미래의 식량에 대한 각본

The global food economy is in a state of flux. A growing world population and rising incomes have pushed up demand for food, especially for more diverse diets. Food crops are being used to produce biofuels. Restrictive trade policies and speculation on food commodities have also contributed to the recent price spikes in global food markets.¹ Climate change, weather extremes, and natural resource degradation have created more challenging conditions for farmers. These factors are reflected in the higher and more volatile food prices of the past few years, after decades of stable and relatively low prices for major food commodities (Figure 1).²

Figure 1 - World prices for agricultural commodities, 1977–2012

Source: World Bank, GEM Commodities, http://data.worldbank.org/data-catalog/commodity-price-data (accessed January 18, 2013). Prices for 2012 are through August 2012. Note: Prices are in real 2005 US dollars. Download a larger version of Figure 1

Given the unsettled recent past, what lies ahead for the global food economy, especially in light of the rapid socioeconomic changes happening in emerging regions that will drive future diets and energy prices? Clearly, a whole range of policy choices and other factors will affect the world food system, and many of them will deal with sustainability issues raised at the 2012 United Nations Conference on Sustainable Development (Rio+20). These issues include sustainable agricultural production growth and the environmental impacts of human consumption patterns. The discussion of sustainable diets has been particularly strong within the European Union, where more consumers tend be highly conscious of their environmental footprint and where some forward-looking studies have closely examined the issue of meat consumption and its environmental impacts.³ In this chapter, we address the implications of future consumption patterns beyond the European Union and explore the potential future impact of supply-side agricultural productivity improvements and higher energy prices on agriculture.

This chapter examines the dynamics of the new global food economy by looking at what would happen to global food prices, trade, and food security under four alternative scenarios for the period between 2010 and 2050. To construct these scenarios, we used IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), a model of the world food system that generates projections of global food supply, demand, trade, and prices.⁴

The Growing Role of Emerging Economies

In addition to showing global results, this chapter highlights results for Brazil, China, and India because, according to our simulations, growth in food demand and supply in these big emerging economies will be a major force behind future food security outcomes.

Brazil has become a huge player in global agricultural export markets. In two decades, the country has undergone an agricultural revolution to become a leading exporter of soybeans, beef, chicken meat, fruit, and pork, as well as biofuels. Brazil owes its agricultural advancement to investments in agriculture and related sectors, reduced trade restrictions, and macroeconomic policy reforms that generated economic stability. These policies triggered infrastructure development, advances in crop research, and adoption of innovative technologies, and gave the country a comparative advantage in the production of soybeans, sugar, meats, and other major crop commodities.⁵

China has experienced rapid agricultural growth but faces challenges as it seeks to meet the food needs of a growing population that is becoming wealthier and more urban. The country’s future food security, trade position, and possible influence on international food markets are likely to depend on its future agricultural growth, its patterns of food demand growth, and the sensitivity of its trade balances and world prices to its environmental and growth outcomes.

In India, growth in agricultural productivity has been slow during the past two decades, and crop yields remain below those in most countries in Asia. At the same time, as their incomes rise, Indians are diversifying their diets. Demand is growing for poultry and dairy products, in addition to traditional staples, fruits, and vegetables. India remains home to the highest number of food-insecure people.⁶

Box 1

Modeling the Future: How Can We Improve Food Policy?

Gerald Nelson

The outcomes of most decisionmaking processes, from how to allocate scarce research resources to when to implement new directions in national polices, have consequences that play out well into the future. Unexpected events can alter expected outcomes. The idea of strategic foresight activities (also called “scenario exercises”) is to improve the payoff of decisionmaking by examining the range of potential outcomes. In the recent past, groups have used scenarios to explore many topics, including ecosystem challenges, energy futures, and water scarcity.

Strategic foresight activities use both qualitative and quantitative approaches to assess plausible futures and outcome ranges for decisionmaking. Quantitative models, such as the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) used in this chapter, have the advantage of explicitly specifying important relationships whose interactions are difficult to grasp qualitatively, clarifying the range of potential outcomes.

