Stockholm Resilience Centre Sustainability Science for Biosphere Stewardship

Master’s Thesis, 60 ECTS Social-ecological Resilience for Sustainable Development Master’s programme 2018/20, 120 ECTS

Investigating future land use scenarios: consequences for food production and grassland preservation in the steppe biome, Orenburg province of Southwestern Russia

Nataliia Pustilnik

ACKNOWLEDGEMENTS

First and foremost, I would like to thank my supervisors – Ingo Fetzer and Robert Pazur – for their guidance, comments, suggestions and general support during this sometimes not so easy journey.

To Miriam Huitric for support and encouragement along the way.

To all my amazing peer students for their insightful comments, wit and perspectives they brought that helped me to be aware of my own biases.

To partners from the CLIMASTEPPE project – Alexander Prishchepov for sharing his time and expertise and Ksenia Mjachina for good advice and willingness to help.

To Sergey Levykin for sharing his knowledge about land use, agriculture, conservation and history of Orenburg steppes.

To Matthias Bürgi for the hospitality during my stay at WSL and also for great comments and advice.

To wonderful researchers at SRC and at WSL who shared their time and expertise with me – Emma Sundström for helping with GIS and Simona Bacau for all the useful comments and help with the CLUEMondo.

CONTENTS

INDEX OF FIGURES ...... 5

INDEX OF TABLES ...... 6

ABSTRACT ...... 7

1. INTRODUCTION...... 8

2. THEORETICAL FRAMEWORK ...... 10

2.1 Land change modelling ...... 10

2.2 Scenarios ...... 11

3. CASE STUDY AREA ...... 14

4. METHODS ...... 16

4.1 Epistemological and ontological background ...... 16

4.2 Methodological approach...... 17

4.2.1 General approach and overview of the data sources ...... 17

4.2.2 Scenario quantifications ...... 21

4.2.3 Modelling location suitability ...... 26

4.2.4 Spatial restrictions, conversion resistance and transition matrix ...... 29

4.3 Methodology for data and literature gathering ...... 31

4.4 Critical reflections on methods and data sources ...... 31

4.4.1 Critical reflections on methods ...... 31

4.4.2 Critical reflections on data sources ...... 32

5. RESULTS ...... 33

5.1 Grains and pulses ...... 33

5.1.1 Grains and pulses: production...... 33

5.1.2 Grains and pulses: yields ...... 33

5.1.3 Grains and pulses: area ...... 34

5.2 Sunflower ...... 35

5.2.1 Sunflower: production ...... 35

5.2.2 Sunflower: yields ...... 36

5.2.3 Sunflower: area ...... 37

5.3 Livestock ...... 38

5.4 Changes within land use types ...... 39

5.5 Scenario differences ...... 40

5.6 Land change processes ...... 42

6. DISCUSSION ...... 46

6.1 Local, national and global perspectives in land use futures ...... 46

6.2 The interplay between demand for crops, meat and preservation of grassland ...... 47

6.3 Limitations of the approach adopted and future perspectives ...... 50

6.4 Discussion of regression analysis ...... 51

6.5 Regime shifts in land systems ...... 51

6.6 Positionality ...... 51

7. CONCLUSION ...... 52

8. LITERATURE CITED ...... 54

9. APPENDIX ...... 67

Appendix 1. Land use in Orenburg province in 2017 ...... 67

Appendix 2. Short descriptions of the five main scenarios’ narratives ...... 68

Appendix 3. Comparison of global Shared Socio-Economic Pathways and National grain strategy scenarios ...... 71

Appendix 4. Future population and GRP values for Orenburg province ...... 74

Appendix 5. Trends in grain yields for municipal districts until 2050 ...... 75

Appendix 6. Ethical Review – final review ...... 77

Word count: 9928

INDEX OF FIGURES

Figure 1 Overview of the information flow in CLUMondo modelling framework ...... 11 Figure 2 Map of case study area ...... 14 Figure 3 Main sources used for development of scenarios ...... 18 Figure 4 General methodological approach and data sources used in the study……………….....19 Figure 5 Decision-making scheme about quantitative predictions ...... 21 Figure 6 A diagram of possible transitions between different land use types in Orenburg province...... 31 Figure 7 Grains and pulses production in Orenburg province...... 33 Figure 8 Grains and pulses yield in Orenburg province ...... 34 Figure 9 Relative change in area under grains and pulses ...... 35 Figure 10 Sunflower production in Orenburg province ...... 36 Figure 11 Sunflower yield in Orenburg province ...... 37 Figure 12 Relative changes in sunflower area ...... 38 Figure 13 Total livestock numbers ...... 39 Figure 14 Absolute changes in arable land, pasture, grassland with scattered trees and natural grassland in 2050 compared to 2017 ...... 40 Figure 15 Land use change processes in five scenarios and initial land use map...... 42 Figure 16 Hotspots for arable land expansion into grassland areas...... 43 Figure 17 Hotpots for potential grassland restoration ...... 44 Figure 18 Hotspots for abandonment of agricultural land ...... 45 Figure A1 Initial land use map of Orenburg province………………………………………………...67 Figure A5 Grain yields’ trends in municipal districts based on extrapolation of historical data……………………………………………………………………………………………………………...74

INDEX OF TABLES

Table 1 Overview of the main data sources...... 20 Table 2 Growth rates for grain and sunflower yields, production, and livestock numbers .... 22 Table 3 Grazing intensity among different types of agricultural producers ...... 24 Table 4 The aggregate numbers of meat and milk resources production...... 25 Table 5 List of all explanatory variables for land suitability modelling ...... 28 Table 6 Regression modelling results for each land use type ...... 29 Table 7 Conversion resistance values ...... 30 Table 8 Relative changes in all land use types in 2050 compared to 2017 ...... 40 Table 9 Per cell Kappa comparison of similarities among future maps of different scenarios and with the initial land use map ...... 41 Table A1 Land use types in the initial land use map of Orenburg province…………………….....66

Table A3.1 Comparison of the main scenario elements………………………………………………70

Table A3.2 Comparison of the assumptions…………………………………………………………....71

Table A4 Population and GRP in Orenburg in 2050………………………………………………..…73

Table A5 Results of grain yields’ extrapolations………………………………………………………74

ABSTRACT

Many land systems experience massive ecological pressure due to ongoing land use changes for the increasing demand for food, but also need to sustain essential ecosystem services. Computer-based model scenarios help to anticipate the consequences of different socio- economic future transition pathways for humans and nature and evaluate trade-offs between various demands on land. In many grassland ecosystems, the processes of agricultural abandonment in less attractive regions coexist with agricultural intensification in others. At the same time, the ecological value of natural grassland is rarely considered in decision making. By using the CLUMondo land use modelling framework I mapped the future composition of the land system of Orenburg province under five socio-economic scenarios with different ranges of food production intensification. The outcomes allowed me to identify hotspot areas for arable land expansion, grassland restoration, and agricultural abandonment. Most agricultural expansion is prevalent in three scenarios with high ambition for food production, and, without active policy interventions, some natural grassland areas in northern parts of the province are likely to be converted to cropland. In a scenario with low demand for food production, large areas in southern parts could be abandoned creating good opportunities for grassland restoration on former cropland, but possibly having negative socio-economic consequences, such as people’s migration to northern parts of the province. In a scenario with lesser ambition for crop production, but an increase in meat production, agricultural abandonment is less widespread and will even include some additional conversion of cropland to pasture. With appropriate policies aimed at supporting sustainable grazing practices (together with favourable global socio-economic conditions), such scenario can provide an opportunity for satisfying demands for food, providing livelihoods, and ensuring the flow of ecosystem services by grassland ecosystems.

Key words: land use change, scenarios, shared socio-economic pathways, land use-change modelling, steppe

In addition to climate and air, our steppes also have soils that contain water and mineral (including agricultural) resources, bedrock (geology, chemistry and physics), and, therefore agriculture’s capacities and practices are very diverse and sometimes completely different. Finally, they also contain life, a unique plant and animal world with its own rules and habits and demands to humans in general and to agriculture specifically. …All the above-mentioned factors are tightly connected and intertwined to the extent that it is hard to distinguish their individual impacts on human lives. Therefore, when studying and managing those factors one should consider nature as one, whole and inseparable, not only its parts. One must equally respect and study all her elements – Dokuchaev, Our steppes: before and now, 1892

1. INTRODUCTION

Land use change, mainly for crop production, has become an issue of global importance (Ramankutty et al. 2006) and one of the nine Earth-system processes that can change in a non- linear, abrupt way potentially having disastrous consequences for life on Earth (Rockström et al. 2009). Land system science studies complex interactions between humans and terrestrial ecosystems including land use change, its drivers and consequences, through a variety of interdisciplinary approaches (Verburg et al. 2013).

Globally grassland biomes are most impacted by land use change and historically have been most converted by humans for agricultural production. Over the last 300 years, natural grasslands’ area decreased by 26 to 61% by being turned into cropland and grazing land (Ramankutty et al. 2006). But grassland ecosystems provide livelihoods for millions of people and are also the source of other ecosystem services, such as erosion control, carbon sequestration, water flow regulation (Hönigová et al. 2012; Bengtsson et al. 2019), and habitat provision for many animals and biodiversity (Squires and Feng 2018). The Russian Federation has one of the largest areas of grassland habitats in the world (Reinecke et al. 2018). Although the steppes of Russia historically have been modified by a complex interaction of various factors, including humans (Dokuchaev 1936), these ecosystems have been threatened mostly due to cropland expansion. Arable land in the steppe region of European Russia has increased from 9 to 49% during the last three centuries (Moon 2013). A decrease in livestock numbers after the collapse of the Soviet Union has led to the restoration of large areas of natural steppe vegetation on former grazing areas and arable land (Prishchepov et al. 2013; Pazur et al. accepted). On the other hand, the decreased grazing pressure threatens some steppe areas with 8 shrub encroachment, the spread of weed and alien plant species (Smelansky and Tishkov 2012). Moreover, biomass accumulation leads to an increasing number of fires (Brinkert et al. 2016). Processes of abandonment of low-productive land co-exist with land use intensification and reclamation of fallow land on more suitable plots (Levykin et al. 2018).

Modelling has become an important tool in understanding the drivers and impacts of land use changes (Brown et al. 2013). With the increasing number and accessibility of land change models, it becomes important to combine place-based, regional, and global land use research to better understand the interaction between local land change processes mediated by policies and global demand (Meyfroidt et al. 2013; Verburg et al. 2013; Popp et al. 2017). While there has been substantial research focused on land change in tropical regions (Lambin, Geist, and Lepers 2003), research on land use change in the steppe region of European Russia has received less attention. The studies mostly focused on the national level which does not consider the specifics of land use in the steppe biome and on single processes or aspects of land systems, such as mapping past land cover change (Sieber et al. 2013; Lesiv et al. 2018), assessing agriculture potential (Deppermann et al. 2018) or assessing determinants of abandonment (Prishchepov et al. 2013). A comprehensive approach to identify constraints and trade-offs associated with potential re-cultivation of cropland in Russia was used by Meyfroidt et al. (2016) who pointed to the importance of regional-scale assessments.

The overall research question is to understand land system configuration of Orenburg province in the steppe region of southwestern Russia under different socio-economic scenarios and identify the locations of main land use changes. The specific objectives are:

1. Developing local socio-economic scenarios and estimating demands for land services (crop production and livestock) in each of the scenarios. 2. Identifying areas and locations of main land use changes, such as agricultural expansion, agricultural change or abandonment to understand potential trade-offs between different demands on land.

