reserves in rivers
and reservoirs amounted to only 80% of the long-term average (Schvidenko et al, 2018). In-
tensive processes of drying and morbidity of major forest tree species, including pine, spruce,
oak, and beech, are observed in forests. The southern part of the country is under particular
risk, with ongoing losses of forests due to drought estimated by remote sensing at about 20-
30% of forested area.
Under the RCP 4.5 projections, conditions suitable for forest growth will remain only in the
Carpathians, western forest-steppe, western part of Polissya (in the form of a new climate
type), and parts of the north of Chernihiv and Sumy oblasts. The areas of wet and fresh
climate types are expected to decrease (as shown in Annex 4), and dry climate types to in-
crease. The total area with dry conditions will occupy 63.2% of the country’s territory in the
middle of the century, and 70.5% at the end of the century.
Under the RCP 8.5 projection, the process of aridification will accelerate. Dry climate types
are expected to account for about 69.6% of the country’s territory in the middle of the century
and 89.7% at the end of the century. Accordingly, the area of climate conditions suitable for
forest growth will decrease significantly, up to 30.4% in the middle of the century and 10.3%
at the end of the century.



20
     	 Other indices which have a strong influence on tree growth, such as the continentality index, were also taken into account for
       this analysis. Details of the continentality index are given in Annex I.




52                 Ukraine. Building Climate Resilience in Agriculture and Forestry
            elative Changes of Vorobjov’s Heat Availability Index to Climate,
Figure 30: R
           1991-2010

                              RCP 4.5 Near future: 2021-2040                                                                                                      RCP 8.5 Near future: 2021-2040



                                                               Chernihivska                                                                                                                        Chernihivska
               Volynska                                                                                                                            Volynska
                          Rivnenska                                               Sumska                                                                      Rivnenska                                               Sumska

                                      Zhytomyrska                                                                                                                         Zhytomyrska

                                                     Kyivska                                                                                                                             Kyivska
       Lvivska                                                                 Poltavska                                                   Lvivska                                                                 Poltavska
                                                                                                 Kharkivska                                                                                                                          Kharkivska
                           Khmelnytska                                                                                                                         Khmelnytska
                  Ternopilska                            Cherkaska                                                                                    Ternopilska                            Cherkaska
                                         Vinnytska                                                                  Luhanska                                                 Vinnytska                                                                  Luhanska
       Ivano-Frankivska                                                                                                                    Ivano-Frankivska
 Zakarpatska                                                   Kirovohradska                                                         Zakarpatska                                                   Kirovohradska
                    Chernivetska                                                  Dnipropetrovska                                                       Chernivetska                                                  Dnipropetrovska
                                                                                                         Donetska                                                                                                                            Donetska


                                                              Mykolaivska                                                                                                                         Mykolaivska
                                                                                             Zaporizka                                                                                                                           Zaporizka

                                                     Odeska                 Khersonska                                                                                                   Odeska                 Khersonska




                                                                                    Crimea                                                                                                                              Crimea




                             RCP 4.5 Mid-century: 2041-2060                                                                                                      RCP 8.5 Mid-century: 2041-2060



                                                               Chernihivska                                                                                                                        Chernihivska
               Volynska                                                                                                                            Volynska
                          Rivnenska                                               Sumska                                                                      Rivnenska                                               Sumska

                                      Zhytomyrska                                                                                                                         Zhytomyrska

                                                     Kyivska                                                                                                                             Kyivska
       Lvivska                                                                 Poltavska                                                   Lvivska                                                                 Poltavska
                                                                                                 Kharkivska                                                                                                                          Kharkivska
                           Khmelnytska                                                                                                                         Khmelnytska
                  Ternopilska                            Cherkaska                                                                                    Ternopilska                            Cherkaska
                                         Vinnytska                                                                  Luhanska                                                 Vinnytska                                                                  Luhanska
       Ivano-Frankivska                                                                                                                    Ivano-Frankivska
 Zakarpatska                                                   Kirovohradska                                                         Zakarpatska                                                   Kirovohradska
                    Chernivetska                                                  Dnipropetrovska                                                       Chernivetska                                                  Dnipropetrovska
                                                                                                         Donetska                                                                                                                            Donetska


                                                              Mykolaivska                                                                                                                         Mykolaivska
                                                                                             Zaporizka                                                                                                                           Zaporizka

                                                     Odeska                 Khersonska                                                                                                   Odeska                 Khersonska




                                                                                    Crimea                                                                                                                              Crimea




                               RCP 4.5 Far future: 2081-2100                                                                                                       RCP 8.5 Far future: 2081-2100



                                                               Chernihivska                                                                                                                        Chernihivska
               Volynska                                                                                                                            Volynska
                          Rivnenska                                               Sumska                                                                      Rivnenska                                               Sumska

                                      Zhytomyrska                                                                                                                         Zhytomyrska

                                                     Kyivska                                                                                                                             Kyivska
       Lvivska                                                                 Poltavska                                                   Lvivska                                                                 Poltavska
                                                                                                 Kharkivska                                                                                                                          Kharkivska
                           Khmelnytska                                                                                                                         Khmelnytska
                  Ternopilska                            Cherkaska                                                                                    Ternopilska                            Cherkaska
                                         Vinnytska                                                                  Luhanska                                                 Vinnytska                                                                  Luhanska
       Ivano-Frankivska                                                                                                                    Ivano-Frankivska
 Zakarpatska                                                   Kirovohradska                                                         Zakarpatska                                                   Kirovohradska
                    Chernivetska                                                  Dnipropetrovska                                                       Chernivetska                                                  Dnipropetrovska
                                                                                                         Donetska                                                                                                                            Donetska


                                                              Mykolaivska                                                                                                                         Mykolaivska
                                                                                             Zaporizka                                                                                                                           Zaporizka

                                                     Odeska                 Khersonska                                                                                                   Odeska                 Khersonska




                                                                                    Crimea                                                                                                                              Crimea




                                             Change in heat supply of warm season (monthly sums) to base period: 1991-2010 [%]

                                                         0% — 7%                                 9% — 11%                      13% — 15%                  17% — 19%                          37% — 39%


                                                         7% — 9%                                11% — 13%                      15% — 17%                  35% — 37%                          39% — 41%




                                                         Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                                                                          53
            elative Changes of Vorobjov’s Moisture Availability Index to Climate,
Figure 31: R
           1991-2010

                               RCP 4.5 Near future: 2021-2040                                                                                                               RCP 8.5 Near future: 2021-2040



                                                                 Chernihivska                                                                                                                                       Chernihivska
                Volynska                                                                                                                                   Volynska
                           Rivnenska                                                 Sumska                                                                            Rivnenska                                                       Sumska

                                        Zhytomyrska                                                                                                                                   Zhytomyrska

                                                       Kyivska                                                                                                                                           Kyivska

        Lvivska                                                                   Poltavska                                                       Lvivska                                                                           Poltavska
                                                                                                     Kharkivska                                                                                                                                             Kharkivska
                            Khmelnytska                                                                                                                                    Khmelnytska
                   Ternopilska                             Cherkaska                                                                                              Ternopilska                                   Cherkaska
                                           Vinnytska                                                                    Luhanska                                                         Vinnytska                                                                             Luhanska
        Ivano-Frankivska                                                                                                                              Ivano-Frankivska
 Zakarpatska                                                     Kirovohradska                                                              Zakarpatska                                                             Kirovohradska
                     Chernivetska                                                     Dnipropetrovska                                                              Chernivetska                                                          Dnipropetrovska
                                                                                                             Donetska                                                                                                                                               Donetska


                                                                Mykolaivska                                                                                                                                        Mykolaivska
                                                                                                Zaporizka                                                                                                                                           Zaporizka

                                                       Odeska                  Khersonska                                                                                                               Odeska                   Khersonska




                                                                                       Crimea                                                                                                                                              Crimea




                               RCP 4.5 Mid-century: 2041-2060                                                                                                            RCP 8.5 Mid-century: 2041-2060



                                                                 Chernihivska                                                                                                                                  Chernihivska
                Volynska                                                                                                                               Volynska
                           Rivnenska                                                 Sumska                                                                        Rivnenska                                                       Sumska

                                        Zhytomyrska                                                                                                                               Zhytomyrska

                                                       Kyivska                                                                                                                                       Kyivska

        Lvivska                                                                   Poltavska                                                   Lvivska                                                                          Poltavska
                                                                                                     Kharkivska                                                                                                                                      Kharkivska
                            Khmelnytska                                                                                                                             Khmelnytska
                   Ternopilska                             Cherkaska                                                                                       Ternopilska                                   Cherkaska
                                           Vinnytska                                                                    Luhanska                                                    Vinnytska                                                                              Luhanska
        Ivano-Frankivska                                                                                                                      Ivano-Frankivska
 Zakarpatska                                                     Kirovohradska                                                          Zakarpatska                                                            Kirovohradska
                     Chernivetska                                                     Dnipropetrovska                                                        Chernivetska                                                           Dnipropetrovska
                                                                                                             Donetska                                                                                                                                           Donetska


                                                                Mykolaivska                                                                                                                                Mykolaivska
                                                                                                Zaporizka                                                                                                                                       Zaporizka

                                                       Odeska                  Khersonska                                                                                                        Odeska                     Khersonska




                                                                                       Crimea                                                                                                                                        Crimea




                                 RCP 4.5 Far future: 2081-2100                                                                                                                 RCP 8.5 Far future: 2081-2100



                                                                  Chernihivska                                                                                                                                     Chernihivska
                Volynska                                                                                                                                   Volynska
                           Rivnenska                                                 Sumska                                                                            Rivnenska                                                       Sumska

                                        Zhytomyrska                                                                                                                                  Zhytomyrska

                                                       Kyivska                                                                                                                                           Kyivska

        Lvivska                                                                   Poltavska                                                       Lvivska                                                                           Poltavska
                                                                                                     Kharkivska                                                                                                                                             Kharkivska
                             Khmelnytska                                                                                                                                Khmelnytska
                    Ternopilska                             Cherkaska                                                                                          Ternopilska                                     Cherkaska
                                           Vinnytska                                                                    Luhanska                                                         Vinnytska                                                                             Luhanska
        Ivano-Frankivska                                                                                                                          Ivano-Frankivska
  Zakarpatska                                                     Kirovohradska                                                             Zakarpatska                                                            Kirovohradska
                     Chernivetska                                                     Dnipropetrovska                                                              Chernivetska                                                         Dnipropetrovska
                                                                                                             Donetska                                                                                                                                               Donetska


                                                                 Mykolaivska                                                                                                                                     Mykolaivska
                                                                                                Zaporizka                                                                                                                                           Zaporizka

                                                       Odeska                  Khersonska                                                                                                               Odeska                   Khersonska




                                                                                       Crimea                                                                                                                                              Crimea




                                                                           Change in humidity index by Vorobjov (W) to base period: 1991-2010 [%]

                                            -120% — -100%                             -100% — -80%                        -80% — -60%   -60% — -40%                               -40% — -20%                               -20% — 0%




54                                     Ukraine. Building Climate Resilience in Agriculture and Forestry
    Effect of Climate Change on Key Forest Species
5.3 
The boundaries of zones with satisfactory conditions for English oak are expected to further
shift toward the northwest, while the conditionally unsuitable zone will expand to the south.
Conditions in the Carpathians are becoming more favorable for this species. In the middle of
century climate, comparatively minor changes are anticipated (Figure 33), including narrowing
of the zone of unsatisfactory conditions in the Carpathians and the southern steppe. Accord-
ing to the projection under RCP 8.5 at the end of the century, the conditions in the Carpathians
are expected to be satisfactory in the highlands and optimal on the plains; and most of Ukraine
will be characterized by conditionally unsuitable and unsatisfactory conditions21 (Figure 33
and maps in Annex 4).
Under RCP 8.5 projections, the areas with optimal conditions for European beech will diminish
to 2% of Ukraine’s territory. A shift toward the northwest was already taking place in the base-
line period (1990-2010). In particular, the optimal zone for European beech in the western part
of the forest-steppe decreased. During the same time period, conditions for beech improved in
the Carpathians, from unsatisfactory to satisfactory. A further shift of boundaries and shrinking
of the area suitable for forest beech growth is anticipated. In the mid-century, under RCP 4.5,
conditions suitable for beech could be preserved in 23.8% of Ukraine’s territory (Figure 33). By
the end of the century, the western part of the forest-steppe and Polissya will consist of mostly
unsatisfactory and conditionally unsuitable zones, and the Carpathians and a small area in
the western forest-steppe will consist of zones ranging from optimal to satisfactory (Figure 32
and maps in Annex 4).
Conditions suitable for Scots pine growth will remain in the Carpathians (ranging from optimal
to satisfactory), the western part of the forest-steppe, and Polissya (predominantly unsatisfac-
tory). Between 1990 and 2010, boundaries shifted slightly toward the north and the zones with
optimal and suboptimal conditions narrowed. The south had small zones of conditionally un-
suitable areas for pine growth. In the future, minor changes are expected, leading to improved
conditions including the expansion of optimal zones in the Carpathians and suboptimal zones
in most of Polissya and the western part of the forest-steppe. Conditions will be satisfactory in
the forest-steppe and unsatisfactory in the steppe. It is expected that borders will further shift
to the northwest, and the conditionally unsuitable zone will expand. By mid-century, under
RCP 4.5 the conditionally unsuitable zone will cover about 37.1% of Ukraine’s territory, and
under RCP 8.5, 45.4% (Figure 33 and maps in Annex 4).
In the mid-century, under both RCP 4.5 and RCP 8.5, only the Carpathians will remain a suit-
able zone for Norway spruce. In the rest of Ukraine, climate conditions will be conditionally
unsuitable for spruce. Regional studies of climate change impacts on forests in the Ukrainian
Carpathians came to a similar conclusion. Spruce forests areas are expected to decrease
from more than 60% to 25% under RCP 4.5, and to 10% under RCP 8.5 (Kruhlov et al. 2018).
In the end of the century, climate conditions suitable for spruce are projected to disappear in
the north/northwest (Polissya), and the western part of the forest-steppe, with most Ukrainian
territory becoming conditionally unsuitable (Figure 33 and maps in Annex 4).




21
     	 See Figure 32 for the full suitability scale for growing forest species; the scale ranges from conditionally unsatisfactory to
       optimal.




                                    Ukraine. Building Climate Resilience in Agriculture and Forestry                            55
The impact of climate change on forests in Ukraine is exacerbated by a simultaneous loss of
ecosystem services. Additionally, based on some estimates, carbon sequestration by forests
could decrease significantly. Box 4 summarizes some of the impacts which are not addressed
in this report, but could negatively affect resilience of natural landscapes, reduce agricultural
productivity, and threaten biodiversity in Ukraine.




           mpact of Climate Change on Areas with Growing Potential for English Oak
Figure 32: I
           and European Beech, 2100




56          Ukraine. Building Climate Resilience in Agriculture and Forestry
               mpact of Climate Change on Areas with Growing Potential
    Figure 33: I
               for Selected Forest Species, in 2050

                                                                           English oak                                                                     Scots pine                                    European beech                                                              Norway spruce


                                       100%                                                                                   2.6%                            2.2%                 2.1%                                                                    2%                                                                    1.6%
                                                                                                                                                                                                                                     2.6%                                      1.7%                         1.6%
                                                                                                                                                                                   1.5%                 4.3%                                                                                                                     1.5%
                                                                                                                                                               2%                                                                                         2.3%                                               2%
                                                                                                   8.1%                       3.2%                                                                                                   3.7%                                      3.2%

                                                                                                                                                                                                        6.6%                                              7.6%
                                                                              18.4%


                                              28.1%                                                                                                                                20.9%
                                                                                                                                                                                                                                     17.5%
                                                                                                   19.6%                                                     27.2%

                                                                                                                                                                                                        21.9%
                                                                                                                                                                                                                                                          22.3%
                                       75%                                                                                    38.1%
                                                                              18.1%
                                                                                                                                                                                                                                                                                                                                                      Suitability scale
                                                                                                                                                                                                                                                                                                                                                      for growing
                                                                                                                                                                                                                                     19.2%
                                                                                                                                                                                                                                                          1.3%                                                                                        forest species:
Share [%] of the Ukrainian territory




                                              21.3%                                                23.1%                                                                           29.5%
                                                                                                                                                                                                                                                                                                                                                            optimal
                                                                                                                                                                                                                                      1%
                                                                                                                                                                                                        23.7%
                                                                              21.8%                                                                          30.7%                                                                                                                                                                                          suboptimal
                                       50%
                                                                                                                                                                                                                                                                                                            95.5%                96.4%
                                                                                                                                                                                                                                                                               93.9%                                                                        satisfactory

                                                                                                                                                                                                        1.1%
                                                                                                                                                                                                                                                                                                                                                            unsatisfactory
                                                                                                   21.5%                      36.3%
                                              27%
                                                                                                                                                                                                                                                                                                                                                            extremely
                                                                                                                                                                                                                                                          64.6%
                                                                                                                                                                                                                                                                                                                                                            unsatisfactory
                                                                              21.7%
                                                                                                                                                                                                                                                                                                                                                             conditionally
                                                                                                                                                                                                                                     55.9%
                                                                                                                                                                                                                                                                                                                                                             unsuitable
                                       25%
                                                                                                                                                                                   45.4%
                                                                                                                                                                                                        42.4%
                                                                                                                                                             37.1%


                                              20.3%                                                26.8%

                                                                              19.2%                                           19%




                                              2.5%
                                       0%
                                               Current climate 1991-2010


                                                                               RCP 4.5 2041-2060


                                                                                                    RCP 8.5 2041-2060




                                                                                                                               Current climate 1991-2010


                                                                                                                                                               RCP 4.5 2041-2060


                                                                                                                                                                                    RCP 8.5 2041-2060




                                                                                                                                                                                                         Current climate 1991-2010


                                                                                                                                                                                                                                      RCP 4.5 2041-2060


                                                                                                                                                                                                                                                           RCP 8.5 2041-2060




                                                                                                                                                                                                                                                                                Current climate 1991-2010


                                                                                                                                                                                                                                                                                                             RCP 4.5 2041-2060


                                                                                                                                                                                                                                                                                                                                  RCP 8.5 2041-2060




                                                                                                                        Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                                                                                                                 57
 Box 4: Impact of Climate Change on Forests and Decline of Ecosystem Services

  Carbon sequestration. Ukrainian forests provide very high potential for carbon sequestration – in 2018
  about 50 thousand tons of CO2-equivalent per year, a figure 21% lower than in 1990 (NIR 2020). This is
  one of the highest values of forest sink in Europe and is explained by a large share of forests with a re-
  stricted regime of wood harvest (~50%) and share of young and middle-aged forests (~70%) (Shvidenko
  et al. 2017). However, following the traditional trends of forest sector development in Ukraine assuming
  an extensive model of forest management and a “business as usual” scenario (assuming no adaptation),
  the next 30 years will lead to a more than twofold decline in carbon sequestration potential (Shvidenko et
  al. 2014). It is very likely that the situation will worsen by the end of this century, as there are high risks
  of reaching a “tipping point,” when an ecosystem is driven to a new state or collapses entirely. Figure 32
  indicates change potential for growing the selected forest species by geographic area.
  Productivity of forests. The warmer and drier climate will affect the productivity of forests and make pest
  outbreaks more common. The area of forest affected by pests and diseases doubled from 4% in 2000 to
  8% in 2011. More such changes will take place in the future, as temperatures reach 5°C and 10°C earlier
  in the year (see Table 5). Projected changes in monthly mean temperatures can disrupt the synchroni-
  zation of tree leaf development and lead to a rise in diseases and pathogens, including fungal infections
  (Shvidenko et al. 2018a).
  Water regulation. Forest degradation can cause water scarcity, as forests are of paramount importance
  in providing water regulation functions. They can contribute to maintaining sustainable crop yields and the
  ecological stability of landscapes (Shvidenko et al. 2018b). Forest degradation will disturb hydrological
  cycles and associated impacts on agricultural yields and can have a significant impact on the economy.
  However, these effects are not easily modeled even by specialized ecosystem-economy models and the
  magnitudes of effects can be only anticipated (Johnson et al. 2021).
  Impact on natural habitats and preservation of biodiversity. The loss of forest species (see Figure 32),
  which is itself a loss of biodiversity, promotes the ongoing losses (including extinctions) of dependent spe-
  cies and ecosystems. Biodiversity loss reduces the ecosystem’s resilience to shocks and limits provision
  of valuable ecosystem functions and services to people. Loss of forest cover induces loss in pollination
  sufficiency, especially important for agricultural crops that are dependent on wild pollination (Johnson et
  al. 2021).




