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Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030 A study commissioned by the CGIAR Systemwide Livestock Programme

ILRI PROJECT REPORT

ISBN: 92–9146–285–3

The International Livestock Research Institute (ILRI) works to enhance the roles livestock play in pathways out of poverty in developing countries. ILRI is a member of the CGIAR Consortium. ILRI has two main campuses in East Africa and other hubs in East, West and southern Africa and South, Southeast and East Asia. ilri.org

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Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030 A study commissioned by the CGIAR Systemwide Livestock Programme

Mario Herrero,1 Philip K. Thornton,1 An Notenbaert,1 Siwa Msangi,2 Stanley Wood,2 Russ Kruska,1 J. Dixon,3 D. Bossio,4 J. van de Steeg,1 H. Ade Freeman,1 X. Li,3 and P. ParthasarathyRao5

1 2 3 4 5

International Livestock Research Institute, Nairobi, Kenya International Food Policy Research Institute, Washington, DC, USA Centro Internacional de Mejoramiento de Maiz y Trigo, Mexico DF, Mexico International Water Management Institute, Colombo, Sri Lanka International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India

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ISBN 92–9146–285–3

Citation: Herrero, M., Thornton, P.K., Notenbaert, A., Msangi, S., Wood, S., Kruska, R., Dixon, J., Bossio, D., van de Steeg, J., Freeman, H.A., Li, X. and Rao, P.P. 2012. Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030: A study commissioned by the CGIAR Systemwide Livestock Programme. Nairobi, Kenya: ILRI.

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Contents Tables

Figures

Executive summary

viii

1 Introduction

2 Framework for studying the dynamics and impacts of change in crop–livestock systems

2.1 Key drivers of change in crop–livestock systems

3 Global trends in agriculture, agro-ecosystems services and human wellbeing

3.1 Trends in human demography, livelihoods and economic parameters

3.2 Trends in agriculture

3.3 Environmental trends and crop–livestock systems

4 Methods and scenarios for evaluating changes in mixed crop–livestock systems and human wellbeing

4.1 Methods

4.2 Brief IMPACT model description

4.3 Descriptions of IMPACT scenarios used for drivers study

4.4 Allocation of the FPU-level impact outputs to regions and systems

5 Results

5.1 Farming systems and the distribution of human population

5.2 World food prices

5.3 Livestock numbers and their production under alternative scenarios 2000–2030

5.4 Crop production

5.5 Impacts on human wellbeing

6 Conclusions

References

Appendix A: Definition of the regions

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Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Tables Table 1. Key drivers of change in crop–livestock systems

Table 2. Population size and life expectancy between 1950 and 2000 for different world regions

Table 3. Population numbers in different farming systems in developing countries

Table 4. Changes in food consumption in developing countries

Table 5. Area, people, poverty and livestock within agricultural production systems

Table 6. Share of milk and meat outputs by production systems in selected regions

Table 7. Livestock population and production in different production systems in developing countries

Table 8. Livestock population and production in different agro-ecological zones

Table 9. Global trends and projections in the use of cereal as feed

Table 10. Key productivity parameters for pigs and poultry in different world regions

Table 11. Regional climate change projections from the IPCC’s Fourth Assessment

Table 12. Global projections of energy demand in 2015 and 2030

Table 13. Global biofuel production and crops

Table 14. Human, livestock and total energy consumption in selected farming systems

Table 15. Average nutrient balances of some SSA countries

Table 16. Estimated relative contribution of pig waste, domestic wastewater and non-phosphorus emissions in water systems

Table 17. Assumptions for reference case and the scenario variant with high agricultural investment combined with other AKST-related factors (used as IRRIGATION EXPANSION scenario)

Table 18. Changes to average income demand elasticities for meat and vegetarian foods by IAASTD region under low growth in meat demand

Table 19. Indices used

Table 20. Farming systems: area and human population for different regions of the world under alternative scenarios to 2030

Table 21. World food prices by scenario

Table 22. Bovine numbers by farming system under different scenarios 2000–2030

Table 23. Milk production by farming system under different scenarios 2000–2030

Table 24. Meat production by farming system under different scenarios 2000–2030

Table 25. Livestock production, farming systems vs. lamb production

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 26. Livestock production, farming systems vs. small ruminants

Table 27. Chicken numbers by farming system under alternative development scenarios

Table 28. Egg production by farming system under alternative development scenarios

Table 29. Poultry production under alternative development scenarios

Table 30. Numbers of pigs by farming system under alternative development scenarios

Table 31. Livestock production, farming systems vs. pork production

Table 32. Global area of maize by system by region by scenario 2000–2030

Table 33. Global maize production by system by region by scenario 2000–2030

Table 34. Global area of wheat by system by region by scenario 2000–2030

Table 35. Global production of wheat by system by region by scenario 2000–2030

Table 36. Global area of rice by system by region by scenario 2000–2030

Table 37. Global rice production by system by region by scenario 2000–2030

Table 38. Global area of sorghum by system by region by scenario 2000–2030

Table 39. Global sorghum production by system by region by scenario 2000–2030

Table 40. Global area of millet by system by region by scenario 2000–2030

Table 41. Global millet production by system by region by scenario 2000–2030

Table 42. Global area of barley production under different scenarios

Table 43. Global production of barley by system by region by scenario

Table 44. Global area of cassava by system by region by scenario 2000–2030

Table 45. Global cassava production by system by region by scenario 2000–2030

Table 46. Global area of sweetpotato by system by region by scenario 2000–2030

Table 47. Global sweetpotato production by system by region by scenario 2000–2030

Table 48. Global area of potatoes by system by region by scenario 2000–2030

Table 49. Global production of potatoes by system by region by scenario 2000–2030

Table 50. Stover production in the developing world 2000–2030 under alternative development scenarios

Table 51. Metabolizable energy from stover by system by region by scenario to 2030

Table 52. Predicted number of malnourished children under 5 by system, region and scenario 2000–2030

Table 53. Percentage of malnourished children under 5 relative to human population numbers by system by region by scenario 2000–2030

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figures Figure 1. Conceptual framework for studying the impacts of drivers of change in crop–livestock systems

Figure 2. Overview of a framework and how it relates to a specific crop–livestock system

Figure 3. Overview of a framework and how it relates to a specific crop–livestock system under a technology intervention

Figure 4. Expenditure gains in 42 developing countries for a one per cent increase in GDP growth

Figure 5. Growth in agricultural GDP in developing countries

Figure 6. Proportion of total population in developing countries that is rural

Figure 7. Rural poverty rates from 1993 to 2002

Figure 8. Association between National Average Dietary Energy Supply and GDP, per capita

Figure 9. Domestic consumption of meat and cereals in developing countries between 1980 and 2005

Figure 10. Per capita food consumption in developing countries between 1961 and 2003

Figure 11. Changes in the value of exports of crops in developing countries between 1960 and 2004

Figure 12. Trends in selected drivers of food provision worldwide, 1961–2001

Figure 13. Regional cereal yields between 1960 and 2005

Figure 14. Arable and permanent cropland per capita of the agricultural population

Figure 15. Growth rates of yields for major cereals in developing countries

Figure 16. Global trends in food production and price in relation to undernourishment

Figure 17. Disaster losses, total and as a share of GDP between 1985 and 1999 in the world’s ten richest and poorest nations

Figure 18. Length of growing period (days per year) for 2000

Figure 19. Areas within the LGA (in yellow) and MRA (in green) systems projected to undergo more than 20% reduction in the length of growing period to 2050

Figure 20. Global changes in food consumption from 1961 to 2003

Figure 21. Investing in irrigation based on FAO and World Bank data

Figure 22. Some examples of scenario options

Figure 23. Trends in yield and nutrient stocks for two soil types

Figure 24. Modern inputs have expanded rapidly but have lagged in SSA

Figure 25. Changes in fallow land to 2030

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Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 26. Overview of the ‘food’ side of the IMPACT model

Figure 27. Schematic representation of the linkage of the food and water modules in the augmented IMPACT model (IMPACT-Water)

Figure 28. Flow chart of the process used in establishment of the production systems

Figure 29. The distribution of farming systems, as classified for this study, for 2000 and 2030

Figure 31. Rates of growth in meat and milk production under the references scenario 2000–2030

Figure 32. Density of poultry 2000–2030 for the baseline scenario

Figure 33. Density of pigs 2000–2030 for the baseline scenario

Figure 34. Global cereal production–2000

Figure 35. Mixed systems in the developing world produce the food of the poor

Figure 36. Rates of cereal production to 2030 by farming system under the reference scenario

Figure 37. Composition of cereal stover availability by system and region 2000

Figure 39. Per capita kilocalorie consumption by scenario

Figure 40. Density of malnourished children under five, 2000–2030

Figure 30. Density of ruminants 2000–2030 for the baseline scenario

Figure 38. Global availability of metabolizable energy from stover for ruminants and its change to 2030

viii

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Executive summary Introduction The CGIAR Systemwide Livestock Programme (SLP) commissioned a desk study titled ‘Drivers of change in mixed crop–livestock systems’. The study was to be developed by a multi-disciplinary task force from across the CGIAR centres. The objective of the study was to identify places and production systems in the developing world which, due to global changes, may not be able to supply food for the growing population or, in doing so, the sustainability and maintenance of key ecosystem functions would be compromised. The project works are the cross roads of agriculture and livestock, poverty and the environment. This report 1) develops a conceptual framework for studying the effects of drivers of change in mixed crop–livestock systems; 2) analyses the past trends of key indicators of change in mixed crop–livestock systems; and 3) uses these trends, along with modelling approaches and other tools, to develop a series of scenarios of how mixed systems in different regions might evolve, and what their constraints and opportunities could be. This information can be used to guide some basic priority setting for the SLP and for the CGIAR in more general terms.

What is the problem? The world’s population is predicted to increase by 50% over the next quarter of a century to reach 9 billion by 2030. During this period, and if the livestock revolution fully materializes (Delgado et al. 1999), in developing countries there is likely to be a rapid increase in demand for livestock products driven by increasing urbanization and rising incomes. On top of this, the impacts of a range of driving forces, such as water availability, climate change and technological innovations, on smallholder crop–livestock production may be substantial. Variations in these drivers will inevitably affect smallholder farms. The challenge is to ensure that the resource-poor, i.e. the mixed crop–livestock smallholder sector, which currently provides the majority of milk and meat in the tropics, is able to meet the increased demand for these products. To do so the sector will need to intensify but at the same time ensure that household food security, sustainable natural resource management and rural livelihoods are not compromised. The framework for the study was based on that of the Millennium Ecosystem Assessment, which was subsequently used for other major assessments such as the Global Environment Outlook 4 (UNEP 2007) and the International Assessment of Agricultural Knowledge, Science and Technology for Development (2008). It shares common features with the frameworks of the Intergovernmental Panel on Climate Change (2007) and the Comprehensive Assessment of Water Management in Agriculture (2007). It is based on the notion that a set of drivers, both direct and indirect, can make systems change over time. The local development context determines how, where and which drivers play the most important role in which system. Different drivers exert different kinds of ‘pressures’ on key aspects of agroecosystems. These pressures include changes in land use, changes in resource and input use, and increased competition for biomass (food, feed and energy). In turn, these pressures have impacts on different agro-ecosystem services, such as climate regulation, watershed protection, and crop pollination. Depending on the magnitude of the pressures and the impacts on agro-ecosystems services, human wellbeing (measured, for example, by income, health, food security,

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

and vulnerability) can be affected in different ways. Positive interventions can be made either by trying to regulate the effects of the drivers and pressures or by generating options for adapting the agro-ecosystems services to the impacts of the changes. We used the IMPACT-Water model coupled with a farming systems classification and a range of spatial disaggregation methods for looking at alternative scenarios of change in mixed crop–livestock systems to 2030. We built upon the results of the IAASTD. The scenarios we used were 1) the reference scenario, which tries to mimic business-as-usual conditions of growth in agriculture, incomes and population. Additionally we investigated the consequences of an increased demand for biofuels, an increased expansion of irrigation to produce more food and feed, and a decreased demand for livestock products. The following are the main messages from the study. Mixed crop–livestock systems are and will continue to be the backbone of sustainable pro-poor agricultural growth in the developing world to 2030. Two-thirds of the global population live in these systems. They not only produce most of the milk and meat globally but also produce a significant proportion of the key staples of the world. Rates of growth in demand, production and consumption of agricultural products are significantly higher in these systems than in others. These systems will surpass the developed world in the production of cereals and some livestock products by 2030. 1. Mixed intensive systems in the developing world face significant pressures. These pressures are larger in some systems than in others but are all caused by the rising demands of the human population: its income shifts and rates of urbanization. For example, mixed intensive systems in South Asia are reaching a point where production factors are seriously limiting production as land per capita decreases. Significant trade-offs in the use of resources (land, water, nutrients) exist in mixed crop–livestock systems, especially as the demands for biomass for food, feed and energy increase. 2. Prices of food–feed crops are likely to increase at faster rates than the prices of livestock products. Due to the multiple competing demands for food, feed and energy, increases in the prices of commodities will be more marked for food–feed crops than for any other products, including livestock. 3. Rates of change in crop, and therefore stover, production are likely to vary widely from region to region to 2030. Large increases in stover production are likely to occur in Africa as a result of area and productivity increases mainly in maize, sorghum and millet. Other large increases will occur across systems in Central and South America but less so in the mixed extensive systems of East Asia. Stover production will stagnate in some areas, notably in the mixed extensive and intensive systems of South Asia, which together have the largest numbers of ruminants in any system in the world. 4. Increase in ruminant numbers has outpaced the rate of growth of availability of stover per animal in many places. This means that either stover will become less important as a feed in these systems or it will be substituted by other feeds in the diet, or that there will be significant feed deficits in some places. 5. Land availability and water will be key constraints to the production of alternative feeds for ruminants in the most intensive systems. Mixed intensive systems in South Asia, which depend on irrigation to a great extent, and which are supposed to produce 113 million tonnes of milk and 4.5 million tonnes of beef to feed increasing human populations, will have to support all their production from feed sources other than stover, as stover production only meets the maintenance requirements of the animals. If this production levels were to materialize, water demands from livestock would rise several fold (billions of litres) to produce fodders for animals and would compete directly with irrigation for the production of crops for multiple uses.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

6. Fodder markets are likely to expand in areas of feed deficits as demand for animal products increases. Substantial local heterogeneity exists in supply and demand of feeds for ruminants. Areas of surplus are likely to trade with areas of feed deficits as prices of stovers and green feeds increase. Public investments will be required to create incentives and reduce transaction costs of moving feeds over long distances. 7. The livestock revolution—at least for ruminants—could potentially exclude the poor in terms of the benefits of consumption of meat. If green fodders became scarce due to land and water shortages and more grains are fed to ruminants to match production prices of animal products may further increase, bypassing the abilities of the poor to consume more milk and meat. This would present significant challenges in mixed systems, particularly in Asia. 8. Rates of malnutrition relative to population increases are highest in agropastoral systems followed by the mixed intensive systems. In agropastoral systems, malnutrition may be caused by increased vulnerability, lack of primary productivity, poor market access and lack of economic growth but with large land holdings (Thornton et al. 2006). In mixed intensive systems, too many people, especially poor, relative to the resources available may be the principle cause of malnutrition. South Asia and sub-Saharan Africa (SSA) exhibit particularly large rates of malnutrition across these systems. 9. Expansion of biofuels is likely to reduce household food consumption in most systems. Increased production of biofuels may raise the price of staple commodities, which will particularly affect the poor due to their low purchasing power. This effect may be stronger in rural and poor urban households that are net buyers of food. 10. Highly intensive systems will require solutions that give high efficiency gains without using any more land and water. More intensive crop management practices, such as efficiency gains in pig and poultry production may reduce pressure on land resources. 11. Some systems may need to de-intensify or stop growing to ensure the sustainability of agroecosystems. Developing sound, simple and equitable schemes for payments for ecosystems services could be part of the solution. Understanding the limits of land intensification is necessary along with developing a set of intensification thresholds to prevent irreparable environmental damage. 12. Important productivity gains could be made in the more extensive mixed rainfed systems. Resource constraints in some mixed intensive systems are reaching a point where livestock production could decrease and where environmental degradation may have deleterious impacts on humans. In more extensive systems, with less pressure on the land, yield gaps of crops and livestock in different regions are still large. Pro-poor policies and public investments in infrastructure will be essential to create systems of incentives, reduce transaction costs and improve risk management in these systems. Integration of production in these systems to supply agro-ecosystems services such as feeds and food to the more intensive systems should be promoted. 13. Crop improvement programs could play a key role in helping meet the multiple demands for biomass. Developing multi-purpose or more specialized crop varieties for the production of food, feed and energy may significantly decrease competition for these resources if they become limited. 14. The dynamics of agriculture and other sectors are changing at unprecedented rates and are becoming more difficult to project. Integrated assessments are becoming a key step towards understanding change but these studies are increasing in complexity and are difficult to put together comprehensively across sectors. 15. Better targeting of studies and refining the methods used in this study are essential steps for better understanding change in farming systems. A more comprehensive understanding of the interactions between drivers, ecosystem services and agricultural systems will enable better prioritization of sustainable options to meet the simultaneous demands of different sectors, but especially to meet the needs of the poor and the environment.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

1 Introduction This desk study was prepared by a multi-disciplinary team from across CGIAR to identify places and production systems in the developing world which, due to global changes, may not be able to supply food for the growing population or, in doing so, the sustainability and maintenance of key ecosystem functions would be compromised. The team worked at the cross roads of agriculture and livestock, poverty and the environment. The report 1) develops a conceptual framework to study the effects of drivers of change in mixed crop–livestock systems; 2) analyses past trends of key indicators of change in mixed crop–livestock systems; and 3) uses these trends, along with modelling approaches and other tools, to develop a series of scenarios of how mixed systems in different regions might evolve, and what their constraints and opportunities could be. The guiding principles for the study were the following: • The study should be built around a conceptual framework on how farming systems are likely to evolve. • It must describe the impacts of drivers of change and their effects at different scales and on different systems, but with special emphasis on crop–livestock systems. • It should build on historical information as well as on scenarios of future changes. • It should seek to introduce systems change concepts in the CGIAR centres’ research and development agendas by providing information on what drives systems to change in different parts of the developing world, and how this occurs. • It needs to be able to identify priority intervention points for coping with change in different systems. • It should seek to find where synergistic activities between CGIAR centres will be of primary importance to deliver products for adapting to change in crop–livestock systems. • It should build on the recent major assessments of global change such as the Intergovernmental Panel on Climate Change (IPCC), the International Assessment of Agricultural Knowledge, Science and Technology for Development (IAASTD), the Millennium Ecosystem Assessment (MEA), and the Comprehensive Assessment of Water Management in Agriculture (CA).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

2 Framework for studying the dynamics and impacts of change in crop–livestock systems A range of forward-looking international global assessments covering different aspects of the global use of resources and its significance to humanity have been conducted recently. Aspects covered have included agriculture and development (World Development Report 2008), agriculture, science and technology (IAASTD), ecosystem services (Millennium Ecosystem Assessment, MEA), environmental outlooks (Global Environmental Outlook [GEO-4], UNEP 2007), water management, and climate change (IPCC 2007). The IAASTD and GEO-4 are based around the conceptual framework developed for the MEA and some similarities exist with the IPCC (2007). The present study uses a similar framework, but with the specific objective of looking in more depth at the effects of drivers of systems change on crop–livestock systems. It is useful to explicitly link the framework used in this study to those of other major assessments. This will enable us to have some coherence when comparing and integrating results from these other studies. To our knowledge, this is the only assessment that attempts to identify changes at the production systems level for the whole of developing world. This is a key difference to most other assessments, which provide aggregated data at the country or regional level. The basic conceptual framework is presented in Figure 1. The key aspects of the framework are: • Mixed crop–livestock systems (and other systems) are diverse, and their structure, function and potential are shaped by their development context. • There is a set of drivers, both direct and indirect, that can make systems change over time. Direct drivers are those that have a direct measurable effect on different aspects of agro-ecosystems and humans. Indirect drivers act as key influences on one or many other drivers. For example, increased demand for livestock products (a direct driver) is the product of increases in human population and their income increases (indirect drivers). • The local development context determines which direct and indirect drivers play a more important role in which system, in which location and in which ways. • Different drivers of change exert different kinds of ‘pressures’ on key dimensions of agro-ecosystems. These can range from land use change, resource and input use to competition for biomass (food, feed and energy). For example, as global demands for food increase along with competition for biomass and resources and for use of inputs, greenhouse gas emissions are affected positively or negatively, or not changed, depending on location. • These pressures have impacts on different agro-ecosystems services. These services can be divided into four categories: provisioning (e.g. of food/feed, water, or fuel); regulating (e.g. of the climate); cultural (e.g. spiritual, aesthetic, and recreation values); and supporting services (e.g. primary production and soil formation).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

• Depending on the magnitude of the pressures and the impacts on agro-ecosystem services, human wellbeing can be affected in different ways (e.g. incomes, health, food security, vulnerability etc.) and this in turn can have important feedbacks, especially on the indirect drivers of change. • There are several pathways to generate solutions to respond locally and globally to the effects of the drivers of change. These are through technologies, policies, and institutional arrangements that promote intensification, diversification, expansion, regulation and exit from agriculture (Dixon et al. 2001). • These key entry points operate through regulating the effects of the drivers and the pressures or through generating options for adapting the agro-ecosystems services to the impacts of the changes. For example, price policies may help regulate water demands, or mitigation strategies can be developed to prevent increases in greenhouse gas emissions from crop–livestock systems. We may want to promote alternative crop varieties to increase the production of grain and fodder for humans and animals. These three different alternatives present different instruments to provide a solution and consist of a different entry point (drivers, pressures and agroecosystems responses). These can be solutions that transcend scale in some cases (from global to local), though the impacts on people and systems will be felt differentially depending on location and context.