2. THEORETICAL FRAMEWORK

2.1 Land change modelling

Land change modelling is a tool to understand and predict changes in land cover and land use. Land cover change means a change in vegetation of a certain area, such as forest to cropland, while land use change is related to land use intensity and may not directly affect land cover, e.g. change from extensive pasture to intensive pasture. Some land change models try to represent both land cover and land use in a land system approach (van Asselen and Verburg 2012).

Traditionally, predictions of future land change were based on global trends, such as projections of population change and economic development or on local and regional factors, such as distance to roads and historical trends (Dalla-Nora et al. 2014). However, land systems are the result of a combination of both global and local drivers that act synergistically leading to various land system configurations (Meyfroidt 2016; Geist and Lambin 2002). There are also institutional factors (social changes and governmental policies) which are usually hard to predict in models since they happen abruptly, but which also impact the resulting land system configuration (Lambin and Meyfroidt 2010). All these factors contribute to uncertainties in modelling outcomes which suggests that for local studies preferences should be given to models that can combine global and local drivers with local natural conditions.

CLUMondo (Conversions of Land Use and its Effects) modelling framework (see Figure 1) simulates changes in land systems in response to an exogenous demand, land system characteristics, and a series of biophysical and socio-economic variables (J. van Vliet and Verburg 2018). Land systems are represented as a combination of different land use types relevant for a particular area (e.g. cropland, extensive and intensive pasture, urban land). The model allows exploring scenarios with different types and quantities of demands for land. The demand can be expressed in the actual area for each land use type or in ecosystem services that they provide (e.g. tonnes of grain produced or livestock numbers). Thus, one can model not only land cover change but also land use change. Also, it can consider local spatial policies and restrictions (such as nature parks and restricted areas). Moreover, since it is a dynamic model it allows considering the history of a system (e.g. the past land use can influence the possibility or conversion to another use) (E. Koomen and Stillwell 2007). The rules that define which land type can change into which and how long does it take are defined in a so-called transition matrix. Conversation resistance can express how hard it is to convert one land use type to any

10 other and can be approximated by the costs of conversion. For example, build-up areas would have high conversion resistance. These parameters are usually based on expert knowledge and literature. Finally, like many other land change models of a similar approach, it can represent local conditions by taking into consideration local spatially explicit biophysical and socio- economic characteristics which determine the suitability of each location for a specific land use type through logistic regression. The model was developed by Peter Verburg and has been applied on a global (Eitelberg, van Vliet, and Verburg 2015; van Asselen and Verburg 2013) and national (Ornetsmüller, Verburg, and Heinimann 2016; Price et al. 2015) scales.

Figure 1 Overview of the information flow in CLUMondo modelling framework (Jasper van Vliet and Malek 2015). Spatial policies and restrictions describe local restrictions and rules regarding the specific area. Land use type-specific conversion settings can specify which land use type can change into which, how long does it take, how difficult it is to convert it. Land use demand is a parameter that is defined separately based on trends, scenarios, other advanced models. Local suitability is a set of spatial biophysical and socio-economic parameters (such as soil fertility, distance to roads etc.) that is used in a regression model to find out the suitability of each location for each specific land use type.

2.2 Scenarios

When modelling future land use change, complexity and uncertainty of decision making is a major problem decreasing the reliability of model outcomes. One way to deal with it is to

11 explore various future scenarios. Scenarios are plausible versions of future based on coherent set of assumptions about key drivers and relationships (IPCC 2012). With the development of first global socio-economic scenarios, such as Millennium Ecosystem Assessment and IPCC Special Report Emissions Scenarios, they have been increasingly used to predict the dynamics and consequences of land use change on global, regional and local scales (Popp et al. 2017). Recently a new scenario framework has been proposed in the form of Shared Socioeconomic Pathways (SSPs) which consist of narratives that describe key assumptions and quantitative projections of changes in population, GDP, and urbanization (O’Neill et al. 2017). Although SSPs were created with reference to adaptation and mitigation to climate change, they can be used to analyse other sustainable development problems, such as land use change (O’Neill et al. 2017).

There are numerous ways to develop local socio-economic scenarios. They can be generally divided into top-down and bottom-up approaches (Absar and Preston 2015). While bottom-up approaches are useful for creating storylines relative for local stakeholders, they often result in a greater number of storylines that are not easily compared to global or regional studies (Alcamo 2008). The top-down approach can be used to limit the number of future visions and make sure they are nested in global ones (Kok et al. 2019), as well as in contexts where participatory approaches are difficult to implement because political regime or other circumstances (Voinov and Bousquet 2010).

Downscaling global scenarios to a local level is one of the major challenges. Research by Zurek and Henrichs (2007) identifies five approaches to connect scenarios from different scales: equivalent, consistent, coherent, comparable and complementary. They note that “although scenarios were developed independently, they can still be mapped onto each other to understand their differences and similarities”. Equivalent approach is when assumptions and outcomes in the higher and lower scale scenario are the same. In theory, this can be achieved by statistical methods. Consistent approach is when main assumptions, driving forces and their trends are consistent in both scenarios. Coherent approach is when both scenarios share the same assumptions, but context is enhanced by literature and expert interviews. Comparable scenarios are often independent and developed for different scales but connected by the similar framework (e.g. use similar concepts, drivers, main focal issues), and complementary scenarios are developed independently with different assumptions, but present a complementary perspective on the issue. Several studies combine equivalent, consistent and coherent approaches to extend global SSPs to develop regional New European socio-economic scenarios 12

(Kok et al. 2019), subnational (Absar and Preston 2015), national (Frame et al. 2018) and local (Nielsen et al. 2019) scenarios. Comparable and complementary approached can be useful for connecting scenarios developed by different teams and for different scales but addressing the same topic (for example, agriculture) (Zurek and Henrichs 2007).

3. CASE STUDY AREA

Orenburg province covers the area of 123,700 km2 and is located in the southwestern part of European Russia. The population is 1.97 million people with 60.1% people living in cities (Orenburgstat 2019). The climate is continental with average temperatures in January between -14 and -16℃ and in July between +20 and +22℃. Annual precipitation decreases from northwest to south-east from 450 to 260 mm (Orenburgstat 2014). Aridity index changes from 0.2 to 0.65 with most of the province characterized by semi-arid climate and north-west being in dry sub-humid climate (Figure 2a) (Trabucco and Zomer 2019). The province is part of the Eurasian grassland belt and is dominated by steppe landscapes: genuine forbs-bunchgrass steppes in northern and central parts and dry forbs-bunchgrass steppes in southern parts (Figure 2b) (Smelansky and Tishkov 2012). Black chernozem soils cover most of the area with chestnut soils prevalent in the south (Shoba and Dobrovolʹskiĭ 2011).

a) c) Figure 2 Map of case study area: a) Orenburg province, its main cities and aridity index according to UNEP classification; b) Orenburg province (red) within the borders of the Eurasian steppe (green) (Zhao et al. 2017); c) A picture of Orenburg steppe in late spring/early summer†. Land use system of Orenburg province has undergone many changes due to socio-economic and political developments. Land use management changed from predominantly nomadic pastoralism to increased share of farming in the 18th and 19th centuries. Followed by collectivised industrial farming and the “Virgin Land Campaign” (1954-1960) aimed at

† Sergey Metik, Orenburg steppe in the beginning of summer, May 11, 2016 Wikimedia Commons accessed November 18, 2020 https://commons.wikimedia.org/wiki/File:Оренбургская_степь_в_начале_лета_- _panoramio.jpg

14 transforming the remaining natural grasslands into productive cropland and pasture (Moon 2013). After the collapse of the Soviet Union, massive agricultural areas were abandoned (Alcantara et al. 2013; Prishchepov et al. 2013). In Orenburg province, this process especially affected grazing areas due to collapse of the livestock sector (Chernov and Shkilev 2013). Since 2000 land use system in Russia undergone a major transformation due to ownership change, capital inflows and government policies (Wengle 2018) and a re-cultivation process started (Meyfroidt et al. 2016). Some abandoned areas, which transitioned to secondary steppe ecosystems over the 20 years, were brought back for crop production. Cropland area even increased through pasture conversion (Chibilyov et al. 2013).

Despite many transformations, Orenburg province still has the third-highest share of grazing areas among all subjects of the Russian Federation, but also the third-largest grain area and the second-largest sunflower area (FSSS 2019) (see also Figure A1 and Table A1). There are also areas of unploughed or restored natural grassland that have high conservation value (Reinecke et al. 2018). The province can satisfy local food demand and exports to other regions and countries (Orenburgstat 2019). However, the numbers of cattle and horses who are important elements for sustaining grassland ecosystems in the absence of extinct ungulates (Mack and Thompson 1982), despite the state support, continue to decline (Chernov and Shkilev 2013). Moreover, meat consumption preferences of the local population are changing towards higher consumption of poultry and pig (Orenburgstat 2019). The ban on meat imports introduced by the Russian government in 2014 did not significantly change the situation for the province.

Less productive arable lands (especially areas ploughed during the 60s campaign) are impacted by increasing droughts (Alcamo et al. 2007). Research about the evolution of dry steppe ecosystems shows that they can experience regime shifts with a drastic change of structure and diversity in response to climate change, especially aridity (Barbolini et al. 2020). Southern parts of the province have low aridity index and are on the border with the desert-steppe natural zone (see Figure 2a). Therefore, climate change could potentially lead to a shift from semi-arid to arid climate which would threaten agricultural production, food security and water resources (Neverov 2015; Loboda et al. 2017).

Chibilev (2018) and Krasnoyarova et al. (2019) argue that the existing land use system is poorly fitted to the environmental conditions of the steppe region especially in the context of climate change and is a result of destructive policies of agricultural expansion in the 60s and current agrarian lobby (O. Visser, Mamonova, and Spoor 2012).

4. METHODS

4.1 Epistemological and ontological background

This thesis aims to estimate the quantity and location of future land use changes under a set of specific socio-economic conditions by investigating the observed patterns and relationships in the land system. It integrates a normative approach (different hypothetical projections of future) and positive approach (prediction based on evidence-based accounts of land systems) (Brown et al. 2013). Concerning the latter by applying logistic regression method I follow the general approach to causality in land system science (and complex systems science), i.e. that outcome can be achieved by a combination of different causes (also referred to as equifinality) (Meyfroidt 2016). This approach is based on fallibilism which holds that knowledge is provisional or hypothetical and theories can never be proven, but we eliminate false theories and choose the ones which have the highest explanatory power (Thornton 2018). The normative approach utilizes the scenario building method to be used in simulations of future land use. From epistemological standpoint, scenarios are dubious with regards to the knowledge they produce, however as thought experiments based on a coherent and consistent set of assumptions they can contribute to the growth of our body of knowledge by helping to reduce uncertainty and complexity (Aligica 2005).

Finally, in the land-use modelling framework, I use experts' knowledge to modify different models’ conditions for specific context and goals. The outcome of my research is a result of my effort bounded by my experience and background and other participants knowledge and values. Therefore, from ontological view, it is related to constructivism which holds that reality does not exist outside the bias of the researcher (Azadi et al. 2017). My epistemological standpoint in this research was originally related to a participatory paradigm as I planned to engage with local stakeholders for building local scenarios, however, since due to legislative issues the full participatory approach was no used, my epistemological view evolved to that of pragmatism, i.e. I believe that the validity of knowledge can be assessed by its effectiveness (e.g. by improving the management of the studied land system).