5.4 Impact on Forest Fires
Data for the period 2007-2020 shows a general decrease in the number of forest fires but a
sharp increase in the area burned. The analysis of data on forest fires in Ukraine for the period
2007-2020 shows that the 2020 fire season was the most catastrophic in the country’s mod-
ern history, with a number of fires classified as large on the national forest fire classification
scale occurring in the northern and eastern parts of the country, resulting in unprecedented
environmental, social and economic damage. This is consistent with the recent trend in other
European countries, where large forest fires have become more frequent in recent decades
(de Rigo et al. 2017).
Regional forest fire density data shows the highest concentration of incidents in the southeast
regions of the country. Research by the Forest Ecology Laboratory of URIFFM determined
that the density of forest fires increases from the northwest to the southeast. (See Annex 4.)
Typically, forest fires are most highly concentrated in Khersonska (1.9 cases per 1,000 ha of




58           Ukraine. Building Climate Resilience in Agriculture and Forestry
     Figure 34: Total Burned Areas and Numbers of Forest Fires in Ukraine

                  10000                                                                                                             100000




                                                                                                                                        Burned area, ha
         Number of fires




                           0                                                                                                        0
                               2007    2008    2009   2010   2011   2012   2013   2014    2015   2016   2017   2018   2019   2020
      Burned area              12731   4521    4575   1239   612    3311   220    16677   2625   1101   5474   1367   1065 74623
      Number of fires          5024    3231    4922   2368   1761   1743   806    1486    2225   945    2371   1297   1261   2598




     Source: State Forest Resources Agency of Ukraine, 2020.




forested area), Zaporizska (1.4 cases per 1,000 ha), Dnipropetrovska (1.3 cases per 1,000
ha), Luhanska (1.1 cases per 1,000 ha), and Donetska oblasts (1.1 cases per 1,000 ha).
The projected changes in climatic conditions in Ukraine will likely have significant implications
for forest fires risk. Climate change projections for Ukraine show a consistent trend of in-
creases in annual average temperature under both RCPs, with progressively higher increases
toward the end of the century (2.1±1.8°C under RCP 4.5 and 4.3±2.1°C under RCP 8.5). The
spatial distribution of temperature rise under both emission scenarios are similar over time,
with the highest temperature increases in the northeast and the lowest in the west, northwest
and areas near the Black Sea coast. Rising temperatures in the summer will result in heat-
waves and increased aridity in the south and east of Ukraine. The southern regions will expe-
rience an average daily maximum temperature above 34°С in July, with the southern steppe
remaining the hottest until the end of the century. The Sixth Assessment Report (AR6) by the
IPCC indicates that every additional increase of 0.5°C in global average temperature causes
discernable increases in the intensity and frequency of heat extremes such as heatwaves and
ecological droughts (IPCC 2021). These factors will, in turn, enlarge the stock of forest fuel
available for combustion in wildfire events (de Rigo et al. 2017).
The PESETA study assumes a high level of vulnerability to forest fires in the northwestern
parts of Ukraine, particularly in Polissya with its large areas of pine forests with high risk of fire.
The study projects a rise in the number of days per year with high to extreme wildfire danger
nearly everywhere in Europe due to higher temperatures and increased dry spells. The vul-
nerability is measured as percentage of biomass lost in case of fires. The risk of fires is even
higher for the pine forests growing in these regions, although the total area of pine forests here
is not as large as that in Polissya.




                                              Ukraine. Building Climate Resilience in Agriculture and Forestry                                            59
5.5 Analysis Limitations
Due to the lack of reliable forest inventory data in Ukraine (World Bank, 2020), the study was
not able to address the impact of climate change on forest productivity and ecosystem servic-
es with the same analytical approach and for the same spatial disaggregation used in other
parts of this study. This analysis could be conducted in follow-up studies. Similarly, the impact
of climate change on forest fires was assessed using historical data. This analysis could be
further improved by complementing climate indexes developed in this study with specific in-
dexes for potential frequency and intensity of forest fires in Ukraine.




60          Ukraine. Building Climate Resilience in Agriculture and Forestry
CHAPTER 6:
THE SPATIAL DISTRIBUTION
OF AGRICULTURAL IMPACTS:
OBLAST-LEVEL ANALYSIS
6.1. Spatial Distribution of Potential Benefits for Agriculture
The analysis of climate change from the preceding sections presents two important findings
for the agriculture and land use sector in Ukraine:
(i) Ukraine could benefit from increased productivity of winter wheat, if cropping areas shift to
the north-west (Figure 35).
(ii) other export crops (maize, sunflower and soybean) could benefit if measures are taken to
maintain optimal water balance.



     Figure 35: Relative Changes in Wheat Productivity, Through 2030



                                                                                 Chernihivska
                              Volynska
                                          Rivnenska                                                 Sumska

                                                      Zhytomyrska


                                                                      Kyivska

                      Lvivska                                                                    Poltavska
                                                                                                                    Kharkivska
                                            Khmelnytska
                                  Ternopilska                              Cherkaska
                                                          Vinnytska                                                                    Luhanska
                       Ivano-Frankivska
                Zakarpatska                                                      Kirovohradska
                                   Chernivetska                                                      Dnipropetrovska
                                                                                                                            Donetska


                                                                                Mykolaivska
                                                                                                               Zaporizka


                                                                       Odeska
                                                                                              Khersonska




                                                                                                      Crimea




     Key: Darker shades of green indicate a higher increase in wheat productivity in the oblast,
           relative to the baseline. Productivity is measured in millions of tons. Red borders
           show oblasts with the highest yield increases through 2030 in the high projection
           with most beneficial climate conditions for agriculture.




                                Ukraine. Building Climate Resilience in Agriculture and Forestry                                                  61
The northwest oblasts will experience warmer winters with more precipitation, creating condi-
tions favorable for winter crops. Increase in wheat yield [tons/ha] (see Figure 18) and change
in crop land allocation will allow for high wheat productivity [millions of tons] in these oblasts.
Change in climate periods induced by new climate conditions (see Table 4 and 5) will have
a positive effect on sowing and harvesting of winter wheat. These favorable conditions are
distributed regionally, with the northwest oblasts Zhytomyrska, Chernihivska, Zakarpatska,
Ivano-Frankivska, and Volynska benefiting most.
Measures to maintain optimal water balance under climate change could result in an increase
in agricultural production. These potential benefits are estimated by comparing the WOFOST
modelled values in 2030 under RCP8.5 of water-limited production and with the production
under optimal water availability.22 The WOFOST model delivers yield projections under opti-
mum water availability for three selected crops: maize, soybean, and sunflower. The change
in value is estimated by multiplying the change in total production by the change in real crop
prices. The analysis then proceeds to determine the potential benefits if certain measures are
taken to maintain optimal water balance in the agricultural sector to address the projected
climate change.
Under the optimal water availability scenario, compared to the no changes to water manage-
ment scenario for the three selected crops, benefits could reach US$112 million per year until
2030 in the mean projection. This amounts to about 0.8% of 2019 GDP in agriculture, forests
and fishery. According to the latest data (WDI 2021), the sector’s GDP comprises US$13.8
billion and contributes 9% to Ukraine’s GDP. Over the 10-year period from 2026- 2035, the
benefits from maintaining an optimal water availability measure calculated by the WOFOST
model amount to as much as US$550.7 million, with a range of US$354- 780 million (Table 6
and Annex 5).
In other simulations of yield (both low and high projections), the economic impact of maintain-
ing optimal water availability can amount to US$264-504 million or 2-4% of Ukraine’s GDP
for agriculture in 2019 (Annex 5). The extent of the benefits of these water balance measures
depends on the type of crop. The highest benefit in relative terms (39.6%) is expected for
soybean. Suitable measure for maintaining optimal water availability can lead to an increase
of 26% to 40% in the values of agricultural output (Table 6). The largest absolute benefit (dif-
ference between the optimal water availability vs. the loss under water stress scenarios) is
expected for maize, estimated at a US$92.7 million loss.
The benefits of maintaining optimal water availability also have strong regional differences.
These differences are illustrated in Figure 36. In the figure, the oblasts are ordered by the
change in value of agricultural output in each projection relative to the base year 2010 values.
The changes in values of agricultural output under optimal water availability are presented in
blue. Generally, the benefits are distributed unevenly among oblasts and crops. As indicated
by the yellow and blue bars, for maize, Kyivska, Cherkaska and Poltavska oblasts would enjoy
the largest benefits from maintaining optimal water availability. Figure 36 shows the change
in value in US$ million. However, for sunflower, Khersonska, Mykolaivska and Odeska would
benefit the most from implementing adaptation measures. Zakarpatska oblast also shows
a significant benefit; however, the initial value of sunflower production is low. For soybean,
Chernivetska, Ternopilska and Khemelnytska show the largest gain. Adaptation measures
22
     	 Price changes for the RCP 4.5 scenario are not available, therefore the analysis focused on the RCP 8.5 scenario.




62                 Ukraine. Building Climate Resilience in Agriculture and Forestry
would likely have the most notable benefits in Khersonska oblast (see Figure 36). For some
oblasts, these measures may not produce significant benefits, specifically: Rivnenska, Lvivs-
ka, Zakarpatska, Ivano-Frankivska and Volynska oblasts for soybean; Lvivska and Volynska
for sunflower; and Chernihivska for maize.



               ffect of Measures to Maintain Optimal Water Balance on Change in the
     Table 6: E
              Value of Agricultural Output for Selected Crops (for the mean yield pro-
              jection)


                       Value of          Change* in                   Adjusted Change†              Impacts of maintaining
                       Agricultural      the Value of                 in the Value of               optimal water availability
                       Output            Agricultural Output Agricultural Output
                                                                                                    (per year)      (10-year total) ‡

                       US$ million       %            US$ million     %           US$ million       US$ million     US$ million

                       2010              2030         2030            2030        2030              2030            2026-2035

     Maize             1700.8            18.7%        317.8           13.2%       225.1             92.7            453.8

     Soybean           34.6              26.5%        9.2             39.6%       13.7              4.6             22.3

     Sunflower         809.1             3.8%         30.8            5.7%        46.1              15.2            74.6

     Total             2544.5            10.9%        277.8           6.5%        165.3             112.5           550.7




      Change [%] in the value of agricultural production as a percent of 2010 value of agricultural production. Value in Million US$ is
     *
      given for real prices.

      The estimated adjusted change in the value of water scarce agricultural production as a percentage of the value in 2010 of
     †
       agricultural production by oblast in 2030 with maintaining water availability measures in the agricultural sector.

      The net present value (to base year 2019) of cost of inaction over the period of climate projections for the agricultural outputs
     ‡
       2026-2035, with 6% interest rate. An assessment with 3% and 10% interest rates is provided in Annex 5.




                                Ukraine. Building Climate Resilience in Agriculture and Forestry                                    63
            hange in Value of Agricultural Output in 2030 Relative to 2010 for the
Figure 36: C
           Mean Projection Scenario: Optimal Water Availability vs. Water Scarcity
           Projection Scenario23

                                                                                                                                                                                    Maize




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                                                                                                                                                           Water scarcity                   Optimal water availability




23
     	 For each projection, oblasts are ordered by the change (%) in value of agricultural output relative to the value of agricultural
       production in 2010. The circle defines a baseline - 0%. For maize, negative percentage changes signal losses in the value of
       agricultural output. Implementing adaptation measures can be expected to reduce the losses to the value of maize production
       as an effect of climate change. For sunflower, implementation of adaptation measures results in greater gains in the value of
       agricultural output – the case in all but three oblasts that show losses: Ternopilska, Khemelnytska and Vinnytska. For soy-
       bean, all oblasts experience a positive change in the value of agricultural production that increases if adaptation measures
       are introduced.




64                         Ukraine. Building Climate Resilience in Agriculture and Forestry
                                                                                                                                                                        Sunflower
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                                                                                                                                                               Water scarcity
                                                                                                                                                  Water scarcity                   Optimal
                                                                                                                                                                       Optimal water        water availability
                                                                                                                                                                                     availability




                  Authors’ estimates using IFPRI data and Ukrainian statistics on agricultural
          Source: 
                  croplands in 2019.




    Spatial Distribution of Potential Risks from Climate Change
6.2 
    for Agriculture
Using the results from climate impacts on agriculture, “hotspot” oblasts are grouped based
on the: i) change in oblast GDP due to the projected changes in agricultural production; ii)
change in agricultural production values; and iii) change in household incomes, poverty, and
inequality (Table 7). As discussed in the preceding chapters, the south and the east of Ukraine
are expected to experience these changes more than the north and west of the country. All
oblasts are grouped and ranked by the magnitude of the impacts on these parameters. The
detailed tables for this analysis are provided in Annex 5. This analysis is intended to provide
information on climate “hotspots” where potential risks from climate change are the highest
based on the impact on agriculture (yield and value of production) and the resultant impact on
household income and inequality. This analysis does not account for other factors which could
affect agricultural production such as availability of skilled labor, supply chains, or access to
finance.
The assessment results until the mid-21st century24 under RCP 8.5 were selected to identify
the potentially most impacted oblasts. This RCP was chosen following recent international
studies of climate impact, e.g., PESETA IV in the EU and IFPRI IMPACT (EU Science Hub
2021, IFPRI 2015), which consider RCP8.5 as a core scenario for climate risk analysis.

24
     	 There are many challenges to extending the agricultural impact assessment beyond 2050 and distributional analysis beyond
       2030. The uncertainty becomes too high to permit sensible statistical estimations. This challenge is well recognized in
       the scientific literature and described by the IPCC (2007) as “…scientifically controversial to assign a precise probability
       distribution to a variable in the far distant future determined by social choices such as the global temperature in 2100…”




                                                                                     Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                                                                                                                                                                                65
            ifference in the Value of Agricultural Production Between Optimal
Figure 37: D
           Water Availability and Water Scarcity Projections in US$ million/year1


                                                 Maize                    Soybean                 Sunflower
                    Chernihivska
                     Zakarpatska
                Ivano-Frankivska
                        Zaporizka
                     Mykolaivska
                        Donetska
                          Lvivska
                         Volynska
                    Chernivetska
                        Luhanska
                       Rivnenska
                      Khersonska
                          Crimea
                      Ternopilska
                          Odeska
                Dnipropetrovska
                   Kirovohradska
                       Kharkivska
                    Khmelnytska
                          Sumska
                    Zhytomyrska
                        Vinnytska
                        Poltavska
                       Cherkaska
                          Kyivska
                                       0

                                             5

                                                   10

                                                         15

                                                                20




                                                                                                0

                                                                                                        1

                                                                                                                2
                                                                      0

                                                                              1

                                                                                      2




1
    	   Changes in Figure 36 are given in relative values (%). Therefore, when estimating the effect of adaptation changes for each
        crop and oblast, it is helpful to consider absolute changes – differences in the value of agricultural production between optimal
        water availability and water scarcity projections in million US$ per year, e.g., although Chernivetska oblast shows a positive
        relative change of 42% in the value of agricultural output of sunflower relative to 2010, sunflower has a minor change in the
        absolute value of the agricultural output, especially in comparison with Mykolaivska and Odeska oblasts (Figure 37).




66                  Ukraine. Building Climate Resilience in Agriculture and Forestry
According to Jafino et al., (2021), a strong synergy between development policies and climate
change adaptation, i.e., practical inseparability of development and adaptation strategy, may
make benefits of adaptation less noticeable. The observable impact of adaptation reflects only
residual impact of climate change after autonomous adaptation is implemented on a national,
sectoral or sub-national level. Most of the initial climate damage that may accrue in a coun-
terfactual “no adaptation” scenario is not present in development scenarios built to consider
changing climatic conditions. The RCP8.5 emissions pathway, coupled with the low agricultur-
al yield projections scenario, could be considered a stress test that reveals residual damage
and highlights the vulnerability of different sectors and oblasts. This approach addresses the
uncertainty of climate projections and climate impact assessments.
The effects of climate change on agriculture will have a greater impact on some oblasts than
on others. Table 7 shows the top five oblasts across three selected ranking lists: (1) highest
share of agriculture GDP at oblast and at national level; (2) biggest decrease in agriculture
production; and (3) largest change in combined poverty indicators. Kirovohradska, Zhytomyrs-
ka and Lvivska appear in more than one of these three top-five groups, indicating higher over-
all vulnerability of their agricultural sectors to climate change impacts. Kirovohradska oblast
has the highest agricultural GDP in Ukraine (see Annex 5) and the value of its agricultural
production will also be considerably impacted by changing climatic conditions. Lvivska and
Zhytomyrska will be most exposed to the adverse impacts of climate change, with potential
losses of agricultural production value amounting to 34% and 48%, respectively, in the near
future period. The substantial losses in agricultural value will have implications for individual
household incomes and poverty. Kirovohradska oblast is ranked highest in Group 1 and ap-
pears again in Group 3, indicating high impacts on household income. In Group 2, Zhytomyrs-
ka and Lvivska will experience the largest reductions in their agricultural production and value
due to the changes in local climatic conditions. They are also ranked the highest in Group 3,
which indicates significant potential impacts on household incomes.
The top five oblasts with the highest share of agricultural GDP are Khersonska, Kirovohrads-
ka, Poltavska, Vinnytska and Cherkaska (Figure 38; see Annex 3 for complete data). In the
near future (2021-2040), these oblasts are likely to experience significant losses in house-
hold incomes and negative changes in poverty and inequality indicators due to the projected
changes in the value of agricultural production. Although the relative reductions in the values
of agricultural production in these oblasts are not among the highest in Ukraine, the climate
change impacts on the respective oblasts’ GDPs in absolute values will be the strongest due
to the high shares of the agricultural sector in their local economies.
The top five oblasts that will experience the largest decreases in agricultural production values
attributed to climate change until the mid-21st century are Zhytomyrska, Kyivska, Chernivets-
ka, Rivnenska and Lvivska (Figure 39).25 The agricultural production values under considera-
tion are from the low projection, which reflects the lowest production potential of the selected
crops. These values describe the worst-case scenario, in which the potential reduction in the
agricultural production values will be the greatest for the selected oblasts. The decline in the
value of agricultural production can be up to 48%, in the case of Zhytomyrska oblast.


25
     	    All oblasts are ranked by the reductions in the value of agriculture production in both the near future and mid-century. Annex
         7 provides the details on the integrated index for ranking all oblasts by the magnitude of the impact in in both near future and
         the mid- century.




                                      Ukraine. Building Climate Resilience in Agriculture and Forestry                              67
          blasts Most Affected by the Impacts of Climate Change on Agriculture,
Table 7: O
         by Category


Oblasts ranked by highest share           Oblasts ranked by biggest decrease in     Oblasts ranked by biggest change in combined
of agriculture GDP at oblast and at       agriculture production†                   poverty indicators‡
national level*

Group 1                                   Group 2                                   Group 3

Khersonska                                Zhytomyrska                               Lvivska

Kirovohradska                             Kyivska                                   Zhytomyrska

Poltavska                                 Chernivetska                              Kharkivska

Vinnytska                                 Rivnenska                                 Luhanska

Cherkasska                                Lvivska                                   Kirovohradska




  ased on the data represented in Annex 3 that describes the share of agricultural sector in the national and local GDP.
*B

  ased on the findings of the analysis of changes in agricultural production due to climate change. These oblasts show consistent
†B
 reductions in the value of the agricultural production in 2030 and 2050 under RCP 8.5, assuming no endogenous adaptation
 measures.

 Based on the results of the distributional analysis. These oblasts will undergo the biggest changes in poverty indicators,
‡
 including poverty headcount, poverty gap, and severity of poverty.




68                Ukraine. Building Climate Resilience in Agriculture and Forestry
In the near-future period, Zhytomyrska, Kyivska, and Lvivska oblasts will undergo signifi-
cant changes in climatic conditions, with Kyivska oblast facing a new and drier climate type.
Although the agricultural sector in these oblasts accounts for relatively minor shares in ei-
ther the local or national GDP, the projected changes in agricultural production values will
have significant implications for inequality measures. The anticipated loss in household in-
comes and rise in poverty headcount in Zhytomyrska and Kyivska will be substantial. With a
consistent rise in dry and hot conditions, Kyivska and Chernivetska oblasts will be exposed
to extremely high temperatures, as indicated by the increasing number of tropical nights.
The top five oblasts with the most significant loss in household incomes and the highest
increase in poverty and inequality are Lvivska, Zhytomyrska, Kharkivska, Luhanska, and
Kirovohradska (Figure 40). Agriculture accounts for less than 5% of GDP in these oblasts
and the oblasts are ranked highest in term of potential household income loss due to rising
food prices caused by adverse climate change impacts on agricultural production. The rank-
ing reflects the combined impacts of climate change and induced changes in the agricultural
sector on the key poverty indicators, including poverty headcount, poverty gap, and severity
of poverty. Annex 3 presents the detailed ranking of all oblasts in Ukraine based on these
indicators. In the near future, all oblasts in this group will be exposed to warmer and drier
climates. These changes in climatic conditions will be most pronounced in southern Ukraine.



     Figure 38: Share of Agriculture in National and Local GDP, by Oblast


                                                                                     Chernihivska
                                  Volynska
                                              Rivnenska                                                 Sumska

                                                          Zhytomyrska


                                                                          Kyivska

                          Lvivska                                                                    Poltavska
                                                                                                                        Kharkivska
                                                Khmelnytska
                                      Ternopilska                              Cherkaska
                                                              Vinnytska                                                                    Luhanska
                           Ivano-Frankivska
                    Zakarpatska                                                      Kirovohradska
                                       Chernivetska                                                      Dnipropetrovska
                                                                                                                                Donetska


                                                                                    Mykolaivska
                                                                                                                   Zaporizka


                                                                           Odeska
                                                                                                  Khersonska




                                                                                                          Crimea




          Darker shades of blue denote a higher share of agricultural sector in GDP of Ukraine
     Key: 
          and oblast. Red borders show the oblasts analyzed in detail in the integrated crite-
          ria assessment tables.




                          Ukraine. Building Climate Resilience in Agriculture and Forestry                                                            69
Figure 39: Reduction in Agriculture Production Values, by Oblast, Through 2030


                                                                              Chernihivska
                        Volynska
                                     Rivnenska                                                   Sumska

                                                 Zhytomyrska


                                                                   Kyivska

                Lvivska                                                                       Poltavska
                                                                                                                  Kharkivska
                                       Khmelnytska
                             Ternopilska                                Cherkaska
                                                      Vinnytska                                                                        Luhanska
                 Ivano-Frankivska
          Zakarpatska                                                         Kirovohradska
                              Chernivetska                                                        Dnipropetrovska
                                                                                                                            Donetska


                                                                             Mykolaivska
                                                                                                             Zaporizka


                                                                    Odeska
                                                                                           Khersonska




                                                                                                   Crimea




     Darker shades of blue denote a higher negative impact on agricultural production
Key: 
     and its value in the oblast. Red borders show the oblasts analyzed in detail in the
     integrated criteria assessment tables.

            ombined Changes in Household Income, Poverty, and Inequality,
Figure 40: C
           Through 2030

                                                                               Chernihivska
                          Volynska
                                     Rivnenska                                                    Sumska

                                                     Zhytomyrska


                                                                   Kyivska

                 Lvivska                                                                       Poltavska
                                                                                                                    Kharkivska
                                        Khmelnytska
                              Ternopilska                               Cherkaska
                                                       Vinnytska                                                                       Luhanska
                  Ivano-Frankivska
          Zakarpatska                                                         Kirovohradska
                               Chernivetska                                                        Dnipropetrovska
                                                                                                                            Donetska


                                                                             Mykolaivska
                                                                                                             Zaporizka


                                                                   Odeska
                                                                                             Khersonska




                                                                                                    Crimea




     Darker shades of blue denote a higher impact on poverty headcount, poverty gap, and
Key: 
     severity of poverty in the oblast. Red borders show the oblasts analyzed in detail in
     the integrated criteria assessment tables.