Figure 1. Conceptual framework for studying the impacts of drivers of change in crop–livestock systems

Global Regional Local

Indirect drivers� Development context and systems diversity

Human wellbeing

Responses

Food security Poverty Incomes and employment Human health Resilience and vulnerability Income diversification Social and gender equality

Direct drivers� Volume and pattern of demand Changes in local land use and cover Consumption patterns Water availability Technology adaptation and use Climate change

Impacts

Trends

Agro-ecosystems services Food production (crops and livestock) Fibres, oils, minerals Biomass / energy Ecosystems services (water, biodiversity, air quality etc environmental regulation.)

Demographic (urbanization/ migration) Economic processes (consumption, production, markets, trade) science and technology cultural, social, political, institutional

Impacts

Scenarios

Pressures Land use Resource extraction Biomass competition Use of external inputs Emissions Biodiversity

Actions Actions

Context specific options / solutions Technologies, policies and institutions

Actions

Adapted from GEO-4 (UNEP 2007) and the IAASTD (2007).

Figure 2 gives a very simple example of the framework and how it relates to a specific crop–livestock system. Consider just the local level, and a group of mixed systems in a region that is experiencing high population growth (the indirect driver). This affects two direct drivers. One is increasing local demand for livestock products. But at the same time, the average size of land holdings is decreasing, and the fallow period is being reduced further and further. The effects of the drivers are (1) capacity in the local market so that extra production could easily be absorbed; and

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

(2) real pressure on soil fertility that is tending to decline. The impact of declining soil fertility is that food and feed production is declining, and as a result, food security of these smallholders is being compromised and their income is declining. Figure 2. Overview of a framework and how it relates to a specific crop–livestock system

Local Indirect driver Population Human well-being Food security Income

Direct driver Local demand Size of holdings & fallow period

Agro-ecosystem service Food/feed production

impacts

Pressure Soil fertility

Figure 3 represents the situation after a specific action: here, assume that there is some technology that is taken up that increases the efficiency of on-farm use of manure (this could be something related to manure storage technology that reduces nutrient losses between collection of the manure and its application on plots, for example). This has a direct effect on soil fertility, and allows soil fertility to be maintained. This in turn implies that food and feed production can be maintained, and this has positive impacts on food security and household incomes. Note that here, there is an additional positive feedback from increased food/feed production on manure quality, and this feeds back to soil fertility maintenance via the manure efficiency box (hence the feedback loop on the left of the figure). Note also that in this example, there are really no effects of the ‘action’ on either the direct or the indirect drivers, so there are no feedback arrows on the right-hand side of the figure. Further, there are no direct connections between the drivers and human wellbeing in this example (in either figure), as all the effects are mediated through the agro-ecosystem services box (i.e. these are direct agricultural effects).

2.1 Key drivers of change in crop–livestock systems The challenges facing economic development in general and livestock-based systems in particular, seem to be increasingly complex. There are many drivers of change operating at a variety of levels (see Hazell and Wood 2008). Some of these are highlighted below.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Population and demographics: The world’s population will reach 7 billion by 2012, and in Africa alone, human population is projected to double to nearly 2 billion by 2050. This is being accompanied by rapid urbanization, which is expected to continue in many developing countries. The year 2008 is a watershed—for the first time, more than half the global human population (3.3 billion) is now living in urban areas. By 2030, this number will have increased to almost 5 billion: the next few decades will see unprecedented urban growth, particularly in Africa and Asia. Figure 3. Overview of a framework and how it relates to a specific crop–livestock system under a technology intervention

Livestock product demand: The demand for livestock products is rising globally and will increase significantly in the coming decades because of income shifts, population growth, urbanization and changes in dietary preferences; this increased demand will largely be based in developing countries (Delgado 2005). The trends in demand will be for both increased quantity, especially as incomes rise, and for increasing quality, particularly among urban consumers who purchase livestock products from supermarkets. Such factors have enormous consequences for both the volume of global food demand and its composition: these increases in cereals and meat will need to be produced from the same land and water resources as currently exist. While the increased demand will probably be met mostly by increases in chicken and pig production, ruminant populations are also likely to increase substantially. Changes in food prices: The general trend in relative food prices has been a downward one since the early 1970s (Hazell and Wood 2008), but the period from mid-2007 to today has seen quite remarkable increases in grain prices, largely a reflection of changes in demand. The price of rice has risen in dollar terms from a relative level of 100 in January 2007 to nearly 290 in April 2008 (The Economist, 19 April 2008, p 30), attributed largely to population and income increases and the ‘voracious’ appetites of western biofuels programs. The increases have been so rapid that the impacts on the poor and on farming in general are hard to gauge. The relationship between food prices and high energy prices are complex and difficult to foresee, but high energy prices are very likely to be a continuing feature of the global economy from now on. Climate change: The world’s climate is continuing to change at rates that are projected to be unprecedented in recent human history. Model projections of the Fourth Assessment Report of the IPCC (2007) suggest an increase in global average surface temperature of between 1.8 and 4.0°C from the present to 2100, the range depending largely on the scale of fossil fuel burning between now and then and on the models used. Moreover, the impacts of climate

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

change are likely to be highly spatially variable. At mid to high latitudes, for example, crop productivity may increase slightly for local mean temperature increases of 1–3°C, while at lower latitudes, crop productivity is projected to decrease for even relatively small local temperature increases (1–2°C) (IPCC 2007). In the tropics and subtropics in general, crop yields may fall by 10 to 20% to 2050 because of warming and drying, but there are places where yield losses may be much more severe (Jones and Thornton 2003). Changes in climate variability are also projected; although there is considerable uncertainty about these changes, the total area affected by droughts is likely to increase, as are the frequency of heavy precipitation events. Increased frequencies of heat stress, drought and flooding will have adverse effects on crop–livestock productivity over and above the impacts due to changes in mean variables alone (IPCC 2007). Climate change is likely to have major impacts on poor croppers and livestock keepers and on the ecosystems goods and services on which they depend. These impacts will include changes in the productivity of rain-fed crops and forage, reduced water availability and more widespread water shortages, and changing severity and distribution of important human, livestock and crop diseases. Major changes can thus be anticipated in agricultural systems related, for example, to livestock species mixes, crops grown, and feed resources and strategies. Changes in technology: Historically, new and improved technology has been a key driver of agricultural productivity growth (Hazell and Wood 2008). Many publicly funded international and national agricultural research centres have taken important steps in recent years to better address issues of sustainability related to technology design and development. There have been also considerable developments in the field of natural resource management in recent years. The trend is, however, for the continuing globalization and privatization of agricultural science; the private sector has much less incentive to undertake this kind of NRM or ‘public goods’ research. Scenario analysis in the IAASTD shows quite clearly that declining investments in agricultural science and technology may have serious implications: agricultural supporting services tend to degrade rapidly, and absolute childhood malnutrition levels may increase, possibly surpassing the malnutrition levels at the end of the twentieth century. In general, much better outcomes in developing country food security can be achieved for relatively modest investment levels (in global terms), trading off improved crop productivity with slightly lower investment levels in irrigation. The issue is how to achieve and make best use of the levels of investment that are required, given the need for an increased role of the private sector in such research and possible intellectual property concerns vs. international public goods. Changes in sociocultural conditions: The impacts of changes in sociocultural conditions may be profound, but such changes are almost impossible to predict, and their implications may be so far-reaching as to make a mockery of careful assessments based on quantitative models and long-cherished (but erroneous) assumptions and analytical frameworks. These changes can occur at various levels. For example, recent changes in life-style expectations are inducing the Maasai of southern Kenya and northern Tanzania to become croppers and businessmen for example, so as to be much better linked to the market economy and the possibility of generating cash for themselves (BurnSilver 2007). In developed countries, the last 30 years have seen astonishing decreases in the importance that society in general attaches to agriculture and agricultural research. The average age of farmers in North America is about 60. At the same time, the resource base for agricultural research in the North has been undergoing long-term erosion—the plant pathologists, crop breeders, animal scientists, and agronomists of tomorrow simply are not to be found in anything like sufficient numbers. An aging farming population is also the case for many places in the tropics and subtropics, with massive movements off the land to the cities in search of more lucrative income-generating opportunities. The drivers of such changes are partly economic, but they are also partly brought about by complex changes in the sociocultural values of populations. In summary, agricultural systems are being pulled this way and that in a highly dynamic and complex world. There are difficult trade-offs that have to be weighed up and decided upon if goals related to poverty reduction, social equity, economic growth, and environmental sustainability are to be achieved. There is a need for evidence-based inputs into decision-making at all levels in the hierarchy—from local scales up to the global negotiations required if equitable sustainable development is to be more than a pipedream. There is a considerable amount of work to be done to provide these inputs, including targeting work and scenario modelling, particularly in relation to assessing the impacts of interventions in the future and in evaluating the trade-offs that will inevitably arise between different groups of stakeholders with vastly different objectives and access to resources.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 1. Key drivers of change in crop–livestock systems

Source: Hazell and Wood (2008), originally modified from Wood et al. (2005).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

3 Global trends in agriculture, agro-ecosystems services and human wellbeing 3.1 Trends in human demography, livelihoods and economic parameters Human population Today’s global population is three times larger than it was at the beginning of the 20th century but most of that growth has been in the past 50 years. From 1900 to 1960 the population increased by little more than a billion (from 1.75 to 3 billion) whereas from 1960 to 2010 it grew by three times as much from 3 billion to 6.8 billion people (US Census Bureau 2010). Although the rate of growth has declined from a peak in the 1960s of more than 2% to the current 1.1%, absolute growth is such that by 2030 the global human population is predicted to reach 9 billion (UNEP 2008). Table 2 shows that between 1950 and 2000 the world population increased from 2.5 billion to more than 6 billion. However, the rate of increase in population has not been commensurate across all regions: the population of industrialized countries increased by less than half in those 50 years whereas that of developing countries nearly tripled. Although Africa shows the highest rate of population growth for that period, increasing by 360% to nearly 800 million in 2000, in terms of absolute numbers of people, Asia is the forerunner: in 2000 it contained 3.5 billion people, three-quarters of the developing world’s population and 60% of the world’s population. Increases in life expectancy contribute significantly to the growth in population in some places. Globally, life expectancy increased from 46 to 65 years in the second half of the 20th century. Again a large disparity exists between industrialized and developing countries. In 1950, people in industrialized countries lived, on average, to be 66 years old and by 2000 this had increased by only nine years to 75. In developing countries, the population started from a much lower level, with a life expectancy in 1950 of 41; by 2000, this had increased by 22 years to 62, a much greater increase than in industrialized countries. The high increase in life expectancy in developing countries has for the most part been led by Asia: citizens of Asia can expect to live 24 years longer than they did in 1950 whereas Africans can only expect to live another 12 years, to 50.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 2. Population size and life expectancy between 1950 and 2000 for different world regions

In demographic terms, Asia shows the most noticeable changes in the past half century as, from an already dominant position in terms of population size, it has experienced the largest increase in absolute numbers of people and the largest increase in life expectancy. In developing countries, most farming systems can be classified into one of the following three categories: livestock only, i.e. agropastoral; mixed rainfed, i.e. where livestock are raised together with crops and where only rainfall is used for irrigation; and mixed irrigated, i.e. where livestock and crops are produced together and artificial irrigation is used (Table 3). Of these three systems, the vast majority of people, over 95%, live in mixed systems and, with the exception of East and South Asia, more people live in rainfed than irrigated systems. However, large regional variations exist. In SSA, only 6.4 million people live in irrigated systems compared to more than 400 million in rainfed farms. This is markedly different from West and North Africa, where roughly similar numbers, around 100 million people, live in each type of system. Table 3. Population numbers in different farming systems in developing countries

Source: Thornton et al. (2002).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 3 also shows the number of poor living within each of these agricultural systems. In agropastoral systems, 63 million people, more than a third of the total, are classified as poor. In mixed systems, the numbers are much larger but the percentages are slightly lower: 31% of people are poor in rainfed systems and 23% in farms that use irrigation. Again, substantial regional variations exist: in SSA and Latin America, regardless of the type of farming, almost half of the farming population is poor, whereas in East Asia, the poor only comprise between 8 and 14% of farmers. Progress in agricultural growth has been dominated by the significant increases in growth in Asia, especially in mixed crop–livestock systems in China. Figure 5 shows that growth in agricultural GDP per capita is lowest in SSA. In most cases, countries with high rates of agricultural value added per capita of agricultural production, such as China, were also good performers in rural poverty reduction. Figure 4. Expenditure gains in 42 developing countries for a 1% increase in GDP growth

Figure 5 Growth in agricultural GDP in developing countries

GDP per capita The graph shows that between 1981 and 2003 for 42 developing countries, a 1% growth in GDP originating in agriculture increased the countries’ expenditures within the lowest third of the expenditure declines at least two and a half times more than growth originating in the rest of the economy, i.e. GDP growth originating in agriculture benefits the poorest half of the population substantially more than the wealthiest (World Bank 2007). This stresses the importance of agriculture (and livestock production) for the poor and raises evidence of why investments in propoor development interventions need to be related to revitalizing their agricultural sectors (World Bank 2007).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Growth of agricultural GDP in SSA is highly variable among countries and over time. Over the past 25 years, only Nigeria, Mozambique, Sudan and South Africa have maintained agricultural growth rates per capita of agricultural population above two per cent per year; many other countries have had significant periods of negative growth associated with conflicts or economic crises (World Bank 2007).

Rural and urban migrations For the first time in history more people live in cities than in rural areas. Figure 6 shows that although populations in developing countries are still predominantly rural, rates of immigration to urban areas have been very high since the 1950s. In Latin America and the Caribbean, rural populations now only stand at about 20% of the total population and in developing countries as a whole, at just over 50%. Within the next 20 years this number is predicted to further decrease to the extent that more people will be living in urban areas than rural. Figure 6. Proportion of total population in developing countries that is rural

Of the 3 billion rural inhabitants in developing countries, an estimated 2.5 billion are involved in agriculture: 1.5 billion living in smallholder households and 800 million working in smallholder households (World Bank 2007). Poverty rates in rural areas have declined over the past decade, mostly because of impressive gains in economic growth in China. However, 75% of the world’s poor still live in rural areas and rural poverty rates remain high in South Asia and SSA. Rural poverty reduction contributed more than 45% to overall poverty reduction in 1993–2002, with only a small share of that resulting from rural–urban migration. Rural–urban income gaps have narrowed in most regions, except Asia (World Bank 2007).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 7. Rural poverty rates from 1993 to 2002

Food consumption Supply of food As shown in Figure 8, Arcand highlighted that a strong correlation exists between income and nutrition: as the amount of food supplied per person increases so does per capita income. Thus increasing average daily energy supplies (DES) can act as a driver of economic growth. In particular, Arcand calculated that increasing the DES to 2700 k cal per person per day in countries that were below that level, could increase the rate of economic growth by up to 1.13% per year. Figure 8. Association between National Average Dietary Energy Supply and GDP, per capita

Demand for food Increasing population sizes result in a direct increase in demand for food. At the same time increasing incomes change diets and alter the demand for different foods. In particular, demand for the consumption of high value products increases as incomes rise (Delgado et al. 1999). For example, the growth rate of per capita consumption of animal food products is determined by economic factors such as incomes and prices and lifestyle changes. Figure 9 shows that in developing countries, per capita consumption of meat and horticulture increased rapidly between 1980 and 1995 (Delgado et al. 1999).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 9. Domestic consumption of meat and cereals in developing countries between 1980 and 2005

Table 4 shows that between 1962 and 2000 in developing countries the per capita consumption of cereals, milk and meat increased but with a heavy skew towards milk and meat products. Root and tuber consumption decreased. Being animal products, an increase in demand for milk and meat requires an increase in the supply of animal fodder. In developing countries, all the crop products required to feed animals to meet this increasing demand come from mixed crop–livestock systems. Table 4 Changes in food consumption in developing countries 1962

1970

1980

1990

2000

Consumption kg/person/year Cereals

132

145

159

170

161

Roots and tubers Starchy roots Meat Milk

18 70 10 28

19 73 11 29

17 63 14 34

14 53 19 38

15 61 27 45

Source: Steinfeld et al. (2006).

Figure 10 shows how per capita food consumption in developing countries is shifting to fruits and vegetables, meat, and oils. Although the rate of growth of consumption of oils and meats dropped between 1976–1990 and 1991–2003, it was still more than 1% per year; that of fruit and vegetables continued to increase to reach a high of 3% in the period 1991–2003. Figure 10. Per capita food consumption in developing countries between 1961 and 2003

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Increasing consumption implies increasing demand for food. About 60% of the rural population in developing counties consists of farmers living in areas of good agricultural potential and with access to markets. In these areas, good opportunities exist for farmers to diversify to higher value products such as milk, meat, fruit and vegetables, and oils. By doing so they can offset a decline which has been seen in prices for cereals and traditional exports such as tea, coffee, rubber and tobacco (World Bank 2007). Livestock are closely interwoven with the socio-economic status of rural people in developing countries. Livestock contribute to the livelihoods of at least 70% of the world’s rural poor and their livelihoods are enhanced by strengthening their capacity to cope with income shocks. Most people as well as most poor live in mixed systems. In terms of area, rangeland systems are the largest land use system on Earth, most milk and meat, however, comes and will continue to come from, mixed systems (Seré and Steinfeld 1996; Delgado et al. 1999). Figure 11 shows how high value exports are expanding rapidly in developing countries (World Bank 2007). Diversification into higher value commodities and off-farm activities is increasingly becoming a key option in mixed farming systems, and, to a lesser extent, in marginal pastoral systems. Figure 11. Changes in the value of exports of crops in developing countries between 1960 and 2004

Due to high population densities in the mixed systems, higher demands and trade-offs arise in terms of biomass use (food, feed and energy) and ecosystems services. Table 5. Area, people, poverty and livestock within agricultural production systems

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

3.2 Trends in agriculture Crop production The increase in human population creates substantial pressure on food and ecological systems, especially in mixed crop–livestock systems. The pressures can differ depending on factors such as the level of development, environmental conditions, resource endowments, and the parallel effects of other drivers such as climate change. Globally, ecosystems have met the rising demand for food over the last 50 years. Figure 12 shows that the availability of basic food items such as cereals has increased at a faster rate than population growth and that yields have increased whilst the area of land being harvested has remained more or less constant (i.e. that production is been successfully intensified). GDP has increased and the price of staple food items for many people is lower than ever. Figure 12. Trends in selected drivers of food provision worldwide, 1961–2001

Globally, cereal production and yields have been consistently and significantly increasing in the past 50 years. The exception to this is in SSA where production, already lower than elsewhere, has only increased marginally. This has led to a widening of the yield gap between SSA and the rest of the world (World Bank 2007). In most cases increasing yields have been through intensification (increased input use, access to irrigation and crop varietal changes). In SSA, the increases in production have generally been through increases in area planted. These differences are a result of differences in production systems in terms of their agricultural potential, their market access, infrastructure, and population density. Driven by population growth and expanding markets, traditional agricultural production grew by bringing more land under cultivation. However, in SSA and South Asia, the expansion of agricultural land relative to population density is now decreasing (Figure 14). Therefore, the increasing demand for crop production can only be met by intensification of the current mixed systems. At the same time, land now used for agriculture is threatened by pollution, salinization and soil degradation from poorly managed intensification. These factors all affect productivity and reduce potential yields. Soil degradation through nutrient mining is a major problem in SSA, though much of it is reversible through better soil management and fertilizer use. Figure 14 also shows how the area in land under cultivation has increased relative to population size in Latin America, Europe and Central America. However, in some places, notably in Asia’s mixed rainfed systems, population densities are so high that increases in production through area expansion are not possible.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 13. Regional cereal yields between 1960 and 2005

Source: World Bank (2007). Figure 14. Arable and permanent cropland per capita of the agricultural population

With growing resource scarcity, future food production depends more than ever on increasing crop yields and livestock productivity, especially in mixed systems. However, although absolute yields of cereals have been increasing in developing countries, the rate of increase of these yields has been slowing significantly since 1980 (Figure 15). Whether future technological options will be available to increase crop yields without significant expansion in cropping area still remains to be seen.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 15. Growth rates of yields for major cereals in developing countries

Increasing global food production relative to population size has kept food prices down since the early 1970s. Moreover, as Figure 16 shows, between 1980 and the mid-1990s there was a positive correlation, albeit with some lag, between the global number of undernourished people and food price per capita. This trend was less obvious in SSA. Figure 16. Global trends in food production and price in relation to undernourishment

From the mid-1990s onwards, the figure for undernourished people began to rise again, and currently even though food prices, which have fluctuated significantly, are now low, the poorer sectors of society are still not in a position to buy the basic staples. Unequal income distribution remains a problem and is increasing.