In 2012, the IFPRI-led CGIAR Research Program on Policies, Institutions and Markets began developing a strategic foresight platform,¹ which will build on work from the Global Futures for Agriculture project that evaluated promising technologies, investments, and policy reforms for simultaneously improving agricultural productivity and environmental sustainability.² The strategic foresight activities will augment the quantitative tools and methodologies from Global Futures to allow consistent assessments of potential research investments in agricultural technologies. The Policies, Institutions, and Markets program, involving all CGIAR centers and research programs, will institutionalize the following tools and techniques and extend them to national agricultural research centers and the private sector:

• Use existing software models that simulate crop productivity in varying environments to assess the effects of climate change at high spatial resolution.
• Simulate “virtual crops”—that is, characteristic-specific crops that have not yet been developed—to test their potential performance in different environments.
• Link results of models that simulate biological or physical changes of crop cultivation, water availability, and other factors with models that reflect the impact of such changes on the economy and society.
• Assess the potential socioeconomic consequences for human well-being if a new (virtual) crop variety were to become part of global agriculture.
• Allow users of a web-based application to customize scenarios to help answer their specific questions.

Despite these advances in technology, significant challenges remain. The existing crop models do not allow for assessments accounting for increased resistance to pests and diseases and do not incorporate the potential benefits of important changes in agricultural systems. The existing climate change datasets lack information on extreme weather events, and existing socioeconomic models do not sufficiently capture the complex interactions between agricultural and natural resource systems and the economy outside of agriculture. And, last but certainly not least, all of these analyses are based on woefully inadequate data. These are some of the challenges the CGIAR Research Program on Policies, Institutions and Markets must address to ultimately help policymakers decide which policy options can best establish future food security.

Gerald Nelson is a senior research fellow in the Environment and Production Technology Division of the International Food Policy Research Institute, Washington, DC.

1 - For more information on the CGIAR Research Program on Policies, Institutions and Markets, visit www.pim.cgiar.org. [Back]
2 - For more information, visit www.ifpri.org/pressrelease/global-futures .[Back]

Alternative Scenarios of the Future

The analysis begins with a baseline scenario, which assumes that countries maintain their current trends in agricultural policies and investments from 2010 to 2050. We use this scenario as a basis for comparison with three other scenarios: (1) a higher-agricultural-productivity scenario, (2) a higher-energy-prices scenario, and (3) a lower-meat-demand scenario. Each of these scenarios represents a significant driver that can push or pull the trajectory of world food markets from either the demand or supply side. Although we could show what might happen through a conceptual or theoretical model, we have chosen a quantitative illustration to better convey the distribution of likely impacts and how quickly they might occur.

Baseline Scenario
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Baseline Scenario: Maintaining Current Policies

The major drivers of this baseline scenario are income growth, population increase, productivity gains in many agricultural activities, and biofuel sector expansion.⁷ Projected rapid growth in meat and milk demand due to income and population growth and urbanization is projected to put pressure on prices for feedgrains and oilseed meals. Biofuel production will compete for land and water resources, according to the simulated results. Projected higher energy prices will increase the cost of production and make biofuels more competitive. Projected growing water and land scarcity will increasingly constrain growth in food production.

These dynamics, combined with the continuation of current policies and investments under this scenario, would result in higher projected world prices for agricultural commodities by 2050 (Figure 2). High demand for meat and livestock feed means that projected price increases would be greatest for soybean meal, maize, and pork. These rising prices reflect an assumption that most developing countries would be unable to rapidly meet growing domestic food demand. Thus major exporters would play a critical role in meeting the world’s food consumption needs.

In most regions the baseline scenario projects a slightly increased demand for cereals as food (these results do not include demand for cereals as livestock feed). In Africa south of the Sahara, per capita demand for cereals would increase significantly (Table 1). In East Asia and the Pacific and Latin America and the Caribbean, however, income growth and changes in dietary patterns would lead to a projected decline in per capita demand for cereals. At the same time, income growth would cause projected meat demand to rise for all regions, and especially for East Asia and the Pacific (in absolute terms), and for South Asia (in percentage terms), where per capita income growth is projected to be highest.