70           Ukraine. Building Climate Resilience in Agriculture and Forestry
CHAPTER 7:
ACTIONS TO BUILD CLIMATE
RESILIENCE IN AGRICULTURE AND
FORESTRY
This report details many of the projected changes in climate Ukraine will experience over the
course of the 21st century. It provides a detailed assessment of the potential impacts these
changes could have on the country, with a focus on agriculture, a key driver of the economy
and jobs. Empowered with highly granular data on a range of climate indicators across 7,400
geographic points generated for this study using the latest available global and regional cli-
mate models, and analysis for three time periods and three climate scenarios, Ukraine can
adapt to meet the projected risks of temperature increase, shifts in seasons, and changes in
precipitation patterns. With proactive planning, the country may even be able to benefit from
the long-term impacts of climate change on agriculture and forestry. Recommended adap-
tation actions for Ukraine, based on the country context and international good practice are
outlined below.
Recommendations are grouped into three sections:
1.	 Strengthen institutions, policy, and planning:
   •	   Establish a national level institutional mechanism for climate policy
   •	   Establish a mechanism to integrate climate change action within the Ministry of Agri-
        culture Policy and Food
   •	   Include climate risk assessment in oblast development planning
2.	 Increase scientific capacity and research:
   •	   Enhance capacity of national scientific institutions on climate change
3.	 Promote transition to climate-smart agriculture and forestry:
   •	   Promote climate-smart agriculture
   •	   Promote farmer information systems and precision agriculture technologies
   •	   Improve targeting of subsidy programs and develop insurance products for climate
        risks
   •	   Include agroforestry and forest management in adaptation planning




                        Ukraine. Building Climate Resilience in Agriculture and Forestry   71
    Strengthen Institutions, Policy and Planning
7.1 
Establish a national level institutional mechanism to coordinate climate change
policy and actions across all line ministries. Enabling fiscal risk assessment of climate
actions, policy and planning and climate budget tagging will be necessary in order to prepare
critical sectors such as energy, infrastructure, health, and agriculture for climate impacts.
Establish a mechanism to incorporate climate change action within the Ministry of Ag-
riculture Policy and Food (MAPF). Strengthening climate expertise and functions will equip
MAPF with the necessary knowledge and technical capabilities to support effective and coher-
ent climate policies and programs for farmers. It will also be important for MAPF to regularly
carry out agriculture sector climate vulnerability assessments and develop action plans (every
five years).
Include climate change risk assessment in oblast-level development planning. It will be
important to carry out more comprehensive impact assessment reviews at the oblast level to
identify specific climate risk considerations for development planning, tailoring action to the
sectors that face highest risk in the oblast. While this study is not an in-depth assessment of
vulnerability, the analysis has identified oblasts with varying levels of vulnerability, based on
the share of agriculture in their respective GDPs and resulting household income inequalities:
•	   Khersonska, Kirovohradska, Poltavska, Vinnytska, and Cherkasska: could face
     greater negative impacts. Adopting climate-smart agricultural practices for maintaining op-
     timal water balance should be among the focus areas for development planning.
•	   Zhytomyrska, Kyivska, Chernivetska, Rivnenska, and Lvivska: economic impacts
     could be less profound due to a lower share of agriculture in their respective GDP. Howev-
     er, climate change could still cause significant changes to agricultural production, entailing
     the need for diversification of their production structure.
•	   Kirovohradska, Zhytomyrska, and Lvivska: would need to focus on developing overall
     adaptation capacity based on their vulnerability to climate change.
More comprehensive impact assessment reviews should be carried out at the oblast level to
identify specific climate risk considerations.


    Increase Scientific Capacity and Research
7.2 
Enhance institutional capacity for collecting, maintaining, analyzing, and disseminat-
ing climate data through a National Climate Resource Center. Strengthen the Ukraine
Hydrometeorological Institute (UHMI) and the Ukrainian Hydrometeorological Center (UHMC)
as a National Climate Resource Center (NCRC). Both institutions are under the jurisdiction of
the State Emergency Service of Ukraine and combining them under the umbrella of an NCRC
can ensure systematic research on hydrometeorology, agrometeorology, and climate science,
including up-to-date climate projections, assessment of risks and impacts at the sectoral, na-
tional, and regional levels. This will help strengthen the capacity and resources of the UHMI
and UHMC to analyze and manage big data for climate planning. This study filled an important
data gap by generating over two terabytes of highly granular data on a range of climate indi-
cators for Ukraine using the latest available global and regional climate models. Continuous




72           Ukraine. Building Climate Resilience in Agriculture and Forestry
    Box 5: National Climate Policy and Coordination: A Variety of Approaches

    Indonesia. The State Ministry for National Development Planning/National Development Planning Agen-
    cy (BAPPENAS) is responsible for implementation and monitoring and evaluation of the National Action
    Plan for Climate Change Adaptation (RAN-API), including dissemination to provincial governments. The
    BAPPENAS formed a core group with the Ministry of Environment, the Agency for Meteorology, Clima-
    tology and Geophysics and the National Council on Climate Change when initiating the RAN-API. It
    organized meetings with central ministries, provincial governments, universities, and non-governmental
    organizations (NGOs) (UNFCCC 2014).
    Japan. The National Plan for Adaptation to the Impacts of Climate Change was formulated to systemati-
    cally address the impacts of climate change. The National Institute for Environmental Studies (NIES) and
    its Center for Climate Change Adaptation are responsible for analyzing and providing information about
    climate change impacts and adaptation. The NIES also provides technical advice to local governments
    and Local Climate Change Adaptation Centers to help formulate their climate adaptation plans and sup-
    port the implementation of adaptation measures by central and local governments and other stakeholders
    (CCCA 2021).
    The Netherlands. The National Climate Adaptation Strategy (NAS 2016) and the Delta Program (DP
    2010) are at the center of the Dutch Climate adaptation policies. These documents were prepared through
    an inclusive participatory process. The implementation of the NAS is governed by a board of directors
    from all relevant ministries of the Dutch Government, and the Ministry of Infrastructure and the Envi-
    ronment has the coordinator role. Sub-national Provinces and Cities develop and implement their own
    programs, based on NAS. The DP is jointly planned and implemented by the municipalities, district water
    boards, provinces, and the central government. Climate change impacts and resilience are integrated
    into environmental assessment procedures, disaster risks management, and some sectoral planning.
    (Climate-ADAPT 2021).
    Mexico. The National Climate Change Strategy (2007) proposes concrete adaptation and mitigation
    measures for all sectors. Climate change strategies and action plans have also been developed at the
    subnational level for some cities and states. The Inter-Ministerial Commission on Climate Change is re-
    sponsible for formulating and coordinating the implementation of national climate change strategies and
    incorporating them in sectoral programs; promoting national climate change research; and promoting
    GHG emission reduction projects. The Commission receives advice from the Consultative Council on
    Climate Change, composed of scientists and representatives of civil society and the private sector. The
    CCF has a technical committee chaired by the Ministry of the Environment and Natural Resources, with
    representatives from many agencies (GoM 2020; UNDP 2021).




analysis and updating of this data will be needed for sub-national adaptation planning, for
which significant hardware and software capacity will be required within these institutions. It
will also help Ukraine participate in and take advantage of the EURO-CORDEX experiment
and develop highly disaggregated climate projections that could be used to estimate climate
risks in different sectors of the national economy and on a sub-national level.




                           Ukraine. Building Climate Resilience in Agriculture and Forestry                73
    Promote Transition to Climate-Smart Agriculture
7.3 
    and Forestry
As a long-term adaptation strategy, Ukraine can increase its agriculture resilience through
an integrated approach of natural resource management and sustainable soil management.
Ukraine is committed to improving measures to rebuild irrigation infrastructure as one of the
main technologies to counter climate change and improve agricultural production efficiency.
However, irrigation alone is not sufficient to support resilient agricultural production. Additional
measures and technologies to help Ukraine adapt to climate change are proposed below.
Promote climate-smart agriculture (CSA) including agroforestry (planting combinations of
trees and crops), drought-resistant varieties of key crops, cover crops, etc., and increase
landscape diversity and connectivity to increase the ability of ecosystems to adapt to chang-
ing climate conditions and stresses. Maintaining or restoring riparian areas, wetlands, peat-
lands and floodplains helps maintain water balance and reduce soil erosion. Give incentives
to farmers through agrotourism and ecotourism programs to manage non-arable lands for
maintaining biodiversity and natural habitats. These approaches have been shown to benefit
agriculture from environmental and climate stresses.
Promote farmer information systems and precision agriculture technologies. Provide
farmers with reliable and accessible information about, and systems to support, climate-smart
agriculture, including crop land allocation, to enhance their capacity for adaptation. Based on
the information in this study, changes in crop land allocation, and shifting vegetation periods
and growing seasons for major crops should allow farmers to increase resilience to changing
climate (See Annex 1.2). Farmers need information so they can make these adaptations. An
information system for farmers through mobile, online and in-person extension services will be
key to raising awareness and initiating action on the ground. Promoting the use of precision
agriculture (including Variable Rate Technology (VRT), remote sensing and drones), would
help in moving Ukraine towards more climate-friendly technologies by reducing waste of wa-
ter and other inputs. Ukraine can leverage its significant capacity and large pool of talent in
information technologies to develop and maintain such systems.
Improve targeting of subsidy programs and develop insurance products for climate
risks. The Government already provides financial support for the development of agriculture
in Ukraine through direct subsidies, low/free-interest loans, and other instruments. Howev-
er, financial assistance remains difficult to access for most agricultural producers, especially
small farmers. A targeted program with banks and agriculture departments could ensure that
loans and subsidies are linked to adoption of climate-smart technologies and approaches.
This will offset the initial risk for the farmers and the lending institutions.
Residual risk insurance could increase farmers’ resilience to climate change via the coverage
of residual risks not addressed by adaptation actions. This type of insurance could be consid-
ered in oblasts where adverse weather events such as droughts and long-lasting heatwaves
are expected, and there is limited capacity to adapt.




74          Ukraine. Building Climate Resilience in Agriculture and Forestry
    Box 6: Examples of Climate-Smart Agriculture

    The following groups of adaptation measures have been documented to strengthen the resilience of agri-
    cultural systems in many locations around the world.
    Soil Management: Interventions should aim at enhancing soil fertility and water availability, reducing run-
    off and erosion. Well-documented interventions with such benefits include contour ploughing or contour
    tillage on sloping land, contour bunding, conservation tillage, surface mulching, and revegetation and
    reforestation of areas around farmland (i.e., shelter belts), among others.
    Water Management:
      •	   Bio mulching (covering fields with biodegradable mulch films and other biomaterials)
      •	   Conservation farming practices (a combination of direct seeding and covering crops with different
           tillage systems: no-till, mini-till, strip-till, etc.)
      •	   Precision agricultural practices that minimize water and material inputs
      •	   Planting drought-tolerant species and varieties with long growing periods
    Forestry and Agroforestry: Incorporating trees in farming systems has been shown to improve soil qual-
    ity, which leads to higher and more stable crop yields. Agroforestry practices also increase the moisture
    absorptive capacity of soil and reduce evapotranspiration, while tree canopy covers help reduce soil tem-
    perature for crops planted underneath and decrease runoff velocity and soil erosion from heavy rainfall.
    Source: Adapted from CGIAR Research Program on Climate Change (2021).




Include agroforestry and forest management in adaptation planning. The country can
also engage in agroforestry, a win-win measure for both climate change adaptation and miti-
gation of negative impacts on agricultural and forest productivity due to higher temperatures,
increased aridity, and soil erosion. Agroforestry includes planting orchards with cultivation of
perennial grasses, plantations of bioenergy crops, developing agroforestry practices on the
agricultural land occupied by self-planted forests and other solutions. Planting orchards will
diversify production, reduce risks of climate change and increase food security. However, the
greatest potential to develop agroforestry is generated by the restoration of protective shel-
terbelts. Shelterbelts are important for improving soil quality and thermal regulation, retaining
or increasing soil moisture content, increasing crop production, generating additional incomes
from forest and non-timber products, and protecting biodiversity.
As the forest sector requires long-range sustainable management and climate risk planning, it
is especially important to include climate risk management in the forthcoming Forest Strategy
2030 and associated plans for reforestation/afforestation in the country. A regularly updated
national forest inventory will be key, in addition to field trials to monitor growth and plan plant-
ing of timber. Increasing capacity in geospatial technologies is essential for management of
forest fires. It is crucial to plan for this sector as it impacts the hydrological balance and soil
conditions for agriculture.




                            Ukraine. Building Climate Resilience in Agriculture and Forestry                 75
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org/en/677701558601578072/Ukraine-Economic-Update-Spring-2019-en.pdf.

World Bank. (2020). Ukraine Country Forest Note. http://hdl.handle.net/10986/34097.

The World Bank. (2021a). The World Bank in Ukraine. https://www.worldbank.org/en/coun-
try/ukraine/overview#3.

The World Bank. (2021b). World Development Indicators. https://databank.worldbank.org/
source/world-development-indicators#.

The World Bank. (2021c). Data Bank. Metadata Glossary. https://databank.worldbank.org/
metadataglossary/gender-statistics/series/SI.POV.GINI.
The World Bank. (2021d). Ukraine Systematic Country Diagnostic: 2021 Update.
https://www.worldbank.org/en/news/infographic/2021/09/06/ukraine-scd-2021.

World Food Program (IFAD) (2011). Weather Index-based Insurance in Agricultural Devel-
opment. A Technical Guide. https://documents.wfp.org/stellent/groups/public/documents/
communications/wfp242409.pF

Yang, W. et al. (2010). Distribution-based scaling to improve usability of regional climate
model projections for hydrological climate change impacts studies, Hydrology Research,
41 (3-4): 211–229.




84          Ukraine. Building Climate Resilience in Agriculture and Forestry
ANNEX 1.
METHODOLOGY
A 1.1 Climate Projection
As projections of climate change depend heavily on future human activities, climate mod-
els are run against scenarios that make certain assumptions about how these activities will
evolve. Climate models rely on several different scenarios, each making a number of assump-
tions for future greenhouse gas emissions, land-use, technological development, population,
economic development, and other driving forces. Such scenarios form the basis for future
atmospheric GHG concentration projections. The scenarios from the Special Report on Emis-
sions Scenarios (SRES) were used in the IPCC Third Assessment Report (TAR), published in
2001, and in the IPCC Fourth Assessment Report (AR4), published in 2007. For the Fifth As-
sessment Report (AR5), a new set of scenarios was developed, the so-called Representative
Concentration Pathways (RCPs) that consisted of: i) the RCP 2.6 scenario, which assumes
a strongly declining emissions trend, compatible with a 2°C global warming limit by 2100; ii)
the RCP 4.5 scenario, which assumes a slowly declining emissions trend, compatible with
2.4 °C global warming limit by 2100; iii) the RCP 6.0 scenario, which assumes a stabilizing
emissions trend, compatible with a global 2.8°C warming limit by 2100; and iv) the RCP 8.5
scenario, which assumes a rising emissions trend, compatible with a global 4.3°C warming
limit by 2100.
Climate data is processed on a daily basis for a base period and three future time horizons.
These include 1991-2010 (base period), 2021-2040 (to allow a range value for the year 2030
to be calculated), 2041-2060 (to allow a range value for the year 2050 to be calculated), and
2081-2100 (to allow a range value for the year 2090 to be calculated). Key climate variables
(i.e., temperature and precipitation) in future periods are measured against the base period
1991-2010 to determine the extent of changes. The historical period 1961-1990 is also used
to compare the results with older studies and assess the projected future changes against
the changes that have already happened between this period26 and the base period. Such
comparison is significant, considering the climate in Ukraine has been changing considerably
since late 1980s. It should be noted that for several reasons, the base period used for the
forestry and agricultural assessment are different. Specifically, the base period for forestry
analysis is 1961-1990, as many field data were obtained and methodologies developed during
this time. For agricultural analysis, the base period is 2006-2015, since 10-year periods are
sufficient for significant changes to take place in the sector, and thus, it also makes the most
sense to compare the projected changes against the most recent period with available data.
Climate projections are obtained by running numerical models of the Earth’s climate, which
may cover either the entire globe or a specific region. These models are referred to as: i)
Global Climate Models (GCMs), also known as Atmosphere-Ocean General Circulation Mod-
els (AOGCMs) and Earth System Models (ESMs), which provide projections with resolution

26
     	 The World Meteorological Organization (WMO) advocates using a historical base period (1961-1990) for assessing climate
       change, as well as the most recent 30-year period, in order to standardize and harmonize across institutions.




                                  Ukraine. Building Climate Resilience in Agriculture and Forestry                      85
of around 100km2 covering a variety of landscapes; and ii) Regional Climate Models (RCMs),
which are applied over a limited area, taking into account the large-scale climate information
from GCMs as initial and boundary conditions, and provide projections at much higher reso-
lutions. Presently, modelling is conducted through a series of Coupled Model Intercomparison
Projects (CMIP), of which the latest is CMIP6.
The climate projections in this study are based on the European Coordinated Regional Down-
scaling Experiment (Euro-CORDEX) time series27 with the most advanced RCMs covering
Ukraine. GCMs can only simulate earth processes in coarse grid-cells, which are not suitable
for local impact assessment studies. Dynamical downscaling, using RCMs with boundary and
initial conditions from GCMs as inputs, increases the resolution of climate projections. RCMs
provide information on much finer scales, including more detailed specifications of land and
water bodies and simulation of mesoscale processes (Navarro-Racines et al. 2020), to support
more detailed impact assessment and adaptation planning. RCM outputs have been made
available recently through the Coordinated Regional Downscaling Experiment (CORDEX), a
program sponsored by the World Climate Research Program (WCRP) to produce improved
regional climate change projections for all land regions worldwide. Euro-CORDEX is one of
the 14 domains of the international CORDEX initiative with the most advanced RCMs provid-
ing the highest resolution, at 0.11 (~12.5km), and covering the entire territory of Ukraine. The
Coordinated Regional Downscaling Experiment (CORDEX) framework provides a basis for
selecting the combined ensembles of various RCMs and overarching GCMs and assessing
the level of associated certainty.
Simplifications, assumptions, and choices of parametrizations have to be made when con-
structing climate models, resulting in model and forecast errors. Climate models are numerical
models that parameterize the relevant physical processes and their interplay and feedback
to project weather and climate from time scales of days to centuries. The uncertainties in
constructing and running these models are inherent and manifold and originate from different
initial and boundary conditions, as well as structural uncertainties (IPCC 2007b; EURO-COR-
DEX 2021). Initial condition uncertainty is related to the value of observations used to initialize
numerical climate models. This type of uncertainty is most relevant for forecasts over the
shortest time scales, but not significant for long-term climate projections, which are often
averaged over decades and therefore are largely insensitive to variations in initial conditions.
Uncertainty in boundary condition is introduced when datasets are used to replace an interac-
tive part of the system. Parameter uncertainty stems from the parameterization of small-scale
processes in all components of the climate system using bulk formulas when these processes
cannot be explicitly resolved due to computational constraints. Structural uncertainty refers
to any uncertainty originating from the choices in the model design. As a true climate system
is highly complex, it is impossible to describe all the system processes in a climate model.
Thus, choices must be made on what processes to include and how to parameterize them
(Kunreuther et al. 2014).
Multi-model ensembles are used in climate projections to improve the skill, reliability, and con-
sistency of model forecasts. A multi-model ensemble is a set of model simulations from struc-
turally different models (i.e., different initial and boundary conditions and parameterization).
27
     	 The high-resolution and bias-adjusted CORDEX data only became available in late 2019. This study takes advantage of this
       new data for the analysis and provides significantly more insights compared to previous studies, where limited availability and
       the complexity of dealing with large datasets have hindered the broader use of this source.




86                 Ukraine. Building Climate Resilience in Agriculture and Forestry
Combining models to enhance climate projections rests on the assumption that errors tend
to cancel if the choices are made independently for constructing each model, and uncertainty
should decrease with an increasing number of models. Experiences from weather- and cli-
mate-related applications also show that seasonal forecasts and El Ninõ Southern Oscillation
(ENSO) predictions from multi-model ensemble are generally better than those from single
models. Studies indicate that multi-model ensemble performs dramatically better when consid-
ering an aggregated performance measure over many diagnostics, as illustrated in Figure 1.
Multi-model ensembles also help quantify model uncertainty. Uncertainty in projected climate
variables (i.e., temperature and precipitation) can be estimated using quantitative metrics such
as (inter-model) standard deviation and range. In this study, we estimate the range or spread
in the projections for each climate variable from the different RCM-GCM combinations in the
ensemble to quantify the degree of uncertainty. This range is herein referred to as the “ensem-
ble range.” The ensemble range represents all possible realizations of the simulated climate
variables under each RCP in each time horizon under study, while the means of the ensemble
represent the most probable values of the average changes for the modeled variables.
We have evaluated the performance of five driving GCMs from the CMIP5 ensemble, using
the R-based GCMeval tool. These five GCMs were initially selected by the scientist commu-
nity for a high-resolution regional climate change ensemble established for Europe within the
EURO-CORDEX initiative. In general, the GCMeval tool is used to assess and choose a sub-
set of GCMs from the CMIP5 based on their relative performance (in terms of the spread of
the projected temperature and precipitation changes), compared to the entire ensemble. This
tool is opensource and available online at https://gcmeval.met.no. The GCMeval tool is cur-
rently under further improvements, so not all CMIP5 models are included, and the results are
aggregated over just SREX IPCC regions28 for prescribed periods and seasons. In this study,
we utilize the outputs for the Central Europe region, which is much larger but covers the en-
tire territory of Ukraine. Another caution is that estimations of the GCMeval tool are available
for two slightly different time periods, specifically 2021-2050 and 2071-2100 over the present
period 1981-2010. However, it is currently one of the best tools for selecting and assessing a
subset of CMIP5 GCM ensemble for Ukraine. Overall, the subset of five GCMs corresponds
reasonably well with the entire CMIP5 ensemble and shows consistency and balance in their
representation of temperature and precipitation changes. The ensemble ranges of the subset
are slightly lower (38-55% for temperature) and higher (35-63% for precipitation), compared to
those of the entire CMIP5. In term of mean values, the precipitation values of the 5 GCM sub-
set are also similar to those of the CMIP5 ensemble, with slightly higher values (wetter con-
ditions) in winter and annual estimates. For temperature, both summer and annual means of
the subset and the entire CMIP5 ensembles are very close under both RCP 4.5 and RCP 8.5
in both periods.
To form RCM ensembles, this study employs a so-called “fitness-for-purpose” method. This
means when we want to project changes in only one independent in climate variable (i.e.,
air temperature or precipitation), all available RCMs are included in the ensemble – up to 43
28
     	 The 26 SREX regions include Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America
       (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE
       Brazil (NEB), West Coast South America (WSA), South- Eastern South America (SSA), Northern Europe (NEU), Central
       Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF),
       Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia
       (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU).