Livestock production Crop and livestock production tends to be heavily interlinked in most developing countries. As can be seen in Table 6, at a global level, mixed crop–livestock systems account for the bulk of meat and milk production, and in Asia in particular, the use of mixed systems is especially dominant. Grazing-only systems are prevalent in SSA providing nearly two-thirds of cattle meat and three-quarters of milk production.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 6. Share of milk and meat outputs by production systems in selected regions

Source: ParthasarathyRao et al. (2005).

Evidence suggests that grazing systems are gradually evolving into mixed systems partly as a consequence of population increases and land fragmentation (ParthasarathyRao et al. 2005). In farming systems in developing countries the level of intensity of the system is related to the end livestock product. Table 7 shows that beef tends to come from the more extensive systems (though both mixed and livestock only) whereas more than half of all milk production comes from irrigated systems. Chicken and pork are largely produced in industrial systems. These differences reflect the availability of markets as well as the agricultural suitability of the land. Table 7. Livestock population and production in different production systems in developing countries

In developing countries, the majority of ruminants are found outside temperate regions. This implies they tend to be in arid or semi-arid regions which have very low primary productivity and low yields per animal. Table 8 shows that beef production in the temperate zones is equal to that of the arid and humid zones together, although cattle numbers are three times higher in the latter regions. Thus the large numbers of cattle in extensive, livestock-only systems do not necessarily confer high productivity.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 8. Livestock population and production in different agro-ecological zones

The landless and industrial systems of pork and poultry meat are mostly found in humid zones or in the temperate regions and highlands. Globally, livestock numbers are increasing and at the same time, a larger share of the world’s cereal production is being used for animal feed. This reflects the large increases in (relatively intensive) pig and poultry production to meet human demand. This will create important changes in mixed systems especially, as they produce the bulk of cereals in developing countries. Past and projected figures for cereal demand for feed can be seen in Table 9. China has been the forerunner in this area, using more cereal for feed than all of Latin America, i.e. about half of the total for the developing world. That dominance is projected to continue to 2020 but by that time consumption of cereals for feed is projected to have doubled from its 1997 levels. Table 9. Global trends and projections in the use of cereal as feed

Some marginal systems might also benefit from this increased demand, as more land might be converted to produce more crops. Although large increases in pig and poultry numbers are creating a demand for more feed, significant improvements have been made in the productivity per kilo of feed consumed for these animals. Table 10 shows increases in productivity parameters for pigs and poultry in different world regions. Gains in production efficiency between 1980 and 2005 were made across all regions, but in Latin America and South Asia in particular, these gains were especially large. These increased efficiencies should help to defray the increased demand for grains as feed for pigs and poultry.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 10. Key productivity parameters for pigs and poultry in different world regions

3.3 Environmental trends and crop–livestock systems Climate change Table 11 summarizes the findings of the IPCC’s Fourth Assessment (Christensen et al. 2007) in terms of the potential changes in weather as a result of climate change on Africa, Asia and Central and South America. Even though the magnitude of the economic losses may be higher in rich countries than poor, natural disasters tend to have, and are predicted to do so in the future, a greater effect on poorer nations. Poorer countries generally have a worse infrastructure, less advanced technology, and fewer resources with which to finance recovery than rich ones. Figure 17 shows that although between 1985 and 1999 the world’s 10 richest nations lost almost two and a half times more money than the 10 poorest nations, in terms of per cent of GDP, the richest nations lost only a sixth of that of the poorest. Figure 17. Disaster losses, total and as a share of GDP between 1985 and 1999 in the world’s ten richest and poorest nations

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 11. Regional climate change projections from the IPCC’s Fourth Assessment

The climate is changing and the number of extreme events that result in an increase in natural disasters is predicted to increase.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 18. Length of growing period (days per year) for 2000

Source: Thornton et al. (2006).

Figure 19 shows the areas within pastoral and mixed rainfed production systems in arid and semi-arid regions of Africa that are projected to undergo more than a 20% reduction in length of growing period by 2050, under the HadCM3, A1 scenario. Figure 19. Areas within the LGA (in yellow) and MRA (in green) systems projected to undergo more than 20% reduction in the length of growing period by 2050

These are mostly marginal areas of the Sahelean belt and southern Africa in which pastoral systems and marginal mixed-crop–livestock systems predominate.

Energy use Economic and population growth, together with a high demand for transportation services and policies (e.g. subsidies) are the top three factors directly driving the growth in demand for bio-energy. Strong world economic growth has pushed up energy consumption, global economic development, especially in developing countries (notably China and India) has helped drive global renewable energy investment. The European Union and the United States are the heaviest investors in this sector followed by China and India.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

From population growth alone aggregate energy demand will hit 14 billion tonne oil equivalents in 2030, a 32% increase from 2006. According to the World Energy Outlook 2007, the total 2030 world energy demand will be more than 17 billion tonne oil equivalents, led by China and OECD countries (Table 12). Table 12. Global projections of energy demand in 2015 and 2030

* Mtoe is millions of tonne oil equivalents. Source: International Energy Agency’s 2007 World Energy Outlook.

Oil price volatility arising from social and political instability in some oil producing countries has also pushed interest towards bio-energies. The largest projected increase in energy demand occurs in the transportation sector. Security of fuel for transport has attracted much attention in developing countries. China and India, which together with United States comprise the top three energy consuming countries (International Energy Agency) will consume about 70% of the projected energy demanded by the transport sector from 2005 to 2025. Growth rates of energy demand are expected to be 5% and 4.4% per year for this period for China and India, respectively. So, under mounting pressure to improve domestic energy security and combat global climate change, countries are now turning to ethanol and biodiesel as alternative fuel sources. Table 13. Global biofuel production and crops

Source: Licht (2006 ).

In 2006, the United States passed Brazil to become the world’s number one producer of bio-ethanol.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

The principal crops used to produce biofuels are maize, wheat, sugarcane, cassava and sweet sorghum (for bioethanol) and rapeseed, soybean, and sunflower seed for biodiesel. Since a number of these crops, such as maize, wheat, and cassava, are also major animal feeds, competition for crops for feed and for biofuel production now exists. This has had the effect of pushing up the price of livestock feed and, consequently, of livestock products. Cellulose conversion is becoming an economically feasible technology for biofuel production, and may in turn result in competition for fodder and pasture. Controversy regarding biofuels comes from the food security for food deficit area, increasing food prices, greenhouse gas emissions and biofuel cost-efficiency (IFPRI 2006; Rosegrant et al. 2006; ODI 2007; Peskett et al. 2007; Tokgoz et al. 2007; Dixon et al. 2008). Impact on crop–livestock production is a key point for the human food consumption and livestock industry sustainable development. Specific impacts need to be investigated at country- and farming systemlevel based on local energy and resource availability. Table 14 gives examples of the most important regions of the developing world that have significant potential for further biofuel development. Table 14. Human, livestock and total energy consumption in selected farming systems

Water use Over the last 40 years the world food supply has increased by about 25% in relation to population growth, from 2250 calories per person per day to approximately 2800 calories per person per day. Although this increase has occurred uniformly across much of the world, Figure 20 shows that whilst the global food supply is only just reaching the threshold for national security, distribution inequalities mean that in many countries in Asia and SSA food supply is still below that needed for food security. Today, each calorie of food takes approximately 1 litre of water to produce, indicating that the annual amount of water used to produce the world’s food is approximately 7,000 cubic kilometres. Approximately 20% of this is used in irrigated agricultural systems. Much of the last decades’ increased production of food has come from the expansion of irrigated agriculture. Over the last 50 years there have been enormous developments in water technology for agricultural production. Even after the World Bank dramatically slowed its lending for irrigation infrastructure in the mid-1980s, the global area under irrigation continued to grow. And while the world’s population has more than doubled since 1950, food production outstripped population growth, resulting in a marked decline in food prices. This decline is only just beginning to reverse. Loss of water from natural reserves because of large-scale irrigation and cumulative agricultural activities are now being seen to impact on aquatic ecosystems. One index of aquatic ecosystem health, the Living Planet Index of Freshwater Species has declined dramatically.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 20. Global changes in food consumption from 1961 to 2003

Figure 21. Investing in irrigation based on FAO and World Bank data

In the Comprehensive Assessment of Water Management in Agriculture (2007), scenarios were presented that define the land and water required at a global level to produce enough food to feed the population in 2050. In an optimistic rainfed scenario reaching 80% maximum obtainable yields while relying on minimal increases in irrigated production, the total cropped area would have to increase by only 7%, and the total increase in water use would be 30%, with direct water withdrawals increasing by only 19%. In contrast, focusing on irrigation first could contribute 55% of the total value of food supply by 2050. But that expansion of irrigation would require 40% more withdrawals of water for agriculture, surely a threat to aquatic ecosystems and capture fisheries in many areas. The factors that contribute to optimistic and pessimistic estimates of total water needs are primarily differences in water productivity. Without gains in water productivity, water resources devoted to agricultural production will likely increase by 70 to 90%. On top of this is the amount of water needed to produce fibre and biomass for energy. Figure 22. Some examples of scenario options

Source: Comprehensive Assessment of Water Management in Agriculture (2007).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Land use and soil nutrients Over the last 20 years, increasing human population, economic development and emerging global markets have driven unprecedented land-use change (UNEP 2007). Continuous cropping without adequate restorative practices may endanger the sustainability of agriculture. Nutrient depletion is a major form of soil degradation in mixed crop– livestock systems. A soil nutrient balance is a commonly used indicator to assess changes in soil fertility. Constructed N, P and K balances for 37 SSA countries revealed that soil fertility is generally following a downward trend on the African continent. Table 15 indicates average nutrient balances of some SSA countries. Table 15. Average nutrient balances of some SSA countries

Source: FAO (2003).

Anticipated human population increases and continued economic growth are likely to further increase exploitation of land resources over the next 50 years (UNEP 2007). Figure 23 indicates the trends in yield and nutrient stocks for two soil types. Figure 23 Trends in yield and nutrient stocks for two soil types

There is no remedy for soils that are deficient in nutrients other than adding the necessary inputs. Efforts to improve soil fertility have focused on the replenishment of nutrients by the use of inorganic fertilizers and organic manure. This has been very successful in many parts of the world, and is responsible for a large increase in agricultural production. Yields may double or triple on a sustained basis by even modest application of fertilizer (UNEP 2007). However, across most of the tropics, the use of inorganic fertilizers is limited by availability and costs, although inorganic fertilizers often have favourable value-to-cost ratios.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Low soil fertility is a major contributor to the low productivity of African production systems (IAC 2004). The yield gap for certain crops like cereals between SSA and other regions has widened. Globally, improved varieties have been widely adopted except in this region (World Bank 2007). Figure 24 shows that the use of agricultural inputs have also expanded rapidly, but lagged in SSA. Figure 24. Modern inputs have expanded rapidly but have lagged in SSA

Research showed that major impediments to improved soil fertility management include low levels of farmers’ human, physical and financial capitals, lack of investment in science and technology and poor uptake of products derived from them, low agricultural commodity prices relative to fertilizer and other input prices, lack of pro-agriculture policies, and the failure to view the maintenance of soil fertility as an important public good. In certain production systems it is not nutrient depletion that is the cause of land degradation but eutrophication. Rivers, lakes and coastal waters receive large quantities of nutrients from the land as, for example, in East Asia, where pig and poultry operations produce overwhelmingly more nutrient discharge than other sources of pollution. Table 16 shows the estimated relative contribution of pig waste to nitrogen and phosphorus emissions in water systems. Table 16 Estimated relative contribution of pig waste, domestic wastewater and non-phosphorus emissions in water systems

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 25 shows changes in fallow land in mixed rainfed production systems between 2000 and 2050 (Thornton et al. 2002). Increases in population density have increased the pressure on land for cultivation, and farm sizes have diminished. The traditional practice of crop rotation has also decreased in large parts of the world (green areas on the map), and fertilizers and pesticides are used instead. However, to date there are still large areas (grey on map) that use a system of regularly changing the crops grown on a piece of land to utilize and add to the nutrients in the soil and to prevent the build-up of insect and fungal pests and diseases. Increased intensification will result in a reduction in the use of fallow land over time (blue areas). The increasing pressure on land in the future may lead to an excessive depletion of soil nutrients and loss of soil structure in the event that no proper crop rotation and/or use of fertilizers are applied. An additional risk is that feed resources may become more limited and it may therefore be more difficult to maintain cattle. Often, the traditional component of crop rotation is the replenishment of nitrogen through the use of green manure (legumes) and these legumes are used as fodder crops. Figure 25. Changes in fallow land to 2030

Source: Thornton et al. (2002).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

4 Methods and scenarios for evaluating changes in mixed crop–livestock systems and human wellbeing 4.1 Methods For a number of socio-economic and production indicators the current situation is compared with future projections under different scenarios. These indicators are mapped and summaries per region and production system produced and discussed. The production of the maps and regional summaries follows a two-step process. In a first step, the IMPACT model is used to produce future projections of, amongst others, crop–livestock production, water use, world prices, income and malnutrition. The first sections (section 4.2 and 4.3) describe the IMPACT model, its input and output variables and the different scenarios used in this study. A second step then applies GIS technology to spatially re-allocate the country and food production unit level outputs from IMPACT to different livestock production systems within countries and regions. Section 4.4 describes this process in more detail.

4.2 Brief IMPACT model description The IMPACT model combines an extension of the original International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) with a global water simulation model based on state-of-the-art global water databases (Rosegrant et al. 2002). The water module projects the evolution of availability and demand, with a base year of 2000 (average of 1999–2001), taking into account the availability and variability in water resources, the water supply infrastructure, and irrigation and non-agricultural water demands, as well as the impact of alternative water policies and investments. Water demands are simulated as functions of year-to-year hydrologic fluctuations, irrigation development, growth of industrial and domestic water uses, and environmental and other flow requirements (committed flow). Off-stream water supply for the domestic, industrial, livestock, and irrigation sectors is determined based on water allocation priorities, treating irrigation water as a residual; environmental flows are included as constraints. The food module is specified as a set of 115 country or regional sub-models, within each of which supply, demand and prices for agricultural commodities are determined for 32 crop, livestock, and fish commodities, including all cereals, soybeans, roots and tubers, meats, milk, eggs, oils, oilcakes and meals, sugar and sweeteners, fruits and vegetables, and low- and high-value fish. These country and regional sub-models are intersected with 126 river basins—to allow for a better representation of water supply and demand—generating results for 281 Food Producing Units (FPUs). Crop

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

harvested areas and yields are calculated based on crop-wise irrigated and rainfed area and yield functions. These functions include water availability as a variable and connect the food module with the global water simulation model. The ‘food’ side of the IMPACT model uses a system of supply and demand elasticities incorporated into a series of linear and nonlinear equations to approximate the underlying production and demand functions. World agricultural commodity prices are determined annually at levels that clear international markets (Figure 26). Demand is a function of prices, income and population growth. Growth in crop production in each country is determined by crop prices and the rate of productivity growth. Future productivity growth is estimated by its component sources, including crop management research, conventional plant breeding, wide-crossing and hybridization breeding, and biotechnology and transgenic breeding. Other sources of growth considered include private sector agricultural research and development, agricultural extension and education, markets, infrastructure and irrigation. Figure 26. Overview of the ‘food’ side of the IMPACT model

Model Inputs & Scenario Definition Urban growth and changes in food habits (demand elasticities)

Area elasticities w.r.t. crop prices FAOStat & IFPRI supply, demand and trade data

Income growth projections

Yield elasticities w.r.t. crop, labor, and capital prices

Population projections

Area and yield growth rates

Domestic price f(world price, trade wedge, marketing margin) Demand projection World market clearing loop

Supply projection Net trade (imports, exports)

Kilocalorie demand projection NO

World trade balance

YES

Go to next year

Adjust world price

Model Calculations

Update inputs

IMPACT projects the share and number of malnourished preschool children in developing countries as a function of average per capita calorie availability, the share of females with secondary schooling, the ratio of female to male life expectancy at birth, and the percentage of the population with access to safe water (see also Smith and Haddad 2000; Rosegrant et al. 2001). The ‘water’ side of the IMPACT model interacts with the ‘food’ module by simulating the reductions in area and yield that result from deficits in water supply given that the total water requirements for maximum potential yield may not be met and that other non-agricultural demands for water that must be satisfied within the given basin. Whereas the ‘food’ model simulates trade in a non-spatial way, the ‘water’ model allocates water in each spatial unit according to the crop irrigation, livestock, industrial and municipal demands that are projected. A simple schematic showing the linkage of the ‘food’ and ‘water’ modules of IMPACT is provided in Figure 26.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

The model is written in the General Algebraic Modelling System (GAMS) programming language and makes use of the Gauss-Seidel algorithm. This procedure minimizes the sum of net trade at the international level and seeks a world market price for a commodity that satisfies market-clearing conditions. IMPACT generates annual projections for irrigation, livestock, and non-agricultural water withdrawals and depletion as well as irrigated and rainfed crop area, yield, production, demand for food, feed and other uses, prices, and trade; and livestock numbers, yield, production, demand, prices, and trade. The model incorporates data from FAOSTAT (FAO 2003), commodity, income, and population data and projections from the World Bank (World Bank 2000), the Millennium Ecosystem Assessment, and the UN (UN 2000) and USDA (USDA 2000), a system of supply and demand elasticities from literature reviews and expert estimates (see Rosegrant et al. 2001), and rates for malnutrition from ACC/SCN (1996) and WHO (1997) and calorie-child malnutrition relationships developed by Smith and Haddad (2000). Figure 27. Schematic representation of the linkage of the food and water modules in the augmented IMPACT model (IMPACT-Water)