As major exporters and importers, Brazil, China, and India would be critical forces in agricultural markets (Figure 5 on page 95). Under the baseline scenario Brazil would export a net 21 million metric tons of maize and 55 million metric tons of soybeans in 2050. At the same time, China would import a net 37 million metric tons of maize and 59 million metric tons of soybeans—clearly serving as an important market for Brazilian producers who are increasingly taking over from US producers.⁸ China and India are projected to export significant quantities of rice.

Food security in IMPACT is measured through two indicators: the number of malnourished children and the population at risk of hunger.⁹ Under the baseline scenario, food security would improve in most regions (Table 2). In Africa south of the Sahara, however, the decline in the number of malnourished children would be relatively slow. The reduction in the number of people at risk of hunger would also be slow for many regions, and in Africa south of the Sahara, as well as in the Middle East and North Africa, the number of people at risk of hunger would actually rise.

Scenario 1: Higher Agricultural Productivity

Scenario 1
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Recent increases in agricultural commodity prices have drawn attention to the importance of raising crop productivity and the role of agricultural research and development (R&D) in bringing about these productivity gains. Given that land use change is an important part of the discussion on the future sustainability of agricultural production, getting “more from less”—that is, expanding production by raising yields rather than by cultivating more land—will be a critical challenge. The higher-productivity scenario reflects agricultural investments that increase yields for major crops in all countries at rates that would keep inflation-adjusted crop prices in 2050 close to the level of the crop prices in 2010 in the baseline scenario. Figure 3 shows the changes in rice, wheat, maize, and soybean production in Brazil, China, and India that would be needed to keep prices at that level. Maize production would need to be 16–28 percent higher than in the baseline scenario, and wheat production would need to rise by 6–18 percent. These findings underline the importance of investing in agriculture and agricultural R&D to keep food prices relatively low in the long term.

The scenario’s projected higher yields would lower world commodity prices by increasing production and available supply in world markets. By 2050 cereal prices are projected to be 20–36 percent lower than in the baseline scenario (Figure 4). The simulated lower prices for cereals as feedstock would expand livestock and dairy production, causing meat and milk prices to fall. Lower bean and seed prices would lead to higher demand for oilseeds and greater production of vegetable oils, which would push down prices for soybean oil and rapeseed oil.

The higher-productivity scenario would also lead to changes in imports and exports of rice, wheat, maize, and soybeans by Brazil, China, and India, compared with the baseline scenario (Figure 5). Brazil plays a major role in soybean markets, along with the United States. In the higher-productivity scenario, it is projected that Brazil would increase its net exports of soybeans, and China would respond to Brazil’s increased production by raising its net imports of soybeans. For rice, expanded Chinese and Indian exports would help increase supply in international markets and lower world prices, which would aid many developing countries that are net food importers. Brazil’s net exports of maize would increase slightly. China, a major maize importer in 2050, would lower its imports with higher domestic supply, relieving some pressure on world markets. Changes in wheat trade would be relatively small. It is expected that Brazil and China would increase their net imports of wheat slightly, while India would decrease its net imports marginally.

The overall effect of the higher-productivity scenario would be to improve food security in all regions (Table 3). Higher yield growth that lowers agricultural commodity prices and raises food consumption leads to significantly lower numbers of malnourished children and people at risk of hunger. In fact, it is projected that the population at risk of hunger globally declines by 24 percent compared with the baseline scenario. Agricultural R&D and investment is clearly important not only for countries that can increase domestic production, but also for net importing countries, which benefit from productivity gains elsewhere.¹⁰

Scenario 2
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Scenario 2: Higher Energy Prices