                                   Ukraine. Building Climate Resilience in Agriculture and Forestry                      87
RCM runs for RCP 4.5 with bias-adjusted data. When the projected results are intended to
be used as inputs for further modeling (i.e., crop productivity), all meteorological variables
from the same RCM runs are utilized. Even when a less sophisticated model is used (i.e., for
forestry), where there is no need to directly use daily data (since multi-year monthly values
give enough temporal resolution to estimate the differences among scenarios), we still use the
same number of RCM runs in ensembles for air temperature and precipitation for both RCP4.5
and RCP8.5.
Two types of EuroCORDEX datasets for seven climate variables were obtained from the Earth
System Grid Federation (ESGF) website (https://esgf-node.llnl.gov/search/esgf-llnl/). These
include: i) the bias-adjusted outputs for daily precipitation and daily mean, maximum, and
minimum temperatures; and ii) the raw outputs (without bias adjustments) for daily surface
wind speed, relative humidity (RH), and downward shortwave solar radiation (RSDS). COR-
DEX-adjusted outputs covering Ukraine were available for five GCMs and seven RCMs. The
different combinations of these models produce 96 datasets for RCP 8.5, 132 for RCP 4.5,
and only 12 for RCP 2.6. As only three RCM datasets were available for RCP 2.6, only daily
precipitation and daily mean, maximum, and minimum temperatures are calculated for this
scenario (see Table 8). The CORDEX raw outputs were available for five GCMs and three
RCMs. There are 33 datasets for RCP 4.5 and 56 for RCP 8.5 from the combinations of these
models.



          umber of CORDEX Datasets Processed by Combination of RCMs
Table 8: N
         and Overarching GCMs


                                           Number of CORDEX bias- adjusted          Number of CORDEX
                                           datasets                                 datasets without bias
                                                                                    adjustment

                                           RCP 2.6      RCP 4.5     RCP 8.5         RCP 4.5      RCP 8.5

 Mean temperature                           3           43           34             -            -

 Maximum temperature                        3           23           14             -            -

 Minimum temperature                        3           23           14             -            -

 Precipitation                              3           43           34             -            -

 Surface wind speed                         -           -            -              11           22

 Relative humidity                          -           -            -              11           12

Downward shortwave solar radiation         -            -           -               11           22




88               Ukraine. Building Climate Resilience in Agriculture and Forestry
For historical and baseline data, we used the E-OBS v20.0e (EC&D 2021) gridded dataset
with the same spatial resolution (0.11o) as the RCM data from EuroCORDEX. There was no
data in grid cells for some climate variables from RCMs in these past periods. For example,
data for relative humidity (RH) and sunshine duration (SD) during 1961-1990 are absent from
the RCM datasets, but are needed for forestry assessment. In this case, the most suitable
data available are used based on a physical consistency approach. In particular, since RH
does not have a significant inter-annual variability, we use multi-year means over just 5 years
(2006-2010) from 11 available RCMs in RCP 4.5 runs for both past periods. For SD, we inter-
polate in grid cells the data of 38 Ukrainian stations for the period 1991-2013 (Rybchenko and
Savchuk 2015).
Bias-correction is necessary to make the climate projections more realistic, as RCM outputs
are also subject to errors due to uncertainties associated with both the structure of the RCMs
and the boundary conditions of the driving GCMs. Bias-correction improves the realism and
sometimes, resolution of climate model outputs (i.e., when projections are made at coarser
spatial resolution), using different types of statistical techniques, assuming that those outputs
are already plausible representation of future climate characteristics. Existing bias correction
methods cannot fundamentally correct future climate change trends (Navarro-Racines et al.
2020).
The data for air temperature and precipitation has been bias-adjusted by the data provid-
er EuroCORDEX using the Distribution-Based Scaling (DBS) method.29 The DBS approach
reproduces the variations generated from RCMs and preserve their adjustments to the key
hydro-meteorological variables, precipitation and temperature, to obtain more realistic inputs
for hydrological modeling (Yang et al. 2010). These bias-adjusted data are inputted in to the
WOFOST model for crop yield simulations. The DBS method clearly improves the representa-
tion of temperature and precipitation distribution, as shown in Figure 41. Column (a) repre-
sents the raw data received directly from the RCM model developed by Centre National de
Recherches Météorologiques (CNRM). Column (b) represents the CNRM model data that
was bias adjusted with the DBS method. Columns (c) and (d) show ensembles of 8 and 34
bias-adjusted RCMs (including the CNRM model) for RCP 8.5. Column (e) shows the reanal-
ysis ERA5 data30 that approximate observational data for the 2006-2015 period. Comparing
temperature and precipitation maps, we notice that RCMs usually have more difficulties in rep-
resenting precipitation, not only extremes, but also seasonality and even annual averages.31
It is evident from Figure 1 that the bias-adjusted precipitation distribution map of the individual



29
     	 General information on bias-adjustment is provided at https://cordex.org/data-access/bias-adjusted-rcm-data/. A summary
       of bias adjustment methods applied to CORDEX simulations can be found at http://is-enes-data.github.io/CORDEX_adjust_
       summary.html.
30
     	 Climate reanalyses combine past observations with model simulations to generate a consistent time series of multiple climate
       variables. Reanalyses are among the most-used datasets in the geophysical sciences since they provide a comprehensive
       description of the observed climate as it has evolved during recent decades, on 3D grids at sub-daily intervals. ERA5 is the
       latest climate reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), providing
       hourly data on many atmospheric, land-surface and sea-state parameters together with estimates of uncertainty (https://
       www.ecmwf.int/en/research/climate-reanalysis).
31
     	 In previous assessments of projected precipitation distribution based on the FP6 project ENSEMBLES data only four out of
       14 RCMs were able to represent the annual cycle of precipitation in Ukraine (http://www.geology.com.ua/en/7195-2/).




                                    Ukraine. Building Climate Resilience in Agriculture and Forestry                          89
RCM (b) is visually closer to that of the ERA5,32 indicating the benefits of the DBS method in
markedly improving RCM outputs with cold and wet biases for hydrological modeling (Yang
et al. 2010). Moreover, the level of similarity to the maps by ERA5 increases with the larger
number of RCMs, as shown in column (d), compared to column (c). However, the maps of the
8 RCM ensemble (c) are sufficiently similar to those of the ERA5. This shows that the subset
of 8 RCMs is a reasonable representation of the full 34 (43) RCMs ensemble and can be used
to assess agricultural impact. Finally, even bias-adjusted outputs in the full ensemble of 34
RCMs are still colder and wetter than the ERA5 reanalysis data, showing that warming and
drying in this period in Ukraine were higher than simulated.


Figure 41: Effect of Use of Multi Model Ensembles for Temperature
and Precipitation

                                                       Temperature




                                                       Precipitation




               (a)                      (b)                    (c)                          (d)                       (e)

       Raw data from CNRM’       CNRM’ model bias    Ensemble 8 RCMs, including     Ensemble of 34 RCMs,   Approximated observation
              model              adjusted by DBS45        CNRM’s model            including CNRM’s model         data (ERA5)




Further bias-correction by the delta method was conducted within the framework of this study.
The delta-method involves deriving a change factor, or a “delta” from the GCM and then add-
ing it to the observation dataset. The change factor is defined as the difference between the
long-term mean of a climate variable in the future and the base period. In this study, the ob-
servational data for Ukraine for the base period 1991-2010 is obtained from the E-OBS v20.0e
gridded dataset with the same spatial resolution (https://www.ecad.eu/download/ensembles/
download.php). Subsequently, the differences in temperatures (in degrees Celsius) and pre-
cipitation ratios (mm per month or year) in the future periods from the RCMs were added to
(for temperature) and multiplied by (for precipitation) values in the base period. This procedure

32
     	 For the periods after 2010, E-OBS climatological data clearly diverges from the reanalysis ERA5 data that heavily relies on
       modern satellite data (see https://climate.copernicus.eu/climate-reanalysis). One of the reasons could be an absence of up-
       to-date meteorological data for Ukraine in the European Database E-OBS after 2010. That is why we used E-OBS only till
       2010, and ERA5 for subsequent years.




90                   Ukraine. Building Climate Resilience in Agriculture and Forestry
has resulted in some reduction in number of grid points mainly due to the differences in coast-
line masks of the Black and Azov Seas in E-OBS and RCMs.
The use of both bias-adjusted (temperature and precipitation) and non-adjusted variables
(wind speed, RH, and RSDS) in this study is justified. The combination of bias-adjusted and
raw data can be an issue when impact models are to provide outputs on a daily basis. In this
study, multi-year means of most climate variables are used for forestry analysis. For agricul-
ture, where daily data were inputs for the impact model and many processes were parame-
terized based on thresholds, it was more crucial to have proper distributions of precipitation
and temperature rather than consistency across variables, some of which are less influential
on agricultural model outputs.
The ensemble ranges of annual mean temperature and precipitation totals under RCP 4.5 and
8.5 in three periods are presented in Figure 2 and Figure 3. The ensemble range of warming
levels in Ukraine slightly grows under RCP  4.5 and substantially increases under RCP  8.5
by the end of the century (see Figure 42). The ensemble range of annual precipitation totals
under RCP 4.5 show a rather stabilizing trend from the middle to the end of the century. In
contrast, the ensemble range under RCP 8.5 widens significantly toward the far future period,
indicating that half of the RCMs in the ensemble project up to 56 percent higher precipitation
levels (see Figure 3), and annual precipitation totals are likely higher under under RCP 8.5
than RCP 4.5.



                 ean Annual Air Temperature Change (left) and Values for Percentiles
     Figure 42: M
                over the RCM Ensembles (right) for Three Periods and Two RCPs

                                  RCP 4.5
                         16


                         14


                         12


                         10                                   Projected temperature range
      Temperature [°C]




                                                                           max-min
                          8
                                                                           95th-5th
                                  RCP 8.5                                  percentile

                         16                                                75th-25th
                                                                           percentile

                         14                                                median


                         12


                         10


                          8
               2021-2040:     2041-2060:        2081-2100:
               Near future    Mid-century        Far future




                               Ukraine. Building Climate Resilience in Agriculture and Forestry   91
                                          RCP4.5                                      RCP8.5


percentile                                2021-2040       2041-2060   2081-2100       2021-2040      2041-2060         2081-2100


max                                       11.1            12.0        12.7            11.3           12.4              15.4

95pctl                                    10.9            11.7        12.5            11.1           12.1              14.9

75pctl                                    10.2            10.9        11.5            10.4           11.3              13.9

50pctl                                    9.7             10.3        10.9            9.8            10.7              13.1

25pctl                                    9.2             9.7         10.2            9.3            10.2              12.3

5pctl                                     8.3             8.6         9.2             8.2            9.0               11.1

min                                       8.3             8.6         9.2             8.2            9.0               11.1

range                                     2.8             3.4         3.5             3.0            3.4               4.2


Note: The plot displays the distribution of data based on 5-95th percentile range in or-
ange and a five-number statistic summary: minimum, first quartile (25th percentile), me-
dian (50th percentile), third quartile (75th percentile), and maximum. The plot directly
compares three time periods under each RCP scenario.


            ean Annual Precipitation Change (left) and Values for Percentiles
Figure 43: M
           over the RCM Ensembles (right) for Three Periods and Two RCPs

                                                          RCP 4.5

                            1000


                             800


                             600
Precipitation change [mm]




                                                                                       Projected precipitation range
                             400                                                                   max-min

                                                                                                   95th-5th
                                                                                                   percentile
                                                          RCP 8.5
                                                                                                   75th-25th
                            1000                                                                   percentile

                                                                                                   median

                             800


                             600


                             400


                            2021-2040:                2041-2060:        2081-2100:
                            Near future               Mid-century        Far future




92                                          Ukraine. Building Climate Resilience in Agriculture and Forestry
                           RCP4.5                            RCP8.5

                           2021-       2041-     2081-       2021-        2041-     2081-
              percentile
                           2040        2060      2100        2040         2060      2100

              max          919         920       942         921          944       1036

              95pctl       883         882       888         880          906       1011

              75pctl       741         732       736         737          744       770

              50pctl       655         649       651         649          651       664

              25pctl       573         569       572         565          566       566

              5pctl        436         431       439         416          429       420

              min          420         431       439         416          429       420

              range        498         489       503         505          515       616




              Note: The plot displays the distribution of data based on 5-95th percentile range in blue
              and a five-number statistic summary: minimum, first quartile (25th percentile), median
              (50th percentile), third quartile (75th percentile), and maximum. The plot directly compares
              three time periods under each RCP scenario.


                   ist of CORDEX-Adjusted Outputs Based on Combinations
         Table 9: L
                  of GCM-RCM-Ensemble-Adjustment33
                CORDEX-Adjust output                                                          RCP 4.5                    RCP 8.5                    RCP 2.6



         Id     GCM           Ensemble   RCM                 Adjustment
                                                                                                    t max




                                                                                                                               t max




                                                                                                                                                          t max
                                                                                                            t min




                                                                                                                                       t min




                                                                                                                                                                  t min
                                                                                              tas




                                                                                                                         tas




                                                                                                                                                    tas
                                                                                                                    pr




                                                                                                                                               pr




                                                                                                                                                                          pr
         1      CNRM-CM5      r1i1p1     CLMcom-CCLM4-8-17   v1-METNO-QMAP-MESAN-1989-2010                                                                                 

         2      CNRM-CM5      r1i1p1     CLMcom-CCLM4-8-17   v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

         3      CNRM-CM5      r1i1p1     CNRM-ARPEGE51       v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

         4      CNRM-CM5      r1i1p1     CNRM-ARPEGE51       v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

         5      CNRM-CM5      r1i1p1     SMHI-RCA4           v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

         6      CNRM-CM5      r1i1p1     SMHI-RCA4           v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

         7     CNRM-CM5       r1i1p1     SMHI-RCA4           v1-METNO-QMAP-MESAN-1989-2010                                                                                 

         8     CNRM-CM5       r1i1p1     SMHI-RCA4           v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

         9     EC-EARTH       r1i1p1     KNMI-RACMO22E       v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

         10    EC-EARTH       r1i1p1     KNMI-RACMO22E       v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                




33
     	 Simulations chosen for the agriculture research are highlighted in gold. Mean (tas), maximum (t max) and minimum (t min)
       temperature and precipitation (pr) highlighted in green for three scenarios were available and used.




                                         Ukraine. Building Climate Resilience in Agriculture and Forestry                                                             93
     CORDEX-Adjust output                                                             RCP 4.5                    RCP 8.5                    RCP 2.6



Id   GCM            Ensemble   RCM                   Adjustment




                                                                                            t max




                                                                                                                       t max




                                                                                                                                                  t max
                                                                                                    t min




                                                                                                                               t min




                                                                                                                                                          t min
                                                                                      tas




                                                                                                                 tas




                                                                                                                                            tas
                                                                                                            pr




                                                                                                                                       pr




                                                                                                                                                                  pr
11   EC-EARTH       r1i1p1     KNMI-RACMO22E         v1-METNO-QMAP-MESAN-1989-2010                                                                                 

12   EC-EARTH       r1i1p1     KNMI-RACMO22E         v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

13   EC-EARTH       r3i1p1     DMI-HIRHAM5           v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

14   EC-EARTH       r3i1p1     DMI-HIRHAM5           v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

15   EC-EARTH       r3i1p1     DMI-HIRHAM5           v1-METNO-QMAP-MESAN-1989-2010                                                                                 

16   EC-EARTH       r3i1p1     DMI-HIRHAM5           v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

17   EC-EARTH       r12i1p1    CLMcom-CCLM4-8-17     v1-METNO-QMAP-MESAN-1989-2010                                                                                 

18   EC-EARTH       r12i1p1    CLMcom-CCLM4-8-17     v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

19   EC-EARTH       r12i1p1    SMHI-RCA4             v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

20   EC-EARTH       r12i1p1    SMHI-RCA4             v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

21   EC-EARTH       r12i1p1    SMHI-RCA4             v1-METNO-QMAP-MESAN-1989-2010                                                                                 

22   EC-EARTH       r12i1p1    SMHI-RCA4             v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

23   IPSL-CM5A-MR r1i1p1       IPSL-INERIS-WRF331F   v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

24   IPSL-CM5A-MR r1i1p1       IPSL-INERIS-WRF331F   v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

25   IPSL-CM5A-MR r1i1p1       SMHI-RCA4             v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

26   IPSL-CM5A-MR r1i1p1       SMHI-RCA4             v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

27   IPSL-CM5A-MR r1i1p1       SMHI-RCA4             v1-METNO-QMAP-MESAN-1989-2010                                                                                 

28   IPSL-CM5A-MR r1i1p1       SMHI-RCA4             v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

29   HadGEM2-ES     r1i1p1     CLMcom-CCLM4-8-17     v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

30   HadGEM2-ES     r1i1p1     KNMI-RACMO22E         v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

31   HadGEM2-ES     r1i1p1     KNMI-RACMO22E         v2-SMHI-DBS45-MESAN-1989-2010                                                                                 

32   HadGEM2-ES     r1i1p1     SMHI-RCA4             v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

33   HadGEM2-ES     r1i1p1     SMHI-RCA4             v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

34   HadGEM2-ES     r1i1p1     SMHI-RCA4             v1-METNO-QMAP-MESAN-1989-2010                                                                                 

35   HadGEM2-ES     r1i1p1     SMHI-RCA4             v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

36   MPI-ESM-LR     r1i1p1     CLMcom-CCLM4-8-17     v1-METNO-QMAP-MESAN-1989-2010                                                                                 

37   MPI-ESM-LR     r1i1p1     CLMcom-CCLM4-8-17     v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

38   MPI-ESM-LR     r1i1p1     MPI-CSC-REMO2009      v1-IPSL-CDFT21-WFDEI-1979-2005                                                                                

39   MPI-ESM-LR     r1i1p1     MPI-CSC-REMO2009      v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

40   MPI-ESM-LR     r1i1p1     MPI-CSC-REMO2009      v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

41   MPI-ESM-LR     r1i1p1     SMHI-RCA4             v1-SMHI-DBS45-MESAN-1989-2010                                                                                 

42   MPI-ESM-LR     r1i1p1     SMHI-RCA4             v1-IPSL-CDFT22-WFDEI-1979-2005                                                                                

43   MPI-ESM-LR     r2i1p1     MPI-CSC-REMO2009      v1-SMHI-DBS45-MESAN-1989-2010                                                                                 




94                Ukraine. Building Climate Resilience in Agriculture and Forestry
                 imulations Prepared for RCP 4.5 for CORDEX-Adjust Output (left) and
     Figure 44: S
                for Euro-CORDEX Output (right)




                 imulations Prepared for RCP 8.5 for CORDEX-Adjust Output (left) and
     Figure 45: S
                for Euro-CORDEX Output (right)




Additional climate and vulnerability indicators were estimated from temperature and precipita-
tion variables from the model ensembles: continental climate Ivanov index (especially for the
impact assessment on forestry) and the De Martonne aridity index (especially for the impact
assessment on agriculture).
To assess the impacts on forests, climate continentality must be taken into account as an
additional limiting factor for the growth of this tree species. Climate has been getting less
continental, as revealed by comparing the two past climatic periods of 1961-1990 and 1991-
2010 (Figure 46). The estimated values of the Ivanov Continentality Index on the territory of




                        Ukraine. Building Climate Resilience in Agriculture and Forestry   95
Ukraine, which is calculated as a combination of annual (ATR) and daily (DTR) temperature
ranges, varies from 100 to 168. The indicator generally grows in the direction from the north-
west to southeast. The lowest values are observed in the Carpathian Mountains area, as well
as in the northwest (Volynska oblast, partially adjacent areas), where the values are in the
range of 100-120. The highest values of the Continentality index, up to 160-168, are in eastern
and southern Ukraine. In some parts of the coast, the values of this index are lower due to the
influence of the Black and Azov Seas on ATR and DTR.
Climate continentality34 will exhibit a more a contrasting pattern in Ukraine in the future espe-
cially under the RCP 8.5 scenario. The zone with low values of 120-130 observed in the past
only in the northwest is projected to expand towards southeast and cover not only Volynska,
but Rivnenska and Lvivska oblasts. At the same time, the continentality index is expected
to increase significantly in the south (Khersonska and Zaporizka oblasts, Crimea) and east
(Donetska and Luhanska oblasts) of Ukraine, mostly due to rising DTR, especially under the
RCP 8.5 scenario.
To assess the impacts on agriculture and forests the De Martonne aridity index (Figure 47)
along with additional indicators has been used. The De Martonne aridity index combines an-
nual precipitation total and mean temperature has shown drier conditions in the past for the
south, north and some west oblasts and is projected to stay at the same level for all areas
and over all projections in Ukraine. It reflects a combination of predicted increasing temper-
ature combined with increased precipitation. This combination explains the impact of climate
change on the sectors of the economy, dependent on temperature and water regimes, like
agriculture and forestry. For agriculture, the analysis used daily temperature, precipitation,
and humidity indicators, as well as solar radiation and surface wind, which have been elabo-
rated under this study. For forestry, climatic indicators based on monthly air temperature and
precipitation as well as relative humidity and sunshine duration during certain periods of the
year are required including highly important growing season length and its start (end) date for
different temperature thresholds. A set of around 70 indicators has been generated.