4.3 Descriptions of IMPACT scenarios used for drivers study 1. Drastic biofuel expansion. This scenario takes the actual national biofuel plans of those countries which have installed capacity and accelerates the growth of feedstock demand over different periods within the projections horizon. Feedstock demands for biofuel production are taken at their historical levels from 2000 to 2005, whereas the demand by 2010 is taken to be 50% higher than the gradual rate of 1% annual expansion that would otherwise be assumed. This rate of expansion is doubled between 2015 and 2020, and gives a fairly strong projection of feedstock demand from the key crops used in biofuel production, namely sugarcane, maize, cassava (for ethanol) and oil products (for biodiesel). 2. Irrigation expansion. This scenario is taken from one of the variants to the ‘reference’ (or baseline) case used in the International Assessment for Agricultural Science and Technology for Development (IAASTD). Whereas the reference case describes a trend of slowly declining rates of growth in agricultural research (and extension), the ‘higher’ variants for Agricultural Knowledge, Science and Technology (AKST) consider expanded investments in agriculture over the period 2000 to 2050. The variant that we use in this scenario corresponds to higher levels of crop–livestock yields as well as expanded investments in complementary sectors, such as irrigation. The improvements in irrigation infrastructure are represented by accelerated growth in irrigation area and increasing

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

efficiency of irrigation water use. Other improvements that are introduced under this scenario are accelerated growth in access to drinking water, and changes in the enrolment levels of secondary education for females, both of which are important determinants for human wellbeing outcomes, such as child malnutrition. The details of what is included in this scenario are shown in Table 17. Table 17. Assumptions for reference case and the scenario variant with high agricultural investment combined with other AKST-related factors (used as IRRIGATION EXPANSION scenario) 2050

Parameter changes for growth rates

2050 REFERENCE CASE

Drastic biofuel expansion

Low meat demand

High AKST + other services (IRRIGATION EXPANSION)

GDP growth

3.06 % per year

3.31 % per year

Livestock numbers growth

Base model output numbers growth 2000–2050

Base model output numbers growth 2000– 2050

Increase in numbers growth of animals slaughtered by 30%

Livestock: 0.74%/year

Milk: 0.29%/year

Increase in animal yield by 30%

Base model output yield growth rates 2000–2050:

Cereals: %/year: 1.02

R&T: %/year: 0.35

Soybean: %/year 0.36

Vegetables: %/year 0.80

Sub-tropical/tropical fruits: 0.82%/year

Food crop yield growth

Cereals: %/year: 1.02 R&T: %/year: 0.35 Soybean: %/year 0.36 Vegetables: %/year 0.80 Sub-tropical/tropical fruits: 0.82%/year

Increase yield growth by 60% for cereals, R&T, soybean, vegetables, ST fruits and sugarcane, dryland crops, cotton Increase production growth of oils, meals by 60%

Irrigated area growth (apply to all crops)

0.06

Increase by 25%

Rainfed area growth (apply to all crops)

0.18

Decrease by 15%

Basin efficiency

Increase by 0.15 by 2050, constant rate of improvement over time

Access to water

Increase annual rate of improvement by 50% relative to baseline level, (subject to 100 % maximum)

Female secondary education

Increase overall improvement by 50% relative to 2050 baseline level, constant rate of change over time unless baseline implies greater (subject to 100 % maximum)

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Biofuel feedstock demand

2000–2005: Historical level

2005–2015: 50% higher than reference case

2000–2005: Historical level

2005–2050: 1%/year expansion

2015–2050: 100% higher than reference case

2005–2050: 1%/year expansion

Rate of decline of income elasticity of demand for meat

Developed regions: 150% of reference case

Rate of decline of income elasticity of demand for non-meat products

Developed regions: 50% of reference case

2000–2005: Historical level 2005–2050: 1%/year expansion

Developing regions: 110% of reference case

Developing regions: 90% of reference regions

3. Low meat demand. This scenario is also taken from the IAASTD and is an additional variant to the reference case. In this scenario, the rate at which the demand for livestock products increases with income is slowed, over time, whereas the rate at which dietary preferences for fruits and vegetables changes is accelerated. This acts to decrease the share of meat products in the diets of the population, and strengthen preferences for non-meat products. The global slowdown in the growth of meat demand is implemented through adjustments to the way in which income demand elasticities for meat and vegetarian foods change over time. Income demand elasticities for meat products (beef, pork, poultry, sheep and goat) decline at a faster rate than they do under the reference case. Simultaneously, income demand elasticities for vegetarian foods (fruits and vegetables, legumes, roots and tubers, and cereal grains) decline at a slower pace than under the reference case, whereas the elasticities for animal products such as dairy and eggs are left the same. This happens globally using a differentiated set of multipliers for developed vs. developing regions, and assumes that the slowdown in meat demand is stronger in the developed regions, compared to that in developing regions. Regional average income demand elasticities for meat and nonmeat foods are shown in Table 18 for the aggregate regions used in the IAASTD study. The effect, in general, is that the meat income demand elasticities in developed regions decline at a rate that is 150% of the baseline case, whereas those for non-meat foods decline only half as fast. In developing regions, the rates of decline are taken to be 110% and 90% of the baseline rates for the meat and non-meat commodities respectively.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 18. Changes to average income demand elasticities for meat and vegetarian foods by IAASTD region under low growth in meat demand

Meat

2000

2010

2030

2050

Central West Asia and N. Africa (CWANA) East and South Asia and Pacific (ESAP) Latin America and Caribbean (LAC) N. America and Europe (NAE) Sub-Saharan Africa (SSA) Low meat CWANA demand ESAP LAC NAE SSA

0.7223 0.5538 0.5679 0.2761 0.8121 0.7223 0.5538 0.5679 0.2761 0.8121

0.6673 0.5145 0.5129 0.2402 0.7966 0.6554 0.4953 0.5046 0.2178 0.7931

0.5576 0.4507 0.4023 0.1732 0.7634 0.5253 0.4064 0.3781 0.1227 0.7529

0.4806 0.4169 0.2914 0.1161 0.7221 0.4375 0.3844 0.2562 0.0533 0.7044

Baseline

CWANA

0.2486

0.2299

0.2063

0.2025

ESAP LAC NAE SSA Low meat CWANA demand ESAP LAC NAE SSA

0.2243 0.1579 0.2733 0.3359 0.2486 0.2243 0.1579 0.2733 0.3359

0.2003 0.1421 0.2547 0.2775 0.2337 0.2138 0.1436 0.2687 0.2834

0.1660 0.1322 0.2235 0.2027 0.2149 0.2046 0.1345 0.2599 0.2164

0.1222 0.1324 0.1930 0.1751 0.2134 0.1848 0.1337 0.2477 0.1887

Baseline

Vegetarian foods

4.4 Allocation of the FPU-level impact outputs to regions and systems In order to redistribute the FPU-level impact outputs, a two-step process was followed using geographical information system technology. Firstly, the FPU-level data was spread out to create a continuous raster layer. In a second step this raster data was overlaid with the system layers and country boundaries, and summary statistics per country/system combination were calculated.

Spatial reallocation of FPU-level indicators to continuous rasters All the spatial reallocations are done using existing spatially disaggregated baseline layers for the year 2000 that are most related to the IMPACT variables. For example, IMPACT maize estimates by FPU would be ‘spread out’ within the FPU area weighted by the best known ‘sub-national or sub-FPU’ maize layers available for the current situation (year 2000). The reallocations take this form:

[re-allocated layer] = [totals per cell of the disaggregated baseline layer] * [Impact prediction per FPU]/ [sum of baseline layer by FPU]

Livestock IMPACT runs delivered number of animals slaughtered, milk, and eggs etc. per FPU. These were converted to numbers of live animals according to the ratio of live animals to slaughtered animals as provided by IFPRI (this ratio was assumed to be invariant to 2030). The number of live animals in the year 2000 was re-allocated within the FPU according to the FAO gridded livestock of the world ‘observed’ database (FAO 2007).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Crops We used the IMPACT area and production results for wheat, rice, maize, sorghum, millet, potato, sweetpotato, and cassava. Barley had to be estimated based on results for ‘other grains’ and the area and production fractions covered by barley within these. You and Wood (2004) recently completed the spatial allocation of 20 main crops grown worldwide. The pixel-scale allocations were performed through the compilation and fusion of relevant spatially explicit data, including production statistics, land use data, satellite imagery, biophysical crop ‘suitability’ assessments, population density, and distance to urban centres, as well as any prior knowledge about the spatial distribution of individual crops (You et al. 2007). The resulting dataset consist of global estimates of area, production and yields of rice, wheat, maize, sorghum, millet, barley, groundnuts, cowpeas, soybeans, beans, cassava, potato, sweetpotato, coffee, sugarcane, cotton, bananas, cocoa, and oil palm at a resolution of five minutes. The FPU-level crop production and area estimates from IMPACT were spread out according to these layers. Feed from cereals In addition to the crop–livestock numbers, the local availability of feed resources from crop residues was compared with the ruminant density. This comparison gives us a first rough estimate about local feed deficits. In combination with feed transfers/trade and overall demand (in terms of human consumption), this is the first piece of information that can feed into trade-off analysis, impact assessment and comparison of strategic interventions. In other words, this is a first step to answer questions like: • How much grain has to be imported to meet the demand from livestock, while keeping the crop productivity constant? And where could it come from? • What is the impact of yield increase or introduction of dual purpose crops? • What is the impact of a drought/climate change on yield of crops, pasture productivity and hence livestock productivity? For this study we only considered cereals. We looked at stover, brans and cakes. For stover, not only the dry matter (DM), but also metabolizable energy (ME) was calculated. Based on reallocated layers of cereals the feed supply was estimated using the following formulae:

Prod i * c- fact i * util i

* Dm - fact i * Dm - fact i * enerval

100 Prod i * br - fact i * Dm - fact i

100 Prod i * c- fact i * util i

Prod i * bp - fact i * Dm - fact i

With

i Є wheat, rice, maize, sorghum, millet, barley Prodi: production of grain in MT

c-facti: conversion factor indicating how much straw is produced compared to crop yield (derived from harvest indices)

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

utili: utilization factor—the fact that cereals are grown in a particular area does not mean that these are actually used as feed resources. Other competing uses are as soil amendments or as fuel for cooking br-facti: proportion of the grain that is turned into agro-industrial by-products (brans) bp-facti: proportion of the crop yield giving by-products, e.g. oilcakes

Dm-facti: dry matter content of fresh straw

Enerval: energy value of the stover expressed in MJ/MT dry matter

It is important to note that the analysis excludes cut and carry forages, small grazing areas found in mixed systems and purchased feeds (grain supplements and purchased fodder). Table 19. Indices used Crop

C-fact

Utilization factor

Br-fact

Bp-fact

dm_fact

enerval

Wheat

1.3

0.05

0.1

0.9

Rice

1.4

0.05

0.9

7.5

Maize

0.05

0.9

8.2

Sorghum

0.05

0.9

7.4

Millet

0.05

0.9

7.4

Barley

1.3

0.9

6.6

Malnutrition IMPACT’s malnutrition output, in terms of number of malnourished (underweight) children below age five was spatially disaggregated according to the Center for International Earth Science Information Network’s (CIESIN) underweight data layer (CIESIN 2005). Water For domestic water use, human population totals (GRUMP 2005) were used for the disaggregation. Industrial use was re-allocated according to the population numbers within the GRUMP urban area extents (Balk et al. 2004). Livestock water was spread out according to the total number of animals (bovine, small ruminants, poultry and pigs expressed in LU’s). For irrigation, finally, the GMIA version 4 ‘hectares irrigated per cell’ was used for re-allocation (Siebert et al. 2007).

Zonal statistics A production systems layer was created (see description below) and overlaid with country boundaries and the spatially re-allocated layers to come up with totals per system per country. These totals were then further summarized by region. The definition of the regions used is presented in Appendix A.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

A dynamic systems classification

This section comes mostly from Herrero et al. (2008) but has been adapted for this study. Seré and Steinfeld (1996) developed a global livestock production system classification scheme. A method was devised for mapping the classification based on agro-climatology, land cover, and human population density (Kruska et al. 2003). The classification system can be applied in response to different scenarios of climate and population change to give very broad-brush indications of possible changes in livestock system distribution in the future. This method was recently revised by Thornton et al. (2006) and this study uses those modifications. Below is a brief outline of the data sets and methods used. The livestock production system proposed by Seré and Steinfeld (1996) is made up of the following types of systems: landless monogastric, landless ruminant, grassland-based, mixed rainfed, and mixed irrigated systems. The grasslandbased and mixed systems are further categorized on the basis of climate: arid/semi-arid (with a length of growing period < 180 days), humid/sub-humid (LGP > 180 days), and tropical highlands-temperate regions. This gives 11 categories in all. This system has been mapped using the methods of Kruska et al. (2003). This classification has been used previously in poverty and vulnerability analyses (Thornton et al. 2002, 2006), for prioritizing animal health interventions (Perry et al. 2003) and for studying systems changes in West Africa (Kristjanson et al. 2004). It is used in this study for disaggregating methane emissions by production systems, which have different land areas, population densities, number of livestock, diets for ruminants and may evolve at different rates. The Seré and Steinfeld livestock system classification says little about the location of intensive and/or industrial agricultural systems. This breakout is, however, very important for several reasons: systems exist that may be expected to undergo rapid technological change, or exhibit rapid uptake of technology, or be particularly susceptible to the diseases of intensification and/or the emergence of new disease risks. We therefore implemented a classification that includes a measure of intensification potential. 1. Agropastoral and pastoral systems, in which natural resources are constrained and people and their animals adopt adaptation strategies to meet these constraints. 2. Mixed crop–livestock systems, in which natural resources are most likely to be extensively managed. 3. Mixed crop–livestock systems, in which natural resources can be managed to intensify the productivity of the system. 4. Others, which includes an amalgamation of all the others, e.g. urban, forest-based and landless systems. The agropastoral/pastoral systems correspond to the three rangeland-based categories (LGA, LGH, LGT) of Seré and Steinfeld where simultaneously less than 10% of the total land area is covered by crops (according to the crop layers from You and Wood 2004). The crop–livestock systems correspond to the six mixed rainfed and mixed irrigated (MR and MI, both by arid/semiarid, humid/sub-humid, and temperate/highland) categories of Seré and Steinfeld together with all the areas that have more than 10% of the area under crop (according to the crop layers from You and Wood 2004). To derive the mixed ‘intensifying’ systems, we added two indicators, one to do with relatively high agricultural potential, and another one related to market access, on the basis that mixed systems that are in high-potential areas and are close to large population centres and markets, will have a high potential of intensifying production. Areas with high agricultural potential were defined as being equipped with irrigation (as in Seré and Steinfeld) or having a length of growing period of more than 180 days per year (according to the LGP layers of Jones and Thornton). Good

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

market access was defined using the time required to travel to the nearest city with a population of 250,000 or more. We applied a threshold of eight hours. We used the travel time to urban centres with a population of more than 250,000 inhabitants. The distinction between extensive and intensive systems presented here is looking at potential intensification. The flow chart below (Figure 28) shows the process of deriving the different production system categories starting from Seré and Steinfield. Figure 28. Flow chart of the process used in establishment of the production systems

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

5 Results 5.1 Farming systems and the distribution of human population The distribution of farming systems, as classified for this study, can be observed in Figure 29. Table 20 also shows the area and human population by system and region. Figure 29. The distribution of farming systems, as classified for this study, for 2000 and 2030

Grazing systems occupy the largest area on earth. Relative to crop–livestock systems, they occupy more than double the land. SSA, West Asia and North Africa have the largest areas of pastoral and agropastoral systems but these are mostly in arid regions of very low or low productivity. Their carrying capacities are inherently low. Central and South America have important cattle producing areas based on grasslands of moderate potential. Mixed intensive systems have the lowest land area but they contain more than half of the world’s population (2.6 billion). This very high population will increase by almost a billion people by 2030 while remaining virtually with the same amount of land. Population growth elsewhere will also increase significantly to the point that people living in

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

mixed systems (both intensive and extensive) will comprise roughly 80% of the global population. The large population densities in these systems place, and will keep on doing so, a very high pressure on agro-ecosystem services, notably on food production, water resources, and biodiversity. Although larger in area, agropastoral systems will also become more densely populated, possibly leading to increased land fragmentation and the subsequent loss of traditional livelihood strategies, especially in SSA. These aspects have been documented by Reid et al. (2008). Table 20. Farming systems: area and human population for different regions of the world under alternative scenarios to 2030 Farming system

Region

Area 2000 (106 km2)

Area 2030 (106 km2)

Population 2000 (106 people)

Population 2030 (106 people)

(Agro-) pastoral

CSA

5.4

40.5

65.8

5.5

41.3

53.6

0.5

19.2

34.8

SEA

0.2

2.2

3.0

SSA

13.4

12.5

80.2

140.8

WANA

10.2

10.1

111.7

199.3

Total

35.2

34.3

295.1

497.3

Mixed extensive

CSA

3.5

3.6

100.7

155.2

1.7

1.9

195.4

264.6

1.6

371.9

543.6

SEA

1.2

1.0

85.3

92.0

SSA

5.1

5.8

258.7

484.8

WANA

0.9

87.2

129.9

Total

14.0

14.9

1099.2

1670.0

Mixed intensive

CSA

2.4

221.2

286.3

2.3

2.1

938.5

1020.5

1.8

844.6

1248.9

SEA

1.1

1.3

347.2

499.1

SSA

1.5

1.7

168.2

327.1

WANA

0.6

154.4

257.6

Total

9.8

2674.0

3639.5

Other

CSA

8.8

125.8

174.0

1.5

104.2

111.7

0.4

69.5

103.4

SEA

1.9

40.4

57.9

SSA

4.1

109.2

190.3

WANA

0.2

31.3

45.0

16.9

16.8

480.3

682.3

Total

Key: CSA: Central and South America; EA: East Africa; SA: South Asia; SEA: South East Asia; SSA: sub-Saharan Africa; WANA: West and North Africa.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

There are large differences between regions and systems. These reflect the variability in agricultural potential, population densities and access to markets of the different regions. On the one hand, mixed intensive systems in fertile areas with suitable lengths of growing period and relatively low population densities abound in Central and South America, while in South and East Asia, land availability per capita is a constraint. SSA has suitable land for increased intensification but constraints like lack of investment, markets and service provision prevent better utilization of these resources. It is essential to acknowledge these structural differences, as options and opportunities for sustainable growth in productivity and poverty reduction are largely dependent on them. Other systems, such as forests, occupy significant land areas, notably in Latin America and SSA. As demand for food, feed, and energy increase, these areas, usually with very high agricultural potential but somewhat poor market access, become under significant pressure to convert to agriculture and livestock to satisfy the needs of people living in other rural systems or in the increasingly populated urban areas.

5.2 World food prices World food prices are presented in Table 21 and are taken directly from IAASTD. With the exception of milk, the prices of crops and livestock products will increase significantly to 2030 as a result of competing demands (e.g. food, feed and fuel), and production factors (e.g. lack of water, nutrients and low animal productivity in some regions). The largest price increases are observed in cereals, some oil crops, and tubers like sweet potato where demand comes from multiple sources, notably from the feed industry and the energy sector (first generation biofuels). For example, the prices of maize, wheat, sorghum, sweet potato and oil grains is likely to more than double by 2030. It is important to note that all mandate crops for the CGIAR are the ones experiencing the largest price increases. At the same time these are the ones with potential for developing dual or triple purpose crop varieties. Under the biofuel scenario, with increasing demands for grains for energy production, some of these prices, notably maize and oil grains, increase dramatically. This will have serious repercussions for poor consumers whose food security will be compromised as they will not have the ability to purchase basic staples. The prices of animal products are also likely to increase but less so, as a result of less sources of competing demands, in this case only a fraction of the increasing human population, and the relative change in demand relative to the change in supply. Livestock breeds that are more efficient in converting feeds to animal products will experience lower price increases since they buffer the increased needs for feeds through increasing productivity per animal. This is particularly true for poultry and pigs, and for milk production, all of which can be produced in larger volumes by relatively modest modifications in the quality of diet. In the case of small ruminants, an increased supply from pastoral and mixed systems will lower price increases relative to other products. Even though the relative price increases of animal products are lower, with the exception of milk, the baseline prices are higher. Although incomes are increasing, this also has important repercussions for the poorer communities which, apart from milk and perhaps eggs and poultry, will have difficulty in accessing other sources of animal protein. Beef and lamb, with their inefficient production and substantial use of natural resources (water and land), will become almost niche markets for the rich in developing countries. Even though the relative price increases of animal products are lower, with the exception of milk, the baseline prices are higher. Although incomes are increasing, this also has important repercussions for the poorer communities which, apart from milk and perhaps eggs and poultry, will have difficulty in accessing other sources of animal protein. Beef and lamb, with their inefficient production and substantial use of natural resources (water and land), will become almost niche markets for the rich in developing countries.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 21. World food prices by scenario

US Census Bureau (2010).