4.2 Methodological approach

4.2.1 General approach and overview of the data sources

To develop narratives of land use scenarios for Orenburg province I use three global shared socio-economic pathways describing ‘middle-of-the-road’, ‘regional rivalry’, and ‘fossil- fuelled development’ futures (O’Neill et al. 2017) extended by scenarios in the Long-term strategy for grain complex development of Russian Federation until 2035 (hereafter referred to as National grain strategy) that describe ‘pessimistic’, ‘basic’ and ‘optimistic’ trajectories. To link the two scenario frameworks, I follow the equivalent approach for scenario elements which were identified in the global framework (GDP and population) and coherent approach for elements absent in the framework. I compare the developed nested scenarios with the State programme “Development of agriculture and regulation of agricultural and food commodities markets of Orenburg province 2018-2024” (hereafter referred to as State agricultural program and, respectively, the “Province” scenario) and the European and Central Asian Agriculture Towards 2030 and 2050 by J. Bruinsma (referred to as “FAO” scenario) (see Figures 3 and 4). Since scenarios have different timelines, data in the National grain strategy and the State agricultural program was extrapolated to 2050. Because the study is focused on a local level, I decided to focus on a 2050 timeframe that is relevant for policy decisions.

In general, ‘Optimistic’ and ‘Pessimistic’ scenarios can be considered extreme visions of future where agricultural production, driven by socio-economic changes, increases or decreases significantly. The ‘Intermediate’, “FAO” and “Province” scenarios are all visions of a “trend” scenario however from different perspectives: local, national and regional. “FAO” reflects a regional perspective with a moderate growth of crop production and livestock driven by production of concentrated feed (connected to demand for meat), biofuel and export. “Province” is based on local perspective and has similar to “FAO” predictions for grain and livestock but puts more emphasis on sunflower production. However, moderate increase in grain cropland is explained by the fact that all the grain production growth will come not from expansion of cropland but from “improved use of agricultural lands, development of seed production, increased fertilizer use, and increase in irrigated land up to 25000 ha”. The “Intermediate” scenario has moderate predictions for crop production, but the lowest demand for livestock reflecting the current trends for decreasing herds (for short narrative descriptions of each scenario, please, see Boxes 1-4 in Appendix 2).

Figure 3 Main sources used for development of scenarios mapped on time and scale. Approach to comparing SSPs and national grain scenarios

The main challenge for matching global SSPs and national grain scenarios is the limited focus of the latter. While global SSPs are organized according to their outcomes for adaptation or mitigation of climate change, national scenarios focus on outcomes for grain production. To identify whether global SSPs and national scenarios can be matched I first compare main scenario elements (drivers) mentioned in both sets (see Table A3.1) and then their assumptions (Table A3.2). Both national and global scenario sets consider population, urbanization, consumption and diet, development of technologies, land use and environmental policies to be important drivers. Although the National grain strategy does not explicitly mention GDP, I conclude that it plays an important role leading to the realization of basic, pessimistic or optimistic scenarios (E.g. “The pessimistic scenario reflects economic recession followed by a decrease in incomes”). The ‘basic’ scenario in the strategy is very similar to SSP2 or ‘middle- of-the-road’ scenario (hereafter referred to as “Intermediate”). The National grain strategy does not provide explicit descriptions of how the drivers mentioned in 'basic' scenario will change leading to ‘pessimistic’ scenario. However, it mentions that “change of the above-mentioned conditions can lead to the realization of a pessimistic scenario characterized by deterioration of conditions for development, long-term slow-down of growth of Russian and global agricultural markets”. The ‘pessimistic’ scenario can be considered similar to SSP3 or ‘regional rivalry’ scenario (hereafter referred to as “Pessimistic”). The ‘optimistic’ scenario is the hardest to match with SSPs because it only provides information in quantitative terms, leaving out the 18 reason for these changes. Considering that pessimistic scenario is connected to slower economic growth, I conclude that optimistic scenario is connected to higher economic growth, and, therefore, is matched with SSP5 or “fossil-fuelled development” scenario (hereafter referred to as “Optimistic”).

For quantification of scenarios’ demands for livestock and cropland and background analysis, I use various data sources such as national statistics, worldwide databases and products developed by partners of the CLIMASTEPPE project (see Figure 4 and Table 1). I also conducted one remote interview with Dr Sergey Levykin, head of the laboratory of agroecology and land use of the Steppe Institute of the Ural Branch of the Russian Academy of Sciences (hereafter referred to as the Steppe Institute) in November 2019.

Figure 4 General methodological approach and data sources used in the study. The “Scenario narratives” box includes sources used for scenario storylines. Boxes “National statistics” and “Databases” show sources that were used for quantifying scenario demands. All of them contribute to scenario quantifications (Research objective 1). “CLUMondo” gives an overview of sources needed for land use modelling within the CLUMondo modelling framework (Research objective 2) and “Results” box show the spatial outcomes of different scenarios.

Table 1 Overview of the main data sources and parameters that were extracted from them and used for quantifications of scenarios’ demands.

Data source Parameters extracted Time period Demand to from the source used from which the the data data source source contributes SSPs database as provided by National-level data on 2017-2050 Livestock IIASA Energy Program GDP and population (https://tntcat.iiasa.ac.at/SspDb) Yield Gaps and Climate Bins for Spatial data about Based on Cropland Major Crops Data as provided by maximum attainable 1997-2003 (http://www.earthstat.org/yield- grain and sunflower gaps-climate-bins-major-crops/) yields (5*5 min resolution) Municipal statistics as provided by District-aggregated‡ data 2014-2017 Cropland Federal State Statistical Service on grain and sunflower (FSSS) at yields and production https://rosstat.gov.ru/scripts/db_inet2/ passport/munr.aspx?base=munst53 Statistical book “Regions of Province-aggregated data 2000-2018 Cropland, Russia. Socio-economic on Gross Regional livestock indicators. 2019” at Product (GRP), https://rosstat.gov.ru/storage/medi population, livestock, abank/1dJJCOvT/Region_Pokaz_2 food consumption, grain 019.pdf and sunflower production and yields Statistical yearbooks of Orenburg Province-aggregated data 2000-2019 Cropland, province for 2014 and 2019 as about meat and milk livestock provided by the Territorial unit of resources’ use structures FSSS in Orenburg province (Orenburgstat) http://orenstat.old.gks.ru/wps/wcm /connect/rosstat_ts/orenstat/ru/stati stics/ Bioclimatic potential yields District-aggregated Based on Used for prepared by Nefedova T.G. bioclimatic potential 1997-2000 background provided by partners of the grain yields analysis CLIMASTEPPE project with author’s consent District-aggregated historical data District-aggregated 1990-2013 Used for on yields prepared by Elena historical grain yields analysis of Ponkina for the CLIMASTEPPE yield trends project

‡ The data is aggregated at the level of 35 municipal districts and 13 urban districts. Municipal districts are one or several self-governed territories where the urban population is less than two-thirds of the total. Urban districts are territories with an urban population higher than two-thirds of the total.

Land system configuration is modelled with CLUMondo dynamic spatially explicit land use and land cover change model (version: May 2017 (http://environmentalgeography.nl/files/data/public/exemanual)). CLUMondo requires several inputs: initial land use map, location suitability for each land use type based on socio- economic and biophysical characteristics, conversion parameters and total demands for land use types (see Figures 1 and 3). The first input was developed by Robert Pazur (see Figure A1 and Table A1) and the second input was partially provided by the Steppe Institute within the CLIMASTEPPE project (see Table 4).

4.2.2 Scenario quantifications

Cropland area (including area under grains and pulses and sunflower) and pasture and hayfields are the most relevant demands for the land system of Orenburg province. Popp et al. (2017), Stehfest et al. (2019), and Riahi et al. (2017) link them to production, economic growth, population change, consumption, yields, land use and environmental policies, and international trade. Since National grain strategy does not provide information about all the demands the decision-making scheme for the three SSPs/national scenarios was following (Figure 4):

Figure 5 Decision-making scheme about quantitative predictions for SSPs/national scenarios (“Optimistic”, “Intermediate” and “Pessimistic”).

Calculation of cropland area

Demand for cropland in the province depends largely on grains and sunflower as they cover 98% of all cropland (grain – 77%, sunflower – 21% (Orenburgstat 2019)).

푐푟표푝 푝푟표푑푢푐푡𝑖표푛 Total demand for cropland is calculated as future 푦𝑖푒푙푑

Future grain crop production is based on growth rates provided in the main sources for each scenario with linear extrapolation of trends until 2050 for three SSPs/national scenarios and for the “Province” scenario. Future sunflower production in “Optimistic”, “Intermediate” and “Pessimistic” scenarios in the absence of actual numbers was based on growth rates for grains to ensure consistency within scenarios (see Table 2).

Table 2 Growth rates for grain and sunflower yields and production, and livestock numbers (cattle, sheep and goats, pigs) in five scenarios: “FAO”, SSPs/national (“Optimistic”, “Intermediate”, “Pessimistic”), “Province” and the baseline year used in their estimates. “FAO” SSPs/national “Province” Production of 0.53% ann. growth Based on growth Based on real grains and rates from the production estimates pulses National strategy from the programme with linear with linear extrapolation extrapolation until until 2050 2050

Yields of grains 0.84% ann. growth Based on growth Same as production and pulses rates from the growth rates National strategy with linear predictions until 2050

Production of 1.1% ann. growth Growth rates Based on growth rates sunflower assumed to be the from the programme same as for grains with linear extrapolation until 2050 Yields of 0.71% ann. growth Growth rates 0.71% ann. growth sunflower assumed to be the same as for grains

Livestock (cattle, Cattle – 0.04% ann. Calculated from Based on growth rates sheep and goats, growth, sheep and population, GDP, from the programme pigs) goats – 0.26% ann. meat consumption with linear growth, pigs – and productivity extrapolations until 0.05% ann. growth 2050

Base year 2015/17 average 2017 2018 22

Yield predictions are difficult as they depend on factors, such as future climate conditions, technologies, new crop varieties, irrigation, farming practices and policies (van Dijk et al. 2017; Alexandratos and Bruinsma 2012). Grain and sunflower yield growth rates are based on the main sources for each scenario (Table 2). However, as yields cannot increase indefinitely I used average maximum attainable yields for each district from the EarthStat database (Monfreda, Ramankutty, and Foley 2008) (see Table 1 and Figure 3). Maximum attainable yields are defined as “a climate-defined potential yield compared to other farmers growing that crop in areas of similar climate.” The predictions are similar to local studies of yield gaps (Schierhorn et al. 2014). However, Lobell, Cassman, and Field (2009) and van Dijk et al. (2017) suggest that this potential is rarely reached. With diminishing response to inputs, farmers do not invest in inputs anymore and the yields stop to grow around 80% of their potential. For grain yields, I use values that are 84% of attainable yield (FAO estimates for Russia) and once that value is reached yields cannot increase. For sunflower yields, I assume that in SSPs/national and “Province” scenarios yields can reach 80% of their potential. For “FAO” scenario I take that sunflower yields will reach 64% of their potential values, which is the 2050 prediction for Russia (see Table 2).

I also compare results for yields with bioclimatic potential values to add a local perspective on yield trends. Bioclimatic potential is defined as sustained grain yields on regionally representative parcels under natural conditions of soil type, heat and moisture without the use of any agricultural technologies (Ioffe, Nefedova, and Zaslavsky 2004).

Finally, to better understand the province’s conditions, I analyse historical trends in yields based on statistical data for each municipal district from 1990 to 2013 (see Table 1). For each municipality I fit a non-linear model with the square function of the following type: Yield=a+b*Year+c*Year2.

Calculation of pasture and meadow area

Demand for pastures depends on domestic demand (affected by population, incomes and consumption preferences), changes in productivity (influenced by development and spread of technologies), and net trade (including external and internal policies affecting export and import) (Popp et al. 2017).