One critical change in agricultural markets has been the recent strengthening of ties between the energy and agriculture sectors. The higher-energy-prices scenario aims to illustrate how energy prices affect agricultural production, even as agriculture provides energy products in the form of biofuel feedstocks. This scenario thus incorporates two links between agriculture and energy markets. The first link is energy prices’ impact on biofuel demand and production. The scenario assumes a 100 percent increase in crude oil prices by 2035. Because higher oil prices make biofuel production more profitable, this assumption increases demand for feedstocks in the biofuel sector by an average of 67 percent for all countries and crops by 2035. (The scenario considers only first-generation biofuels made from, for example, maize, soybeans, and rapeseed, among other agricultural feedstocks.) The second link is energy prices’ impact on fertilizer prices, which affect the cost of crop production. The scenario increases the annual growth rate of fertilizer prices by 75 percent throughout the period 2000–2050.

The large jump in energy prices in this scenario would significantly raise the prices of agricultural commodities, especially vegetable oils and maize (Figure 6), mainly because of the increased demand for crop-based feedstocks for biofuel production. Another factor is lower crop production due to the rise in production costs. Higher prices for livestock feed would push up the production costs of livestock and dairy producers and lead to higher meat and milk prices.

We also look in more detail at the scenario’s impacts on imports and exports of rice, wheat, maize, and soybeans in Brazil, China, and India. Higher demand for soybean oil by Brazil’s biodiesel industry would cause a projected drop in Brazilian soybean exports of 1 million metric tons compared with the baseline scenario (Figure 7). It is also projected that China would import 0.7 million metric tons fewer soybeans, and India would reduce its exports slightly. With the United States using its maize for its own domestic biofuel industry, Brazil would benefit from higher maize prices and expand its share of the maize market by increasing its net exports. China and India would lower their net imports of maize because higher world market prices would reduce demand. Rice would be less affected by the expansion of biofuel production. Nonetheless, by raising production costs, higher fertilizer prices would lead to reduced rice production and thus lower net exports from China and India. Brazil would lower its imports of wheat, whereas China and India would increase theirs.

The higher-energy-prices scenario would have serious consequences for food security. With higher prices and thus lower food consumption, the number of malnourished children would increase in all regions in 2050 compared with the baseline (Table 4). The population at risk of hunger is also expected to increase for all regions and by 14 percent for the world as a whole.

Scenario 3
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Scenario 3: Lower Meat Demand

Dietary change is an important driver of demand for agricultural products that may have significant implications for the future sustainability of agriculture. The global structure of food demand is undergoing fundamental changes, driven largely by economic growth in developing countries. As consumers’ incomes rise, they tend to shift their consumption from maize and other coarse grains to wheat and rice.¹¹ When urbanization further raises their incomes and leads to lifestyle changes, consumers make a secondary shift from rice to wheat—and their consumption of meat products also begins to increase.¹² The rise in consumption and production of meat leads to higher demand for coarse grains to be used for animal feed, rather than for direct human consumption.¹³ In the developed, or high-income, countries, meat consumption is already very high, and growth in per capita meat and cereal consumption has slowed,¹⁴ so these trends mean that developing countries will play a much larger role in global food markets.

Along with these fundamental shifts in long-term demand, strong concerns have been raised that meat-intensive diets in high-income countries put upward pressure on prices for cereals and coarse grains, as well as contributing to the high prevalence of chronic diseases¹⁵ and increasing greenhouse gas emissions from the livestock sector.¹⁶ Some analysts have asserted that cutting meat consumption in high-income countries, either through voluntary dietary changes or through policies such as taxes on livestock, would release cereals from livestock feed to food for poor people in developing countries.¹⁷ The lower-meat-demand scenario examines whether reducing meat consumption in high-income countries and the emerging economies of Brazil and China would improve food security and reduce pressure on prices.

This scenario looks at two policy variations for cutting meat demand by 50 percent (1) in high-income countries and (2) in high-income countries plus Brazil and China. Brazil and China are included because they are important emerging economies, and they represent both a significant producer (Brazil) and a consumer (China) of meat products. These two countries play an important role in the world food demand and supply balance now and will likely do so in the future. (India is not considered here because it is not a major contributor to global meat demand.)