5.3 Livestock numbers and their production under alternative scenarios 2000–2030 Distribution of cattle and its production Figure 30 presents the density of ruminants for 2000 and projected to 2030 for the baseline scenario. Table 22 shows changes in their numbers under different scenarios while Tables 23, 24, and 25 present their production of milk, beef and lamb, respectively. Most cattle are in the mixed crop–livestock systems, with the highest numbers in the most intensive systems. Due to the relatively small area they occupy, their density is very high, as with human population. In contrast, agropastoral systems have a large number of cattle but also distributed in a much larger area. Animal densities in mixed systems are close to five to sixfold to those of the pastoral areas. This is partly due to the agro-ecological conditions of agropastoral areas which support fewer animals and to the more intensive feeding practices employed in mixed systems. Intensification of cattle production needs not to be mediated by increased use of land in these systems. This is a key characteristic of animal production systems.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 22. Bovine numbers by farming system under different scenarios 2000–2030 NAME

REGION

Cattle 2000 (106 of LU)

Baseline 2030 (106 of LU)

Biofuels 2030 (106 of LU)

Irrigation expansion 2030 (106 of LU)

Low meat demand 2030 (106 of LU)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

64.18 12.67 6.19 1.70 36.68 8.46

95.28 23.56 7.83 2.57 37.03 10.61

93.74 23.34 7.80 2.54 36.65 10.50

96.59 26.95 9.43 3.21 40.04 11.01

94.84 23.49 7.82 2.56 36.92 10.58

129.88

176.88

174.57

187.22

176.21

67.24 20.32 71.96 10.20 55.53 5.32

109.04 44.72 73.30 14.30 70.16 5.64

106.73 44.29 70.75 14.13 69.19 5.53

109.73 51.08 92.09 17.57 76.25 5.83

108.40 44.57 72.54 14.26 69.90 5.61

230.55

317.17

310.61

352.54

315.27

CSA

69.43

99.26

97.65

100.61

98.83

EA SA SEA SSA WANA

34.38 109.52 13.84 11.71 6.01

63.08 118.46 25.06 15.97 7.31

62.47 115.10 24.72 15.80 7.22

72.03 145.93 30.73 17.09 7.36

62.88 117.45 24.97 15.92 7.29

244.89

329.14

322.97

373.75

327.34

41.83 9.79 8.65 7.07 6.77 1.39

63.79 19.26 9.59 11.08 9.03 1.46

63.09 19.07 9.31 10.93 8.94 1.43

65.14 21.96 11.77 13.81 9.66 1.51

63.60 19.19 9.50 11.05 9.01 1.45

75.50

114.20

112.78

123.85

113.80

242.68 77.16 196.32 32.80 110.69 21.18 280.94

367.38 150.62 209.18 53.01 132.19 25.03 278.67

361.22 149.17 202.96 52.32 130.58 24.68 274.26

372.08 172.01 259.20 65.33 143.04 25.71 271.59

365.67 150.13 207.31 52.84 131.75 24.92 277.35

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total

Mixed intensive

Total Other

CSA EA SA SEA SSA WANA

Total Total—all regions

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 23. Milk production by farming system under different scenarios 2000–2030 Baseline 2030 (103 MT)

Biofuels 2030 (103 MT)

Irrigation expansion 2030 (103 MT)

Low meat demand 2030 (103 MT)

Farming system

Region

Milk in 2000 (103 MT)

(Agro-) pastoral

CSA

12,615.9

16,880.9

16,610.8

19,650.7

16,801.4

2207.8

6207.9

6161.0

4085.8

6194.8

5578.3

9558.7

9187.6

13,906.6

9444.4

SEA

572.62

398.52

565.09

547.35

4487.74

10,297.06

9993.62

10,227.26

10,043.70

CSA

12,031.19

28,027.63

26,487.53

27,846.57

27,357.07

12,344.27

29,734.44

26,770.13

29,505.26

28,857.78

Total Total—all systems

Low meat demand 2030 (106 MT)

143.92

Total Other

Irrigation expansion 2030 (106 MT)

CSA

Total

Mixed intensive

Baseline 2030 (106 MT)

Total Mixed extensive

Poultry 2000 meat (106 MT)

1535.54

6786.93

4131.02

6765.61

6718.87

SEA

3841.18

8204.00

8618.37

8165.59

8047.51

SSA

1627.00

2903.07

3743.98

2875.92

2809.48

WANA

3669.71

7482.02

7293.31

7390.90

7174.27

Others

31,773.69

44,319.38

42,877.50

40,450.45

43,905.25

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Pig numbers and production under different scenarios Table 30. Numbers of pigs by farming system under alternative development scenarios Baseline 2030 (106 of LU)

Biofuels 2030 (106 of LU)

Irrigation expansion 2030 (106 of LU)

Low meat demand 2030 (106 of LU)

Farming systems

Region

Pigs 2000 (106 of LU)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

4.13 2.45 0.01 0.32 0.69 0.02

5.25 2.90 0.00 0.46 0.72 0.02

5.23 2.82 0.00 0.45 0.71 0.02

4.97 3.45 0.00 0.35 0.84 0.02

5.24 2.88 0.00 0.46 0.71 0.02

7.62

9.34

9.24

9.63

9.32

5.03 21.80 1.17 2.86 1.31 0.01

6.69 27.12 1.78 4.22 2.25 0.01

6.64 26.37 1.77 4.22 2.22 0.01

5.57 32.22 1.61 2.95 2.23 0.01

6.68 26.94 1.77 4.22 2.24 0.01

32.17

42.07

41.23

44.60

41.87

5.70 54.71 2.26 7.20 1.09 0.01

8.19 59.01 3.49 12.49 1.41 0.01

8.12 57.38 3.48 12.48 1.39 0.01

6.43 69.97 3.16 8.70 1.33 0.01

8.18 58.61 3.49 12.49 1.41 0.01

70.95

84.60

82.85

89.60

84.18

4.91 8.95 0.17 2.33 0.68 0.00

6.55 10.42 0.24 3.44 0.97 0.00

6.52 10.14 0.24 3.43 0.96 0.00

5.78 11.86 0.22 2.65 1.08 0.00

6.55 10.35 0.24 3.44 0.96 0.00

17.04

21.63

21.29

21.58

21.55

19.77 87.91 3.60 12.71 3.76 0.04 73.77

26.68 99.46 5.51 20.61 5.35 0.03 67.71

26.51 96.72 5.49 20.58 5.28 0.03 65.97

22.74 117.51 5.00 14.65 5.48 0.04 65.95

26.65 98.79 5.51 20.60 5.33 0.03 67.16

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all regions

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 31. Livestock production, farming systems vs. pork production Farming system

Region

Pork in 2000 (103 MT)

Baseline 2030 (103 MT)

Biofuels 2030 (103 MT)

Irrigation expansion 2030 (103 MT)

Low meat demand 2030 (103 MT)

(Agro-) pastoral

CSA

625.5

1226.2

1220.3

1200.3

1224.9

1009.6

1677.6

1631.1

2128.9

1666.3

14.5

31.8

31.5

34.5

31.8

SEA

60.6

152.6

151.4

117.0

152.3

SSA

125.7

196.8

193.4

235.3

195.7

WANA Total Mixed extensive

16.2

13.6

3732.1

3284.7

1261.9

2434.0

2421.4

2339.4

2431.4

10,598.6

18,225.5

17,719.8

23,126.8

18,102.5

222.4

611.9

609.2

617.9

611.5

SEA

786.7

1849.9

1850.0

1314.8

1850.3

SSA

246.6

640.1

628.8

686.1

636.6

WANA

4.1

4.0

3.9

6.0

3.9

13,120.3

23,765.3

23,233.1

28,090.9

23,636.2

CSA

1366.3

3013.3

2988.9

2692.7

3008.5

26,159.8

38,903.8

37,825.1

49,334.1

38,641.6

356.8

1003.9

1000.0

1010.8

1003.4

SEA

2029.5

5327.2

5329.6

3819.6

5328.7

SSA

211.7

384.5

377.3

385.4

382.4

WANA

17.6

5.3

5.1

6.2

5.2

30,141.8

48,638.1

47,525.9

57,248.7

48,369.7

1055.1

2115.3

2106.9

2108.6

2113.6

CSA EA

4359.8

6848.6

6661.2

8587.0

6803.1

52.8

144.6

144.0

145.9

144.5

SEA

622.2

1427.7

1423.6

1117.3

1426.9

SSA

72.5

156.7

154.8

177.7

156.1

WANA

75.3

68.4

66.7

95.2

67.9

6237.7

10,761.4

10,557.2

12,231.7

10,712.1

CSA

4308.7

8788.8

8737.5

8341.0

8778.4

42,127.8

65,655.6

63,837.1

83,176.7

65,213.5

646.5

1792.3

1784.7

1809.0

1791.2

Total Total—all systems

13.3 3241.0

CSA

Total Other

13.8 3298.8

Total Mixed intensive

10.8 1846.6

SEA

3499.0

8757.4

8754.6

6368.6

8758.2

SSA

656.5

1378.1

1354.3

1484.5

1370.8

WANA

107.8

91.4

88.9

123.6

90.7

Others

38,971.20

45,262.30

44,122.50

50,229.20

44,905.10

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Notwithstanding the problems of spatial allocation of monogastrics in this study, chicken and pig numbers are highest in the most intensive systems and mainly in East Asia, while significant numbers of poultry also exist across several systems in Latin America (this is a reflection of the difficulty of allocating monogastrics from industrial systems, which predominate in these regions). Under all scenarios, the growth rates of monogastric animal numbers are slower than the growth in pork and poultry meat production. This is a reflection of gains in feed conversion efficiency over short generation intervals in these species. Pigs have the ability to achieve genetic progress in feed conversion in a short time due to their short cycles of production. At the same time feeding practices have improved significantly, mainly in industrial production systems. This ultimately leads to requiring less animals and less grains to produce the increasing volume of poultry and pork meat globally. Due to cultural factors and dietary preferences, some regions do not experience large growth in pig numbers and production, while in others like SEA, pig production more than doubles though its volume of production is low. In terms of alternative scenarios, the main trends are that the biofuels scenario does not significantly reduce the volume of poultry and pork meat production. Although in direct competition for the demand of grains for energy vs. feed for pig production, the increased conversion efficiency of pigs over time, smoothes this response and diminishes the demand for feed. This also translates in modest price increases in the price of poultry and pork meat. On the other hand, the irrigation expansion scenario, with its increased crop production, increases further the production of this commodity, especially in the more intensive systems with more access to irrigation. The low meat demand scenario reduces slightly the volume of poultry and pork meat produced in comparison to the baseline scenario. These results suggest that these large efficiency gains in monogastrics reduce pressure on agro-ecosystems services, especially in the mixed intensive systems. Not even under a drastic biofuels scenario, the trade-off between feed demand and energy becomes so detrimental for poultry and pork production, though these multiple demands contribute significantly to large increases in the price of grains, in this case cereals which compose up to 60% of the diet of poultry and pigs (Steinfeld et al. 2006). Increases in the prices of grains have a serious detrimental effect on human wellbeing and their accessibility to food. The poor get squeezed as they simply do not have the money to buy more expensive staples. This trade-off is essential for the development of a pro-poor livestock revolution and should be the subject of significant research.

5.4 Crop production This section focuses on the production of the main cereals and root crops (cassava and sweetpotato), as these are the crops facing the largest demands from multiple sources and as a consequence the largest price increases. At the same time these are the crops, apart from some legumes, that largely represent the interests of the SLP as sources of food, feed and fuel and also the interests of different CGIAR crop improvement centres. Area and production of these main crops are presented in Tables 32 and 33 for maize, Tables 34 and 35 for wheat, Tables 36 and 37 for rice, Tables 38 and 39 for sorghum, Tables 40 and 41 for millet, Tables 42 and 43 for barley, Tables 44 and 45 for cassava, Tables 46 and 47 for sweetpotato and Tables 48 and 49 for potatoes.

General characteristics of global crop production trends under the reference scenario In general terms, mixed crop–livestock systems in the developing world are significant producers of global cereals (1a). About 50% of global cereal production is produced in the developing world, with the mixed intensive crop–livestock systems contributing 35% of global cereal production. This is a significant characteristic as it means that at least half of the global production of staples is in the hands of millions of smallholder farmers in largely fragmented landscapes in rural areas of Africa, Asia and Latin America. This characteristic demands very novel forms of support in services, technology and policies, as it is difficult to reach large numbers of small heterogeneous farms.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 32. Global area of maize by system, region and scenario 2000–2030

1659 1138 2681 28 787 202

Irrigation expansion 2030 (103 ha) 1552 1165 2488 28 703 195

Low meat demand 2030 (103 ha) 1557 1106 2503 26 728 192

6054

6495

6131

6113

12,501 2559 1489 2751 16,649 694

14,951 4107 1751 2510 18,771 851

16,133 4167 1903 2689 20,610 848

15,207 4251 1851 2590 18,512 816

15,258 4011 1775 2549 19,094 802

36,644

42,941

46,350

43,227

43,490

12,210 20,466 2923 5050 6619 1271

14,344 21,938 2971 5950 7757 1421

15,486 22,772 3231 6361 8453 1475

14,453 22,805 3149 6125 7650 1417

14,600 21,637 3017 6041 7888 1390

48,539

54,382

57,777

55,600

54,573

1040 657 1166 88 329 12

1282 800 1297 106 411 13

1391 831 1414 113 446 13

1304 833 1316 110 407 12

1307 788 1320 108 418 12

3291

3909

4209

3983

3953

27,014 24,643 7751 7909 24,355 2147 43,985

32,107 27,970 8477 8592 27,655 2485 52,321

34,669 28,908 9229 9191 30,296 2538 56,530

32,516 29,054 8804 8853 27,272 2440 53,973

32,722 27,542 8615 8724 28,128 2396 52,751

Farming system

Region

Maize 2000 (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

1263 961 2173 20 758 170

Reference run 2030 (103 ha) 1530 1125 2458 26 716 200

5345

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all systems

CSA EA SA SEA SSA WANA Others

Biofuels 2030 (103 ha)

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 33. Global maize production by system, region and scenario 2000–2030 Farming system

Region

Maize 2000 (103 ha)

Reference run 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion Low meat 2030 demand 2030 (103 ha) (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

4383 5215 4884 55 1773 720

10,504 8,960 9,892 153 3,637 1,748

11,983 9,301 11,755 173 4,395 1,702

10,782 9293 10,444 168 3686 1593

10,750 8533 10,283 157 3796 1559

17,031

34,893

39,310

35,967

35,078

CSA

34,022

72,333

82,121

74,295

74,204

EA SA SEA SSA WANA

10,796 2285 6419 22,407 1707

25,859 5006 11,413 48,667 4211

27,576 5865 12,957 58,319 4208

27,111 5521 12,193 49,551 3905

25,117 5136 11,734 50,660 3815

77,634

167,489

191,045

172,576

170,665

37,150 97,112 5775 13,701 10,961 7964

86,694 161,619 11,666 33,514 22,948 15,708

98,614 180,827 13,707 37,922 27,341 16,291

88,294 172,803 12,899 35,649 23,315 15,388

88,671 162,066 12,031 34,406 23,950 15,192

172,663

332,149

374,701

348,349

336,315

4275 4164 1845 426 1029 49

9711 7841 3659 1030 2398 113

11,167 8346 4347 1165 2843 108

10,092 8065 3878 1101 2431 98

9997 7546 3802 1057 2487 98

11,788

24,753

27,975

25,666

24,987

CSA EA SA SEA SSA

79,830 117,287 14,789 20,600 36,169

179,242 204,279 30,222 46,111 77,651

203,886 226,049 35,674 52,217 92,898

183,464 217,271 32,741 49,112 78,983

183,622 203,261 31,253 47,354 80,892

WANA Others

10,440 329,928

21,779 572,989

22,309 517,107

20,985 528,799

20,663 532,588

Total Mixed extensive

Total

Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all systems

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 34. Global area of wheat by system, region and scenario 2000–2030 Name

Region

Wheat 2000 (103 ha)

Reference run 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand 2030 (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

1570 851 3549 28 163 2767

1699 677 3242 32 295 4003

1714 681 3315 33 293 3987

1630 709 3222 33 303 4310

1709 679 3272 32 295 3989

8928

9948

10,022

10,207

9976

4551 5508 12,481 70 1337 13,687

4898 6248 11,485 79 2015 16,167

4941 6276 11,919 81 2013 16,162

4717 6571 12,620 82 1923 16,275

4927 6263 11,788 80 2023 16,165

37,633

40,892

41,393

42,188

41,246

3064 19,598 15,893 0 789 6332

4236 15,985 12,722 0 933 7560

4277 16,058 13,198 1 931 7578

4013 16,597 13,896 1 883 7740

4261 16,023 13,045 1 935 7566

45,676

41,436

42,043

43,130

41,831

331 530 2561 0 26 165

415 416 2130 0 39 187

416 418 2205 0 40 187

396 429 2197 0 38 184

416 417 2180 0 40 187

3613

3187

3265

3243

3240

CSA

9516

11,247

11,349

10,756

11,313

EA SA SEA SSA WANA Others

26,486 34,484 98 2314 22,951 111,849

23,327 29,579 112 3283 27,917 120,854

23,432 30,637 114 3277 27,915 121,524

24,306 31,935 116 3146 28,508 119,755

23,382 30,286 112 3293 27,907 121,131

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all systems

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 35. Global production of wheat by system, region and scenario 2000–2030 Farming system

Region

Wheat 2000 (103 ha)

Reference run 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand 2030 (103 ha)

Biofuels 2030 (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

1614 7860 5910 1 342 11,393

2677 8088 9014 2 656 28,258

2578 8865 9064 1 565 28,333

2703 8331 9195 2 579 27,177

2758 8545 9567 2 594 27,780

27,120

48,694

49,405

47,988

49,245

10,827 11,040 9596 26 2385 18,523

17,392 16,848 12,614 51 5688 38,163

16,918 17,470 13,520 41 5588 38,610

17,597 16,425 12,587 41 5799 37,664

17,908 16,840 13,084 42 5920 38,467

52,397

90,756

92,145

90,114

92,260

8575 79,126 69,568 73 1451 15,239

13,477 85,680 88,161 135 2733 32,691

12,880 88,419 96,885 106 2548 31,858

13,378 84,622 89,831 109 2703 30,942

13,699 86,768 93,369 111 2766 31,651

174,031

222,878

232,697

221,586

228,364

820 2161 4224 3 296 960

1345 2220 6331 5 449 2416

1310 2338 6372 4 435 2439

1358 2232 6454 4 440 2394

1389 2290 6723 4 451 2466

8464

12,766

12,898

12,882

13,323

21,836 100,187 89,297 103 4474 46,115 309,589

34,891 112,836 116,120 193 9526 101,528 419,637

33,685 117,092 125,841 153 9136 101,239 432,884

35,037 111,611 118,066 156 9521 98,178 423,885

35,752 114,443 122,743 159 9730 100,363 424,468

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total

Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all systems

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 36. Global area of rice by system, region and scenario 2000–2030

Name

Region

Rice 2000 (103 ha)

Reference run 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand 2030 (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

459 541 5402 198 397 124

450 522 3872 201 344 140

456 519 3756 200 332 135

451 550 3692 200 351 136

453 519 3840 201 340 139

7120

5529

5399

5379

5492

2281 6079 13,305 12,276 3,945 415

2253 6307 14,350 10,062 4,905 436

2283 6295 13,960 9991 4858 421

2238 6702 15,053 9917 4763 423

2270 6288 14,239 10,057 4893 431

38,301

38,313

37,808

39,096

38,177

2755 22,653 35,255 27,606 2072 851

2945 17,534 37,130 30,013 2638 851

3005 17,516 36,425 29,704 2577 807

2965 18,536 38,577 29,675 2538 824

2974 17,504 36,875 29,981 2622 838

91,192

91,111

90,035

93,116

90,795

417 1364 2871 629 273 4

449 1169 2084 646 304 4

462 1168 2022 640 294 4

448 1236 2001 640 305 4

455 1167 2067 646 302 4

5557

4657

4592

4635

4640

5911 30,636 56,834 40,709 6687 1394 5055

6097 25,532 57,436 40,923 8192 1432 4514

6206 25,498 56,164 40,536 8062 1368 4544

6102 27,024 59,323 40,433 7957 1386 4564

6151 25,478 57,020 40,885 8157 1412 4544

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total

Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all systems

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 37. Global rice production by system, region and scenario 2000–2030 Farming system