Total demand for pastures and meadows is calculated as

푁푢푚푏푒푟 표푓 푐푎푡푡푙푒, 푝𝑖푔푠, 푠ℎ푒푒푝 푎푛푑 푔표푎푡푠 (퐿푆푈) 퐿푆푈 퐴푣푒푟푎푔푒 푔푟푎푧𝑖푛푔 𝑖푛푡푒푛푠𝑖푡푦 ( ) ℎ푎 23 with grazing intensity defined as a total number of cattle, pigs, sheep and goats in LSU§ divided per hectare of pastures and meadows in 2017. As grazing pressure varies depending on the type of agricultural producer, I use average numbers (Table 3).

Table 3 Grazing intensity among different types of agricultural producers in Orenburg province in LSU/ha and the share of pastures that belongs to them in % (Orenburgstat 2018; 2018)

Type of agricultural producer Grazing intensity % of owned pasture out of total pasture area Agricultural organizations 0.23 72 Farmers 0.23 21.2 Private households 2.5 6.8 Average grazing intensity 0.38

The number of cattle, pig, sheep and goat is based on future meat production divided by carcass weights. For cattle, an additional number of milk cows is added based on milk consumption and milk per cow production. The numbers of animals are then translated to herd total sizes based on estimates in Levykin and Kazachkov (2006).

Domestic demand for meat is affected by population, incomes and consumption preferences. There is a strong positive correlation between gross regional product (GRP) and meat consumption per capita (r=0.94, R2=0.88, based on 1996-2017 data). However, fast growth in GRP results only in a small increase in meat consumption. This can be explained by low elasticity of meat consumption to GRP or the fact that real incomes of people are lower possibly due to high inflation rates. Average meat elasticity (2000-2017) to real GRP is 0.79, which implies that there is low responsiveness of meat consumption to income growth, which can possibly be explained by the fact that it is close to saturation.

I calculate future per capita meat consumption based on future GRP growth rates multiplied by the elasticity of meat consumption in relation to real GRP (expressed in constant prices of 2016, RUB) following the methodology used by Nozaki (2016).

§ Livestock units is s the grazing equivalent of one adult dairy cow producing 3 000 kg of milk annually, without additional concentrated foodstuffs. The coefficients for translating different animals nutritional and feed requirements to a LSU are taken form the Eurostat glossary (https://ec.europa.eu/eurostat/statistics- explained/index.php/Glossary:Livestock_unit_(LSU)): cow = 1 LSU, sheep and goat = 0.1 LSU, pigs = 0.3 LSU

Domestic meat demand will reach FAO’s prediction for average meat consumption in Russia (84 kg/capita) in all scenarios by 2025 and will continue to grow to values between 94 and 180 kg/capita. However, meat consumption has a tendency of slowing down as incomes reach a certain level or even going slightly down (Alexandratos and Bruinsma 2012). Therefore, I assume that in “Intermediate” scenario once this value is reached it will remain stable, in “Pessimistic” it will be 80 kg/capita (similar to Poland whose current GDP is close to 2050 prediction for Orenburg), and in “Optimistic” - 90 kg/capita (current levels of consumption in Western Europe). For GRP values and population size of Orenburg province see Table A4. For future productivity estimates, I use Bruinsma’s predictions for carcass weights in 2050 for Europe and Central Asia (Bruinsma 2012).

Demand for milk products is important for estimating the number of cows in the future. Average milk consumption in the province is high compared to the rest of Russia (302 to 229 kg/cap) and hasn’t changed significantly since 2004 (FSSS 2019). Therefore, for all scenarios, I use 3-year average consumption of milk and milk products (303 kg/cap) multiplied by population estimates and divided by milk production per cow. For “Intermediate” and “Optimistic” scenarios I assume that milk production will grow linearly and reach current European levels of 5000 kg/cow (Bruinsma 2012). For “Pessimistic” scenario I assume 2018 levels of 3623 kg/cow (FSSS 2019).

To estimate the total meat and milk production, it is necessary to consider net trade which depends on export and import. Currently, the province is largely self-sufficient in meat and milk products compared to 2000 (see Table 4). I assume that in all scenarios change in demand will be followed by a similar change in production and therefore self-sufficiency rate will remain stable.

Table 4 The aggregate numbers of meat and milk resources production, demand and net trade for Orenburg province (in tonnes) and province’ self-sufficiency rate in these resources (Orenburgstat 2019; 2014).

Resource structure 2000 2010-2013 2014-2018 Meat and meat products Production 76600 137025 142940 Demand (consumption and industrial use) 97200 137720 136450 Net trade -21100 1925 5100 Self-sufficiency rate, % 79 100 104

Milk and milk products Production 739000 748180 831150 25

Demand (consumption and industrial use) 733500 719460 790075 Net trade -5300 27140 40755 Self-sufficiency rate, % 101 100 104

Since CLUMondo modelling framework only allows for demands to change in one direction (increase, decrease or no change) all demands were transformed using linear interpolation to fill in the gaps between 2050 and 2017.

Area estimates for other land uses in the province

There are other land use types defined in the initial land use map (Figure A1) for which demands are not expected to change in the scenarios: grassland with scattered trees, natural grassland, scattered trees and shrubs, forest, settlements, water and rock. Although grassland with scattered trees can be used for grazing for the simplicity it not considered in the calculations. Natural grassland, despite its high conservation value, is not officially recognized as a separate land category in Russia and is generally considered an “empty” or “unused” land (Levykin, Kazachkov, and Semenov 2019). Province’s Environmental Protection Program until 2024 plans to increase protected areas territory only from 2.2 to 2.5%. Therefore, there is no demand for this land use type in all scenarios. According to the province’s Forest Plan for 2018-2028, forests cover 5.2% of the province’s territory. All of them belong to the category of protective forests the main functions of which are erosion control, water protection and recreation. Therefore, in all scenarios, there is a stipulation on no net loss of forest. Since the population in all scenarios decreases, I assume no demand for additional settlement area.

4.2.3 Modelling location suitability

CLUEMondo requires estimates for suitability of each location for each specific land use type. Location suitability is the probability of finding a specific land use type as calculated through a binomial logit model where initial land use map is a dependent variable and a set of biophysical and socio-economic characteristics are explanatory variables.

Explanatory variables are usually local biophysical or socio-economic conditions that are considered to be important for explaining the land use pattern. An initial set of explanatory variables was chosen based on previous research (Pazur et al. accepted) and consultations with CLIMASTEPPE project members. They include climate, soil and relief variables, socio- economic parameters, infrastructure variables and historical variables related to management practices (see Table 5). All socio-economic variables were based on the year 2017. Grazing

26 pressure which was based on 2016 (the year of the latest country-wide agricultural census). The process for regression analysis was the following.

1) All layers used as explanatory variables were normalized to make it less sensitive to the scale. 2) Explanatory variables were checked for correlation to ensure that no two variables have a correlation coefficient higher than 0.07. 3) For each land use type, a binomial logit model was fitted. I use a binomial logit model since the probability of finding the specific land use type in a location has only two outcomes (land use type present or not). Those variables which had significance values higher than 0.01 were excluded from the model. 4) The models’ explanatory power was accessed by the area under the Receiver Operating Curve (AUC). AUC estimates how well the logistic regression classifies positive or negative outcomes and can range from 0.5 to 1 with higher values meaning better performance of the model (Hosmer, Lemeshow, and Sturdivant 2013). Regression modelling results and the final sets of variables used later in land allocation procedure (including the signs of regression coefficients) and explanatory power values for each land use type are given in Table 6.

Table 5 List of all explanatory variables for land suitability modelling and their characteristics. Data quality types are given as quantitative categorical (‘cat’) data and qualitative numerical (‘num’).

Variable Definition Source Type of Resolution data Socio-economic variables Total population Total number of people divided by the unit’s area Federal State Statistic Service (see Table 1) num Municipal density unit Rural population Total number of people living in rural settlements divided by the Federal State Statistic Service num Municipal density unit’s area unit Migration coefficient Difference between number of immigrant and emigrants in Federal State Statistic Service num Municipal relation to the total population of the unit unit Working population Percentage of people between 16-59 to the total population of the Federal State Statistic Service num Municipal unit unit Climate variables Selyaninov A sum of precipitation during months when temperature was Developed previously within the CLIMASTEPPE num 200*200 m hydrothermic higher than 10℃ divided by the sum of temperatures during the project coefficient same period Soil variables Soil rank Soils are ranked from 1 to 7 based on their organic matter Developed previously within the CLIMASTEPPE cat 200*200 m content (1 is the best quality soil, 7 – the worst) project Distance to water 1 if the water is present within 2 km distance, 0 if absent OpenStreetMap (https://download.geofabrik.de/) cat 200*200 m within 2 km Relief Elevation The hight above the sea level in meters SRTM DEM (USGS) developed previously num 200*200 m within CLIMASTEPPE project Slope The steepness of each cell in degrees SRTM DEM (USGS) developed previously num 200*200 m within CLIMASTEPPE project Infrastructure/accessibility Distance to railway Absolute distance to nearest railway stations of mainline rail OpenStreetMap (https://download.geofabrik.de/) num 200*200 stations services in meters Distance to major Absolute distance to nearest primary, secondary and tertiary OpenStreetMap (https://download.geofabrik.de/) num 200*200 m roads roads in meters Distance to settlements Absolute distance to cities, towns and villages in meters OpenStreetMap (https://download.geofabrik.de/) num 200*200 m Management practices and historical Grazing pressure The number of cattle, pigs, sheep and goats in LSU divided by All-Russian Agricultural Census 2016 num Municipal total unit’s agricultural area unit Tselina Districts where the “Virgin Lands” (“Tselina”) agricultural Developed previously within the CLIMSTEPPE cat Municipal campaign took place in the 60s project unit

Table 6 Regression modelling results for each land use type (+ means positive regression coefficients, – means negative regression coefficient and 0 means the variable was excluded based on significance criterion) and explanatory power value (AUC) on a range from 0 (no explanatory power) to 1 (full explanatory power).

Explanatory Settlement Arable Pasture Grassland Scattered Forest Water Rock Natural variables/Land land and with trees trees grassland use type meadow Distance to - + + + - - - 0 + railway stations Distance to - + - - - + + 0 + major roads Distance to - + + - - - - 0 - settlements Distance to + - + + + + + + - water within 2 km Elevation 0 0 + - - 0 - + + Grazing + - + - 0 - - + + pressure Migration 0 - - - 0 + + - + coefficient Total + - - 0 + + 0 - 0 population density Rural - + - + 0 + - - - population density Selyaninov - + - 0 + + + - - hydrothermic coefficient Slope 0 - + + + + - 0 + Soil rank - - + + + + + + - Tselina 0 - + - - - + - + Working + - + + 0 - - + + population Explanatory 0.7868 0.7881 0.7994 0.6965 0.7208 0.8405 0.7787 0.7810 0.6672 power (AUC)

4.2.4 Spatial restrictions, conversion resistance and transition matrix

Spatial restrictions include two protected areas of federal importance – Orenburgsky natural reserve and national park “Buzuluksky bor” – with a total area of 949.3 km2 (Environmental Protection Plan until 2024). All land use transitions are forbidden within the area.

Conversion resistance or elasticity reflects how easy or costly it is to change the existing land use type to any other use. It is a value that ranges from 0 (very easy to convert) to 1 (no conversion is allowed). Settlement, water, rock and forest have the highest resistance of 1 (see Table 7).