The simulation shows that if meat consumption were lowered in high-income countries, Brazil, and China, the drop in food prices would be notable (Figure 8). Given that this scenario focuses on reducing meat consumption, prices of livestock commodities would decline the most. But because demand for livestock feed would also decline, prices for cereals (particularly coarse grains such as maize) would drop as well.

What would happen to per capita meat demand in Brazil, China, and India under the lower-meat-demand scenarios? Our simulations show that lowering meat demand in high-income countries alone would make more meat available on world markets and lower prices, leading to higher meat consumption in Brazil, China, and India (Figure 9). If meat demand were reduced in high-income regions plus Brazil and China, Indian consumers would benefit from lower prices for animal products and livestock feed as well as from higher levels of meat consumption. (In India, of course, most meat consumption would be nonbeef.)

The lower-meat-demand scenarios would improve food security (Table 5). By reducing cereal prices and raising cereal consumption, these scenarios would lead to shifts in dietary preferences, increases in food availability, and eventually better nutritional status, particularly in developing countries.¹⁸ If meat demand were cut in high-income countries alone, the number of malnourished children in Africa south of the Sahara would fall by about 1 percent compared with the baseline scenario. Reducing meat demand in Brazil and China as well would reduce the number of malnourished children in Africa south of the Sahara by nearly 3 percent and worldwide by about 1.3 percent.¹⁹ Lower meat demand would also reduce the population at risk of hunger, with the largest drop occurring in Africa south of the Sahara.

Conclusions

Agricultural markets are undergoing a transformation. Long-run dynamics are changing demand-supply relationships, and new countries are emerging as major importers and exporters. Given these new realities, the four scenarios examined in this chapter show how different socioeconomic trends, policy actions, and investment choices could affect food prices and food security by 2050. In some cases, the results are dramatically different from the baseline scenario and call for closer attention on the part of market analysts, agricultural researchers, and policymakers.

One main takeaway message is that continuing upward pressure on food prices is likely to be the new norm, especially if trends in agricultural productivity growth continue to lag behind the demand-side drivers of change. If current agricultural, biofuel, and energy policies and trajectories of dietary change continue, inflation-adjusted prices of meat and grain are likely to rise. Although the baseline scenario projects fewer people at risk of hunger in most regions, it projects an increase in the Middle East and North Africa and Africa south of the Sahara.

Increased public and private investments in agricultural productivity growth, as in the higher-productivity scenario, would help to push agricultural commodity prices much lower than in the baseline scenario. Expanding funding for agricultural research and technology, extension services, rural infrastructure, irrigation, and water-use efficiency, among other efforts, can also lead to higher agricultural production and greater food security than in the baseline scenario.

Growing biofuel markets and the increasing share of energy in the costs of agricultural inputs such as fertilizer have intensified the linkage between energy and agricultural markets. Together with higher energy prices, this stronger linkage could make food prices even higher and more volatile than they have been in recent years, as shown in the higher-energy-prices scenario. The food-versus-fuel debate over biofuels therefore has critical repercussions for the food security of developing countries. Governments can undertake various policy initiatives to alleviate the pressures on food prices and food security, such as eliminating subsidies and trade barriers supporting crop-based biofuels.

Finally, in two lower-meat-demand scenarios, we consider whether reducing meat demand in developed countries is an effective route to improving food security in developing countries. A decline in consumption of livestock products in developed countries would have only small impacts on food security in developing countries. If reduced meat demand were extended to Brazil and China, the reduction in the number of malnourished children and the population at risk of hunger would be somewhat larger. More livestock products would be available in the global market, leading to lower prices in developing countries, where consumers could shift consumption to include more meat. Reduced demand for maize and other coarse grains for livestock feed would also tend to push down the prices of these commodities. However, this scenario would have little impact on the prices of wheat and rice—the main staple foods in most developing countries—and would therefore do little to raise consumption of these crops.