Region

2000 (103 MT)

Reference run 2030 (103 MT)

Biofuels 2030 (103 MT)

Irrigation expansion 2030 (103 MT)

Low meat demand 2030 (103 MT)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

1379 2358 6638 775 616 339

1965 2410 5658 1142 1075 585

2023 2396 5561 1153 959 599

1983 2551 5390 1138 989 607

1987 2370 5637 1148 962 603

12,105

12,836

12,692

12,658

12,708

4130 24,673 21,834 20,401 3760 1020

6161 28,890 32,680 24,185 8442 1687

6434 29,468 32,103 24,350 8165 1691

6179 30,860 34,171 23,964 7963 1693

6269 29,110 32,364 24,287 8110 1707

75,818

102,045

102,211

104,829

101,846

7454 92,366 82,799 71,455 2473 4292

11,239 84,838 121,167 104,774 4909 5993

11,667 86,294 119,959 105,020 4657 5829

11,410 89,347 125,405 103,973 4575 5893

11,413 85,303 120,007 105,143 4662 5985

260,839

332,920

333,427

340,604

332,512

1244 6708 7256 2518 412 11

1970 6839 6574 3535 940 21

2079 6890 6462 3547 900 21

1996 7115 6327 3509 931 22

2011 6811 6545 3548 905 22

18,149

19,880

19,898

19,900

19,841

14,207 126,104 118,528 95,149 7261 5662 21,375

21,335 122,978 166,079 133,636 15,367 8285 22,751

22,203 125,049 164,086 134,070 14,680 8141 23,293

21,568 129,873 171,293 132,584 14,458 8215 23,325

21,680 123,595 164,552 134,124 14,639 8317 23,094

Total Mixed Extensive

CSA EA SA SEA SSA WANA

Total

Mixed Intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—All systems

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 38. Global area of sorghum by system, region and scenario 2000–2030 Sorghum 2000 (103 ha)

Reference run 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand 2030 (103 ha)

CSA EA SA SEA SSA WANA

188 79 13 0 775 128 1182

258 54 11 0 1035 120 1478

256 55 11 0 1030 123 1476

256 53 11 0 1009 139 1468

257 55 11 0 1034 123 1480

CSA EA SA SEA SSA

2292 167 2028 47 19,056

2849 124 1720 21 27,860

2822 126 1755 21 27,446

2887 126 1748 23 27,112

2842 125 1736 21 27,707

WANA

389

362

372

419

371

23,979

32,937

32,542

32,315

32,802

CSA EA SA SEA SSA

1296 460 2180 40 2436

1398 303 1724 30 3993

1387 307 1756 29 3934

1431 314 1752 32 3887

1395 304 1737 30 3976

WANA

193 6606

197 7645

197 7610

209 7625

198 7639

CSA EA SA SEA SSA

160 20 26 1 93

214 13 22 1 169

213 13 23 1 167

215 13 23 1 161

214 13 23 1 168

WANA

2 301

2 421

2 418

2 415

2 420

CSA EA SA SEA SSA

3935 725 4248 88 22,360

4719 495 3477 52 33,057

4677 501 3545 51 32,577

4789 507 3534 55 32,169

4708 496 3506 52 32,885

WANA Others

711 4273

681 3653

694 3639

769 3686

694 3648

Farming system

Region

(Agro-) pastoral

Total Mixed extensive

Total

Mixed intensive

Total Other

Total Total—all systems

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 39. Global sorghum production by system, region and scenario 2000–2030 Sorghum 2000 (103 MT)

Baseline 2030 (103 MT)

Biofuels 2030 (103 MT)

Irrigation expansion 2030 (103 MT)

Low meat demand 2030 (103 MT)

CSA EA SA SEA SSA WANA

474 237 16 0 612 190 1529

1012 243 19 0 1341 239 2855

1028 253 21 0 1365 253 2921

1025 247 22 0 1336 289 2920

1017 246 21 0 1339 249 2872

CSA EA SA SEA SSA WANA

6296 484 1531 78 15312 402 24,102

10,627 586 2102 67 36,269 511 50,161

10,804 613 2225 67 36,498 538 50,746

11,135 615 2276 74 36,173 617 50,890

10,701 596 2155 67 36,161 530 50,210

Farming system

Region

(Agro-) pastoral

Total Mixed extensive

Total

Mixed intensive

CSA

3828

5525

5653

5881

5585

EA SA SEA SSA WANA

1621 1681 69 2506 947 10,653

1385 2174 101 6693 1182 17,059

1460 2292 101 6768 1225 17,498

1523 2348 111 6690 1252 17,804

1421 2221 101 6698 1206 17,232

CSA EA SA SEA SSA WANA

442 190 25 4 246 1 908

897 66 35 5 691 2 1696

915 69 37 5 705 2 1732

930 68 38 5 677 3 1720

904 67 36 5 695 2 1708

CSA EA SA SEA

11,039 2533 3254 151

18,061 2279 4330 172

18,400 2395 4576 173

18,972 2454 4684 189

18,206 2330 4433 172

SSA WANA Others

18,676 1540 16,389

44,994 1935 16,360

45,336 2019 16,612

44,877 2161 16,936

44,893 1988 16,426

Total Other

Total Total—all systems

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 40. Global area of millet by system, region and scenario 2000–2030 Millet 2000 (103 ha)

Reference run 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand 2030 (103 ha)

CSA EA SA SEA SSA WANA

2 134 N/A 11 780 14 955

1 97 N/A 12 983 16 1120

1 97 N/A 12 964 16 1101

2 96 N/A 13 965 17 1102

1 97 N/A 12 978 16 1115

CSA EA SA SEA SSA WANA

22 312 2482 129 17,313 113 20,370

17 229 1871 134 21,103 115 23,467

16 229 1886 133 20,527 116 22,907

18 234 1886 137 20,585 125 22,985

17 229 1882 133 20,934 116 23,311

CSA

EA SA SEA SSA WANA

473 3015 94 1661 18 5264

338 2198 108 2120 21 4787

338 2215 107 2047 21 4731

344 2214 111 2051 22 4745

338 2211 107 2096 21 4776

CSA EA SA SEA SSA WANA

0 124 56 16 60 2 257

0 89 49 17 79 2 236

0 90 49 17 77 2 234

0 89 49 17 76 2 235

0 89 49 17 78 2 236

CSA EA SA SEA SSA WANA Others

27 1042 5567 249 19,814 148 1664

21 753 4127 271 24,284 154 1190

20 754 4160 268 23,615 155 1179

23 763 4159 278 23,678 167 1166

21 753 4152 270 24,087 156 1186

Farming system

Region

(Agro-) pastoral

Total Mixed extensive

Total

Mixed intensive

Total Other

Total Total—all regions

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 41. Global millet production by system, region and scenario 2000–2030 Millet 2000 (103 MT)

Reference run Irrigation expansion Low meat demand Biofuels 2030 2030 2030 2030 (103 MT) (103 MT) (103 MT) (103 MT)

CSA EA SA SEA SSA WANA

4 215 6194 10 531 63 7016

5 252 8540 15 1279 93 10,184

5 258 8757 15 1281 95 10,411

6 258 8972 15 1283 104 10,639

5 254 8604 15 1279 95 10,251

CSA EA SA SEA SSA WANA

26 474 1753 141 10,627 25 13,046

30 577 2253 188 24,446 41 27,535

30 589 2340 190 24,129 42 27,320

35 617 2422 193 24,366 45 27,679

30 580 2300 188 24,341 42 27,481

CSA EA

6 896

5 888

5 907

5 945

5 893

SA SEA SSA WANA

2469 61 1710 22 5164

3107 100 4044 35 8179

3222 100 3973 36 8243

3339 105 4011 38 8444

3167 100 4011 35 8210

CSA EA SA SEA SSA WANA

1 171 75 28 100 2 376

1 74 100 36 252 3 466

1 75 103 37 250 3 469

1 80 106 37 251 3 478

1 74 101 37 251 3 467

CSA EA SA SEA

37 1756 10,490 240

41 1790 14,000 338

40 1829 14,422 342

47 1900 14,839 350

40 1800 14,171 339

SSA WANA Others

12,967 112 1649

30,021 173 1892

29,633 176 1904

29,912 190 1907

29,882 175 1893

Farming system

Region

(Agro-) pastoral

Total Mixed extensive

Total Mixed intensive

Total Other

Total Total—all regions

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 42. Global area of barley production under different scenarios Farming system

Region

Barley 2000 (103 ha)

Reference run 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion (103 ha)

Low meat demand 2030 (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

119 92 264 0 14 1424

155 77 254 0 19 2007

160 81 268 0 20 2128

158 77 262 0 20 2040

154 78 259 0 20 2055

1913

2513

2657

2557

2566

463 152 151 6 990 6506

614 168 147 5 1746 8135

647 177 154 5 1834 8644

637 169 154 5 1670 8419

625 172 150 5 1784 8346

8268

10,815

11,462

11,055

11,081

383 797 265 1 107 2549

647 565 222 6 283 3393

676 595 230 6 297 3604

655 569 230 6 270 3495

654 578 225 6 289 3481

4102

5116

5408

5225

5232

84 42 138 0 6 101

127 35 131 0 8 148

134 36 138 0 9 158

132 35 135 0 8 150

129 35 133 0 9 152

370

449

475

461

459

1050 1083 818 7 1116 10,579 39,136

1544 844 753 11 2056 13,684 41,840

1617 889 791 11 2160 14,533 44,384

1583 850 781 12 1968 14,104 42,299

1562 863 767 11 2101 14,034 42,798

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all regions

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 43. Global production of barley by system, region and scenario Farming system

Region

Barley 2000 (103 MT)

Reference run Biofuels 2030 2030 (103 MT) 3 (10 MT)

Irrigation Low meat expansion 2030 demand 2030 (103 MT) (103 MT)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

212 266 513 0 34 1926

553 426 805 0 116 5288

602 464 885 0 124 5717

579 443 839 0 115 5484

569 441 836 0 117 5475

2951

7188

7793

7461

7437

797 349 235 12 1015 7103

2017 680 382 11 3954 19,298

2182 751 422 12 4344 20,806

2130 716 419 12 3866 20,154

2061 712 396 11 4089 20,023

9512

26,342

28,518

27,299

27,294

783 2238 400 1 102 3702

2592 2540 591 28 554 10,228

2759 2801 645 30 607 11,016

2635 2660 636 31 539 10,650

2602 2651 610 29 572 10,593

7226

16,532

17,859

17,151

17,057

147 124 260 0 13 172

456 164 402 1 39 489

493 180 442 1 42 520

478 172 419 1 39 504

465 170 417 1 40 507

715

1551

1678

1613

1599

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all regions

CSA

1939

5619

6037

5821

5696

EA SA SEA SSA WANA Others

2976 1408 14 1164 12,903 113,832

3809 2179 39 4663 35,303 161,840

4197 2394 43 5118 38,059 177,910

3992 2314 44 4559 36,792 166,535

3974 2259 41 4818 36,599 167,632

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 44. Global area of cassava by system, region and scenario 2000–2030 Cassava 2000 (103 ha)

Reference run 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat 2030 (103 ha)

Biofuels 2030 (103 ha)

CSA EA SA SEA SSA WANA

162 39 24 4 238 0 468

174 44 30 5 260 0 513

175 38 28 5 226 0 472

175 39 29 5 231 0 478

177 39 29 5 234 0 484

CSA EA SA SEA SSA WANA

1261 63 49 1156 8881 0 11,410

1301 84 58 878 12,180 0 14,501

1311 71 57 861 10,546 0 12,846

1304 73 58 879 10,804 0 13,118

1321 73 59 884 10,927 0 13,264

CSA EA SA SEA SSA WANA

976 203 49 1737 3227 0 6192

1157 211 48 1835 5262 0 8513

1166 178 48 1798 4549 0 7739

1161 182 49 1836 4658 0 7885

1175 183 49 1844 4715 0 7968

CSA EA SA SEA SSA WANA

232 20 8 73 431 0 764

263 22 10 80 592 0 967

264 19 10 78 512 0 882

264 19 10 80 525 0 897

267 19 10 80 531 0 908

CSA EA SA SEA SSA WANA Others

2631 326 130 2970 12,777 0 24

2896 362 147 2797 18,293 0 25

2917 306 143 2740 15,833 0 25

2904 313 145 2799 16,217 0 25

2941 315 147 2813 16,407 0 25

Name

Region

(Agro-) pastoral

Total Mixed extensive

Total

Mixed intensive

Total Other

Total Total—all regions

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 45. Global cassava production by system, region and scenario 2000–2030 Farming system

Region

Cassava 2000 (103 ha)

Baseline 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA

2310 753 3 103 4657

3283 1051 4 136 6524

3717 1088 5 139 6626

3658 1050 5 133 6330

3601 1059 5 136 6355

WANA

7827

10,997

11,575

11,176

11,155

CSA EA SA SEA SSA WANA

14,077 1370 251 24,634 71,664 0 111,996

20,138 2077 263 15,231 114,214 0 151,923

22,706 2141 350 16,025 114,256 0 155,479

22,402 2062 340 15,479 109,295 0 149,578

22,016 2085 336 15,663 110,212 0 150,312

CSA EA SA SEA SSA WANA

12,006 3619 616 22,072 28,525 0 66,838

19,616 4121 673 45,526 54,811 0 124,748

22,441 4246 881 46,432 56,372 0 130,373

22,122 4090 855 44,827 53,776 0 125,670

21,753 4135 849 45,425 54,241 0 126,402

CSA EA SA SEA SSA WANA

3505 504 37 2008 6220 0 12,275

5686 630 44 2588 11,795 0 20,742

6342 652 57 2661 11,605 0 21,318

6224 629 56 2590 11,088 0 20,586

6142 634 55 2610 11,166 0 20,607

CSA EA SA SEA SSA WANA Others

31,898 6246 908 48,817 111,067 0 323

48,723 7878 984 63,481 187,344 0 535

55,206 8128 1293 65,257 188,859 0 556

54,406 7831 1255 63,029 180,489 0 551

53,512 7913 1245 63,834 181,973 0 541

Total Mixed extensive

Total Mixed intensive

Total Other

Total

Total—all regions

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 46. Global area of sweetpotato by system, region and scenario 2000–2030 Name

Region

Sweetpotato 2000 (103 ha)

Reference run 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand 2030 (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

22 87 0 2 106 0

21 68 0 2 119 0

21 68 0 2 107 0

22 66 0 2 105 0

21 68 0 2 107 0

217

210

199

195

198

90 1148 17 176 4001 1

103 955 14 175 5583 1

103 962 14 175 4838 1

106 932 14 177 4791 1

103 958 14 175 4853 1

5432

6830

6094

6022

6105

161 3878 78 419 1904 10

194 2885 70 384 2661 12

194 2908 71 394 2453 12

201 2819 71 393 2413 12

194 2894 70 394 2456 12

6449

6206

6032

5907

6021

38 114 2 72 70 0

47 87 1 78 98 0

47 88 1 78 90 0

49 85 1 79 89 0

47 88 1 78 91 0

296

312

305

303

305

311 5226 96 669 6081 11 113

365 3995 85 639 8461 13 105

366 4027 87 650 7488 13 105

377 3902 87 650 7398 13 105

366 4007 86 650 7507 13 105

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all regions

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 47. Global sweetpotato production by system, region and scenario 2000–2030 Sweetpotatoes 2000 (103 MT)

Baseline 2030 (103 MT)

Biofuels 2030 (103 MT)

Irrigation expansion 2030 (103 MT)

Low meat demand (103 MT)

CSA EA SA SEA SSA WANA

250 2326 713 17 1036 6 4347

454 2592 1076 30 1694 13 5859

431 2660 1119 30 1649 11 5900

439 2593 1115 30 1640 11 5829

425 2612 1092 29 1622 11 5792

CSA EA SA SEA SSA WANA

787 22,093 158 874 47,327 6 71,246

1584 26,502 204 1574 67,304 17 97,184

1502 27,872 213 1541 66,566 14 97,709

1533 27,183 212 1571 66,292 15 96,806

1482 27,365 208 1525 65,741 14 96,335

CSA EA SA SEA SSA WANA

1217 81,383 815 3069 14,657 257 101,396

2937 84,810 1066 6552 24,725 673 120,763

2825 89,236 1108 6195 26,194 556 126,114

2911 87,060 1118 6219 25,842 547 123,697

2787 87,621 1079 6128 25,751 549 123,914

CSA EA SA SEA SSA WANA

303 8324 17 389 776 0 9808

716 8535 23 720 1609 0 11,604

683 9012 24 713 1573 0 12,005

706 8790 25 724 1556 0 11,801

673 8848 24 705 1546 0 11,795

CSA EA SA SEA SSA WANA Others

2556 114,125 1702 4348 63,796 269 2296

5691 122,439 2370 8876 95,331 703 3032

5441 128,780 2464 8478 95,982 582 3040

5589 125,626 2469 8544 95,330 573 3045

5367 126,446 2403 8387 94,659 574 3006

Farming system

Region

(Agro-) pastoral

Total Mixed extensive

Total Mixed intensive

Total Other

Total Total—all regions

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 48. Global area of potatoes by system, region and scenario 2000–2030 Farming system

Region

Potatoes 2000 (103 ha)

Baseline 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand 2030 (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

174 390 496 2 35 69

180 440 471 2 41 83

177 438 466 2 39 82

181 438 476 2 38 85

178 440 471 2 39 82

1165

1217

1203

1220

1212

396 1554 185 51 653 328

440 2105 257 54 838 341

430 2090 254 54 811 339

433 2100 275 53 790 348

431 2102 255 54 824 340

3167

4035

3978

3999

4007

396 2665 774 72 180 324

411 2530 1040 88 339 400

403 2515 1037 88 331 393

408 2529 1084 87 312 400

405 2528 1040 88 336 394

4411

4808

4766

4819

4793

97 394 20 7 39 12

109 398 26 8 46 16

107 395 25 8 44 16

108 400 26 8 45 16

108 398 26 8 45 16

569

601

595

603

600

CSA EA SA SEA SSA WANA

1063 5003 1474 131 906 734

1139 5473 1793 152 1263 840

1117 5438 1782 151 1225 829

1131 5467 1862 149 1184 849

1122 5469 1792 152 1244 832

Others

10,406

8832

8758

8724

8824

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all regions

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 49. Global production of potatoes by system, region and scenario 2000–2030 Farming system

Region

Potatoes 2000 (103 ha)

Baseline 2030 (103 ha)

Biofuels 2030 (103 ha)

Irrigation expansion 2030 (103 ha)

Low meat demand (103 ha)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

2199 6707 14,734 21 308 1938

3006 8600 19,681 37 506 3432

2887 8660 19,912 37 466 3284

2963 8770 20,352 36 462 3399

2868 8612 19,844 37 466 3263

25,907

35,262

35,246

35,983

35,090

5568 18,840 2827 557 5520 5523

8790 28,038 5552 834 10,042 7873

8246 28,321 5409 845 9830 7586

8333 28,656 6001 822 9495 7773

8202 28,167 5381 839 9839 7538

38,835

61,129

60,237

61,079

59,967

7150 35,454 12,872 913 1245 7720

10,980 35,263 24,722 1707 3715 14,247

10,634 35,628 24,531 1726 3626 13,679

10,780 36,051 26,292 1719 3381 13,967

10,574 35,427 24,332 1717 3630 13,592

65,354

90,634

89,824

92,190

89,273

1582 3153 579 120 219 232

2470 2336 972 206 469 409

2418 2373 980 208 459 404

2449 2388 1012 205 460 419

2404 2361 974 207 460 401

5885

6863

6841

6933

6807

16,500 64,153 31,011 1611 7293 15,413 178,177

25,245 74,237 50,927 2785 14,733 25,961 199,086

24,184 74,982 50,833 2816 14,381 24,953 199,763

24,525 75,865 53,658 2783 13,797 25,558 199,847

24,048 74,567 50,532 2801 14,395 24,795 199,555

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total

Mixed intensive

CSA EA SA SEA SSA WANA

Total Other

CSA EA SA SEA SSA WANA

Total Total—all regions

CSA EA SA SEA SSA WANA Others

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 34. Global cereal production–2000

Food production Cereals Producti 4%

14%

45%

35%

AgroPastoral Mixed Extensive Mixed Intensive Other Developed countries

Mixed systems produce almost 50% of the cereals of the World Most production coming from intensive systems (irrigation, high potential, relatively good market access)

More importantly, these systems produce the main staples consumed by the poor (see Figure 35). Figure 35. Mixed systems in the developing world produce the food of the poor

Mixed systems in the developing World produce the food of the poor Maize Production 3% 13%

54%

Millet Production 1% 6% 26% 19% AgroPastoral Mixed Extensive Mixed Intensive Other Developed countries

28% 2% Rice Production 5% 6% 3% 20%

48% Sorghum Production 3% 31% 44% 2%

66%

20%

Apart from maize, which has multiple uses and large quantities are produced in the developed world (40% produced in the developing world), 86% of rice, 67% of millet and 64% of sorghum are produced in mixed systems of the developing world. The former mostly in mixed intensive irrigated systems in Asia, while millets and sorghums are grown largely in mixed extensive systems globally. This is a true case of ‘poor producers feeding poor consumers’ in the developing world. The share of global cereal production in the developing world will increase even further due to faster rates of growth to 2030 than those in the developed world (Figure 36). However, by 2030, rates of growth of cereal will have stagnated in some places, notably in East and South Asia. Significant growth will be observed in SSA as a result of a combination of small area expansion and increased productivity due to increased use of inputs and technology.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Certainly yield gaps in SSA are higher than in East or South Asia. However, in the mixed intensive systems of SSA, like in the Kenyan highlands, land fragmentation is causing significant constraints to production, forcing farmers to diversify or to exit agriculture (Box 2). Figure 36. Rates of cereal production to 2030 by farming system under the reference scenario

Rates of cereal production diminishing in places due to water and other constraints

Annual changes in Cereal Production 2000 - 2030 Rates lower than those of population growth

Rates of growth of mixed intensive similar to developed countries

Catching up

6 5

4 3 2 1 0 CSA AgroPastoral

EA Mixed Extensive

SEA Mixed Intensive

SSA Other

WANA

Total

Developed countries

Rates of cereal production in the more extensive mixed systems are higher than in the more intensive systems which reflect higher potential for tapping existing yield gaps through technology and policy. In the mixed intensive systems, the green revolution increased yields significantly in Asia, to a point that rates of growth to 2030 will resemble those of developed countries.