Table 7 Conversion resistance values and their explanations for different land use types: 0 - very easy to convert, 1 - no conversion is allowed. Land use type Conversion Explanation for the conversion resistance value resistance Settlement 1 Although residential areas can be abandoned, they have high capital costs Arable land 0.5 Moderate conversion costs. Agricultural organizations and farmers can switch between arable land and pasture if conditions change Pasture and 0.5 Moderate conversion costs. Agricultural meadow organizations and farmers can switch between arable land and pasture if conditions change Grassland with 0.6 More expensive to convert to agricultural land scattered trees (30- 70% of surface) Scattered trees and 0.7 Expensive to convert to agricultural land shrubs (>70% of surface) Forest 1 Forests in the province belong to the category of protection forests, which means that they can only be cut in specific cases (such as making roads or mining) Water 1 Very high conversion costs Rock 1 Very high conversion costs Natural grassland 0.4 Easiest to be converted to other land uses and historically has been subjected to change by humans. Natural grassland is not protected outside of special protected areas Land transition sequences reflect possible land use conversions for the Orenburg province as well as the minimum amount of years for one land use type to become another (for an overview see Figure 5). Pasture and arable land have similar transitions. In case of increased demand, they can expand into natural grassland and grassland with scattered trees through reclamation processes. In addition, if an area belongs to the category of agricultural land according to Russian cadastre and has been overgrown with trees, the trees must be cut. Therefore, there is a transition possible from scattered trees to agricultural land (however, it is offset by high conversion resistance of former). If the demand for pasture and arable land decreases, there are two succession sequences possible: restoration to natural grassland or succession to grassland with scattered trees. The average time for it is 10-20 years (Pazur et al. accepted).

Natural abandonment and grassland restoration, ~15 y reclamation

reclamation fire, conservation abandonment and efforts restoration, ~15 y

Pasture Arable land

reclamation fire, invasive reclamation species, human action reclamation abandonment and succession under specific abandonment and conditions, ~15 y Forest succession under specific conditions, ~15 y Scattered trees Grassland with and shrubs succession under scattered trees and specific conditions, ~30 y shrubs succession under specific conditions, ~15 y reclamation

Figure 6 A diagram of possible transitions between different land use types in Orenburg province. Green arrows indicate natural processes or successions, red arrows show agricultural expansion processes and orange arrows indicate agricultural change. Dotted lines show either unlikely transitions (e.g. scattered trees to agricultural land) or phenomena which depend on many factors and more studies are necessary (e.g. transition of natural grassland to grassland with scattered trees and vice versa).

4.3 Methodology for data and literature gathering

The literature search was organized according to sections in the thesis: scoping search for problem definition, theory and methods. Also, literature suggestions were provided by supervisors and partners of the CLIMASTEPPE project. Secondary data used as the basis for scenario projections were guided by the theoretical and practical approaches to downscaling and quantification of scenarios and were chosen to represent local, regional and national (nested in the global) perspectives.

4.4 Critical reflections on methods and data sources

4.4.1 Critical reflections on methods

First, the area for cropland is calculated as production divided by yields. However, the actual sown area may slightly differ since some of the harvest may be lost and since I use yields aggregated for district level. I compared predicted cropland areas with actual reported sown 31 areas for 2018. The difference among all scenarios is no bigger 18-255 km2 for grains, 443- 565 km2 for sunflower. Second, the sub-national data about sunflower production was lacking. However, to ensure consistency across all five the scenarios it was necessary to include this element. Therefore, I decided to make assumption that sunflower production will change similarly to grain production. For the future research, it might be necessary distinguish this demand. Third, transition from abandoned agricultural land to natural grassland for dry forb- bunchgrass steppes of Orenburg province usually takes 15-20 years. In the model this change happens immediately. In practice if the land was abandoned not a long time ago it is more likely to be brought back to use if there is a demand. However, since according to modelling framework demands can change only in one direction this transition cannot happen. In addition, since the objective of the study was to identify hotspots for different land use changes, I decided not to limit the time transition (this information can be extracted however from the modelling results if necessary, for future research). Finally, the change in crop production in SSPs/national scenarios was based on growth rates and their linear extrapolations. However, for “Pessimistic” scenario growth rates have a very non-linear pattern which could have affected predictions.

4.4.2 Critical reflections on data sources Some of the data regarding meat and milk resources use that had to be collected from province statistical yearbooks to cover the period of 2000-2017 had differences in the produced amounts compared to the statistics provided by Federal State Statistical Service. Sometimes the amounts for the same year were different depending on the year when the yearbook was issued. I limited the use of this source only to the data about export and internal consumption of meat and milk products which could not be obtained from federal statistical services (see Table 2).

5. RESULTS

5.1 Grains and pulses

5.1.1 Grains and pulses: production In “Intermediate” scenario grains and pulses production in 2050 will increase by 1.5 million tonnes (37%), in “Optimistic” – by 2.3 million tonnes (55%) and in “Pessimistic” production will decline by 0.5 million tonnes (-12%) compared to 2017. In both “Province” and “FAO” scenarios grain production will decline by 0.3 million (-8%) (Figure 6). Because scenario sets use different base years (see Table 2 in Methods) there is a steep decline compared to 2017 (which was also a record year for grain harvest in Russia over the last 20 years), however over the time of prediction these differences even out. Comparing production to a 3-year average would show the following results: “Intermediate” + 77%, “Optimistic” + 100%, “Pessimistic” + 13%, “FAO” + 19%, “Province” + 19%.

Grains and pulses production in Orenburg province, 2013-2050 6700000

5700000

4700000

tonnes 3700000

2700000

1700000 2013 2016 2019 2022 2025 2028 2031 2034 2037 2040 2043 2046 2049 Year

"Intermediate" "Optimistic" "Pessimistic" "Province" "FAO" 2015/17 average

Figure 7 Grains and pulses production in Orenburg province (tonnes): 2013-2017 – historical data, 2018-2050 – projections of five different scenarios. 5.1.2 Grains and pulses: yields In “Intermediate” scenario average yields in Orenburg province will increase from 1.61 in 2017 to 1.93 tonnes/ha in 2050. In “Optimistic” yields will grow to 2.01 tonnes/ha in 2050, and in “Pessimistic” yields will fall to 1.57 tonnes/ha (Figure 7). All yield scenarios are lower than potential yields (2.81 tonnes/ha) or their 84% (2.36 tonnes/ha) but higher than bioclimatic 33 potential (1.72 tonnes/ha; exception for bioclimatic potential is “Pessimistic” scenario). There are significant differences between districts. In “Intermediate” 12 out of 35 districts will reach 84% of their attainable yields in 2050, all located in the north-western part of the province. In “Optimistic” 21 districts will reach their 84% of attainable yields. In “Pessimistic”, due to faster-declining growth rates, only one district will reach 84% of attainable yield. In the “Province” scenario yields will increase to 1.7 tonnes/ha and in FAO scenario to 1.64 tonnes/ha in 2050. District-level analysis of the historical grain yields and predictions based on non-linear model- fitting indicate that in 31 out of 35 municipal districts the trend in yields change was positive, which means that yields have been growing since 1990. However, in four located in the eastern corner of the province yields decline (see Figure A5 and Table A5).

Grains and pulses yield in Orenburg province, 2013-2050 3.2 Attainable yield

2.7 84% of attainable yield

2.2 Bioclimatic potential

1.7 tonnesper ha

1.2

0.7 2013 2016 2019 2022 2025 2028 2031 2034 2037 2040 2043 2046 2049 Year

"Intermediate" "Optimistic" "Pessimistic" "Province" "FAO" 84% of attainable yield Bioclimatic potential Attainable yield

Figure 8 Grains and pulses yield in Orenburg province: 2013-2017 – historical data, 2018-2050 – projections of five different scenarios. 5.1.3 Grains and pulses: area

Two of the five scenarios – “Intermediate” and “Optimistic” – predict an increase in total area under grains and pulses by 9% and 15% respectively caused by the demand for grain production outpacing yields growth. “Pessimistic”, “FAO” and “Province” scenarios all have similar 14- 15% decrease in area under these cultures (Figure 8). In “Pessimistic” decrease in the area after 34

2039 is explained by declining production growth rates, which becomes negative, but continued although a slow rise in yields. In “FAO” scenario slowly growing production is outpaced by yields growth and therefore there is a decrease in grain area. In the “Province” scenario, there is no change in the area until 2041 when yields reach 84% of their attainable yields. From 2042 there is a slight increase in area.

Relative change in area under grains and pulses, 2000-2050 130 125 120 115 110

% 105 100 95 90 85

2036 2042 2048 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2038 2040 2044 2046 2050 Year

"Intermediate" "Optimistic" "Pessimistic" "Province" "FAO"

Figure 9 Relative change in area under grains and pulses compared to 2017: 2000-2017 – historical data, 2018-2050 – projections of “Intermediate”, “Optimistic”, “Pessimistic”, “FAO” and “Province” scenarios.

5.2 Sunflower

5.2.1 Sunflower: production In “Intermediate” scenario sunflower production will grow by 310,692 tonnes (37%) compared to 2017, in “Optimistic” – by 458,577 tonnes (55%) and in “Pessimistic” – decrease by 10,062 tonnes (-12%). In the “Province” scenario production will increase by 911,403 tonnes (109%) and in “FAO” it will increase by 150,352 tonnes (19%) in 2050 (Figure 9). The doubling of sunflower in the “Province” scenario can be explained by a different base year, which reflects a recent trend for the rapid increase in sunflower production. Comparing production to a 3-year average would give the following results: “Intermediate” + 45%, “Optimistic” + 66%, “Pessimistic” + 15%, “FAO” + 131%, “Province” + 22%.

Sunflower production in Orenburg province, 2013-2050 1900000

1700000

1500000

1300000

tonnes 1100000

900000

700000

500000 2013 2016 2019 2022 2025 2028 2031 2034 2037 2040 2043 2046 2049 Year

"Intermediate" "Optimistic" "Pessimistic" "Province" "FAO" 2015/17 average

Figure 10 Sunflower production in Orenburg province (tonnes): 2013-2017 – historical data, 2018-2050 – projections of five different scenarios. 5.2.2 Sunflower: yields

Sunflower yields increase to a similar value from 0.98 tonnes per ha (in 2017) to 1.15 in “Intermediate”, 1.17 in “Optimistic” and 1.16 in the “Province” scenario. In “FAO” scenario yields do not change significantly from the base year, which is explained by the usage of 3- year average for “FAO” base year due to high variability in yields. In “Pessimistic” scenario sunflower yields decrease to 0.90 tonnes per ha. In all scenarios, sunflower yields change moderately, and average province yields do not exceed 64% of the attainable yields (Figure 10).

Sunflower yield in Orenburg province, 2013-2050 1.35

1.25 64% of attainable yield

1.15

1.05

tonnesper ha 0.95

0.85

0.75 2013 2016 2019 2022 2025 2028 2031 2034 2037 2040 2043 2046 2049 Year

"Intermedite" "Optimistic" "Pessimistic" "Province" "FAO" 64% of attainable yield

Figure 11 Sunflower yield in Orenburg province: 2013-2017 – historical data, 2018-2050 – projections of five different scenarios. 5.2.3 Sunflower: area In three of the five scenarios areas under sunflower crops in Orenburg province will change similarly: in “Intermediate” scenario sunflower cropland will grow by 6%, in “FAO” – by 8%., and in “Optimistic” – by 12%. In the “Province” scenario area will grow by half (53%) and in “Pessimistic” it will reduce 18% (Figure 11).

Relative change in sunflower area, 2000-2050 180

160

140

120

100 % 80

2024 2050 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 Year

"Intermedite" "Optimistic" "Pessimistic" "Province" "FAO"

Figure 12 Relative changes in sunflower area compared to 2017: 2000-2017 – historical data, 2018-2050 – projections of “Intermediate”, “Optimistic”, “Pessimistic”, “FAO” and “Province” scenarios.