34
     	 Climate continentality is characterized by the average daily temperature range, as well as the annual temperature range.
       Ivanov Continentality Index is calculated using the following equation:

                                  (Ry + Rd + 0.25D0) *100%
                    Kn ivanov=
                                         0.36 ������ + 14

	      where Ry is the annual air temperature range (°C), that is, the difference between the warmest and coldest months; Rd – mean
       daily air temperature range (°C), which is the difference between the average maximum and minimum air temperatures
       for each month that were then averaged for the year; D0 – average annual deficit of relative humidity, %; 0.36������– linear
       dependence of the three aforementioned components on geographical latitude ������,°; 14 – the sum of the components of the
       numerator at the equator.




96                 Ukraine. Building Climate Resilience in Agriculture and Forestry
            he Continental Climate Ivanov Index for Historic Periods (E-OBS) and
Figure 46: T
           Ensembles of the RCMs by Periods of the 21st Century




                 Ukraine. Building Climate Resilience in Agriculture and Forestry   97
Figure 47: De Martonne Aridity Index




98        Ukraine. Building Climate Resilience in Agriculture and Forestry
A 1.2 Agricultural Impact Assessment
The agricultural impact assessment provides a comprehensive and granular regional analysis
of the future production potential for five crops that collectively accounted for 61%of Ukraine’s
agricultural production volume in 2018. The objective of the assessment is to estimate the
losses and gains in crop yield, production, and production values by Ukrainian oblasts under
the RCP 4.5 and RCP 8.5 scenarios in the near future (2030), and the middle of the century
(2050), using 201035 as the base year. The analysis is conducted in three steps: i) estimation
of the changes in yield (tons/ha) for each crop, including uncertainty ranges; ii) estimation of
the changes in agricultural production (tons) for each oblast by combining yield simulation
results with expected changes in the areas for each crop; and iii) estimation of the changes
in the values of production by applying price projections for the crops in 2030 and 2050. The
models employed for the analysis include the WOrld FOod STudies (WOFOST) model, which
was adapted and calibrated by the Ukrainian Hydro Metrological Institute UHMI, and the Inter-
national Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) by the In-
ternational Food Policy Research Institute (IFPRI). The five crops analyzed are barley, maize,
soybean, sunflower, and winter wheat, which in total accounted for 61 percent of production
volume in 2018 (FAO 2021b). The analysis was carried out within the 10-year time periods
that drive agricultural practices and at a highly granular level, covering more than 7,400 grid
cells. Such granular analysis requires an enormous amount of climate data and extensive
modelling with a deep understanding of soil conditions and requirements of specific crops.
The simulations built in simplified endogenous adaptation measures to show the benefits of
appropriate adaptation actions. By integrating simplified endogenous adaptation measures,
such as changes in the allocation of land compared to 2010 in response to changes in relative
yields, the simulations allow for a comparison between adaptation and no adaptation. The
estimated changes in the agricultural production and production values help show the benefits
of adaptation measures in each oblast. The results of this assessment indicate the importance
of forming effective adaptation strategies in the agriculture sector of Ukraine.
The assessment of climate change impact on yield and production is conducted using the
WOrld FOod STudies (WOFOST) model, which was adapted and calibrated for Ukraine by
the Ukrainian Hydro Metrological Institute UHMI. The WOFOST crop simulation model has
been one of the key components for monitoring crops and predicting yield in Europe. It is
implemented in the Monitoring Agricultural Resources (MARS) system. Originally, WOFOST
was developed to simulate crop production potentials in the tropics. However, the biophysical
core of the model is generally applicable, and the model can be easily used to estimate annual
crops in Europe (De Wit et al. 2019). WOFOST is a mechanistic model with a solid biophysical
basis and is widely used to simulate the effects of climate change on the growth, develop-
ment, and yield of major crops like wheat, maize, barley, soybean, sunflower, and others. It
simulates crop growth on the basis of various eco-physiological processes, including pheno-
logical development, carbon (CO2) assimilation (or photosynthesis), transpiration, respiration,
assimilate partitioning, and dry matter production with a time step of one day (Van Diepen et
al. 1987; de Wit et al. 2019). The model simulates the phenological development from sow-
ing to maturity based on crop genetic properties and environmental conditions. The inputs

35
     	 Data is processed for the periods of 2006 – 2015, 2026 – 2035, and 2045 – 2055 with reported central values for 2010, 2030,
       and 2050, respectively.




                                   Ukraine. Building Climate Resilience in Agriculture and Forestry                          99
required for WOFOST include weather, crop, phenology, and agro-management data. Table 9
gives minimum input weather data required for WOFOST. The adaptation and calibration were
conducted through: (i) the generation of a new soil database based on a soil map of Ukraine
1:2,500,000 with spatial resolution 10×10 kilometers (km); the data obtained for 40 soil types
were correlated with WRB (World Reference Base for Soil Resources) soil classification and
correspondent soil physical characteristics; and (ii) the calibration of phenological coefficients
for crops (i.e., sowing date, sum of temperature from sowing to emergence, emergency to
anthesis, and anthesis to maturity) based on phenological observations at local agrometeoro-
logical stations (Kryvobok 2015, Kryvobok et al. 2018).


Figure 48: Crop Growth Processes in the WOFOST Model




Source: Kropff and van Laar, 1993.



Projected changes in climatic conditions are included in the WOFOST simulations to show
the combined effects of changes in atmospheric CO2 concentration, temperature, precipi-
tation, and other meteorological variables on biomass production. Higher levels of CO2 sig-
nificantly increase photosynthesis for wheat, barley, sunflower, and soybean crops (all C3
plant species), but less so for maize crop (C4 plant species), and thus, lead to increases in
the generation of total biomasses and yields. This is referred to as the carbon fertilization
effect. Temperature can influence biomass production in different ways. Higher temperature
has a positive effect on winter crops during cold periods of vegetation and reduces risks of
frost damages for spring crops, but shortens crop maturity time (or vegetation stages), which




100         Ukraine. Building Climate Resilience in Agriculture and Forestry
     Table 10: Minimum Input Weather Data Required for WOFOST


      Input                         Description

      Minimum temperature           Minimum temperature

      Maximum temperature           Maximum temperature

      Sunshine hours                Bright sunshine duration

      Calculated radiation          Daily global radiation

      Wind speed                    Daily mean wind speed at 10 m

      Rainfall                      Daily rainfall

      Vapor pressure                Daily mean vapor pressure




leads to decrease in yields. Higher temperature shifts the sowing, emergence, anthesis, and
maturity dates, which can have different effects on biomass and yield production, depending
on each crop. An increase or decrease in the annual precipitation totals has different effects
on yield production for most parts of Ukraine, but it is more important to estimate its effect in
combination with temperature and other meteorological data. For example, the differences
between precipitation and evapo-transpiration indicate the arid conditions (low values of soil
moisture), which will reduce biomass and yield production. Optimal values of soil moisture
depend on crop development stage (DVS); most crops need high values of soil moisture on
earlier DVS up to anthesis and low values on later DVS.
Daily meteorological input data for the base year 2010 and 2030 and 2050 projections are
generated by 8 RCMs for 7,344 grids. Sowing date, as required phenological information
to start the simulation, is estimated as optimal sowing date assuming optimal temperature,
precipitation and evapotranspiration conditions for each grid which continued during last 10
days. The simulations are finished when crops reach maturity stage. It should be noted that
the assessment methodology cannot directly incorporate climate extremes such as heat and
cold waves, drought, windstorms, and river and coastal flooding.
In this study, the WOFOST model simulates two production levels: potential and water-limited.
The simulation for potential production is only limited by temperature, day length, solar radi-
ation, atmospheric CO2 concentration, and crop features. This simulation assumes that the
soil moisture level is optimal or that water is fully available for crop growth. In the water-limited
simulation, water shortage also plays a role in determining the production outcome. Therefore,
a soil-water balance is calculated that applies to a freely draining soil, where groundwater
is so deep that it does not influence the soil moisture content in the rooting zone. In both
the potential and water-limited simulations, an optimal supply of nutrients is assumed, and
the damages caused by pests, diseases, weeds and/or extreme severe weather events (i.e.,




                             Ukraine. Building Climate Resilience in Agriculture and Forestry   101
flooding, hail, strong wind, etc.) are not considered. So, to make the simulations as realistic as
possible, we define special coefficients between the actual yields, obtained from official sta-
tistics, and simulated yields at the oblast level for base year, and then use them for the 2030
and 2050 two projections. The outputs of WOFOST simulations include crop indicators (i.e.,
biomass-potential productivity level, storage organs biomass-potential productivity level, total
biomass-water limited productivity level, and storage organs biomass-water limited productiv-
ity level), potential leaf area index, water-limited leaf area index, soil moisture, development
stage, main phrenological dates (i.e., sowing, emergence, anthesis and maturity), and total
water requirement.
The yield projections from the WOFOST model have been aggregated to provide estimates at
the oblast level. The modeled yields for each grid point on the map of Ukraine show an overall
potential based on the conditions projected by the climate model. The final yield level for each
oblast is estimated as mean value of all grids within the corresponding oblast. Such aggre-
gation of data, while reducing detailed spatial variability, allows policymakers to examine the
significant differences among the administrative regions in Ukraine regarding climate change
impacts on agriculture, and facilitate decision-making and planning accordingly.


These confidence intervals have been estimated as follows:

                                       Confidence interval = y ± z ∗ σ
                                                                    √n


where ȳ is the mean simulated yield for each region for time periods: 2006 – 2015,
2026 – 2035, and 2045 – 2055, with reported central values for 2010, 2030, and 2050);
z is the confidence (95%); σ is the standard deviation between actual and simulated
yield for 2006-2015. Assuming relative error for 2026-2035 and 2045-2055 periods is the
same as for 2006-2015, we can estimate σ for each period; and n is the sample size|
(10 years).


Variability and uncertainty in the projections of the future yields and production levels in the
face of expected climate changes is reflected in the low, mean, and high projections for each
RCP scenario. Like the climate models, the agricultural model also undergoes an intensive
process of “bias correction”, where it is trained to simulate observational processes. Howev-
er, uncertainties in the agricultural analysis persists. This is due to the fact that the projected
climate variables from the 8 GCM-RCM ensemble are used as meteorological inputs for the
WOFOST model. As such, crop yield projections also have an uncertain range stemming
from the uncertainties associated with climate projections. The uncertainty range (+/- values)
in agricultural modeling results in three sets of projections: low, mean, and high under each
RCP. Thus, the results should not be interpreted as forecasts. Considering all three sets of
projections is a justified and recommended approach (Herger at al. 2015). The mean projec-
tion represents the mean value of the modeled yield potential (or crop productivity) within each
oblast. Low and high projections are the lower (5th percentile) and upper (95th percentile)
limits36 of the modeled yield potential, as determined by the confidence interval. The larger is

36
     	 0-5th and 95-100th percentile ranges are defined as “low likelihood, high impact” outcomes.




102                Ukraine. Building Climate Resilience in Agriculture and Forestry
     Figure 49: Simulation Model (WOFOST) for Crop Yield Assessment




the difference between the low and high projections, the larger is the uncertainty range. Such
range highlights the uncertainty associated with the variations in local soil and climatic condi-
tions, which could influence yield potentials and production outputs, within an oblast territory.
The projected changes in production and production values are calculated using the estimat-
ed changes in land areas under each crop and crop prices in 2030 and 2050 from the IFPRI
IMPACT model. Changes in production are estimated by multiplying the changes in the land
area under each crop (ha) by the projected yields (tons/ha). The change in land areas are
calculated from the IMPACT model, based on the Shared Socioeconomic Pathways 2 (SSP2)
GDP and Population Trends. Data on cropland areas in 2010 (both irrigated and rainfed) by
oblast and by type of grain was used as the base. Finally, the changes in production values
are estimated by multiplying the change in total production by the changes in crop prices.
The IMPACT model uses IPSL Climate Models and Global Environmental Multiscale Models
to estimate the future changes in crop prices. The IMPACT model gives prices of four grains
(maize, barley, wheat, and soybean) for 2010, 2030 and 2050 under two sets of scenarios:
SSP2 RCP 8.5 IPSL and SSP2 RCP 8.5 HGEM. The mean value of these two scenarios
has been used to obtain a single projection under RCP 8.5. Price changes for the RCP 4.5
scenario are not available. For sunflower, the 2010 price was taken from the FAO Producer
Prices Stats , as the IMPACT model data does not include sunflower seed prices. The ratio
of price changes for maize in 2030 and 2050 from IMPACT is then used to get the 2030 and
2050 prices of sunflower.




                        Ukraine. Building Climate Resilience in Agriculture and Forestry   103
Figure 50: The IMPACT Model System by IFPRI



         Climate                             Impact Water                              Water
         Models                                Models                                 Demand




      Crop Models                           IMPACT Global                          Macroeconomic
        (DSSAT)                           Multi-market Model                          Trends




                                               Outputs
                                                Yirelds
                                            Harvested Areas
                                              Production
                                             Commodity
                                                Prices
                                                Trade




  IMPACT is a network of linked economic, climate, water, and crop models. The core of IMPACT is
  a partial equilibrium multi-market economic model that simulates national and global markets for
  agricultural commodities and includes 159 countries. The core model is linked to modular models (i.e.,
  climate, water, crop simulation, land use change, value chain, and others) in a consistent equilibrium
  framework that supports longer-term scenario analysis. Some of the model communication is linear while
  some captures feedback loops. Agricultural production is specified by models of land supply, allocation
  of land to irrigated and rain-fed crops, and determination of yields. Production is modelled at a sub-
  national level, including 320 regions called food production units (FPUs). FPUs are linked to the water
  models and correspond to 154 water basins. Figure 10 shows the links between the various models. The
  links to water and crop models support the integrated analysis of changing environmental, biophysical,
  and socioeconomic trends, allowing for in-depth analysis on a variety of critical issues of interest to
  policymakers at national, regional, and global levels.
  The core model of IMPACT simulates the production, trade, demand, and pricing for 62 agricultural
  commodities across the globe, representing the bulk of food and cash crops. The model specifies
  supply and demand behavior in all markets. Currently in IMPACT, there are three main types of commod-
  ities (i.e., crops, livestock, and processed goods). Crop production in IMPACT is simulated through area
  and yield response functions and is specified sub-nationally at the level of FPUs. This regional disaggrega-
  tion permits linking with water models and provides the added benefit of smaller geographical units for ag-
  gregating climate change impacts, which can vary significantly from one location to another. Land used for
  crop production is divided into irrigated and rain-fed systems, capturing the significant differences in yields
  observed across these cultivation systems and linking directly with the water models, which treat irrigated
  and rain-fed water supplies separately. The system solves for prices, allocations of land, and outputs of
  different agricultural outputs simultaneously, with changes in the allocations of land depending on changes
  in yields of crops and the prices of the crops.




104          Ukraine. Building Climate Resilience in Agriculture and Forestry
A 1.3 Forestry Impact Assessment
The assessment of climate change impacts on Ukrainian forests is conducted for the main for-
est-species, using Vorobjov’s climate-related forestry typology model and Didukh’s model of
suitable environmental condition for plants. Assessing the potential impacts of climate change
on forests needs to consider general trends in climate variables, short-term climate variability,
and the interactions with biotic and abiotic disturbances (Lindner M. et al. 2010). The analysis
is carried out at two levels: i) assessment of changes in core climatic indexes that are impor-
tant for forests based on Prof. D. Vorobjov’s climate-related forestry typology classification
model; and ii) assessment of the favorable climatic conditions for eight main forest-forming
tree species based on the scales of ecological amplitudes for natural flora of Ukraine by Prof.
Ya. Didukh. The main tree species that form most of the forest stands in Ukraine include Scots
pine (Pinus sylvestris L.), common oak (Quercus robur L.), beech (Fagus sylvatica L.), spruce
(Picea abies (L.) H.Karst.), birch (Betula pendula Roth.), black alder (Alnus glutinosa (L.)
Gaertn.), hornbeam (Carpinus betulus L.) and robinia (Robinia pseudoacacia L.). These tree
species are prominent in more than 86 percent of the forest areas37 in Ukraine and constitute
coniferous forests (43 percent, of which 35 percent is pine) and hardwood plantations (43
percent, of which 37 percent are oak and beech). An illustration of the step-by-step process
of assessing forest vulnerability to climate change is shown in Figure 51.




          Figure 51: Workflow for Forests Vulnerability Assessment to Climate Change




37
     	 Lands covered in forest vegetation.




                                   Ukraine. Building Climate Resilience in Agriculture and Forestry   105
The climate-related forest typology classification model of Vorobjov’s is based on the close
connections between forest typologies and climatic conditions (Vorobjov 1961). Specifically,
the forest plot types under homogeneous parent materials and landforms are defined by the
impacts of humidity and heat. The formation of forest types and boundaries of individual forest
plots are tied to climate continentality. Additionally, within the limits of an individual forest type,
the productivity of forest stands is directly connected to the level of heat. Thus, three climate
indexes with the most significant effects on forest growth, condition, productivity, and biodiver-
sity are employed to assess the suitability of future climatic conditions for Ukrainian forests.
These include humidity (Ombro-regime), continentality, and frostiness (Cryo-regime)




  The climate humidity index, or Ombro-regime (Om) is one of the most important en-
  vironmental factors, reflecting the aridity / humidity of climate. This index characterizes
  air humidity associated with precipitation, evaporation and transpiration, soil moisture,
  and groundwater level, etc. The Om index integrates the effects of precipitation and
  thermal resources of a given area and is defined as the difference between annual
  precipitation (W) and evaporation (E0):

                                        Om = W – E0 (mm)
  Evaporation is the potential evaporation from the surface, which has unlimited reserves
  of moisture. Among the methods suggested for calculating E0, the method developed
  by Kolomyts (2010) seems most reasonable for the parts of the country where forests
  are concentrated–specifically, mixed forests, forest steppe, and Carpathian zones:

                             E0 = 1384 – 161,6 * tmax + 6,245 *t2max ,
  where tmax is the long-term average air temperature of the warmest month of the year.
  The method by Kolomyts reflects well the impacts of extreme events (i.e., droughts) on
  forest species.
  The Continentality of climate (Kn) is among several indexes of climate continental-
  ity. The formula suggested by Ivanov (1959) seems most appropriate for territories of
  Ukraine:
                                      (Ap + Ad + 0.25D0) * 100%
                             Kn =
                                             0.36 φ + 14
  where Ap is the yearly amplitude of air temperature (the difference between the warm-
  est and coldest months) in 0С; Ad is daily air temperature (annual average), defined as
  difference between average maximal and minimal temperature in 0С; D0 is the average
  annual deficit of relative air humidity in %; 0.36φ is the linear dependence of all three
  components of geographical latitude φ in degrees; and 14 is the sum of components of
  the numerator at the equator.




106         Ukraine. Building Climate Resilience in Agriculture and Forestry
Based on the three indexes Ombro-regime (Om), Continentality (Kn) and Cryo-regime (Cr),
the lower critical (minimum) and upper critical (maximum) limits and the interval between them
(referred to as “zone of ecological amplitude”) are established for each of the eight forest-form-
ing species, using the methodology developed by Didukh (2011, 2012). The critical limits refer
to the thresholds, above or below which the organisms cannot survive (Didukh 2012). The
ecological amplitude are the boundaries of the environmental conditions within which an or-
ganism can live and function. Understanding such amplitudes of the eight main forest-forming
tree species is essential in diagnosing the conditions of their ecotopes and forecasting the
development of their populations and phytocoenoses. The amplitudes of forest species in
terms of both edaphic and climatic factors are significantly narrower compared to those of
other ecological communities (i.e., meadows, steppe, wetlands). The state of tree species un-
der study and characteristics of forest stands, the ability to form stable forest cenosis, and the
ability to provide ecosystem services vary with the gradients of the ecological amplitude. The
center of the ecological amplitude is where the conditions for growth are optimal. The condi-
tions become less optimal further from the center. The ecological optimum can be assessed
using plant parameters such as vitality, productivity, yield, biomass, height, diameter, density,
abundance, leaf area index, canopy close, or projective cover for grasses, etc.
Based on the Om, Kn and Cr indexes, the degree to which the projected climatic conditions
support healthy and productive growth of the main forest-forming species in Ukraine is de-
termined using the scale of optimal environmental conditions developed by Bondaruk and
Tselischev (2015):
    •	   Optimal (combined index scores of 91-100/100): conditions are optimal for the spe-
         cies (i.e., high viability of the species population with maximum productivity values
         with class I forest land fertility index (bonitet) and others).
    •	   Suboptimal (71-90/100):conditions are close to optimal for the species (i.e., a certain
         decrease in productivity to class I-II bonitet with a sufficiently high viability).
    •	   Satisfactory (51-70/100): conditions are satisfactory for the species (i.e., decrease in
         productivity (i.e., phyto-mass, stock, growth, etc.) of the species to class II-III bonitet).
    •	   Unsatisfactory (21-50/100): conditions are not satisfactory for the species (i.e., re-
         duction of productivity to class III and sometimes class III-IV bonitet, deterioration of
         stand sanitary conditions, and reduced competitiveness).
    •	   Extremely unsatisfactory (1-20/100): conditions are extremely for the species unsat-
         isfactory (i.e., significant decrease in productivity to class III-IV and sometimes class
         IV-V bonitet, further deterioration of stand sanitation conditions, disruptions to the cy-
         cle of phenological development, gradual decrease of natural recovery, weak resist-
         ance to pests and diseases, and reduced competitiveness).
    •	   Conditionally unsuitable (up to 1%): conditions are disruptive for the species (i.e.,
         population regression, loss of productivity (class IV-V bonitet), unsatisfactory stand
         sanitary conditions, damages due to pests and diseases, loss of reproductive capacity,
         disruptions to the cycle of ontogenesis, and loss of cenosis-forming function).
Key climate variables and the average values for Vorobjov’s indexes under RCP 4.5 and RCP
8.5 were calculated for each of the approximately 7,400 grid cells for the base period 1961-
1990, the recent period 1991-2010, 2021-2040 (to allow a range value for the year 2030),




                          Ukraine. Building Climate Resilience in Agriculture and Forestry      107
2041-2060 (to allow a range value for the year 2050), and 2061-2100 (to allow a range value
for the year 2080). As the life cycle of forest development extends over very long periods of
time, we use 1961-1990 as the base period. Additionally, a significant part of the existing for-
ests in Ukraine was formed during the recent period 1991-2010, thus we include this period
in the analysis to allow for sufficient comparisons. The analysis examined areas of suitable
climatic conditions for eight main forest-forming species based on Vorobjov’s indexes (Om,
Kn, and Cr) for all administrative and forestry regions of Ukraine. The open-source Geograph-
ic Information System (Q-GIS) was used to perform spatial analysis and visualize the results.