Although the scenarios included in this chapter are global in nature, special focus is given to Brazil, China, and India and their role in world agricultural markets. As significant producers and consumers, these three countries exert huge influence in agricultural markets. Changes in their dietary patterns, productivity growth, trade, and energy policies are likely to shape global trade patterns and therefore commodity prices. Although the United States and the European Union will remain important players in agricultural markets, the agricultural R&D efforts of Brazil, China, and India are becoming as important, if not more so, as those of the United States and the European Union.

These simulations of the global future show that different choices with regard to agricultural investment, energy, and food consumption can lead to vastly different results for food prices, trade, and food security. Higher investment in agricultural research that boosts productivity growth is projected to significantly improve the future food security situation.

1 - P. Abbott, C. Hurt, and W. Tyner, “What’s Driving Food Prices in 2011?” Farm Foundation Issue Report (July 2011), www.farmfoundation.org/news/articlefiles/1742-FoodPrices_web.pdf; D. Headey, S. Malaiyandi, and S. Fan, Navigating the Perfect Storm: Reflections on the Food, Energy, and Financial Crisis, IFPRI Discussion Paper 889 (Washington, DC: International Food Policy Research Institute, 2009); A. Mittal, “The Blame Game,” in The Global Food Crisis, ed. J. Clapp and M. Cohen (Waterloo, ontario, Canada: Centre for International Governance Innovation and Wilfrid Laurier University Press, 2009); T. Yu, S. Tokgoz, E. Wailes, and E. Chavez, “A Quantitative Analysis of Trade Policy Responses to Higher World Agricultural Commodity Prices,” Food Policy 36, no. 5 (2011): 545–561. [Back]
2 - World Bank, GEM Commodities, http://data.worldbank.org/data-catalog/commodity-price-data, accessed January 18, 2013.
[Back]
3 - H. Westhoek, T. Rood, M. van den Berg, J. Janse, D. Nijdam, M. Reudink, and E. Stehfest, The Protein Puzzle: The Consumption and Production of Meat, Dairy, and Fish in the European Union (The Hague: Netherlands Environmental Assessment Agency, 2011).
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4 - IMPACT covers more than 46 crops and livestock commodities. It includes 115 countries, with each country linked to the rest of the world through international trade, and 281 food-producing units (grouped according to political boundaries and major river basins). Demand is a function of prices, income, and population growth. Population projections are the “medium” variant population growth rate projections from the Population Division of the United Nations. Gross domestic product (GDP) projections are estimated by the authors, drawing upon Millennium Ecosystem Assessment, Ecosystems and Human Well-Being: General Synthesis, Millennium Ecosystem Assessment Series (Washington, DC: Island Press, 2005). Crop production is determined by crop and input prices, the rate of productivity growth, and water availability. For more details on the model, see M. W. Rosegrant and the IMPACT Development Team, “International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description” (Washington, DC: International Food Policy Research Institute, 2012),www.ifpri.org/sites/default/files/publications/impactwater2012.pdf. [Back]
5 - S. M. Borras, “Questioning Market-Led Agrarian Reform: Experiences from Brazil, Colombia, and South Africa,” Journal of Agrarian Change 3, no. 3 (2003): 367–394; N. Beintema, A. Flavio Dias Avila, and P. G. Pardey, Agricultural R&D in Brazil: Policy, Investments, and Institutional Profile (Washington, DC: IFPRI, Embrapa, and FONTAGRO, 2001), http://www.ifpri.org/themes/grp01/grp01_brazil.pdf, accessed July 14, 2006; J. Brooks and O. Melyukhina, “The Effects of Agricultural Policy Reform on Poverty in Brazil,” Workshop on Agricultural Policy Reform and Adjustment, Imperial College, Wye, UK, October 23–25, 2003; E. Dohlman, S. Osborne, and B. Lohmar, “Dynamics