5.3 Livestock

None of the scenarios envisions the restoration of livestock numbers to the levels of the year 2000. Three out of five scenarios predict an increase of livestock units in Orenburg province: “Optimistic” and “FAO” scenarios are close to each other with a total increase of 14,122 and 11,915 LSU respectively and in “Province” scenario livestock will grow the most (by 23,534 LSU). In two other scenarios – “Intermediate” and “Pessimistic” – livestock numbers decrease by 29,479 and 20,183 LSU respectively (Figure 12).

Total livestock numbers in Orenburg province, 2000-2050 1050000

1000000

950000

900000

850000 LSU

800000

750000

700000

650000

2034 2044 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2036 2038 2040 2042 2046 2048 2050 Year

"Intermedite" "Optimistic" "Pessimistic" "FAO" "Province"

Figure 13 Total livestock numbers (cattle, sheep and goats, pigs) in LSU in Orenburg province between 2000 and 2050: 2000-2017 – historical data, 2018-2050 – projections of five different scenarios.

5.4 Changes within land use types

Land use changes in different scenarios are represented in Figure 13 (absolute changes) and Table 8 (relative changes). The biggest differences between scenarios are observed in natural grassland and arable land. For arable land, the biggest increase by 5371 km2 or 14% is in “Optimistic” scenario and the biggest decrease is nearly the same amount – 5766 km2 or 15% in “Pessimistic”. In “Intermediate” scenario arable land increases by extent 3151 km2 or 8% and in “Province” scenario by a lesser extent of 792 km2 or 2%. In “FAO” scenario arable land decreases by 9%. Increase or decrease in arable land is accompanied by a similar change in natural grassland: in “Optimistic” they reduce by 5517 km2 (48%) and in “Pessimistic” by 6456 km2 (57%). There is also, a significant increase in natural grassland in “FAO” scenario (+20%) and a decrease in “Province” scenario (-22%). Similarly to natural grasslands, the highest decrease in grassland with scattered trees is observed for “Optimistic” (-15%) and the highest increase in in “Pessimistic” (+17%) followed by “FAO” scenario (+3%). In other scenarios, the change is less than 1%. For pastures, the differences compared to the 2017 year range from -4% to +3% with the highest decrease in “Intermediate” scenario and increase in “Province”

39 scenario. The lowest amount of change is observed in scattered trees class that changes only in “Optimistic” scenario (-2.7%).

Land use changes in different scenarios in 2050 8000

6000

4000

2000 2 0 km Arable land Pasture and meadow Grassland with scattered Natural grassland -2000 trees and shrubs

-4000

-6000

-8000

"Intermediate" "Optimistic" "Pessimistic" "Province" "FAO"

Figure 14 Absolute changes in arable land, pasture, grassland with scattered trees and natural grassland in 2050 compared to 2017 for all scenarios.

Table 8 Relative changes in all land use types in 2050 compared to 2017 for all scenarios, % Land use “Intermediate” “Pessimistic” “Optimistic” “FAO” “Province” types Arable land 8.21 -15.02 13.99 -8.60 2.06 Pasture and -4.33 -2.89 1.94 1.68 3.27 meadow Grassland -0.19 17.09 -15.04 2.72 -0.55 with trees Natural -7.26 56.59 -48.36 19.89 -22.04 grassland Scattered trees 0.00 0.00 -2.69 0.00 0.00

5.5 Scenario differences

I assess similarities between five future maps and initial map using Kappa statistics in Map Comparison Kit tool (H. Visser and de Nijs 2006). Kappa statistics is a measure of agreement between two maps based on similarities in the spatial allocation of categories and their quantities (Hagen 2002). Values closer to 1 indicate a high level of agreement and values closer to 0 mean that maps are different. The lowest similarities between 2050 and 2017 maps are in

“Optimistic” (Kappa=0.909) and “Pessimistic” (Kappa=0.913) scenarios, which is they describe extremes of demand predictions (Table 9). Comparing scenarios among themselves shows the biggest differences between “Pessimistic” and “Optimistic” (Kappa = 0.831) and the biggest similarities between “Intermediate” and “Province” (Kappa = 0.952).

Table 9 Per cell Kappa comparison of similarities among future maps (2050) of different scenarios and with the initial land use map (2017). Values closer to 1 mean that maps are equivalent while values closer to 0 mean that maps are different.

“Intermediate” “Pessimistic” “Optimistic” “FAO” “Province” 2017 “Intermediate” - 0.885 0.918 0.925 0.952 0.961 “Pessimistic” - 0.831 0.943 0.886 0.913 “Optimistic” - 0.873 0.940 0.909 “FAO” - 0.932 0.960 “Province” - 0.965

A general Kappa statistic doesn’t show which changes contributed the most to observed differences. Therefore, I identify the main land change processes in each scenario using contingency tables in Map Comparison Kit tool and map them into spatial outcomes (Figure 14). The main land change processes are agricultural expansion (when any land use type changes either to arable land or pasture), agricultural change (pasture changes to arable land or vice versa), and regrowth of natural vegetation (restoration of natural steppe or succession to grassland with scattered trees because of agricultural abandonment).

Agricultural expansion is the main process in “Optimistic” and “Province” scenario (Figure 14 c) & e)). In “Optimistic” scenario agricultural expansion is a response to high demands for arable land and livestock (Figure 14 c)). In this scenario, pasture extends into natural grassland and arable land extends into natural grassland, pasture and grassland with scattered trees. In the “Province” scenario since the direction of demand change is the same as in “Optimistic” the location of changes is also similar, but the extent is smaller (Figure 14 e)). In the “Intermediate” scenario the main process is agricultural change (pasture → arable land) since there is an increasing demand for grain and sunflower but decreasing demand for livestock (Figure 14 a)). In “Pessimistic” scenario, decreasing demands for grain, sunflower and livestock production lead to abandonment of agricultural areas which become areas for potential restoration of natural grassland as well as re-growth of grassland with scattered trees. 84% of natural grassland areas are abandoned arable land and 16% is abandoned pasture (Figure 14 b)). In “FAO” scenario there is a restoration of natural grassland due to abandonment of arable land and change of arable land into pasture (Figure 14 d)).

Figure 15 Land use change processes in five scenarios (a-e) and initial land use map (f). Five scenario maps show locations of agricultural expansion, agricultural change (pasture ↔ arable land), and regrowth of grassland with scattered trees and restoration of natural grassland.

5.6 Land change processes

Two most prominent land use change processes are the expansion of arable land into grassland in “Optimistic”, “Intermediate” and “Province” scenarios and restoration of grassland from abandoned arable land in two other scenarios. Figures 15 and 16 show the hotspots for these changes. Here grassland areas comprise both natural grassland and pasture.

Hotspots for arable land expansion (where changes are observed in more than 3 scenarios) are mostly located in the north and northwest in less steep areas with higher humidity and closer to roads (Figure 15). The exception is Ilekskyi district, which is the southwest. Areas marked with dark red are natural grassland that can be converted to cropland in more than 3 scenarios.

Figure 46 Hotspots for arable land expansion into grassland areas with dark red marking an especially valuable natural grassland. Hotspots for potential restoration of grassland are mostly located in the southeast and western corner of the province, in the areas characterized by less fertile soils (dark chestnut type) and more arid climate (Figure 16). However, if the area is close to the waterways it is more likely to be brought back to use as pasture. Dark red areas are places where arable land is converted into pasture in 3 or more scenarios.

Figure 17 Hotpots for potential grassland restoration. Dark red areas also show places where abandoned arable land is particularly suitable for conversion to pasture. Two of the scenarios – “Pessimistic” and “FAO” – predict abandonment of agricultural land (Figure 17). Abandoned land can either be restored to natural grassland or transition to grassland with scattered trees. Restoration of natural grassland is observed in the areas with less fertile soils, more arid climate, away from waterways and major roads. Regrowth of grassland with scattered trees is limited to areas with higher humidity (dry sub-humid climate in the northern corner) and places along the rivers and floodplains.

Figure 18 Hotspots for abandonment of agricultural land (pasture and arable land) in “Pessimistic” and “FAO” scenario. Yellow and orange show areas where abandoned land can transition to grassland with scattered trees and greens show areas for potential restoration of natural grassland.

6. DISCUSSION

Globally land systems experience increasing demand for food production, but also need to sustain the flow of ecosystem services. Scenarios help to anticipate the consequences of different socio-economic futures for nature and humans. In response to global socio-economic conditions and bounded by local natural and socio-economic conditions land systems are changed through people’s actions affecting natural processes and human societies on local and cumulatively on a global level. In this study, I modelled future land use scenarios for Orenburg province in a spatially explicit manner and identified hotspots for arable land expansion, grassland restoration and agricultural abandonment.

6.1 Local, national and global perspectives in land use futures

The five different scenarios represent a wide range of socio-economic developments with different implications for land use (Figure 15). The three visions of “trend” scenario – “FAO”, “Province” and “Intermediate” – all have moderate predictions for the future land use change but emphasize different agricultural sectors. National and local strategies have more “optimistic” outlook on future crop production resulting in expansion of arable land in more suitable northern and central parts of the province (Figure 15 a) and e)). “FAO” has a more moderate look on crop production based on estimates for the whole Europe and Central Asia region resulting in abandonment of some cropland in the south (Figure 15 d)). In January 2020 Russian government approved Doctrine for Food Security one of the provisions of which is prevention of agricultural land reduction. In this case, regional perspective overlooks the importance of governmental priorities and policies, which is emphasized in recent studies that analyse land use change models for Amazon (Dalla-Nora et al. 2014). On the other hand, local perspective has a provision on “no net increase in area under grain crops” but prioritizes sunflower production. Given that sunflower and grain crops together comprise most of all arable land implementing the ambitious plan for increased sunflower production would result either in an expansion of land or in the inability to meet the grain production goals. Therefore, a local perspective often fails to consider interactions between different demands for land services.

“Trend” scenarios also differ in terms of livestock production and consequently pasture areas. “Province” and “FAO” scenarios demonstrate a moderate increase in pastures while in “Intermediate” scenario (which in case of livestock is based on national estimates provided in 46 global shared socio-economic pathways) pasture is turned into cropland in more suitable northern areas. In this case, the national scenario is more consistent with the observed decline in livestock numbers (resulting from population decline, trade policies, meat consumption levels and GDP) and “Province” and “FAO” are more “optimistic” scenarios. Such development shows the limitations of governmental policies since even with the ban on meat import active from 2014 (OECD and Food and Agriculture Organization of the United Nations 2020) there is a limited increase in local production.

Thus, the local program reflects more “optimistic” from the productivity point of view future for agriculture and fails to consider interactions between different sectors. Regional assessments lack insight into national government priorities. Therefore, if the goal of the modelling is informative rather than exploratory, national strategies embedded in global scenarios seem to be preferable to local and regional ones.

6.2 The interplay between demand for crops, meat and preservation of grassland

The difference between abandonment, agricultural expansion and agricultural change scenarios are due to relationships between demands for crop production and livestock. As long as one demand falls and the other increases there is no massive extension or abandonment. However, if both demands increase there is a risk for converting large areas of primary or secondary natural grassland to agricultural land (“Optimistic” and, to a lesser extent, “Province” scenario). It is important to distinguish between the expansion of arable land and the expansion of pasture. Most of the pastures in the province are used extensively or close to ecological optimum (see Table 3). Therefore, expansion of pasture into natural grassland has fewer impacts for natural vegetation compared to turning the areas into cropland. This is true only if grazing is performed within ecosystem capacities and such practices as rotation (between grazing and hay cutting, between different grazing animals) are followed (Levykin et al. 2017). In such a case, expansion of pasture can be seen as an integrative practice for both preserving the natural steppe vegetation and satisfying demand for meat and milk products. It can also be a source of income in southern and south-eastern parts of the province.