A 1.4 Distributional Analysis
The distributional analysis assesses the impact of climate change on households’ real in-
comes through its impacts on the price of foods and agricultural incomes. The agricultural
impacts assessment provides two key outputs: i) increases in the prices of key food products
due to climate change and estimates of price increases for 2030 for key agricultural commod-
ities under RCP 8.5 and RCP 4.5 (based on the IFPRI model); and ii) changes in agricultur-
al incomes due to the climate change effects on yields, production, and production values.
These data were inputs for the distributional analysis of the impacts on households.
Like the agricultural impact assessment, the analysis of income considers three sets of projec-
tions: low, mean, and high. They reflect the uncertainty range in the results of the WOFOST
model simulations for changes in yields, production, and production values for the selected
crops (i.e., barley, wheat, maize, sunflower and soybean) under RCP 4.5 and 8.5 in 2030 and
2050, relative to 2010. Such a range reflects a distribution of likely outcomes. The low and
high projections represent the 5th and 95th percentile of the distribution of yield changes pro-
vided, at a very fine scale for each oblast. Changes in real incomes and indicators of poverty
and inequality are estimated for RCP 8.5 in 2030. The analysis is limited to 2030 because by
2050, the baseline expenditure data cannot be considered a reasonable point of comparison.
The analysis is based on comprehensive data collected for 250 to 500 individual households
for each oblast, which allows for identification of variations in income distribution due to cli-
mate-induced changes in the agricultural sector. The modeled climate impacts data is com-
bined with Ukrainian Household Expenditure Survey (HES) data for the latest available year
(2018) to examine the effects on households at different levels of income. The changes in
values of different commodities (which may be negative or positive, depending on the sce-
nario) in turn affect the income of households to the extent they derive their incomes from the
production of these commodities. The HES provides details of expenditure by commodity and
the amount of income from the sale of agricultural products for each household in each oblast.
Data are anonymized, with between 250 and 500 individual households for each oblast. This
enables an estimation of the real expenditure needed to make up for the increase in prices,
as well as the actual changes in real income due to the change in revenues from agricultural
products. The two sets of data at the household level can be used to assess how the distribu-
tion of income is affected by climate change impacts on the agricultural sector.




108         Ukraine. Building Climate Resilience in Agriculture and Forestry
     Figure 52: Distributional Analysis Workflow



                                                     Climate Change Impacts on
                                                     Key Agricultiral Commodities
                                                              in Ukraine




                                                          Ukraine Household                               Effects of Changes
            Effects on Higher
                                                         Expenditure Survey by                               in Agriculutral
             Prices of Foods
                                                             Oblast 2018                                        Incomes



                                                           Combined Effects in
                                                          Terms of Changes in
                                                              Poverty and
                                                               Inequality




A 1.5 Identification of “hotspot” oblasts
Using the results from climate impacts on agriculture, “hotspot” oblasts are grouped based on
several factors. These factors include: i) change in oblast GDP due to the projected changes
in agricultural production; ii) change in agricultural production values; and iii) change in house-
hold incomes, poverty, and inequality.


     Table 11: Criteria Used in the Integrated Assessment Tables

       Criteria

       Change in                Indicates the emergence of new climate types or continuous expansion of arid areas. Reflects the
       climate types            combined effects of changes in annual precipitation and temperatures.

                                The de Martonne aridity index and its classification of climate types is widely used to describe these
                                joint changes. (See Figure 7).

       Impact of                Reveals the increased likelihood of extreme weather events until the end of the century.
       climate
                                This criterion is based on the estimated changes in two climate indicators: number of frost and tropical
       extremes
                                nights per year for every location in Ukraine. (See Figures 14 and 15 in Chapter 2).




                                Ukraine. Building Climate Resilience in Agriculture and Forestry                                109
Criteria

Value of               Changes in the value of agricultural production stemming from changes in crop yields, which are
agriculture            sensitive to the main climate indicators such as temperature, precipitation, and seasonal shifts. The
                       value of agricultural production explains changes in the incomes of households in the agricultural
                       sector.

                       This parameter is estimated for the selected crops. For 2030, values are given as the cumulative
                       effect of value changes from the low, mean, and high projections, assuming no adaptation measures
                       in place. For 2050, values are given for two estimations: with adaptation and without the adaptation.
                       There are two adaptation measures incorporated in the model-based analysis: an endogenous
                       optimal choice of seeding dates for each crop and optimal adjustments of the land allocated to each
                       crop type in response to the changing climatic conditions.

Availability           One of the key adaptation measures for reducing adverse climate change impacts on agricultural
of irrigation          production.
infrastructure
                       The assessment shows that the availability of water is crucial for minimizing the adverse impacts of
                       climate change, especially in the central and northwestern parts of Ukraine. This factor should be
                       considered for the evaluation of future adaptation measures.

Income loss            Describes changes in household incomes as the results of the changes in food prices and the value
                       of agricultural production. These changes are driven by the variability in the climate indicators.

                       Is based on the comprehensive data collected for 250 to 500 individual households for each oblast.
                       Data is used to identify variations in income distribution among households at different levels of
                       income due to climate change-induced changes in the agricultural sector.

Poverty                Describes the deviation of a household’s income from the subsistence level.
headcount
                       The percentage of all households below the subsistence income level (household equivalent).

Inequality             Describes the deviation of the observed income distribution from the theoretic level of equal
measure –              distribution of income.

changes in the         The Gini coefficient measures the extent to which the distribution of income (or, in some cases,
Gini coefficient       consumption expenditure) among individuals or households within an economy deviates from a
                       perfectly equal distribution. The Gini index measures the difference between the hypothetical line of
                       absolute equality and the actual distribution of the cumulative income over the cumulative number of
                       households receiving the income. A Gini index of 0 represents perfect equality, while an index of 1
                       implies perfect inequality.




110            Ukraine. Building Climate Resilience in Agriculture and Forestry
          Integrated Criteria Assessment of Oblasts with the Highest Share
Table 12: 
          of Agriculture in Their GDP in the Near Future


                    Climate type               Value of          Potential risk      Loss in the         Poverty          Impact on
                 (temperature and             agriculture        reduction via       households’        Headcount         inequality
                    precipitation)                                 irrigation          income                           Gini coefficient

                        2021  -2040         2021-2040 change %    low or high     2021-2040 change %   base%+change %      2021-2040
                the area under this climate        to base          potential           to base             to base     base % / change %
                    type is rising or                                                                                        to base
                       decreasing


                     Semi-humid and
Kirovohradska        Mediterranean
                                                 -25 %                high              -2.0%             17%+2%         0.31     0.5%




                       Humid and
  Vinnytska                                      -28%                 high              -1.8%            11%+0.9%        0.33     1.4%
                      semi-humid



                       Humid and
 Cherkasska                                      -32 %                high              -1.7%            15%+1.6%        0.31     0.6%
                     semi-humid



                       Humid and
  Poltavska                                      -32 %                high              -2.1%            16%+0.7%        0.35     0.8%
                     semi-humid




 Khersonska             Semi-arid                -25%                 low               -1.6%            24%+1.5 %       0.31     0.3%




                          Ukraine. Building Climate Resilience in Agriculture and Forestry                                          111
ANNEX 2.
PROJECTED SEASONAL CHANGES

            hanges in Warm-Season Length in the Recent Period 1991-2010
Figure 53: C
           (E-Obs), Near-Future (RCP 8.5) and the End of the Century (RCP 4.5
           and RCP 8.5)




112       Ukraine. Building Climate Resilience in Agriculture and Forestry
            hanges in Growing Season Length in the Recent Period 1991-2010
Figure 54: C
           (E-Obs), Near-Future (RCP 8.5) and the End of the Century (RCP4.5 and
           RCP 8.5)




                Ukraine. Building Climate Resilience in Agriculture and Forestry   113
            hanges in the Active-Vegetation Season Length in the Recent Period
Figure 55: C
           1991-2010 (E-Obs), Near-Future (RCP8.5) and the End of the Century
           (RCP4.5 and RC8.5)




114       Ukraine. Building Climate Resilience in Agriculture and Forestry
            hanges in the Summer Season Length in the Recent Period 1991-2010
Figure 56: C
           (E-Obs), Near-Future (RCP8.5) and the End of the Century (RCP4.5 and
           RC8.5)




                Ukraine. Building Climate Resilience in Agriculture and Forestry   115
ANNEX 3.
DATA FOR AGRICULTURAL
ASSESSMENT & DISTRIBUTIONAL
ANALYSIS
Table 13: Weight of Agriculture in Relation to GDP (US Dollars) in 2010, per Oblast38

 Oblast                               GDP                               Agricultural value   Weight (%)

 Cherkaska                            2,857,808,904                     592,117,263.27       20.72%

 Chernihivska                         2,106,574,368                     232,681,729.88       11.05%

 Chernivetska                         1,114,455,551                     100,749,797.76       9.04%

 Crime                                4,101,072,695                     296,416,732.28       7.23%

 Dnipropetrovska                      13,812,309,960                    651,102,780.98       4.71%

 Donetska                             16,436,244,077                    435,703,123.88       2.65%

 Ivano-Frankivska                     2,227,200,337                     74,471,167.23        3.34%

 Kharkivska                           8,067,363,553                     533,231,318.82       6.61%

 Khemelnytska                         2,214,641,971                     380,806,627.01       17.19%

 Khersonska                           1,913,487,374                     411,870,822.94       21.52%

 Kyivska                              25,397,707,265                    448,587,023.80       1.77%

 Kirovohradska                        2,051,491,271                     600,015,846.79       29.25%

 Luhanska                             5,996,086,314                     298,154,852.75       4.97%

 Lvivska                              4,945,012,214                     153,535,047.47       3.10%

 Mykolaivska                          2,887,855,492                     355,172,783.72       12.30%

 Odeska                               6,205,995,595                     578,678,861.43       9.32%

 Poltavska                            5,238,799,486                     657,352,685.33       12.55%


38
     	 The five types of crops have been incorporated in an aggregate form.




116                Ukraine. Building Climate Resilience in Agriculture and Forestry
Oblast                                                                              GDP                                                                                   Agricultural value                                   Weight (%)

Rivnenska                                                                           1,804,097,884                                                                         148,288,560.15                                       8.22%

Sumska                                                                              2,284,746,506                                                                         364,953,365.43                                       15.97%

Ternopilska                                                                         1,447,096,233                                                                         228,236,810.39                                       15.77%

Vinnytska                                                                           2,862,959,797                                                                         671,406,894.97                                       23.45%

Volynska                                                                            1,612,504,839                                                                         120,898,988.46                                       7.50%

Zakarpatska                                                                         1,757,683,060                                                                         52,990,606.28                                        3.01%

Zaporizka                                                                           5,431,881,552                                                                         477,918,964.09                                       8.80%

Zhytomyrska                                                                         2,266,873,098                                                                         164,100,290.30                                       7.24%



             ncrease in Expenditure Needed to Keep Wellbeing Constant with Food
  Figure 57: I
             Price Increases
                                                                                 Group 1                                                                                                                  Group 2
                                      3.2%                                                                                                                         1.6%
                                      2.8%
   Increase in real expenditure [%]




                                                                                                                                Increase in real expenditure [%]




                                      2.4%                                                                                                                         1.2%
                                      2.0%

                                      1.6%                                                                                                                         0.8%
                                      1.2%

                                      0.8%                                                                                                                         0.4%

                                      0.4%

                                      0.0%                                                                                                                         0.0%
                                             Quintile 1         Quintile 2        Quintile 3     Quintile 4        Quintile 5                                                Quintile 1   Quintile 2       Quintile 3       Quintile 4       Quintile 5
                                                                              Income Quantiles                                                                                                         Income Quantiles


                                                               Cherkaska             Kyivska          Zakarpatska                                                                         Donetska           Khmelnytska            Odeska

                                                               Chernihivska          Vinnytska        Zhytomyrska                                                                         Khersonska         Lvivska                Rivnenska



                                                                                 Group 3                                                                                                                  Group 4
                                      1.2%
                                                                                                                                                                   1.2%
   Increase in real expenditure [%]




                                                                                                                                Increase in real expenditure [%]




                                      0.8%                                                                                                                         0.8%




                                      0.4%                                                                                                                         0.4%




                                      0.0%                                                                                                                         0.0%
                                             Quintile 1         Quintile 2        Quintile 3     Quintile 4        Quintile 5                                                Quintile 1   Quintile 2       Quintile 3       Quintile 4       Quintile 5
                                                                              Income Quantiles                                                                                                         Income Quantiles


                                                          Chernivetska            Ivano-Frankivska            Kirovohradska                                                               Mykolaivska         Sumska               Volynska

                                                          Dnipropetrovska         Kharkivska                  Luhanska                                                                    Poltavska           Ternopilska          Zaporizka




                                                                         Ukraine. Building Climate Resilience in Agriculture and Forestry                                                                                                          117
Table 14: Agricultural Production by Type of Unit in Ukraine, 2019


                                                                 Production in Tons

 Unit                     Number       Area (Ha)     Ave. Size   Wheat            Maize     Barley   Sunflower   Soya
                          of Units                   (Ha)

 Personal Peasant         3,975,100    6,133,600     1.5         8,060            13,631    3,258    3,373       1,096
 Households

 Personal peasant households as % of total production:           28%              38%       37%      22%         30%

 Farm Companies           38,268       15,877,235 414.9          20,268           22,249    5,659    11,881      2,603

 Agro Holdings            38,428       27,841,691 724.5

 Total                                 49,852,526                28,328           35,880    8,917    15,254      3,699

                                            % of total production: 31%            39%       10%      17%         4%



Sources: https://feodal.online/, Ukrainian Statistics 2021.


Table 15: Percent Changes in Value of Selected Crops39 in Ukraine, 2010-2030

 Oblast                            2010 Mn Hrv.      Percent Change to 2030

                                                     Low                 Medium            High

 Cherkaska                         530.59            -32.09%             15.92%            63.94%

 Chernihivska                      228.53            -37.92%             35.41%            108.75%

 Chernivetska                      97.41             -47.37%             14.28%            75.94%

 Crimea                            287.99            -42.12%             43.37%            128.86%

 Dnipropetrovska                   547.83            -19.65%             36.05%            91.75%

 Donetska                          349.58            -12.30%             41.25%            94.81%

 Ivano-Frankivska                  72.77             -39.49%             22.41%            84.32%

 Kharkivska                        446.91            -28.93%             27.95%            84.83%


39
     	   Selected crops include barley, wheat, maize, sunflower, and soybean.




118                 Ukraine. Building Climate Resilience in Agriculture and Forestry
Oblast                2010 Mn Hrv.   Percent Change to 2030

                                     Low           Medium         High

Khemelnytska          377.89         -37.25%       15.97%         69.19%

Khersonska            384.58         -25.42%       32.79%         91.06%

Kyivska               435.98         -40.36%       11.92%         64.20%

Kirovohradska         515.89         -24.86%       27.47%         79.81%

Luhanska              241.56         -24.52%       41.00%         106.52%

Lvivska               152.28         -34.41%       23.18%         80.78%

Mykolaivska           286.92         -7.86%        43.74%         95.35%

Odesska               510.87         -18.79%       38.43%         95.64%

Poltavska             588.95         -32.67%       19.17%         71.02%

Rivnenska             150.02         -42.46%       12.46%         67.38%

Sumska                349.25         -42.42%       21.00%         84.43%

Ternopilska           227.11         -32.51%       19.52%         71.55%

Vinnytska             648.93         -28.98%       16.89%         62.76%

Volynska              120.67         -36.58%       28.05%         92.69%

Zakarpatska           49.94          -41.56%       27.04%         95.65%

Zaporizka             398.1          -20.41%       39.95%         100.32%

Zhytomyrska           160.66         -48.20%       8.34%          64.88%



Source: World Bank staff calculations




                  Ukraine. Building Climate Resilience in Agriculture and Forestry   119
          Poverty Consequences of Agricultural Impacts of Climate Change
Table 16: 
          (Only Price Effects Considered) RCP 8.5, 2030

                         Poverty Headcount         Poverty Gap              Severity of Poverty
Oblast 
                         Base %       Change %     Base %       Change %    Base %      Change %

Cherkaska                 14.85%       1.06%        15.2%         0.34%      4.0%        0.22% 

Chernihivska              16.48%       0.38%        22.5%         0.40%      8.9%        -0.88% 

Chernivetska              18.18%       0.96%        15.6%         0.17%      3.9%        0.08% 

Dnipropetrovska           15.07%       0.24%        20.3%         0.63%      6.4%        0.28% 

Donetska                  21.18%       1.47%        19.2%         -0.02%     5.5%        0.08% 

Ivano-Frankivska          9.65%        0.00%        13.13%        1.93%      3.1%        0.47% 

Kharkivska                23.17%       1.17%        19.9%         -0.14%     5.9%        0.01% 

Khemelnytska              20.75%       0.00%        20.8%         1.00%      6.4%        0.44% 

Khersonska                24.17%       0.76%        17.5%         0.44%      5.8%        -0.78% 

Kyivska                   15.61%       1.12%        14.9%         -0.39%     3.7%        -0.24% 

Kyiv City                 10.74%       0.31%        19.4%         0.94%      6.7%        0.38% 

Kirovohradska             16.86%       0.38%        21.5%         -0.57%     7.2%        0.11% 

Luhanska                  15.18%       1.56%        16.5%         -0.64%     4.5%        -0.14% 

Lvivska                   12.14%       1.43%        20.8%         -0.98%     6.1%        -0.17% 

Mykolaivska               17.11%       1.07%        14.9%         0.03%      3.4%        0.05% 

Odeska                    18.79%       1.45%        15.5%         0.02%      3.9%        0.07% 

Poltavska                 15.55%       0.00%        15.2%         0.95%      3.8%        0.28% 

Rivnenska                 17.99%       0.36%        18.1%         1.49%      5.1%        1.15% 

Sumska                    15.27%       0.30%        14.1%         0.57%      3.7%        0.14% 

Ternopilska               16.36%       0.91%        17.0%         -0.05%     4.8%        0.03% 

Vinnytska                 11.21%       0.00%        23.0%         0.99%      8.3%        1.29% 

Volynska                  23.11%       1.33%        22.23%        -0.27%     7.7%        -0.07% 

Zakarpatska               12.18%       1.52%        16.5%         -0.51%     4.3%        -0.08% 

Zaporizka                 15.85%       0.27%        19.0%         -0.12%     4.3%        -0.05% 

Zhytomyrska               20.88%       1.20%        23.6%         -0.36%     8.4%        -0.03% 




120              Ukraine. Building Climate Resilience in Agriculture and Forestry
          Poverty Consequences of Agricultural Impacts of Climate Change
Table 17: 
          (Low Scenario) RCP 8.5, 2030

Oblast              Poverty Headcount        Poverty Gap                Severity of Poverty

                    Base %      Change %     Base %        Change %     Base %          Change %

Cherkaska           14.85%       1.59%        15.2%        0.67%         4.0%           0.30% 

Chernihivska        16.48%       2.30%        22.5%        -0.56%        7.9%           -0.15% 

Chernivetska        18.18%       1.44%        15.6%        1.94%         3.9%           0.62% 

Dnipropetrovska     15.07%       0.48%        20.3%        0.70%         6.4%           0.30% 

Donetska            22.28%       0.37%        19.2%        0.18%         5.5%           0.14% 

Ivano-Frankivska    9.65%        0.00%        13.13%       1.93%         3.1%           0.47% 

Kharkivska          23.17%       2.05%        19.9%        -0.25%        5.9%           -0.01% 

Khemelnytska        20.75%       0.41%        20.8%        2.13%         6.4%           0.84% 

Khersonska          24.17%       1.53%        17.5%        0.87%         4.9%           0.33% 

Kyivska             15.61%       1.86%        14.9%        0.85%         3.7%           0.22% 

Kyiv City           10.74%       0.61%        19.4%        1.38%         6.7%           0.48% 

Kirovohradska       16.86%       1.92%        20.6%        -0.21%        7.2%           -0.10% 

Luhanska            15.18%       2.53%        16.5%        -0.39%        4.5%           -0.07% 

Lvivska             12.14%       1.90%        20.8%        -0.46%        6.1%           0.00% 

Mykolaivska         17.11%       1.07%        14.9%        0.28%         3.4%           -0.04% 

Odeska              18.79%       1.45%        15.5%        0.69%         3.9%           0.24% 

Poltavska           15.55%       0.71%        15.2%        2.08%         3.8%           0.61% 

Rivnenska           17.99%       2.52%        18.1%        0.56%         5.1%           0.30% 

Sumska              15.27%       1.80%        14.1%        1.67%         3.7%           0.43% 