of Agricultural Competitiveness: Policy Lessons from Abroad,” Amber Waves (Economic Research Service, USDA) 1, no. 2 (2001): 14–21; S. Helfand and G. Castro de Rezende, “Brazilian Agriculture in the 1990s: Impact of the Policy Reforms,” UC Riverside Economics Working Paper No. 01-34, 2001; US Department of Agriculture, Economic Research Service, “Brazil: Policy” (May 30, 2012), www.ers.usda.gov/topics/international-markets-trade/countries-regions/brazil/policy.aspx#research, accessed January 3, 2013; US Department of Agriculture, Foreign Agricultural Service, Record Brazilian Agricultural Production Spurs Further Export Gains, International Agricultural Trade Report (Washington, DC, 2012),www.fas.usda.gov/info/IATR/012412_Brazil/012412_Brazil.pdf; World Food Prize Foundation, “2006 World Food Prize Winners Opened Brazil’s ‘Closed Lands,’” press release, June 15, 2006, www.worldfoodprize.org/index.cfm?nodeID=24667&audienceID=1&action=display&newsID=8086; S. Zahniser, D. Pick, G. Pompelli, and M. Gehlhar, “Trade Liberalization in the Western Hemisphere: Impacts on US Agricultural Exports,” in US Agriculture and the Free Trade Area of the Americas, ed. M. E. Burfisher, Agricultural Economic Report No. 827 (Washington, DC: US Department of Agriculture, Economic Research Service, 2004). [Back]
6 - Food and Agriculture Organization of the United Nations, FAOSTAT, 2012. [Back]
7 - Rosegrant et al., “IMPACT: Model Description.” To estimate income projections, we drew upon scenario drivers from the Millennium Ecosystem Assessment (see note 4) and updated them based on recent data on economic performance. We used population projections from the “medium” variant projections of population growth from the Population Division of the United Nations. [Back]
8 - FAO, FAOSTAT, 2012. [Back]
9 - The percentage of malnourished children younger than the age of five is estimated from average per capita calorie consumption, female access to secondary education, quality of maternal and child care, and health and sanitation; Rosegrant et al., “IMPACT: Model Description.” The relationship used to project the percentage of malnourished children is based on a cross-country regression relationship from L. Smith and L. Haddad, Explaining Child Malnutrition in Developing Countries: A Cross-Country Analysis, IFPRI Research Report 111 (Washington, DC: International Food Policy Research Institute, 2000). The data used to make this calculation are obtained from the World Health Organization’s Global Database on Child Growth Malnutrition, the United Nations Administrative Committee on Coordination–Subcommittee on Nutrition, the World Bank’s World Development Indicators, the FAOSTAT database, and the UNESCOSTAT database. The population at risk of hunger is computed using the share of the population at risk of hunger, which is based on a strong empirical correlation between the share of malnourished people within the total population and the relative availability of food. It is adapted from the work in G. Fischer, M. Shah, F. N. Tubiello, and H. van Velhuizen, “Socio-economic and Climate Change Impacts on Agriculture: An Integrated Assessment,” Philosophical Transactions of the Royal Society B 360, no. 1463 (2005): 2067–2083. [Back]
10 - J. Alston, T. J. Wyatt, P. G. Pardey, M. C. Marra, and C. Chan-Kang, A Meta-Analysis of Rates of Return to Agricultural R&D, IFPRI Research Report 113 (Washington, DC: International Food Policy Research Institute, 2002). [Back]
11 - B. M. Popkin, “Nutritional Patterns and Transitions,” Population and Development Review 19, no. 1 (1993): 138–157; A. Drewnowski and B. M. Popkin, “The Nutrition Transition: New Trends in the Global Diet,” Nutrition Reviews 55, no. 2 (1997): 31–43.[Back]
12 - J. Huang and H. Bouis, Structural Changes in the Demand for Food in Asia, 2020 Vision Brief 41 (Washington, DC: International Food Policy Research Institute, 1996); World Health Organization, Diet, Nutrition, and the Prevention of Chronic Diseases: Report of a Joint WHO/FAO Consultation, WHO Technical Report Series 916 (Geneva, 2003). [Back]