Area and production of cereals and root crops under reference conditions Maize is a key crop in the biomass competition throughout the world. It is used for human consumption, monogastric feeding and more recently for biofuel production. It is mostly produced in mixed intensive systems with good lengths of growing period. Nevertheless, it is increasingly grown in more extensive marginal environments like in mixed extensive systems and agropastoral areas. In all these systems, maize stover is a key feedstuff for ruminants. East Asia produces the largest amounts of maize. Due to its key role as food, feed and energy, and the increasing demands from these sectors, both area and but especially production will increase significantly by 2030 under the reference scenario. Area expansion will occur mostly in SSA and CSA, while productivity gains will occur, notably in East Asia, where more than 80% of the maize is produced in the mixed intensive systems.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030 1

Box 2. Falling farm sizes, diversification, and poverty in Kenya There are many drivers of change in rural Kenya. Perhaps the strongest of them have been population growth and commercialization. While urban population growth outpaces that of the rural area, rural population continues to grow in absolute number (by 2.3% per year between 1990–2005) each year, leading to a doubling of population density over the past 31 years. New land is being cleared to accommodate some of the pressure, but in many agricultural areas, land holdings are subdivided and passed to multiple heirs. The implication of this reduction of farm size is the focus of this analysis. A survey of over 900 households in 15 districts was used to explore relationships between farm size, diversification and income in Kenya.1 In terms of farm size, the average among the households was about 1.6 with just a small difference across three major provinces: Nyanza 1.5 ha, Western 1.4 ha, Central 1.7 ha. The small land holdings provide strong incentives for intensification (following the Boserup hypothesis). Coupled with that, there have been improved opportunities for commercialized agriculture, emanating from growing urban markets and increased access to them, expanded international demand for selected commodities, and a re-orientation of extension and agricultural development projects towards income generation. Conventional wisdom based on economies of scale in production or marketing would suggest that farmers might respond through specialization—growing best suited food crops and the most remunerative cash crops. The data show, however, that diversification overwhelms specialization in terms of smallholder crop portfolios. Specialization is common in two key zones—the cereal belt of the Rift Valley where farming is on a larger scale and often mechanized and in the higher altitude tea zones where tea farming has been lucrative for years. Elsewhere, diversification is the norm, where on average, farmers harvest seven different crops. Among the sample, 36 different crops were commonly harvested (by a minimum of 40 farmers). Interestingly, diversification is commonplace even on the small farms. The correlation between number of crops grown and size of farm is slightly negative (–0.07). Although, economic theory would suggest that specialization is positively related to income, in this sample, the opposite is true. The more the number of crops grown (or number per hectare), the greater the income from crops (or crops per hectare). The correlation between the variables is 0.15. Diversification strategies differ across Kenyan regions, however. While the number of crops grown per farm is similar across region, in Central Province, there is much more focus on cash crop diversification than in Western Province. For example, cropping area under maize and other cereals is about 66% of smallholder cultivated area in Western Province, but only 46% in Central Province. This matters significantly in terms of poverty alleviation. Recent estimates of poverty rates show that districts in Central Province have the lowest poverty rates in rural areas. In contrast, those in Western Province are very high. In conclusion, smallholder farmers in Kenya have opted for a diversification farming strategy (and this would also include an array of livestock and tree growing practices as well). In general, diversification is associated with greater agricultural income, but the degree of impact on poverty alleviation depends greatly on the market opportunities available and seized by farmers. Frank Place (ICRAF).

Wheat is also produced in large quantities in the most intensive mixed systems of the developed and developing World. Under the reference scenario, its area reduces in intensive systems mainly at the expense of maize, notably in East Asia. Small area increases are observed in more marginal areas in the mixed extensive systems, notably in SSA and in East Asia.

1.The survey was conducted in 2004 in maize growing districts as part of the Research on Poverty, Environment, and Agricultural Technology (RePEAT) project.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

In SSA and WANA areas are increasing across systems. Nevertheless, production of wheat will increase in most systems to 2030. Large increases are observed in developed countries (others category in all systems), WANA and SSA, while production in mixed intensive systems will increase much less than in the extensive systems; this is as a result of water shortage for irrigation. Rice is also mostly produced in the mixed intensive, largely irrigated systems. The largest producers are EA, SA and SEA. In general terms areas will remain stable under the references scenario as a result of the low irrigation expansion, though small increases can be observed in SA and SEA. Rice areas in EA will tend to decrease also at the expense of other crops. Rice production is likely to increase much less than the other cereals as a result of water shortages due to competition for water from other sectors. The largest production increases are likely to be observed in the mixed extensive systems. The majority of rice production will, however, remain in the mixed intensive systems. Sorghum and millet are dryland crops that grow predominantly in mixed extensive and agropastoral systems. SSA is the largest producer of both of these crops. Sorghum and millet stovers represent key feed resources in semiarid tropical areas. For sorghum, substantial area and production increases will occur under the reference scenario to 2030, mostly in SSA. Across Asia, areas will decrease as competition with other crops, notably maize for food, monogastric feed production and biofuels increases. A similar pattern is observed in millet production, with significant increases in SSA and WANA and reductions in Asia, notably in the mixed intensive systems, where productivity and economic gains of growing other crops might be larger. In essence, these are key crops in arid tropical environments of Africa, but maize is replacing them in parts of Asia to satisfy the larger demands from multiple sources (humans, animal and the energy sector). Cassava is an important crop, primarily in mixed extensive systems and agropastoral systems of SSA and in some mixed intensive systems of SEA. The largest increases in production under the reference run will be observed in SSA as a result of increased demands from the increasing human population and animal numbers and the steady trend of using cassava for ethanol production. Sweetpotato, on the other hand, is a key crop in mixed intensive systems of EA, where it is used as a dual purpose crop in smallholder pig systems and also in mixed extensive and intensive system of SSA. Sweetpotato areas under the baseline run are dynamic. SSA will likely increase the area under sweetpotato in all systems but the area under sweetpotato is expected to reduce in EA.

Area and production of cereals and root crops under alternative scenarios Biofuels scenario: increases in the demand of maize grain as a biofuel source will cause that area and production of maize will increase across systems and regions, with the largest gains observed in the mixed extensive (CSA, SSA and the developed world largest producers) and the mixed intensive systems (mainly the developed world ‘others’, EA and CSA) in comparison to the reference scenario. Note that maize production in the mixed intensive systems in the developed world, represented here by the ‘others system’ is twice the amount than in all the developing world combined. In comparison to the reference scenario almost 10% more maize is projected to be produced. Cassava and sweetpotato production, the other key biofuel crops will experience large increases in area and production, mostly in SSA where it is grown in large areas. The other crops will be relatively insensitive to the biofuels scenario, but will still constitute a key component in animal feedstuffs. Irrigation scenario: Water shortages are a serious threat to increasing agricultural production. Under the irrigation scenario, only rice (especially in SA and SEA) and sorghum (SSA) in the mixed intensive system experience increases in production. The increases in production are a combination of moderate irrigation expansion and efficiency.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Box 3. The political economy of land cover and land use changes in Fogera and Lenche Dima, Ethiopia Key message Drivers of change in the Ethiopian highlands differ depending on the agro-ecologies and socio-economic arrangements. For instance, in the Fogera woreda (district) of Ethiopia, a water abundant case study area, introduction of rice in the formerly unproductive flooded plains and the implementation of irrigation schemes along the streams have allowed for multiple cropping and have markedly increased overall water productivity of the agricultural systems. Whilst there has been an increase of rice cropped area from 6 ha in the 1993/94 farming season to 6378 ha in the 2004/05 farming season as shown in Table 1, the livestock grazing area has markedly declined. As a result of expanding crop fields, livestock increasingly depend on low quality crop residues for most of the year. Agricultural intensification through introduction of rice and high value vegetables and increasing population pressure have been the drivers behind the land use/land cover changes. Given the huge grazing pressure on the remaining grasslands and the feed shortage problem, introducing high quality forages into the system while improving veterinary services and reducing the numbers of livestock seem to be promising interventions to improve water productivity in these crop–livestock systems.

Table 1. Trends in rice cultivation in Fogera woreda Years 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–2000 2000–2001 2001–02 2002–03 2003–04 2004–05

No. of peasant associations (PAs) 2 5 5 5 11 13 13 14 14 14 14 14

No. of households

Area (ha)

Yield (quintiles/ha)

30 256 494 1334 2957 4450 6158 9453 9796 11,032 15,000 15,945

6 65 130 487 1113 1670 1968 2907 3037 3340 3480 6378

27 25 13 30 15 25 31 35 35 35 35 45

Source: Ethiopia BMZ baseline report.

On the other hand, in the water scarce Lenche Dima case study, land degradation and drought are the main bottlenecks for agricultural intensification. Over the last decades, the increase in population has been the major driver behind increases in cropland, and corresponding decreases in forests and grasslands. The establishment of exclosures for land rehabilitation have on the one hand resulted in higher feed availability, and on the other hand have further put pressure on communal grazing areas, which have been declining over the years. Inter-community water access conflicts have escalated as most of the water upstream is now being used for irrigation and as water for livestock drinking is very scarce. Within the irrigation schemes themselves, conflicts have arisen due to inadequate water to meet the requirements of all the irrigators. The water scarcity in Lenche Dima enforces the need for improved water storage and regulations and their enforcement with respect to water distribution and the management of livestock drinking ponds. Past rehabilitation efforts have shown that revegetation can result in a more productive use of the water flows in a landscape.

Feeding resources for ruminants—A key pressure point? Stover is a key feed resource for ruminants in mixed crop–livestock systems (Powell and Williams 1995). It comprises between 45–60% of the diets of ruminants in these systems (Blummel et al. 2006). Since the IMPACT model only includes grains in the calculations, we estimated the amounts of stover produced from cereals from the knowledge of crop production and generic harvest indexes by crop to ascertain whether there would be enough feed to feed ruminants in these systems.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

As expected, apart from the developed world which produced the largest quantities of cereals, the amounts of stover are largest in the mixed intensive and mixed extensive systems of the developing world. In intensive systems, the larger productivity of the crops determines the amounts of stover; while in the extensive regions it is mostly the larger is areas that produce the higher quantities of stover. In 2000, the largest stover production areas were the mixed systems of SA, CSA, the mixed intensive system in EA and the mixed extensive system in SSA. Changes in stover production vary widely from region to region for the reference run to 2030. Large increases in stover production are likely to occur in Africa as a result of area and productivity increases mainly in maize, sorghum and millet. Other large increases will occur across systems in SSA and CSA and less so in the mixed extensive systems of East Asia. It is important to note that there are systems where stover production will stagnate, notably the mixed extensive and intensive systems of SA which together have the largest numbers of ruminants in any system in the world. A proper assessment of the adequacy of the amount of stover in each system can only be made when comparing it to the numbers of animals present, by knowing the amounts of metabolizable energy the stover may contribute to, and by knowing the requirements of the animals. For the calculations that follow we assume that a 250 kg LU will require around 15 thousand megajoules of metabolizable energy per year to meet its maintenance requirement. This equates to 41 MJ ME/day per animal and includes small corrections for level of activity. Figure 38 presents the global availability of ME from stover per LU per day. Table 50 presents the results for 2000 and for the reference run to 2030. As expected, apart from the developed world which produced the largest quantities of cereals, the amounts of stover are largest in the mixed intensive and mixed extensive systems of the developing world. In intensive systems, the larger productivity of the crops determines the amounts of stover; while in the extensive regions it is mostly the larger is areas that produce the higher quantities of stover. In 2000, the largest stover production areas were the mixed systems of SA, CSA, the mixed intensive system in EA and the mixed extensive system in SSA. Table 51 presents the metabolizable energy amounts (total and per LU) by system, region and scenario. The key observations we can make are from these numbers are: 1. The increase in animal numbers has outpaced the rate of growth in availability of stover in many places. This means that either stover will become less important as a feed in these systems and it will be substituted by other feeds in the diet, or that there will be significant feed deficits in some places. 2. There are vast differences in the ME availability from stover by region and system. Several have surpluses but others (those with less than 15 thousand MJ/LU, see Table 51, Figure 38) will not be able to meet the maintenance requirements of ruminants from stover alone and will need to obtain all production from alternative feed resources. 3. This may not be a problem in many parts, like in some systems where land is not a constraint as other feed resources can be planted or will be available. However, of alarming concern are places like the mixed intensive systems of South Asia that depend on irrigation to a great extent and which are supposed to produce 113 million tonnes of milk and 4.5 million tonnes of beef to contribute to feeding the ever-increasing populations. All this production will have to come from alternative feed resources apart from stover (which only meet the maintenance requirements of the animals. Other systems face similar dilemmas although they have fewer animals to feed).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 50. Stover production in the developing world 2000–2030 under alternative development scenarios Farming system

Region

Stover in 2000 (106 MT)

Baseline 2030 (106 MT)

Biofuels 2030 (106 MT)

Irrigation expansion 2030 (106 MT)

Low meat demand 2030 (106 MT)

(Agro-) pastoral

CSA EA SA SEA SSA WANA

11.86 20.23 31.71 0.84 6.47 15.62

25.7 27.17 46.77 1.37 13.64 38.29

28.46 28.27 50.88 1.41 14.84 38.27

26.16 28.69 48.3 1.39 13.55 38.49

26.2 26.67 47.75 1.38 13.73 37.15

86.73

152.95

162.14

156.57

152.88

89.93 55.61 41.27 30.74 102.82 31.27

176.36 92.19 61.66 42.93 236.08 69.58

194.5 95.89 63.56 45.72 253.04 71.63

180.7 97.01 65.62 44.07 236.56 70.74

180.1 90.79 61.79 43.56 238.98 69.28

351.63

678.79

724.33

694.7

684.5

CSA

89.8

189.33

210.85

192.6

192.94

EA SA SEA

338.88 166.28 91.31

450.46 233.67 156.91

486.75 241.76 164.66

477.21 249.36 159.82

450.93 235.11 158.76

SSA WANA

31.99 39.41

71.3 79.5

78.73 80.29

71.35 78.68

72.72 77.34

757.67

1181.17

1263.05

1229.00

1187.80

10.6 16.34 14.69 3.19 3.26 1.25

22.61 22.41 19.47 5.18 7.68 3.17

25.34 23.43 20.99 5.42 8.44 3.25

23.36 23.19 19.69 5.28 7.68 3.19

23.18 21.9 19.83 5.24 7.8 3.14

49.32

80.53

86.86

82.39

81.1

Total Mixed extensive

CSA EA SA SEA SSA WANA

Total Mixed intensive

Total Other

CSA EA SA SEA SSA WANA

Total Total—all systems

CSA

202.19

414

459.15

422.82

422.42

EA SA SEA SSA WANA Others

431.06 253.95 126.08 144.54 87.55

592.23 361.57 206.39 328.7 190.54

634.34 377.19 217.21 355.05 193.44

626.1 382.97 210.56 329.14 191.1

590.29 364.48 208.94 333.23 186.91

1063.65

1568.16

1675.94

1586.13

1559.90

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 51. Metabolizable energy from stover by system, region and scenario to 2030 Farming systems (Agro-) pastoral

Mixed extensive

Mixed intensive

Other

Total—all regions

Region

Stover ME/LU 2000

Baseline 2030 Biofuels 2030

Irrigation expansion 2030

CSA EA SA

(103 MJ/LU) 1.40 5.96 26.15

(103 MJ/LU) 2.05 4.21 28.38

(103 MJ/LU) 2.05 3.96 25.63

(103 MJ/LU) 2.10 4.15 29.05

SEA SSA WANA CSA EA SA SEA SSA WANA CSA EA SA SEA SSA WANA CSA EA SA SEA SSA WANA CSA EA SA SEA SSA WANA Others

3.69 1.00 6.04 10.49 17.50 3.96 22.24 11.19 25.99 10.20 66.50 10.94 45.19 15.74 34.61 1.93 10.76 11.87 3.25 3.00 5.25 6.53 32.61 9.82 26.93 7.78 17.17 27.36

3.98 1.96 10.66 12.74 13.14 5.55 22.51 19.56 46.02 15.06 48.85 13.61 44.77 25.14 53.90 2.70 7.57 13.78 3.43 5.11 11.63 8.83 23.05 12.58 28.26 14.14 27.94 39.73

3.25 1.78 10.29 12.94 12.17 4.76 18.90 17.98 44.91 15.09 45.51 11.96 37.54 23.59 52.01 2.73 6.91 11.54 2.83 4.76 11.25 9.96 24.89 13.51 30.19 15.45 28.77 43.02

4.03 1.97 10.36 13.09 12.97 5.61 22.91 19.88 45.95 15.42 49.03 13.81 45.46 25.72 52.50 2.78 7.42 14.15 3.48 5.20 11.58 8.88 21.50 10.91 23.57 13.02 26.99 40.96

Figure 37. Composition of cereal stover availability by system and region 2000

Low meat demand 2030 (103 MJ/LU) 2.31 4.42 31.14 4.16 2.15 10.79 14.36 13.79 5.90 24.31 21.27 48.15 17.06 53.26 14.46 47.66 28.06 55.10 3.07 7.99 15.25 3.65 5.67 12.13 9.05 23.03 12.80 28.71 14.37 27.46 39.63

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 38. Global availability of metabolizable energy from stover for ruminants and its change to 2030

4. Land availability and water will be key constraints to the production of alternative feeds. If this production levels were to materialize, water demands from livestock would rise several fold (billions of litres) to produce fodders for animals and would compete directly with irrigation for the production of crops for multiple uses. On the other hand, if more grains were given to ruminant to match production this is likely to increase the prices of animal products further, thus bypassing the abilities of the poor to consume more milk and meat. In a sense—the livestock revolution—at least from ruminants, could potentially exclude the poor in terms of the benefits of consumption.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

5. Another possible trend (and opportunity) would be a substantially increased trade of fodders and stovers in certain regions to move ‘megajoules’ from surplus to deficit areas (see Box 4). There is growing evidence that this is starting to happen at an accelerated pace in parts of South Asia (Michael Blummel, personal communication). Stovers are traded in India, they cover vast distances, their price is increasing and farmers are starting to pay for quality indicators. 6. In these highly populated, land scarce systems competition or incentives for second generation biofuels from stovers may not happen, as there is not enough feed for ruminants. Prices of stover relative to efficiency and output prices of bio-energy and livestock production will determine the magnitude off this trade-off. Unless the residue after biofuel extraction was useable by ruminants then this could be an option to have both activities simultaneously, at least for a proportion of the farmers. 7. It is clear that the developed World has a surplus of stover that is not used as animal feed due to the poor quality of the material relative to the abundance of high quality, energy dense feed resources available and the needs of high producing animals to consume high quality feeds. Since stover is a surplus commodity that may only compete with conservation agriculture, these regions could invest in second generation biofuel technologies without detrimental trade-offs with their livestock industries.