Ternopilska         16.36%       2.27%        17.0%        0.55%         4.8%           0.24% 

Vinnytska           11.21%       0.93%        23.0%        1.88%         8.3%           0.95% 

Volynska            23.11%       1.78%        22.2%        1.39%         7.7%           0.51% 

Zakarpatska         12.18%       1.52%        16.5%        0.56%         4.3%           0.23% 

Zaporizka           15.85%       1.09%        19.0%        -0.54%        4.3%           -0.21% 

Zhytomyrska         20.88%       2.81%        23.6%        -0.23%        8.4%           -0.03% 


Source: World Bank staff calculations




                     Ukraine. Building Climate Resilience in Agriculture and Forestry             121
          Poverty Consequences of Agricultural Impacts of Climate Change
Table 18: 
          (Mean Scenario) RCP 8.5, 2030

Oblast                  Poverty Headcount         Poverty Gap               Severity of Poverty

                        Base %       Change %     Base %        Change %    Base %       Change %

Cherkaska               14.85%       0.53%        15.20%        0.53%       4.00%       0.27%

Chernihivska            16.48%       0.38%        22.50%        -0.74%      8.90%       -1.31%

Chernivetska            18.18%       0.48%        15.60%        0.00%       3.90%       0.03%

Dnipropetrovska         15.07%       0.00%        20.30%        0.30%       6.40%       0.19%

Donetska                22.28%       -1.29%       19.20%        0.82%       5.50%       0.32%

Ivano-Frankivska        9.65%        0.00%        13.13%        1.93%       3.10%       0.47%

Kharkivska              23.17%       1.17%        19.90%        -0.65%      5.90%       -0.15%

Khemelnytska            20.75%       -0.83%       20.80%        1.27%       6.40%       0.52%

Khersonska              24.17%       0.76%        17.50%        -0.27%      5.80%       -1.00%

Kyivska                 15.61%       1.12%        14.90%        -0.39%      3.70%       -0.24%

Kyiv City               10.74%       0.31%        19.40%        0.68%       6.70%       0.31%

Kirovohradska           16.86%       0.00%        20.60%        -0.38%      7.20%       -0.12%

Luhanska                15.18%       -1.36%       16.50%        0.79%       4.50%       0.27%

Lvivska                 12.14%       0.95%        20.80%        -0.94%      6.10%       -0.17%

Mykolaivska             17.11%       -0.80%       14.90%        0.98%       3.40%       0.23%

Odeska                  18.79%       -0.87%       15.50%        0.78%       3.90%       0.27%

Poltavska               15.55%       0.00%        15.20%        0.00%      3.80%        0.04%

Rivnenska               17.99%       0.36%        18.10%        1.01%      5.10%        0.34%

Sumska                  15.27%       -0.90%       14.10%        0.65%      3.70%        0.19%

Ternopilska             16.36%       -0.45%       17.00%        0.33%      4.80%        0.12%

Vinnytska               11.21%       0.31%        23.00%        1.80%      8.30%        0.82%

Volynska                23.11%       0.00%        22.23%        -0.36%     7.70%        -0.09%

Zakarpatska             12.18%       1.02%        16.50%        -0.51%     4.30%        -0.09%

Zaporizka               15.85%       -0.27%       19.00%        -0.34%     4.30%        -0.09%

Zhytomyrska             20.88%       0.80%        23.60%        -0.17%     8.40%        0.04%


Source: World Bank staff calculations




122             Ukraine. Building Climate Resilience in Agriculture and Forestry
          Poverty Consequences of Agricultural Impacts of Climate Change
Table 19: 
          (High Scenario) RCP 8.5, 2030

Oblast               Poverty Headcount        Poverty Gap              Severity of Poverty

                     Base %       Change %    Base %        Change %   Base %         Change %

Cherkaska            14.85%       0.00%       15.2%         0.11%      4.0%           0.17%

Chernihivska         16.48%       -1.15%      22.5%         -0.66%     8.9%           -1.32%

Chernivetska         18.18%       -1.91%      15.6%         -0.04%     3.9%           0.01%

Dnipropetrovska      15.07%       -1.44%      20.3%         1.49%      6.4%           0.63%

Donetska             21.18%       -0.55%      19.2%         0.47%      5.5%           0.20%

Ivano-Frankivska     9.65%        0.00%       13.13%        1.93%      3.1%           0.47%

Kharkivska           23.17%       -1.17%      19.9%         0.47%      5.9%           0.17%

Khemelnytska         20.75%       -1.66%      20.8%         0.52%      6.4%           0.25%

Khersonska           24.17%       -0.51%      17.5%         -0.47%     5.8%           -1.07%

Kyivska              15.61%       1.12%       14.9%         -0.39%     3.7%           -0.24%

Kyiv City            10.74%       -0.31%      19.4%         0.80%      6.7%           0.44%

Kirovohradska        16.86%       -2.68%      21.5%         0.32%      7.2%           0.53%

Luhanska             15.18%       -3.31%      16.5%         1.12%      4.5%           0.35%

Lvivska              12.14%       -0.48%      20.8%         -0.05%     6.1%           0.05%

Mykolaivska          17.11%       -1.34%      14.9%         -0.25%     3.4%           -0.10%

Odeska               18.79%      -1.73%       15.5%         0.17%      3.9%           0.10%

Poltavska            15.55%      -1.41%       15.2%         -0.90%     3.8%           -0.14%

Rivnenska            17.99%      -1.08%       18.1%         0.01%      5.1%           -0.04%

Sumska               15.27%      -2.40%       14.1%         -0.39%     3.7%           -0.04%

Ternopilska          16.36%      -1.82%       17.0%         -0.61%     4.8%           -0.15%

Vinnytska            11.21%      0.00%        23.0%         -0.59%     8.3%           0.58%

Volynska             23.11%      -2.22%       22.23%        -0.71%     7.7%           -0.18%

Zakarpatska          12.18%      0.00%        16.5%         -0.48%     4.3%           -0.10%

Zaporizka            15.85%      -1.37%       19.0%         -0.05%     4.3%           0.04%

Zhytomyrska          20.88%      -1.61%       23.6%         1.19%      8.4%           0.53%


Source: World Bank staff calculations




                   Ukraine. Building Climate Resilience in Agriculture and Forestry            123
          Base Values of the Gini Coefficient and Changes in the Coefficient
Table 20: 
          RCP 8.5, 2030

 Oblast                                    Percentage Change in Gini

                             Base Value    With Income and Price Effects
                                                                                    Only Price
                                           Agricultural Impact Scenario             Effect

                                           Low           Medium        High

 Cherkaska                   0.31          0.58%         0.19%         -0.13%       0.32%

 Chernihivska                0.3           0.40%         -0.10%        -0.51%       0.11%

 Chernivetska                0.32          0.81%         0.17%         -0.29%       0.30%

 Dnipropetrovska             0.33          0.39%         0.00%         -0.34%       0.25%

 Donetska                    0.34          0.47%         0.21%         -8.27%       0.40%

 Ivano-Frankivska            0.32          -2.75%        -3.23%        -3.51%       -3.05%

 Kharkivska                  0.3           0.42%         0.12%         -0.06%       0.27%

 Khemelnytska                0.3           0.50%         0.19%         -0.10%       0.30%

 Kherson                     0.31          0.34%         0.37%         0.42%        0.34%

 Kyivska                     0.31          0.76%         0.08%         -0.38%       0.22%

 Kyiv City                   0.38          0.60%         0.38%         0.22%        0.43%

 Kirovohradska               0.31          0.46%         0.05%         -0.30%       0.27%

 Luhanska                    0.31          0.60%         -0.10%        -0.62%       0.33%

 Lvivska                     0.31          0.62%         0.32%         0.07%        0.41%

 Mykolaivska                 0.29          0.34%         -0.03%        -0.29%       0.29%

 Odeska                      0.34          0.38%         0.34%         0.33%        0.34%

 Poltavska                   0.35          0.81%         0.14%         -0.39%       0.37%

 Rivnenska                   0.29          0.73%         0.29%         -0.02%       0.39%

 Sumska                      0.31          1.07%         -0.01%        -0.87%       0.29%

 Ternopilska                 0.35          0.55%         0.23%         -0.04%       0.39%

 Vinnytska                   0.33          1.40%         1.15%         0.63%        1.36%

Volynska                     0.33          0.64%         -0.02%        -0.42%       0.26%

Zakarpatska                  0.32          0.71%         0.41%         0.29%        0.53%

Zaporizka                    0.32          0.32%         0.08%         -0.08%       0.22%

Zhytomyrska                  0.33          0.66%         0.18%         -0.08%       0.24%




124              Ukraine. Building Climate Resilience in Agriculture and Forestry
            ange of Change in Income for all Deciles between Low and
Figure 58: R
           High Scenario, RCP 8.5, 2030

                                           5%
                                                                   Cherkaska                                     Chernihivska
                                           2%

                                           0%

                                           -2%

                                           5%                     Chernivetska                                 Dnipropetrovska
                                           2%

                                           0%

                                           -2%


                                           5%                      Donetska                                    Ivano-Frankivska
                                           2%

                                           0%

                                           -2%
Range between High and Low scenarios [%]




                                           5%
                                                                  Kharkivska                                     Khersonska

                                           2%

                                           0%

                                           -2%


                                           5%
                                                                  Khmelnytska                                       Kyivska

                                           2%

                                           0%

                                           -2%


                                           5%                 Kirovohradska                                          Lvivska
                                           2%

                                           0%

                                           -2%


                                           5%                      Luhanska                                      Mykolaivska
                                           2%

                                           0%

                                           -2%


                                           5%                       Odeska                                          Poltavska
                                           2%

                                           0%

                                           -2%


                                                 1   2   3    4      5   6       7   8   9   10    1   2   3    4     5    6    7   8   9   10

                                                                                         Deciles




                                                             Ukraine. Building Climate Resilience in Agriculture and Forestry           125
                                                                       Rivnenska                                        Sumska
                                           5%

                                           2%
Range between High and Low scenarios [%]



                                           0%

                                           -2%

                                           5%
                                                                       Ternopilska                                     Vinnytska
                                           2%

                                           0%

                                           -2%

                                                                        Volynska                                   Zakarpatska
                                           5%

                                           2%

                                           0%

                                           -2%

                                           5%                           Zaporizka                                  Zhytomyrska
                                           2%

                                           0%

                                           -2%


                                                 1   2    3    4   5     6    7      8   9   10    1   2   3   4   5      6    7   8   9   10

                                                                                         Deciles




126                                                  Ukraine. Building Climate Resilience in Agriculture and Forestry
Table 21: Rating of Oblasts with the Highest Share of Agriculture in GDP

 Oblast            GDP ($) in       Agricultural    Share (%) of    Share (%) of   Rating    Normalized
                   2010             value ($S) in   agricultural    agricultural             rating
                                    2010            sector in the   sector in
                                                    oblast GDP      Ukraine GDP

 Kirovohradska     2,051,491,271    600,015,846     29%             0.47%          6.6       1

 Vinnytska         2,862,959,797    671,406,894     23%             0.53%          7.6       0.95

 Cherkaska         2,857,808,904    592,117,263     21%             0.47%          8.5       0.91

 Khersonska        1,913,487,374    411,870,822     22%             0.32%          8.8       0.89

 Poltavska         5,238,799,486    657,352,685     13%             0.52%          10.9      0.79

 Khemelnytska      2,214,641,971    380,806,627     17%             0.30%          10.2      0.82

 Sumska            2,284,746,506    364,953,365     16%             0.29%          10.7      0.8

 Odeska            6,205,995,595    578,678,861     9%              0.46%          12.8      0.7

 Mykolaivska       2,887,855,492    355,172,783     12%             0.28%          12.3      0.72

 Zaporizka         5,431,881,552     477,918,964    9%              0.38%          13.6      0.66

 Ternopilska       1,447,096,233     228,236,810    16%             0.18%          11.7      0.75

 Kharkivska        8,067,363,553     533,231,318    7%              0.42%          14.9      0.6

 Dnipropetrovska   13,812,309,960    651,102,780    5%              0.51%          16.1      0.54

 Chernihivska      2,106,574,368     232,681,729    11%             0.18%          13.9      0.65

 Crime             4,101,072,695     296,416,732    7%              0.23%          15.9      0.55

 Luhanska          5,996,086,314     298,154,852    5%              0.23%          18.2      0.44

 Rivenska          1,804,097,884     148,288,560    8%              0.12%          16.9      0.5

 Zhytomyrska       2,266,873,098     164,100,290    7%              0.13%          17.5      0.47

 Donetska          16,436,244,077    435,703,123    3%              0.34%          20.6      0.32

 Chernivetska      1,114,455,551     100,749,797    9%              0.08%          17.2      0.49

 Volynska          1,612,504,839     120,898,988    7%              0.10%          18        0.45




                          Ukraine. Building Climate Resilience in Agriculture and Forestry            127
 Oblast           GDP ($) in        Agricultural    Share (%) of    Share (%) of   Rating   Normalized
                  2010              value ($S) in   agricultural    agricultural            rating
                                    2010            sector in the   sector in
                                                    oblast GDP      Ukraine GDP


 Kyivska          25,397,707,265    448,587,023     2%              0.35%          22.8     0.22

 Lvivska          4,945,012,214     153,535,047     3%              0.12%          23.3     0.19

 Ivano-                                             3%
                  2,227,200,337     74,471,167                      0.06%          25.3     0.09
 Frankivska

 Zakarpatska      1,757,683,060     52,990,606      3%              0.04%          27.3     0

 TOTAL            127,041,949,396   9,029,442,932

                                                                    Min            6.58     0.00

                                                                    Max            27.25    1.00

                                                                    Mean           15.26    0.58



Rating values do not have specific interpretation and only serve to establish the order
of oblasts by the value of the share of agriculture in GDP of the oblast and Ukraine. The
values are estimated as:
                   r = In(Share (%) of agricultural sector in the oblast GDP) *
                       ln(Share (%) of agricultural sector in Ukraine GDP)




128            Ukraine. Building Climate Resilience in Agriculture and Forestry
Table 22: Rating of Oblasts by the Highest Change in Agriculture Production

                                                Change in the value of agricultural production
                                                without adaptation measures for the low
                                                projection

Oblast              Rating       Normalized     For 2030                For 2050
                                 rating

Zhytomyrska         0.25         1              -59%                    -62%

Kyivska             0.53         0.83           -52%                    -44%

Chernivetska        0.59         0.8            -59%                    -32%

Rivnenska           0.66         0.76           -50%                    -38%

Lvivska             0.72         0.72           -47%                    -38%

Khemelnytska        0.78         0.68           -48%                    -35%

Sumska              0.82         0.66           -55%                    -25%

Volynska            0.83         0.66           -49%                    -32%

Poltavska           0.88         0.63           -50%                    -29%

Zakarpatska         0.92         0.6            -54%                    -23%

Ivano-Frankivska    0.94         0.59           -51%                    -25%

Kirovohradska       0.95         0.58           -45%                    -31%

Kharkivska          1.03         0.54           -50%                    -23%

Vinnytska           1.05         0.53           -42%                    -30%

Ternopilska         1.06         0.52           -44%                    -28%

Cherkaska           1.06         0.52           -44%                    -27%

Chernihivska        1.09         0.5            -52%                    -19%

Luhanska            1.3          0.38           -45%                    -19%




                   Ukraine. Building Climate Resilience in Agriculture and Forestry        129
                                                    Change in the value of agricultural production
                                                    without adaptation measures for the low
                                                    projection

Oblast                   Rating       Normalized    For 2030                 For 2050
                                      rating

Zaporizka                1.33         0.36          -41%                     -22%

Dnipropetrovska          1.33         0.36          -41%                     -23%

Odeska                   1.5          0.26          -39%                     -20%

Khersonska               1.53         0.24          -40%                     -19%

Mykolaivska              1.67         0.16          -33%                     -22%

Donetska                 1.94         0             -36%                     -15%




Min                      0.25         0

Max                      1.94         1

Mean                     1.03         0.54



Rating values do not have specific interpretation and only serve to establish the order of
oblasts by the magnitude of the consecutive impact on the value of agricultural produc-
tion between the two time periods. The values are estimated as:

              r=ln(Change in the value for 2030) * ln(Change in the value for 2050)




130             Ukraine. Building Climate Resilience in Agriculture and Forestry
Table 23: Rating of Oblasts by the Combined Social Changes

Oblast            Rating    Normalized    Poverty Headcount:     Poverty Gap:   Severity of
                            Rating        Change to Base [%]     Change to      Poverty: Change
                                                                 Base [%]       to Base [%]

Lvivska           -0.06     1             1.90%                  -0.46%         0.00%

Zhytomyrska       -0.07     0.97          2.81%                  -0.23%         -0.03%

Kharkivska        -0.07     0.97          2.05%                  -0.25%         -0.01%

Luhanska          -0.09     0.91          2.53%                  -0.39%         -0.07%

Kirovohradska     -0.09     0.9           1.92%                  -0.21%         -0.10%

Mykolaivska       -0.1      0.89          1.07%                  0.28%          -0.04%

Chernihivska      -0.11     0.84          2.30%                  -0.56%         -0.15%

Ternopilska       -0.12     0.81          2.27%                  0.55%          0.24%

Rivnenska         -0.12     0.81          2.52%                  0.56%          0.30%

Zakarpatska       -0.13     0.77          1.52%                  0.56%          0.23%

Donetska          -0.14     0.76          0.37%                  0.18%          0.14%

Kyivska           -0.14     0.76          1.86%                  0.85%          0.22%

Zaporizka         -0.14     0.75          1.09%                  -0.54%         -0.21%

Odeska            -0.14     0.74          1.45%                  0.69%          0.24%

Cherkaska         -0.14     0.74          1.59%                  0.67%          0.30%

Khersonska        -0.15     0.7           1.53%                  0.87%          0.33%

Volynska          -0.18     0.62          1.78%                  1.39%          0.51%

Sumska            -0.18     0.62          1.80%                  1.67%          0.43%

Dnipropetrovska   -0.18     0.6           0.48%                  0.70%          0.30%

Chernivetska      -0.21     0.52          1.44%                  1.94%          0.62%

Kyiv City         -0.22     0.48          0.61%                  1.38%          0.48%

Poltavska         -0.25     0.39          0.71%                  2.08%          0.61%




                     Ukraine. Building Climate Resilience in Agriculture and Forestry         131
 Oblast             Rating    Normalized    Poverty Headcount:    Poverty Gap:    Severity of
                              Rating        Change to Base [%]    Change to       Poverty: Change
                                                                  Base [%]        to Base [%]

 Vinnytska          -0.25     0.38          0.93%                 1.88%           0.95%

 Khmelnytska        -0.3      0.23          0.41%                 2.13%           0.84%

 Ivano-Frankivska   -0.37     0             0.00%                 1.93%           0.47%




 Min                -0.37     0

 Max                -0.06     1

 Mean               -0.16     0.69



Rating values do not have specific interpretation and only serve to establish the order
of oblasts by the magnitude of the impact on three indicators of poverty. The values are
estimated as:
                                 ln( Headcount Poverty Change )
               r=
                    ln( Poverty Gap Change ) * ln( Severity of Poverty Change )




132            Ukraine. Building Climate Resilience in Agriculture and Forestry
ANNEX 4.
DATA FOR FORESTRY ASSESSMENT
 Table 24: Average Annual Air Temperature in Forest Regions of Ukraine

 Time            Carpathian         Polissya     Right-bank   Left-bank   Mountain   Northern   Southern
 periods /                                       Forest-      Forest-     Crimea     Steppe     Steppe
 projections                                     steppe       steppe

 Average annual temperature, Т оС

 1961-1990       6.5±1.5            7.1±0.4      7.7±0.4      7.3±0.5     9.3±0.8    8.4±0.5    10.1±0.5

 1991-2010       7.1±1.4            8.1±0.4      8.5±0.4      8.2±0.4     9.8±0.8    9.1±0.5    10.7±0.5

 RCP 4.5 2021-
                 7.9±1.4            8.9±0.4      9.3±0.4      9.1±0.4     10.5±0.9   10±0.5     11.5±0.5
 2040

 RCP 4.5 2041-
                 8.4±1.4            9.5±0.4      9.9±0.5      9.7±0.4     11.1±0.8   10.6±0.4   12.1±0.5
 2060

 RCP 4.5 2081-
                 9.1±1.4            10.1±0.4     10.5±0.5     10.3±0.4    11.6±0.8   11.2±0.4   12.6±0.5
 2100

 RCP 8.5 2021-
                 8.1±1.4            9.1±0.4      9.5±0.4      9.3±0.4     10.7±0.8   10.2±0.5   11.7±0.5
 2040

 RCP 8.5 2041-
                 9±1.4              10±0.4       10.4±0.4     10.2±0.4    11.6±0.8   11.2±0.4   12.6±0.5
 2060

 RCP 8.5 2081-
                 11.3±1.3           12.3±0.4     12.8±0.5     12.7±0.4    13.9±0.9   13.6±0.4   15±0.4
 2100

 The average temperature of the coldest month, Cr, оС

 1961-1990       -5±1.1             -5.9±0.9     -5.1±0.5     -6.8±0.6    -1.4±0.5   -5.6±1.1   -2.5±1.1

 1991-2010       -3.4±1             -3.4±0.6     -3±0.3       -4.3±0.5    -0.6±0.6   -3.6±0.7   -1.3±0.9

 RCP 4.5 2021-
                 -2.5±1             -2.3±0.5     -2±0.3       -3.1±0.5    0.1±0.6    -2.6±0.7   -0.5±0.9
 2040

 RCP 4.5 2041-
                 -2.3±1.1           -2.2±0.5     -1.8±0.3     -3±0.4      0.5±0.6    -2.3±0.7   -0.2±0.9
 2060




                            Ukraine. Building Climate Resilience in Agriculture and Forestry          133
Time            Carpathian       Polissya      Right-bank   Left-bank   Mountain   Northern   Southern
periods /                                      Forest-      Forest-     Crimea     Steppe     Steppe
projections                                    steppe       steppe