13 - C. Delgado, M. Rosegrant, H. Steinfeld, S. Ehui, and C. Courbois, Livestock to 2020: The Next Food Revolution, 2020 Discussion Paper 28 (Washington, DC: International Food Policy Research Institute, jointly with the Food and Agriculture Organization of the United Nations and the International Livestock Research Institute, 1993). [Back]
14 - J. Bruinsma, ed., World Agriculture towards 2015/2030: An FAO Perspective (Rome: Food and Agriculture Organization of the United Nations; London: Earthscan, 2003). [Back]
15 - M. McCullough, D. Feskanich, M. J. Stampfer, E. L. Giovannucci, E. B. Rimm, F. B. Hu, D. Spiegelman, D. J. Hunter, G. A. Colditz, and W. C. Willett, “Diet Quality and Major Chronic Disease Risk in Men and Women: Moving toward Improved Dietary Guidance,” American Journal of Clinical Nutrition 76, no. 2 (2002): 1261–1271. [Back]
16 - H. Steinfeld, P. Gerber, T. Wassenaar, V. Castel, M. Rosales, and C. de Haan, Livestock’s Long Shadow: Environmental Issues and Options (Rome: Food and Agriculture Organization of the United Nations, 2006). [Back]
17 - See, for example, J. A. Foley, R. Navin, K. A. Brauman, E. S. Cassidy, J. S. Gerber, M. Johnston, N. D. Mueller, C. O’Connell, D. K. Ray, P. C. West, C. Balzer, E. M. Bennett, S. R. Carpenter, J. Hill, C. Monfreda, S. Polasky, J. Rockstrom, J. Sheehan, S. Siebert, D. Tilman, and D. P. M. Zaks, “Solutions for a Cultivated Planet,” Nature 478, no. 7569 (2011): 337–342; M. Gold, The Global Benefits of Eating Less Meat: A Report for Compassion in World Farming Trust (Surrey, UK: Compassion in World Farming Trust, 2004),www.ciwf.org.uk/includes/documents/cm_docs/2008/g/global_benefits_of_eating_less_meat.pdf. [Back]
18 - The IMPACT model infers a trend in levels of malnutrition among the most vulnerable demographic of the population: children under five. The determinants of malnutrition are derived primarily from four key indicators, which were first established by Smith and Haddad in their cross-country estimation work: per capita calorie availability, access to clean drinking water, rates of secondary schooling among females, and the ratio of female-to-male life expectancy. These determinants are consistent with the four-pillared concept of food security underlying FAO’s conceptual framework, where availability is only one factor accounting for food security status among vulnerable populations, and must be evaluated along with access, utilization, and stability. The methodology used for tracking child malnutrition in IMPACT is based on this work and is implemented through an analytical relationship that is parameterized by the statistical coefficients derived by Smith and Haddad’s work. Smith and Haddad, Explaining Child Malnutrition in Developing Countries; Food and Agriculture Organization of the United Nations, “Declaration of the World Summit on Food Security,” Rome, November 16–18, 2009; O. Ecker and C. Breisinger, The Food Security System: A New Conceptual Framework, IFPRI Discussion Paper 1166 (Washington, DC: International Food Policy Research Institute, 2012). [Back]
19 - The IMPACT model does not disaggregate demand to various substrata of the population and thus does not show explicitly what the reduction in meat consumption in Brazil and China would mean for the distributional intake of meat within the population. However, given that the wealthier sections of the population in these countries eat much more meat per capita than the poorer members of the population, their reduction in meat consumption growth means that the poorer households might actually be able to consume more. In other words, the reduction in the price of meat products made possible by lowered consumption among the wealthier members of society would make meat more affordable for others. So even if poorer households slightly increase their meat intake, an overall national reduction of per capita meat intake could occur through the redistribution of consumption within the population. (Washington, DC: World Bank, 2012). [Back]

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