Box 4. Economic value of sorghum stover traded as fodder for urban and peri-urban dairy production in Hyderabad, India M. Blummel (ILRI) and P.P. Rao (ICRISAT) Chopped sorghum stover is the major source of dry fodder for urban and peri-urban dairy production in Hyderabad, India (Tesfaye 1998). Blümmel and Rao (2006) sampled six major Hyderabadi traders of chopped sorghum stover monthly from November 2004 to November 2005 to better understand the value farmers and traders attribute to sorghum stover and to investigate the relationship between price of stover and stover fodder quality. Traditionally sorghum fodder was brought to Hyderabad market in cartloads from villages, a distance of 50 to 100 km away. During 2004 to 2005 the fodder shops traded sorghum stover from regions of Andhra Pradesh, Karnataka and Maharashtra up to 300 to 400 km away. The average stover price per kg dry stover was 3.5 Indian Rupees but the price was dependent on stover quality (digestibility) and season. Our survey during 2004 to 2005 indicated that the average price for sorghum grain in wholesale grain markets around Hyderabad (Tandur, Mahabubnagar, Jedcherla, Jogipet) was about Indian Rupees 6 to 7 per kg. Thus average sorghum stover price is now approximately half that of the average grain price. The price of sorghum stover in Hyderabad’s fodder markets in the late 1970s was about one-fourth of the grain price. We recently revisited the stover trader and prices have further increased reaching an average of Indian Rupee 7 per kg dry stover during November 2008 to January 2009. Sorghum grain prices at the same time averaged Indian Rupee 10 per kg.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Indian Rupee per kg dry sorghum stover

Cost of sorghum stover traded in Hyderabad in 2004 to 2005 and in end of 2008 and beginning of 2009 9 8 7 6 5 4 3 2 Sampled from November 2008 to January 2009 Sampled from November 2004 to November 2005

1 0

Nov Dec Jan Feb Mar Apr May Ju

Jul Aug Sep Oc Nov

Month of trading

4.2 4.2 y = -4.9 + 0.17x; R 2 = 0.75; P = 0.03

4.0

Stover price (IR/kg DM)

P = 0.62

3.8 3.6 3.4 3.2 3.0 2.8 2.6

4.0 3.8 3.6 3.4 3.2 3.0

2.8

3.0

3.2

3.4

3.6

3.8

Crude protein content of stover (%) Figure 1: Relations between crude protein content of stover and price of stover

4.0

2.8 44

Stover in vitro digestibility (%) Figure 2: Relations between in vitro digestibility of stover and price of stover

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

5.5 Impacts on human wellbeing Human wellbeing As discussed in the framework of the study described in section 1, agro-ecosystems responses to the pressures exerted by the drivers of change can be of many dimensions and can alter the condition of human wellbeing. As in IAASTD (2007), in this study human wellbeing is measured as food security (kilocalorie consumption) and the number and per cent of malnourished children under five which is a proxy for poverty. Figure 39 shows the consumption of kilocalories per capita for different regions under different scenarios (IAASTD 2007). There are important differences in consumption between different regions and scenarios. In general terms, SSA has a lower average consumption than all other regions irrespective of the scenario, while EA has the highest. Not all regions increase the calorie consumption per capita under the reference scenario (i.e. WANA, others, very small increases in CSA). Rate of growth in consumption in Asia happens across regions and is the product of overall economic growth. Figure 39. Per capita kilocalorie consumption by scenario 4000 3500 kcal per capita

3000

kcal00

2500

kcal30_b

2000

kcal30_f kcal30_i

1500

kcal30_v

1000 500 0 CSA

SEA

SSA

WANA

Others

region

There are important trends in the scenarios and how they affect consumption. In general terms, the drastic biofuels scenario presented here has detrimental effects on consumption, especially in CSA, SSA, WANA, and others. These effects are mediated via the increased prices in commodities caused by the extra demand for biomass for energy production. At the same time, there is a general trend that irrigation increases will positively affect consumption by increasing the provision of relatively cheaper food for human, at a potential environmental cost of depleting water sources, aquaculture etc. This is a key trade-off that will become more acute in the future as demand for water for domestic and other uses increase.

Child malnutrition Figure 40 presents the number of malnourished children under five for 2000 and 2030, while Tables 52 and 53 present the total numbers of malnourished children and the per cent of malnourished children relative to the human population for different systems, region and scenarios 2000–2030. In general terms, the highest numbers of malnourished children are in the highly populated mixed intensive systems, with a disproportionate majority being in South Asia. Under the reference scenario, most regions, especially Asia make significant inroads in reducing the numbers of malnourished children by 2030. This is in contrast to SSA which has the highest numbers of malnourished children in the agropastoral and mixed extensive systems, and increasing to 2030.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 52. Predicted number of malnourished children under five by system, region and scenario 2000–2030 Malnutrition 2000 (103 children below 5) 2017 320 1168 54 4861 3763 12,183

Reference run 2030 (103 children below 5) 2062 94 1112 46 5808 4084 13,206

2218 109 1156 48 6177 4413 14,121

Irrigation expansion 2030 (103 children below 5) 1654 5 844 33 4499 2753 9788

Low meat demand 2030 (103 children below 5) 2091 90 1121 46 5888 4150 13,385

CSA EA SA SEA SSA WANA

1918 1999 20,998 1768 15,621 1802 44,105

1953 608 16,298 1169 20,099 1810 41,937

2188 705 16,750 1219 21,343 1966 44,172

1474 17 13,636 871 15,361 1151 32,510

1998 576 16,403 1179 20,363 1844 42,365

CSA EA SA

3999 6596 47,475

3370 1829 38,424

3712 2093 39,457

2623 206 32,595

3435 1745 38,662

SEA SSA WANA

9380 7380 1852 76,683

7948 10,942 2069 64,583

8281 11,609 2317 67,469

6457 8019 1094 50,994

8016 11,060 2128 65,046

CSA EA SA SEA SSA WANA

2348 1003 3370 1094 4455 377 12,648

2050 348 2668 864 6950 363 13,243

2285 388 2750 908 7458 399 14,188

1549 99 2190 607 5471 227 10,144

2096 337 2687 873 7050 371 13,413

CSA EA SA SEA SSA WANA

10,282 9918 73,011 12,296 32,317 7794

9435 2879 58,502 10,027 43,799 8326

10,403 3295 60,113 10,456 46,587 9095

7300 327 49,265 7968 33,350 5225

9620 2748 58,873 10,114 44,361 8493

Farming system

Region

(Agro-) pastoral

CSA EA SA SEA SSA WANA

Total

Mixed extensive

Total Mixed intensive

Total Other

Total Total—all regions

Distributions are large. Policy instrument change this distribution.

Biofuels 2030 (103 children below 5)

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Table 53. Percentage of malnourished children under five relative to human population numbers by system, region and scenario 2000–2030 Farming system

(Agro-) pastoral

% malnourished as proportion of total population 2000

% malnourished as proportion of total population 2030 base

% malnourished as proportion of total population 2030 biofuels

% malnourished as proportion of total population 2030 irrigation

% malnourished as proportion of total population 2030 veggie

CSA EA SA SEA SSA WANA Others

2.6 0.9 5.5 2.6 5.3 3.1 2.1 3.4

1.8 0.2 3.0 1.6 3.8 1.9 1.8 2.4

2.0 0.2 3.1 1.7 4.1 2.0 1.9 2.6

1.4 0.0 2.4 1.3 2.9 1.3 1.6 1.8

1.8 0.2 3.0 1.6 3.9 1.9 1.9 2.5

CSA EA SA SEA SSA WANA Others

1.9 1.0 5.7 2.0 6.6 2.4 0.0 2.1

1.2 0.2 3.0 1.3 4.4 1.9 0.0 1.3

1.4 0.2 3.1 1.3 4.6 2.0 0.0 1.4

0.9 0.0 2.5 0.9 3.4 1.1 0.0 1.0

1.3 0.2 3.1 1.3 4.4 1.9 0.0 1.3

CSA EA SA SEA SSA WANA Others

2.3 0.7 5.7 2.7 4.6 1.2 0.4 2.8

1.5 0.2 3.1 1.6 3.4 0.8 0.3 1.7

1.7 0.2 3.2 1.6 3.6 0.9 0.3 1.8

1.2 0.0 2.6 1.3 2.5 0.4 0.2 1.4

1.6 0.1 3.1 1.6 3.4 0.8 0.3 1.7

CSA EA SA SEA SSA WANA Others

1.9 0.8 5.2 2.7 4.0 2.4 0.1 2.1

1.2 0.2 2.8 1.6 3.5 1.6 0.1 1.5

1.4 0.2 2.8 1.7 3.8 1.7 0.1 1.6

0.9 0.0 2.3 1.2 2.7 1.2 0.0 1.1

1.2 0.2 2.8 1.6 3.5 1.6 0.1 1.5

Region

Total Mixed extensive

Total Mixed potentially intensify

Total Other

Total

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Figure 40. Density of malnourished children under five, 2000–2030

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

The highest rates of malnutrition relative to population increases are in agropastoral systems followed by the mixed intensive systems. In one hand it may be the case of increased vulnerability, lack of primary productivity, poor market access and lack of economic growth but large land holdings (agropastoralists, Thornton et al. 2006) while on the other hand it may be simply too many people, specially poor, relative to the amount of resources available (i.e. mixed intensive systems). South Asia and SSA exhibit particularly large rates of malnutrition across these systems. In terms of alternative scenarios, numbers and proportion of malnourished children are higher in the biofuels scenario when compared to the reference scenario. This is caused mostly by the increased competition for grains for bio-energy vs. food and feed, therefore increasing dramatically the prices of the basic staples and some livestock products. The ultimate result is that the poor are denied access to cheap food due to its high cost. The irrigation scenario reduces further the rates and numbers of malnourished children as more food can be supplied to meet demand, though at a potentially high environmental cost as explained before. The low meat consumption scenario releases some of the demand pressures for grains, thus lowering the prices of staple commodities. This increases the accessibility of basic staples for poor people.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

6 Conclusions A range of integrated assessments have studied the state and future of global ecosystems and their capacity to provide key services for humans (food, fibre, energy and others) while maintaining ecosystems functions (MA 2005). A recent study also highlighted the future pressures on global food production and the need for additional investments in science and technology as a prerequisite to meet increasing human demands in a sustainable way. This study used those results to further differentiate the impacts of different drivers of change on specific production systems in terms of what this meant for the sustainable intensification of food production and maintenance of ecosystems services for the developing world. There is a significant need for further differentiation of data from global assessments at the production systems level to be able to design technology, policy and investment options with more focus, that are of greater relevance to the social groups in question. The role of population density, agro-ecological potential, length of growing period and market access as good proxies for describing change, development differences and investment opportunities in production systems in the developing world has been widely demonstrated. The CGIAR study describes three basic land-based production systems and one landless system: •

Agropastoral systems with low population densities, low agricultural potential and poor market access. These areas are characterized by livelihood systems depending mostly on ruminant livestock.

•

Extensive crop–livestock systems with medium population densities, where there is crop cultivation but low yields, extensive livestock production mainly for meat production, low input use, and poor connectedness to markets.

•

Intensive crop–livestock systems with high population densities, high agricultural potential including the use of irrigation sometimes, high input use, intensive livestock rearing predominantly for dairying, and good market access.

•

Industrial systems: as developing countries industrialize, large-scale monogastric production systems spring up and tend to be located close to urban centres to minimize the problems associated with product conservation and transportation (Steinfeld et al. 2006). These are essentially landless systems.

Mixed crop–livestock systems are and will continue to be the backbone of sustainable pro-poor agricultural growth in the developing world to 2030. Their significance cannot be ignored in the global development agenda. Two-thirds of the global population live in these systems. They produce 50% of the world’s cereals and more importantly, produce most of the staples consumed by poor people: 41% of maize, 86% of rice, 66% of sorghum and 74% of millet production. They also produce the bulk of livestock products in the developing world— 75% of the milk and 60% of the meat—and employ many millions of people in long value chains. Rates of growth in production and consumption of agricultural products are significantly higher in these systems than in others, with livestock production and consumption rates doubling those of crops (Delgado et al. 1999).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Traditionally, governments in developing countries have often targeted public investments to the mixed intensive systems as they have been seen as the engines of agricultural growth in these regions, typified by the green revolution in South Asia in the 1970s. At the same time, public investment has historically been significantly higher in crops than in the livestock sector, often by a factor of ten or more. Intensive crop–livestock systems in the developing world are under significant pressures and the substantial growth rates in productivity observed in the past may be attainable no longer. These pressures are larger in some systems than in others but are all caused by the rising demands of the human population, income shifts, and high rates of urbanization. Globally, population in these systems will increase from 2.5 billion people to 3.4 billion by 2030, predominantly in Asia. Intensive crop–livestock systems in South Asia are reaching a point where production factors are seriously limiting production as land per capita decreases. Rice and wheat production in the future may not grow fast enough to meet human demands due to water constraints. At the same time, livestock numbers will increase significantly: cattle and buffalo will increase from 150 to 200 million animals by 2030 while pigs and poultry will increase by up to 40% over the same period. The pressures on biomass to feed these animals are already high and significant trade-offs in the use of resources (land, water, nutrients) exist in these systems, especially as the demands for biomass for food, feed and energy increase. In the high-potential areas of Africa, such as the East African highlands, these phenomena can also be observed. They are manifested in significant reductions in soil fertility, loss of carbon, environmental degradation, reduced production and shrinking farm sizes. Systems with high degrees of intensification will require options with high efficiency gains without using any more land and water. While crop production is reaching its yield increase limits in these systems, there is considerable scope for increasing the efficiency gains in resource use to produce more meat from intensive crop–livestock systems. Monogastrics such as chickens and pigs have doubled the efficiency of conversion of grain into meat in the last 30 years (Steinfeld et al. 2006). This has led to increased use of grains to feed livestock, at the same time producing more meat per unit of grain fed. Growth in this sector has reduced global poultry prices significantly at the expense of increased cereal demands that not only compete with food for humans but may fuel deforestation (Steinfeld et al. 2006). In some regions, livestock species shifts will be required to use resources more efficiently and policies to promote specialization of production will need to be implemented. Specialization and intensive industrial livestock production will also require environmental and trade regulations, as they may lead to concentration of animals and potential environmental problems such as large nutrient loadings in peri-urban areas (Steinfeld et al. 2006). This may affect water quality for human populations and increase the risk of epidemics of emerging diseases that could affect both livestock and humans. Evidence from South Asia suggests that species shifts are already occurring in intensive crop–livestock systems, and these will continue. For example, rates of growth in poultry production to 2030 are projected to be higher than 7% per year, two to three times higher than rates of growth in ruminant or crop production. Not putting our money where our mouth is: a necessary paradigm for sustainable global food production, ecosystems maintenance and poverty reduction? Resource constraints in some land-based mixed intensive systems are reaching a point where crop–livestock production could decrease and where environmental degradation may have deleterious impacts on humans. In more extensive systems, with less pressure on the land, the yield gap for crops and livestock is still large. For example, yields of dryland crops such as sorghum, millet and cowpea could be increased by a factor of three with appropriate use of inputs. Important productivity gains could be made in these more extensive mixed rainfed areas. Pro-poor policies and public investments in infrastructure will be essential to create systems of incentives, reduce transaction costs, and improve risk management in these systems. Integration of production in these systems to supply agro-ecosystems services (feeds, food etc.) to the more intensive systems will also be needed to ensure the viability of the more intensive systems in the future. Considerable changes in public and bilateral resource allocation may thus be required. Governments will need to prioritize investments in a non-traditional way. Instead of allocating most resources to areas that are highly populated or that have high agricultural potential, investing in infrastructure and services in the more extensive areas may be the key for the food security of the future. Early actions in this area are essential to combat increasing risks of food insecurity, especially considering the likely impacts of climate change in some regions (IPCC 2007).

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Rural-to-urban migration rates in extensive crop–livestock systems are high but with the right sets of incentives, such as roads and market creation, infrastructure, health facilities and other services, these could decrease. Nurturing the next generation of food producers in the developing world is of key importance for the food security and poverty reduction of large areas of the globe. At the same time, with these incentives, some pastoralists will grow marginal crops, changing their systems from pastoral to crop–livestock systems. This additional food production, although small, is crucial to the livelihoods of poor people who are largely dependent on livestock. Defining the limits to intensification is crucial for developing regulatory frameworks for sustainable food production and for maintaining ecosystems functions. Intensification of production through science and technology investments (increased input use, changes in crop varieties or animal breeds etc.) has been enormously successful in increasing global food production over the last 200 years. With this has come increased understanding that there can be serious consequences for the environment associated with intensifying systems without limits. Particularly in the developing world, there is a need for understanding and developing a set of criteria to define the thresholds of intensification before irreversible environmental degradation occurs. The limits and criteria will differ depending on location and production system but they should lead to a regulatory framework for systems’ intensification that can be applied at the local level. This framework needs to be accompanied by a robust and practical monitoring and evaluation framework. Within this framework, it may well be that some systems will need to deintensify or stop growing to ensure the sustainability of agro-ecosystems or to protect key resources for the future of specific regions. This will need to be accompanied by the development of options for diversification of income sources for users of key resources through smart schemes for payments for ecosystems services in these regions. Successful examples are starting to appear in the literature (FAO 2007). The viability of global food production, the maintenance of ecosystems services, and the reduction of poverty, involve an increasingly complex and subtle balancing act of promoting well-regulated, differential growth in crop–livestock production, and in investing in food producing systems that traditionally have not received as much attention in the past. These strategies can only be implemented with new, more dynamic policies that weight carefully the trade-offs between agro-ecosystems services and human wellbeing. The rules of the game have changed, as protecting global goods becomes ever-more critical to the survival of the planet.

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

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Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Appendix A: Definition of the regions Region CSA

Country Argentina Barbados Belize Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador French Guiana Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Puerto Rico Suriname Trinidad and Tobago Uruguay Venezuela China Mongolia North Korea

Region

Country

Bangladesh Bhutan India Nepal Pakistan Sri Lanka Brunei Cambodia Indonesia Laos Malaysia Myanmar Papua New Guinea Philippines Thailand Vietnam Angola Benin Botswana Burkina Faso Burundi Cameroon Chad Congo Congo, Democratic Côte d’Ivoire Djibouti Equatorial Guinea Eritrea Ethiopia Gabon

SEA

SSA

Drivers of change in crop–livestock systems and their potential impacts on agro-ecosystems services and human wellbeing to 2030

Region

Country Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mozambique Namibia Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa Sudan Swaziland Tanzania

Region

WANA

Country Togo Uganda Zambia Zimbabwe Afghanistan Algeria Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syria Tunisia Turkey United Arab Emirates Yemen

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