Expansion of the arable land into grassland, on the other hand, has different implications. The areas more likely to be transformed to cropland are in the north-western part of the province (Figure 15), which can be explained by soil type as majority of arable land expansion is happening on more fertile lands with higher organic carbon content (“chernozems”) (Levykin 47 et al. 2018). However, this part of the province already has a large proportion of arable land (Figure 14 f)). Therefore, more agricultural expansion can increase fragmentation of natural grassland areas leading to loss of habitat for many species (Zulka et al. 2014; Wilson et al. 2016) and affecting the potential restoration of the steppe areas (Levykin et al. 2018). Natural grasslands of the province are important sources of biodiversity and other ecosystem services, such as soil fertility, regulation of hydrological cycles and habitat provision (Squires 2018). Once ploughed they require time and resources to bring them back to original condition and the original species composition which can be used for restoring steppe vegetation on other plots or serve as “pools” for automatic restoration and preservation of grasslands is also lost (A. A. Chibilev 2018).

The expansion of arable land in southern areas (such as Ilekskyi district) (Figure 15), considering potential impacts of climate change and declining yields (see Figure A5), either would require resources for additional irrigation or can potentially lead to quick abandonment of ploughed land (Levykin et al. 2018).

If both demands for crops and livestock fall, large areas of agricultural land in southern and south-eastern districts of the province can be abandoned (including extensively used pasture). (Figure 17). Abandonment of arable land can be an opportunity to restore natural vegetation (Queiroz et al. 2014), but, in contrast to agricultural abandonment in forest biomes, widespread abandonment of pasture agriculture in grassland ecosystems can lead to their degradation due to reduced diversity in plant communities (Brinkert et al. 2016), bird populations (Kamp et al. 2011), spreading of weeds and alien species (Sukhorukov Suchorukow 2011). Also, since beef cattle, sheep, goats and horses fill the ecological niche of extinct native species (such as wild horses) and contribute to the preservation of steppe vegetation, pasture abandonment can affect their structure (Smelansky and Tishkov 2012). Agricultural change of secondary grassland to arable land affects carbon sequestration (Schierhorn et al. 2013). Besides, agricultural abandonment accelerates the processes of outmigration from southern and south-eastern districts (Chibilyov et al. 2018) reinforcing the abandonment of rural areas which can have negative socio-economic consequences for the whole region and for people (e.g. urban poverty as a result of migration), as well as lead to loss of local cultural and ecological knowledge which plays an important role in land use planning decisions (DeFries, Foley, and Asner 2004) and reinforcement of so-called “state urban bias” (Wegren 2016).

If following the “Intermediate” scenario, there is a growing demand for grain and sunflower, but livestock numbers are decreasing, then agricultural producers in the northern part of the province may switch from livestock farming to growing grains and sunflower. Some agricultural producers from the south may choose to move north because of higher yields there (see Figure A5). This may lead to the imbalance of economic and social development in the province followed by migration possibly creating so-called “black holes” – districts with declining yields and less demographically active population (Meyfroidt et al. 2016). Thus, although the “Intermediate” scenario is considered “positive” from the agricultural production perspective its implementation may lead to inequalities.

Finally, following the “FAO” scenario, if the demand for cropland is decreasing, but livestock numbers grow moderately, then some agricultural areas in southern and south-eastern parts can be used as pasture while less suitable arable land can be restored to natural grassland (Figure 16). These areas are mostly located on less fertile land in districts with declining yields (Figure A5). Since such lands are surrounded by grassland areas the process of restoration would require less effort and can be automatic. Switching from crop production to livestock would also ensure incomes for farmers in the south given the potential increased aridity in southern districts. Also, livestock farming is less risky for agricultural producers and would not require additional irrigation (Levykin et al. 2018).

Thus, from the resilience perspective, the optimal scenario is stable or decreased cropland area (which can be achieved through sustainable intensification in more fertile northern parts of the province) with pasture with sustainable grazing practices in south and south-east. Restored parts of natural grassland could also ensure stability for grazing and support the flow of ecosystem services from which farmers benefited for a long time, such as water cycle regulation, soil fertility, biodiversity, carbon sequestration, erosion prevention, pollination, water purification, as well as intrinsic services, such as preservation of steppe ecosystem for future generations. Implementation of this vision, however, would depend on many factors including governmental policies supporting ecologically sustainable livestock farming and mechanisms to ensure their implementation (e.g. especially among subsistence farmers who have higher grazing pressure compared to farmers and agricultural organizations), internal and external demand for such production, and recognition of natural grassland as a land category (A. A. Chibilev, Sokolov, and Rudneva 2019).

Currently, the agroecological perspective is largely absent from the main discourse both on the governmental level and among producers themselves, however, this does not mean that similar practices do not exist. Smallholder farmers engaged in traditional horse breeding and beef cattle farming often implement sustainable agricultural practices, although the producers themselves may not recognize them as such (O. Visser et al. 2015). Understanding potential land use changes in different socio-economic scenarios and their locations can enable policymakers to consider socio-ecological interactions when making land-use planning decisions.

In many places in the Eurasian grassland biome grasslands have been degraded or turned into cropland with intensive industrialized farming (Török et al. 2020). In grasslands of Eastern Europe and other parts of Russia, conversion of less suitable arable land to pasture could ensure the flow of supporting and regulating ecosystem services, restore biodiversity and provide a source of income for the local population.

6.3 Limitations of the approach adopted and future perspectives

The decision to use a combination of different sources and methods for demand calculations may have influenced the final cropland and pasture areas in some scenarios. For example, one could expect a much bigger expansion of agricultural areas in “Intermediate” and “Optimistic” scenarios if I used bioclimatic potential instead of maximum attainable yields as a limit since bioclimatic potential yields are on average lower (see Table A5). On one hand, bioclimatic potentials were assessed for 1997-2000 and one can assume that due to technological development they can be higher now (in some highly productive districts bioclimatic potential yields have been reached during the last 5 years). On the other hand, it is possible to suggest that maximum attainable yields estimates overestimate potentials in the southern parts of the province affected by agricultural practices in the past and showing declining trends (some of the southern regions have very low yields and showing a decline (see Figure A5 and Table A5)). More studies are necessary for correct estimation of yields in southern and south-eastern districts considering they are more exposed to a decrease in precipitation and droughts caused by climate change (Neverov 2015).

The decision to use a top-down approach for scenario development has limitations related to the credibility and the usefulness of scenarios on the local level (due to limits of literature search and researcher’s choice of scenarios) (Alcamo 2008). It was partially addressed by using a “local” scenario based on the province’s program for agricultural development. Stakeholder 50 involvement was planned in the initial research proposal, however, due to legislation challenges conducting interviews was not possible. Produced scenarios, however, can now be used as a discussion tool in land-use planning (Nilsson et al. 2017; Voinov and Bousquet 2010).

6.4 Discussion of regression analysis

In the map of the “Pessimistic” scenario, one can observe abandonment of agricultural areas located only in Orenburg municipal district and Orenburg city area (Figure 17). This finding is potentially misleading. Majority of the areas there are abandoned pasture. Based on the regression model pasture has a negative correlation with population density (see Table 6). Areas around the province’s main city have one of the highest population densities. To understand whether the effect of the variable is legitimate it is necessary to run the simulation without the population density factor for pasture and consult local experts.

6.5 Regime shifts in land systems

Predicting land use change is often difficult because land systems can demonstrate non-linear behaviour caused by socio-economic transformations or change in underlying environmental drivers (Müller et al. 2014; Hostert et al. 2011). However, scenarios considered here are not meant to provide the most likely vision of the future. Identifying hotspots for land use change in different socio-economic scenarios can help to monitor them and make informed decisions regarding land-use planning, rural development and agricultural support programs for the province.

6.6 Positionality

The recent discussion raises the issue of engagement of land system scientists with normative issues considering the necessity of transformation to sustainability in land systems (Nielsen et al. 2019). Besides, top-down scenario building requires awareness of researcher’s values, perspectives and assumptions. My choice to focus on trade-offs between arable land and natural grassland is grounded in my belief in the importance of finding a balance between economic and social development and preservation of natural steppe ecosystems for the contributions they make to people living now and in the future and for their intrinsic value.

7. CONCLUSION

In three scenarios with high food production ambitions expansion of agricultural land could lead to the conversion of pasture and natural grassland to cropland in northern and north-central parts of the province. While such scenarios could be seen as positive from the local and national governmental view, the disappearance of grassland ecosystems in the already transformed anthropogenic landscape could affect important functions, such as carbon sequestration, erosion control, water regulation and biodiversity, from which people living in the “black earth belt” benefited for centuries. In a close to “no change” scenario with a small increase in crop production and some increase in meat production, less productive cropland in southern and south-eastern parts of the province can be used as pasture. With appropriate policies aimed at supporting sustainable grazing practices, such a scenario could provide livelihoods for people and support crucial grassland ecosystems’ functions by restoring natural grassland areas on less productive arable lands. Such development could also help to mitigate negative socio- economic consequences of climate change especially in the southern parts that are on the border with the desert-steppe natural zone. In a scenario with decreasing food production, large areas of agricultural land could face the risk of abandonment which could create an opportunity for restoration, but also have negative socio-economic consequences for people. Besides, abandonment of extensively used pasture could also disturb the grassland ecosystems (which co-evolved with grazing animals) and lead to a change in biodiversity of grassland communities. Land use scenarios are not exact predictions of how the land system of Orenburg province will look like, but identified “hotspots” of arable land expansion into grassland, grassland abandonment or restoration can be monitored and considered in decision-making and evaluating synergies and trade-offs of different land use planning decisions, agricultural and conservation policies.

The very different outcomes of three trend scenarios (“Intermediate”, “FAO” and “Province”) indicate that land changes depend highly on the how these projections imagine the development of crop and livestock demands which, through interaction with the local environment and socio-economic factors, result in spatial differences. While regional scenario does not capture the role of national government priorities, local and national perspectives do not consider complex interactions between different land demands. A better integration of crop and livestock farming supporting programs, considering the differences of northern and southern parts of the province and integration of environmental protection policies could help to develop better land use policies that would ensure harmonic socio-economic development within 52 environmental boundaries. Future scenarios of land use change can help local policymakers in developing land use, agricultural and conservation policies and be used for assessing consequences of land use change for people and nature.

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9. APPENDIX

Appendix 1. Land use in Orenburg province in 2017

Figure A1 Initial land use map of Orenburg province with nine land use types (developed by Robert Pazur). Table A1 Land use types in the initial land use map of Orenburg province, their descriptions, areas and percentages of occupied area compared to the total land area of the province.

Land use type Description Area, km2 % Settlement All build-up areas including industrial and commercial 1387 1.1 areas, farms Arable land Area covered by vegetation and bare ground, at the 38386 31.3 beginning and end of the growing season Pastures and meadows Intensively used pastures and meadows 53458 43.6

Grassland with Extensively used grasslands, includes grasslands with 5004 4.1 scattered trees and scattered trees and shrubs (30-70% of surface) shrubs Scattered trees and Scattered trees and shrubs (>70% of surface) 5237 4.3 shrubs Forest Includes closed forest and scattered trees and shrubs (>70% 4741 3.9 of surface) Water Minimal surface area of 5 ha 845 0.7 Rocks Stones 2090 1.7 Natural grassland Unploughed (primary) or restored (secondary) areas with 11409 9.3 typical native forb-bunchgrass vegetation Total 122557 100 67

Appendix 2. Short descriptions of the five main scenarios’ narratives

Box 1. SSP2 or ‘Middle-of-the road’ scenario and National grain strategy “basic” scenario

SSP2 or ‘Middle-of-the road’ scenario: The world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Land use change is incompletely regulated, i.e. tropical deforestation continues, although at slowly declining rates over time. Rates of crop yield increase decline slowly over time, but low-income regions catch up to a certain extent. Caloric consumption and animal calorie shares converge slowly towards high levels. International trade remains to large extent regionalized. In SSP2, international cooperation for climate change mitigation is delayed due to a transition phase to a uniform carbon price until 2040. In this transition phase, emissions from agricultural production are priced at the level of energy sector emissions, while avoided deforestation and afforestation are not incentivized before 2030 (description is taken from O’Neil et al. 2017).