RCP 4.5 2081-
                -1.3±1.1         -0.9±0.5      -0.7±0.3     -1.9±0.5    1±0.5      -1.5±0.6   0.5±0.8
2100

RCP 8.5 2021-
                -2.9±1.1         -2.7±0.6      -2.5±0.4     -3.7±0.5    -0.2±0.6   -3.1±0.7   -1±0.9
2040

RCP 8.5 2041-
                -1.7±1.1         -1.5±0.6      -1.2±0.4     -2.6±0.5    0.9±0.6    -2±0.7     0.2±0.9
2060

RCP 8.5 2081-
                1.4±1.2          2±0.5         2.1±0.4      0.9±0.5     3.2±0.6    1.1±0.6    2.8±0.8
2100

The average air temperature of the warmest month, Тх, оС

1961-1990       16.2±1.7         18.4±0.5      18.7±0.9     19.9±0.6    20±1.1     21.1±0.4   22.2±0.4

1991-2010       17.6±1.6         20.1±0.5      20.3±0.9     21.3±0.5    21.2±1.1   22.5±0.3   23.5±0.3

RCP4.5
                18.4±1.5         20.7±0.5      21±1         22±0.5      22.3±1.2   23.4±0.4   24.5±0.3
2021-2040

RCP4.5
                18.9±1.6         21.2±0.6      21.6±1       22.7±0.6    22.9±1.1   24.1±0.4   25.2±0.3
2041-2060

RCP4.5
                19.5±1.5         21.9±0.6      22.2±1       23.3±0.5    23.4±1.2   24.7±0.4   25.7±0.3
2081-2100

RCP8.5
                18.6±1.6         21.1±0.6      21.3±1       22.3±0.5    22.4±1.1   23.6±0.4   24.8±0.3
2021-2040

RCP8.5
                19.5±1.6         21.9±0.6      22.2±1       23.4±0.6    23.5±1.2   24.8±0.4   25.8±0.3
2041-2060

RCP8.5
                21.8±1.6         24.2±0.7      24.6±1.2     26±0.7      26.5±1.2   27.6±0.5   28.7±0.4
2081-2100




134             Ukraine. Building Climate Resilience in Agriculture and Forestry
          Changes in the Area of Vorobjov’s Heat Availability Index (T) for Forests
Table 25: 
          of Ukraine, %

 Type of climates                                      RCP4.5                   RCP8.5
 by Vorobjov’s heat                1961-       1991-
 availability index for            1990        2010    2021-    2041-   2081-   2021-    2041-   2081-
 forests                                               2040     2060    2100    2040     2060    2100

 c – relatively temperate          1.8         1.2     0.6      0.4     0.4     0.1      0.1     0

 d – temperate                     41.0        10.6    2.1      1.9     1.7     1.3      1.3     0.1

 e – relatively warm               48.5        70.2    63.2     43.0    27.3    53.2     26.1    1.1

 f – warm                          8.7         17.7    31.1     46.1    54.5    40.1     56.1    4.5

 g – very warm*                    0           0.3     3.0      8.6     15.4    5.3      15.8    67.0

 h – hot*                          0           0       0        0       0.6     0        0.5     27.3

 Total                             100         100     100      100     100     100      100     100



* types of climate not described by Vorobjov




                            Ukraine. Building Climate Resilience in Agriculture and Forestry         135
          Changes in Area of Climatic Zones for Vorobjov’s Humidity Index (W)
Table 26: 
          for Forests, %

 Climatic zones of Vorobjov’s      1961-   1991-   RCP4.5                    RCP8.5
 humidity index for forests        1990    2010
                                                   2021-    2041-    2081-   2021-    2041-   2081-
                                                   2040     2060     2100    2040     2060    2100

 Extremely dry (-1)*               0       0       0        0.2      0.7     0.1      0.7     18.7

 Very dry (0)                      12.4    18.6    21.9     28.7     36.1    25.4     35.6    28.7

 Dry (1)                           35.8    31.0    32.4     34.4     33.7    34.8     33.3    42.3

 Fresh (2)                         31.0    40.1    37.3     30.4     24.0    34.1     25.1    7.8

 Moist (3)                         17.2    7.3     5.6      3.9      3.5     3.1      3.0     1.2

 Humid (4)                         0.8     1.0     1.0      1.1      1.4     1.1      1.3     1.2

 Wet (5)                           0.7     1.1     1.0      0.9      0.5     1.0      0.8     0.1

 Very wet (6)                      2.2     0.9     0.8      0.4      0.1     0.5      0.2     0

 Total                             100     100     100      100      100     100      100     100



* not described by Vorobjov




136              Ukraine. Building Climate Resilience in Agriculture and Forestry
            patial-Temporal Dynamics of Vorobjov’s Moisture Availability Index
Figure 59: S
           for Forests




                 Ukraine. Building Climate Resilience in Agriculture and Forestry   137
            patial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 60: S
           of Climate for Scots Pine (Pinus sylvestris L.)




138       Ukraine. Building Climate Resilience in Agriculture and Forestry
            patial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 61: S
           of Climate for English Oak (Quercus robur L.)




                Ukraine. Building Climate Resilience in Agriculture and Forestry   139
            patial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 62: S
           of Climate for European Beech (Fagus sylvatica L.)




140       Ukraine. Building Climate Resilience in Agriculture and Forestry
            patial-Temporal Dynamics of the Suitability Ombroregime (Om)
Figure 63: S
           of Climate for Norway Spruce (Picea abias L.)




                Ukraine. Building Climate Resilience in Agriculture and Forestry   141
Table 27: Distribution of Forest Areas by Classes of Natural Fire Hazard

Forestry regions           Classes of natural fire hazard                                     Average
(by Gensiruk)                                                                                 class of
                           I             II            III           IV            V          natural fire
                                                                                              hazard

                           Forested area, thousand ha /%

Carpathians                164.4/9.8     125.7/7.5     684.1/40.8    701.7/41.9    0.01/0.0   3.15+0.02

Polissya                   654.2/27.3    759.1/31.7    574.424.0     393.0/16.4    17.3/0.7   2.32+0.01

Right-bank Forest-Steppe   101.0/6.3     262.0/16.4    990.0/62.0    239.6/15.0    5.1/0.3    2.87+0.01

Left-bank Forest-Steppe    72.8/8.0      294.0/32.5    420.3/46.5    109.2/12.1    8.6/1.0    2.65+0.02

Mountain Crimea            48.9/19.4     110.2/43.6    93.2/36.9     0.2/0.1       0/0        2.18+0.04

Northern Steppe            77.0/13.7     329.6/58.4    125.0/22.2    31.7/5.6      0.7/0.1    2.20+0.02

Southern Steppe            58.6/31.6     82.6/44.6     29.3/15.8     11.5/6.2      3.4/1.8    2.02+0.04

Ukraine                    1176.9/15.5   1963.2/25.9   2916.4/38.5   1487.0/19.6   35.1/0.5   2.64+0.01



            ensity of Forest Fires in Ukraine by Oblast in Forests Subordinated
Figure 64: D
           to the State Forest Resources Agency of Ukraine, 2007–2020




Source: Forest Ecology Laboratory of URIFFM, 2020




142            Ukraine. Building Climate Resilience in Agriculture and Forestry
ANNEX 5.
BENEFITS OF ADAPTION MEASURES

                  Effect of Adaptation Measures to Maintain the Optimal Water Availability
        Table 28: 
                  on Change in the Value of Agricultural Output for Selected Crops
                  (mean projection)

                     Value of          Change1 in                Adjusted Change1,2          Costs of the Absence of
                     Agricultural      the Value of              in the Value of             Adaptation
                     Output            Agricultural Output       Agricultural Output
                                                                                             (per year) (10-year sum)3

                                                                                                           3%            6%       10%

                     [Million $]       [%]         [Million $]   [%]           [Million $]   [Million $]   [Million $]

                     2010              2030        2030          2030          2030          2030          2026-2035

         Maize       1700.8            -18.7%      -317.8        -13.2%        -225.1        -92.7         -643.0        -453.8   -292.3

         Soybean     34.6              26.5%       9.2           39.6%         13.7          -4.6          -31.6         -22.3    -14.4

         Sunflower   809.1             3.8%        30.8          5.7%          46.1          -15.2         -105.7        -74.6    -48.0

         Total       2544.5            -10.9%      -277.8        -6.5%         -165.3        -112.5        -780.3        -550.7   -354.7



1	
        Change [%] in the value of agricultural production as a percent of 2010 value of agricultural production. Value in million
        US$2010 is given for real prices.
2	
        The estimated adjusted change in the value of water scarce agricultural production as a percent of 2010 value of agricultural
        production by oblast in 2030 with adaptation measures in the agricultural sector directed to maintain the optimal water
        availability.
3
    	   The net present value (to base year 2021) of costs of inaction over the period of climate projections for the agricultural
        outputs 2026-2035.




                                   Ukraine. Building Climate Resilience in Agriculture and Forestry                               143
          Effect of Adaptation Measures to Maintain the Optimal Water Availability
Table 29: 
          on Change in the Value of Agricultural Output for Selected Crops (low
          projection)

                  Value of          Change1 in               Adjusted                 Costs of the Absence of
                  Agricultural      the Value of             Change1,2 in             Adaptation
                  Output            Agricultural             the Value of
                                    Output                   Agricultural             (per year) (10-year sum)3

                                                             Output

                                                                                                    3%            6%       10%

                  [Million $]       [%]        [Million $]   [%]        [Million $]   [Million $]   [Million $]

                  2010              2030       2030          2030       2030          2030          2026-2035

    Maize         1700.8            -75.0%     -127.,3       -51.4%     -874,6        -401.6        -2785.8       -1966.0 -1266.4

    Soybean       34.6              8.9%       3.1           14.8%      5,1           -2.0          -14.1         -10.0    -6.4

    Sunflower     809.1             -24.2%     -195.8        -11.8%     -95,5         -100.3        -695.5        -490.8   -316.2

    Total         2544.5            -57.7%     -1469.0       -37.9%     -965,0        -504.0        -3495.4       -2466.8 -1589.0



1	
        Change [%] in the value of agricultural production as a percent of 2010 value of agricultural production. Value in million
        $2010 is given for real prices.
2	
        The estimated adjusted change in the value of water scarce agricultural production as a percent of 2010 value of
        agricultural production by oblast in 2030 with adaptation measures in the agricultural sector directed to maintain the
        optimal water availability.
3
    	   The net present value (to base year 2021) of costs of inaction over the period of climate projections for the agricultural
        outputs 2026-2035.




144                 Ukraine. Building Climate Resilience in Agriculture and Forestry
          Effect of Adaptation Measures to Maintain the Optimal Water Availabili-
Table 30: 
          ty on Change in the Value of Agricultural Output for Selected Crops
          (high projection)

                  Value of           Change1 in                 Adjusted Change1,2           Costs of the Absence of
                  Agricultural       the Value of               in the Value of              Adaptation
                  Output             Agricultural Output        Agricultural Output
                                                                                             (per        (10-year sum)3
                                                                                             year)

                                                                                                         3%            6%       10%

                  [Million $]        [%]         [Million $]    [%]            [Million $]   [Million $] [Million $]

                  2010               2030        2030           2030           2030          2030        2026-2035

    Maize         1700.8             37.7%       640.7          45.9%          780.8         -140.1      -971.8        -685.8   -441.8

    Soybean       34.6               44.0%       15.2           65.0%          22.5          -7.3        -50.3         -35.5    -22.9

    Sunflower     809.1              31.8%       257.5          46.3%          374.3         -116.8      -810.0        -571.6   -368.2

    Total         2544.5             31.8%       913.4          46.3%          1177.5        -264.2      -1832.1       -1293.0 -832.9



1
    	   Change [%] in the value of agricultural production as a percent of 2010 value of agricultural production. Value in Million
        US $2010 is given for real prices.
2
    	   The estimated adjusted change in the value of water scarce agricultural production as a percent of 2010 value of
        agricultural production by oblast in 2030 with adaptation measures in the agricultural sector directed to maintain the
        optimal water availability.
3
    	   The net present value (to base year 2021) of costs of inaction over the period of climate projections for the agricultural
        outputs 2026-2035.




                                Ukraine. Building Climate Resilience in Agriculture and Forestry                                145
          Change in Value of Agricultural Output Relative to 2010 (Maize): Water
Table 31: 
          Optimal vs Water Scarce Projection

 Oblast             Value of       Change in the value1   Ratio of water-          Adjusted change in
                    Agricultural   [%]                    optima to scarce         the value3 [%]
                                                                 2
                    Output                                yield
                    [Million $]

                    2010           2030                   2030                     2030

                                   low    mean    high    low        mean   high   low    mean      high

 Crimea             5.69           -46    -31     -16     74         71     68     -12    -9        -5

 Chernihivska       0              0      0       0       50         46     41     0      0         0

 Kyivska            121.91         -64    -23     18      49         44     39     -33    -13       25

 Volynska           10.03          -56    -20     16      46         42     38     -30    -12       22

 Khersonska         100.21         -63    -21     22      45         40     34     -35    -12       30

 Zhytomyrska        77             -61    -22     18      44         39     34     -34    -13       24

 Rivnenska          14.23          -63    -21     21      42         38     34     -37    -13       28

 Cherkaska          212.04         -61    -21     20      39         33     27     -37    -14       25

 Sumska             109.35         -74    -17     41      36         31     26     -48    -12       51

 Luhanska           16.88          -124   -22     81      36         30     24     -80    -15       100

 Zaporizka          8.58           -124   -23     78      34         30     25     -81    -16       98

 Mykolaivska        12.73          -91    -16     58      33         28     24     -61    -12       72

 Poltavska          281.03         -65    -15     35      32         28     24     -44    -11       43

 Odeska             43.58          -104   -16     72      30         27     23     -73    -12       88

 Donetska           10.61          -127   -23     81      31         26     22     -87     -17      99

 Zakarpatska        25.65          -69    -3      63      28         26     24     -50     -2       78

 Vinnytska          173.18         -73    -23     27      29         25     22     -51     -17      33

 Kharkivska         88.67          -102   -19     64      28         23     18     -73     -14      76

 Khmelnytska        15.08          -75    -20     34      26         23     20     -56     -16      41




146             Ukraine. Building Climate Resilience in Agriculture and Forestry
      Oblast             Value of           Change in the value1         Ratio of water-               Adjusted change in
                         Agricultural       [%]                          optima to scarce              the value3 [%]
                         Output                                          yield2
                         [Million $]

                         2010               2030                         2030                          2030

                                            low       mean     high      low      mean     high        low    mean      high

      Lvivska            24.59              -70       -15      40        25       22       18          -53    -12       47

      Dnipropetrovska    58                 -119      -19      82        25       21       17          -90    -15       96

      Kirovohradska      165.12             -84       -15      53        22       18       15          -65    -13       61

      Ternopilska        53.86              -80       -18      43        17       15       13          -66    -16       48

      Chernivetska       48.87              -86       -16      53        14       12       11          -74    -14       59

      Ivano-Frankivska   23.91              -75       -9       56        12       12       11          -66    -8        62

      Total              1700.8             -75       -19      38        32       28       24          -51    -13       46



1	
        Change in the value of agricultural production as a percent of 2010 value of agricultural production by oblast in 2030
        for water scarce mean projection. It is taken from the technical report on Agriculture.
2	
        The estimated ratio of the water-optimal yield to the water- scarce yield by oblast in 2030.
3	
        The estimated adjusted change in the value of water scarce agricultural production as a percent of 2010 value of
        agricultural production by oblast in 2030 with adaptation measures in the agricultural sector directed to maintain the
        water optimum.




                              Ukraine. Building Climate Resilience in Agriculture and Forestry                          147
          Change in Value of Agricultural Output Relative to 2010 (Soybean):
Table 32: 
          Water Optimal vs Water Scarce Projection

 Oblast           Value of        Change in the          Ratio of water-optima    Adjusted change
                  Agricultural    value1 [%]             to scarce yield2 [%]     in the value3 [%]
                  Output
                  [Million US$]

                  2010            2030                   2030                     2030

                                  low    mean     high   low     mean     high    low    mean   high

Crimea           1.45             -10    15       40     65      63      61       -3     25     65

Volynska         0                0      0        0      63      61      59       0      0      0

Zhytomyrska      0.06             -2     29       59     62      60      58       -1     46     93

Kyivska          1.42             1      31       61     62      59      57       2      50     95

Rivnenska        0                0      0        0      61      59      57       0      0      0

Sumska           0.39             4      30       56     54      52      50       6      46     83

Khmelnytska      15.71            18     34       50     52      51      50       28     52     75

Lvivska          0                0      0        0      52      51      49       0      0      0

Vinnytska        2.44             14     33       51     51      50      49       21     49     76

Poltavska        5.48             -1     19       38     51      50      49       0      28     56

Khersonska       0.23             17     29       41     53      49      46       26     43     60

Zakarpatska      0                0      0        0      50      49      48       0      0      0

Odeska           0.85             -5     21       47     50      49      47       -3     31     69

Ternopilska      0.05             23     38       52     49      48      48       34     56     78

Chernivetska     0.19             29     42       55     49      48      48       43     62     81

Zaporizka        1.04             7      26       44     50      48      45       11     38     64

Mykolaivska      2                5      28       51     49      47      46       7      41     75

Ivano-
                                  0      0        0      47      47      47       0      0      0
Frankivska       0




148            Ukraine. Building Climate Resilience in Agriculture and Forestry
     Oblast             Value of            Change in the               Ratio of water-optima          Adjusted change
                        Agricultural        value1 [%]                  to scarce yield2 [%]           in the value3 [%]
                        Output
                        [Million US$]

                        2010                2030                        2030                           2030

                                            low     mean      high      low       mean      high       low    mean    high

     Kharkivska         0.55               -10      13       37        49         47        45         -5     19     53

     Luhanska           0.14               -13      32       76        49         46        43         -7     46     109

     Kirovohradska      1.16               13       32       50        47         46        45         19     46     72

     Dnipropetrovska    1.12               1        25       49        47         46        44         2      37     71

     Donetska           0.31               -33      12       56        47         45        44         -18    17     81

     Total              34.59              9        26       44        52         50        47         15     40     65



1	
        Change in the value of agricultural production as a percent of 2010 value of agricultural production by oblast in 2030
        for water scarce mean projection. It is taken from the technical report on Agriculture.
2	
        The estimated ratio of the water-optimal yield to the water- scarce yield by oblast in 2030.
3	
        The estimated adjusted change in the value of water scarce agricultural production as a percent of 2010 value of
        agricultural production by oblast in 2030 with adaptation measures in the agricultural sector directed to maintain the
        water optimum.




                                Ukraine. Building Climate Resilience in Agriculture and Forestry                       149
          Change in Value of Agricultural Output Relative to 2010 (Sunflower):
Table 33: 
          Water Optimal vs Water Scarce Projection

 Oblast              Value of        Change in the          Ratio of water-          Adjusted change
                     Agricultural    value1 [%]             optima to scarce         in the value3 [%]
                     Output                                 yield [%]
                                                                   2


                     [Million US$]

                     2010            2030                   2030                     2030

                                     low    mean     high   low        mean   high   low    mean    high

Crimea               12.31           -56    14       83     78         77     75     -12    24     145

Khersonska           1.25            -36    8        52     61         58     55     -14    13     81

Chernihivska         3.09            -27    5        38     59         56     53     -11    9      58

Kyivska              75.69           -24    3        30     58         55     52     -10    5      46

Volynska             0               0      0        0      54         51     49     0      0      0

Zaporizka            84.65           -34    2        38     54         50     47     -16    3      55

Zhytomyrska          0.21            -28    3        34     53         50     47     -13    5      50

Cherkaska            32              -20    2        23     54         50     46     -9     2      34

Luhanska             57.62           -34    1        36     53         49     45     -16    2      52

Mykolaivska          61.61           -16    8        33     52         49     46     -8     12     48

Rivnenska            0.03            -28    0        28     51         48     46     -14    1      42

Sumska               13.99           -26    4        34     50         47     44     -13    6      49

Donetska             87.61           -30    1        31     50         47     44     -15    1      45

Odeska               64.61           -15    7        29     50         47     44     -8     10     43

Poltavska            61.91           -15    6        28     49         46     44     -7     9      40

Kharkivska           88.56           -21    2        26     47         43     40     -11    4      36

Dnipropetrovska      102.1           -24    3        29     46         43     40     -13    4      41

Vinnytska            18.9            -23    -1       20     45         42     40     -13    -1     28

Kirovohradska        7.82            -18    5        27     42         40     38     -10    7      38




150             Ukraine. Building Climate Resilience in Agriculture and Forestry
        Oblast              Value of            Change in the              Ratio of water-              Adjusted change
                            Agricultural        value1 [%]                 optima to scarce             in the value3 [%]
                            Output                                         yield2 [%]
                            [Million US$]

                            2010                2030                       2030                         2030

                                                low      mean     high     low       mean     high      low    mean     high

     Khmelnytska            32.46               -29      -2       25       41       39       37         -17    -1      34

     Zakarpatska            0.24                -19      11       41       39       37       35         -12    15      55

     Lvivska                0                   0        0        0        39       37       34         0      0       0

     Ternopilska            0.46                -28      -3       22       34       33       32         -18    -2      29

     Chernivetska           1.84                -27      1        28       32       30       29         -18    1       36

     Ivano-Frankivska       0.19                -23      4        31       30       29       29         -16    5       39

     Total                  809.13              -24      4        32       50       47       44         -12    6       46



1	
         Change in the value of agricultural production as a percent of 2010 value of agricultural production by oblast in 2030
         for water scarce mean projection. It is taken from the technical report on Agriculture.
2	
         The estimated ratio of the water-optimal yield to the water- scarce yield by oblast in 2030.
3
    	    The estimated adjusted change in the value of water scarce agricultural production as a percent of 2010 value of
         agricultural production by oblast in 2030 with adaptation measures in the agricultural sector directed to maintain the
         water optimum




                                Ukraine. Building Climate Resilience in Agriculture and Forestry                        151