National grain strategy “basic” scenario: Continuation of the current trend in developing countries when growth of consumption outpaces growth of production. Governmental support for consumption of agricultural commodities. Trade conditions will not deteriorate. Gradual decrease of the influence of political risks. Moderate consumption of flour and cereals and increased production of feed for animals and deep-processed grain products. Grain harvest will be 3.14 tonnes/ha. The export of grain will increase by 79%.

Box 2. SSP3 or ‘Regional Rivalry’ and National grain strategy “pessimistic” scenario

SSP3 or ‘Regional Rivalry’ scenario: A resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues, including food and energy security. Land use change is hardly regulated. Rates of crop yield increase decline strongly over time, especially due to very limited transfer of new agricultural technologies to developing countries. Unhealthy diets with high animal shares and high food waste prevail. A regionalized world leads to reduced trade flows for agricultural goods. In SSP3, forest mitigation activities and abatement of agricultural GHG emissions are limited due to major implementation barriers such as low institutional capacities in developing countries. In addition, they are delayed as a consequence of low international cooperation. In 2020, high income countries start the

68 transition to a uniform carbon price until 2040, whereas low income countries start in 2030 and converge until 2050 (description is taken from O’Neil et al. 2017).

National grain strategy “pessimistic” scenario until 2035: Decline in economic activity will lead to reduction of people’s incomes and change in their consumption patterns: increased consumption of grains as food. There will be a slight increase in industrial processing of grain and grain production for feed. Grain export will remain the same. Grain harvest will be 2.72 tonnes/ha.

Box 3. SSP5 or ‘Fossil-fuelled Development” and National grain strategy “optimistic” scenario

SSP5 or ‘Fossil-fuelled Development” scenario: Driven by the economic success of industrialized and emerging economies, this world places increasing faith in competitive markets, innovation and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Land use change is incompletely regulated, i.e. tropical deforestation continues, although at slowly declining rates over time. Crop yields are rapidly increasing. Unhealthy diets with high animal shares and high waste prevail. Barriers to international trade are strongly reduced, and strong globalization leads to high levels of international trade. In SSP5, all land use emissions are priced at the level of carbon prices in the energy sector. But in contrast to SSP1, international cooperation for climate change mitigation is delayed due to a transition phase to a uniform carbon price until 2040 (description is taken from O’Neil et al. 2017).

National grain strategy “optimistic” scenario until 2035: Higher internal grain consumption is driven by higher consumption of deep-processed grain products (such as alcohol, beer, syrup) and by production of bioethanol. Improved yields due to positive effects of climate change (3.53 tonnes/ha). Grain export will increase by 88%.

Box 4. State agricultural program (“Province” scenario) and the European and Central Asian agriculture towards 2030 and 2050 (“FAO” scenario)

State program for agricultural development of Orenburg province (“Province” scenario): Measures taken to improve stability of agricultural sectors will have a positive influence, however, complicated macroeconomic conditions will probably increase risks for the development of agricultural sector. In crop farming an increased use of modern agricultural

69 technologies, fertilisers, pest and disease protection measures, as well as increase in irrigated agricultural area is envisioned. Increased production of meat due to improved breeds. Grain and sunflower production will increase by 13.2% and 17.1%, livestock production will grow by 3.1%.

European and Central Asian Agriculture towards 2030 and 2050 (“FAO” scenario): The region’s agriculture will be facing demand constraints as its productive potential exceeds effective market. While per caput (direct) consumption of cereals and sugar would remain constant or decline somewhat, consumption of vegetable oils, meats, milk and dairy products could continue to increase, in particular in Eastern Europe and Russia. Exports of cereals from regions such as Russia and Eastern Europe would increase.

Appendix 3. Comparison of global Shared Socio-Economic Pathways and National grain strategy scenarios

Table A3.1 Comparison of the main scenario elements in global SSPs and the National grain strategy.

SSPs: elements relevant to agriculture National grain strategy: factors important and land use for grain markets Population Population Urbanization Urbanization GDP - International trade and globalization International trade Consumption and diet Consumption and diet Environmental and land use policies Agricultural land use limitations, water resources, land use planning Development and transfer of technologies Development of agricultural technologies, including more productive crop varieties Policy orientation & Institutions Political risks, internal tariff policies, infrastructure support policies Fossil fuel constraints Cost of fuel and fertilizer

Table A3.2 Comparison of the assumptions in three scenarios from the National grain strategy (‘basic’, ‘optimistic’ and ‘pessimistic’) and three global SSPs (SSP2 or ‘middle-of-the-road’, SSP5 or ‘fossil-fuelled development’ and SSP3 or ‘regional rivalry’) (adapted from Kok et at. 2019).

Scenario Consumption Governmental Trade Political risks Technologies Match policies ‘basic’ scenario Growth of food Developing No deterioration Gradual decrease of Highly productive Very similar consumption countries support in current trade influence of political crop varieties, globally, increased (increase) policies regarding risks resource-efficient share of animal consumption of tariff and non- technologies, products agricultural products tariff limitations increased fertilizer and plant protection SSP2 or Growing Moderate awareness Global access to Tensions within and Moderate ‘middle-of-the- consumption of of environmental markets is between countries technology road’ animal products issues established; periodically threaten transfer rates slowly reduced to boil over, but do entry barriers to so only rarely, and a/c markets never catastrophically ‘optimistic’ Growing internal Better governmental Higher export of Low political risks Faster adoption of Similar, scenario consumption (due policies supporting grain technologies except for to industrial consumption (higher yields) alternative processing and energy production of development bioethanol). (biofuels) Internal use of grain for food and feed is similar to basic scenario.

SSP5 or ‘fossil- Robust growth in Concern for local Low restrictions Countries cooperate Fast transfer of fuelled demand for environmental issues for international on economic level, technologies development’ services and goods with technological trade regional conflicts especially in cities, solutions decline meat-reach diets, adoption of resource and energy intensive lifestyles around the world ‘pessimistic’ Slightly increased Worse governmental Deterioration in High political risks Slower adoption Similar, but scenario internal policies supporting trade policies of technologies lack of consumption of consumption (export share information grains for food due does not grow) to decline in incomes SSP3 or Material-intensive Low awareness of International Focus on national Slow transfer of ‘regional consumption environmental issues trade is restricted security and a/c technologies rivalry’ competition leads to direct and proxy conflicts

Appendix 4. Future population and GRP values for Orenburg province

Table A4 Population and GRP in Orenburg in 2050 based on national growth rates in the SSP database hosted by the IIASA Energy Program at https://tntcat.iiasa.ac.at/SspDb (population is based on IIASA-WiC model and GDP is based on IIASA GDP model).

Scenario 2017 2050 Change Population (people) SSP2 1,989,589 1,905,688 -4.40% SSP3 1,989,589 1,872,240 -6.27% SSP5 1,989,589 1,924,966 -3.36% GRP per capita, PPP (2017 international $) SSP2 16,426 25,798 +36% SSP3 16,426 24,022 +32% SSP5 16,426 38,312 +57%

Appendix 5. Trends in grain yields for municipal districts until 2050

Figure A5 Grain yields’ trends in municipal districts based on extrapolation of historical data (1990-2013): green colour marks districts where yields grow fast and reach their maximum potentials before 2050; yellow shows districts where yields increase slowly and do not reach their potentials before 2050; pink – districts where yields decline.

Table A5 Results of grain yields’ extrapolations based on non-linear model fitting for each municipal district (2014 - base year and 2050 - prediction) compared to maximum attainable yields and bioclimatic potentials. – yields show a declining trend, – yields increase, but don’t reach the maximum attainable values. Units: tonnes/ha.

Municipal district Grain Grain Attainable Bioclimatic Year in which yield in yield in yield potential yields reach their 2014 2050 maximum attainable values Abdulinsky 10.2 75.6 25.1 19.6 2027

Adamovsky 7.8 -35.6 17.7 17.4 Akbulaksky 5.3 34.2 28.3 15.3 2046 Aleksandrovsky 12 75.2 21.6 18.4 2025 Asekeevsky 18.4 117.3 25.6 19.6 2020

Beljaevsky 4.7 16.2 26.7 15.2 Buguruslansky 16.4 100.3 27.2 19.6 2023 Buzuluksky 15.9 80.5 31.8 18.4 2028 Gaisky 7.5 30.2 19.1 17.4 2035 75

Grachevsky 18.9 98.4 24.9 18.4 2021

Domarovsky 3.8 10.7 27.9 14.5 Ileksky 8 83 27.7 15.2 2029

Kvarkensky 6.8 15 18.8 17.4 Krasnogvardeysky 14.8 90.3 22.7 18.5 2023

Kuvandyksky 6 3.9 22.4 17.4 Kurmanaevsky 10 70.5 23 15.3 2026 Matveevsky 12.6 68.4 24.4 19.6 2027 Novoorsky 5 30.9 18.5 17.4 2037 Novosergievsky 10.7 77.2 20.6 18.5 2023 Oktjabrsky 12.6 60.7 19.2 18.4 2024 Orenburgsky 8.2 45.3 26.2 15.2 2037 Pervomaysky 9.4 51.4 20.6 15.2 2028 Perevolotsky 12.5 85 22.1 18.4 2022 Ponomarevsky 12.3 89.8 24.2 18.4 2025 Sakmarsky 17.5 90.8 21.1 18.4 2020 Sarakyashsky 8.3 34.7 25.1 18.4 2039

Svetlinsky 3.6 -4.2 26 14.4 Severny 9.5 78.5 26.2 19.7 2026 SolIletsky 5.9 66.4 34 15.2 2036 Sorochinsky 17.1 105 19.9 15.3 2019 Tashlinsky 10.6 66.2 22.9 15.2 2025 Totsky 11 82.8 21.1 15.3 2023

Tulgansky 5.9 16.7 20.7 19.6 Sharlyksky 12.7 84.7 23.1 18.4 2023

Jasnensky 4.2 -1.1 20.5 14.4

Appendix 6. Ethical Review – final review

The overall research approach did not deviate largely from the one proposed in the Ethics Review in a way that it would require new ethical considerations. However, I made an adjustment regarding expert interviews. Originally, I planned to conduct semi-structured remote interviews with representatives of the Steppe institute and other local experts from governmental and scientific institutions. However, due to legislative issues, this part of the research was not possible to continue with. The complications related to the recent government legislation regarding collaboration between Russian scientific institutions and foreign organizations. From the conversation with project partners, I also understood that general political tensions between the country of research and country of my origin also led to difficulties in obtaining permission for conducting research. I conducted one remote interview with a representative of the Steppe Institute. I used verbal consent for recording the interview. I also contacted the interviewee at the later stage of research to ask for permission to refer to their statements in my thesis.

The second issue was related to collaboration between different institutions involved in the project. Initially, I signed the collaboration agreement only with one partner organization in the research project. However, along the way issues about which data I can use came up with other project partners. This had to be addressed by signing the agreement including all parties. In later work I also made sure to communicate with all partners about which data products I can use and how can I cite them.

Looking back at the research process from the ethical perspective I think it is important to dedicate time and communicate with all partners if entering an ongoing project since previous relationships and dynamics already exists between parties. Also, it is important to consider the wider political context and gain knowledge about other researcher’s experience in order to understand the practical implications it can have for the research.