OVERVIEW Abdulai Jalloh, Mbène Dièye Faye, Harold Roy-Macauley, Paco Sérémé, Robert Zougmoré, Timothy S. Thomas, and Gerald C. Nelson The part of Africa designated as West Africa is made up of 16 countries— Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo. Its land area is about 5 million square kilo- meters, and its population in 2010 was about 290 million. With the exception of Mauritania, these countries are members of the Economic Community of West African States (ECOWAS). The subregion comprises a diversified agri- cultural base spread over a wide range of agroecological zones with significant potential for improved agricultural productivity. Agriculture is the major source of livelihood for the majority of West Africans. The agricultural sector employs 60 percent of the active labor force but contributes only 35 percent of gross domestic product (GDP). The dis- parity between contribution to GDP and share of population means that many West African farmers are very poor, producing close to subsistence levels and facing numerous constraints such as droughts, soil acidity, and nutrient- depleted and degraded soils that impinge on agricultural development. The most important foodcrops grown and consumed in West Africa are cereals— sorghum, millet, maize, and rice; roots and tubers—cassava, sweet potatoes, and yams; and legumes—cowpeas and groundnuts. Major cash crops are cocoa, coffee, and cotton. Climate change, in terms of both climate means and variability, poses a great threat to farmers in the region. Possible impacts include reduced yields, lower farm incomes, and reduced welfare. There is increasing awareness of these threats among national governments and the regional economic com- munity. Along with other African countries, the West African states have identified medium- and long-term adaptive measures in their national commu- nications to the United Nations Framework Convention on Climate Change. Several of these countries have identified emergency priority measures for adaptation in their National Adaptation Programmes of Action (NAPAs), which center on agriculture, food security, and water resources management. Chapter 1 1 The purpose of this monograph is to help policymakers and researchers bet- ter understand and anticipate the likely impacts of climate change on agriculture and on vulnerable households. This is done by reviewing current data on agricul- ture and economic development, modeling plausible changes in climate between now and 2050, using crop models to assess the impact of climate changes on agri- cultural production, and globally modeling supply and demand for food in order to assess plausible food price trends. For each country, national authors worked with modeling results provided by the International Food Policy Research Institute (IFPRI) and then augmented them with other analysis as necessary. This is a unique initiative that capitalizes on the synergies among the respec- tive countries covered in this study, the Conseil Ouest et Centre Africain pour la Recherche et le Developpement Agricoles (CORAF), and IFPRI to contribute to a climate-resilient agricultural system in West Africa. This chapter provides an overview of the region, its current economic situ- ation, and its vulnerability to climate change. It is designed to provide useful input into the efforts of ECOWAS in developing appropriate policies related to climate for the region. This chapter is followed by one that describes the common methodologies used by the authors of all country chapters and then by the individual country chapters. The monograph ends with a chapter that draws lessons for the region from the individual country studies. The Intergovernmental Panel on Climate Change (IPCC), Climate Change and Agriculture, and Food Security In the Fourth Assessment Report of the IPCC, Working Group 1 reports that “climate is often defined as ‘average weather.’ Climate is usually described in terms of the mean and variability of temperature, precipitation, and wind over a period of time, ranging from months to millions of years (the classical period is 30 years)” (Le Treut et al. 2007, 496). The growth of greenhouse gas emissions is raising average temperatures. The consequences include changes in precipitation patterns, more and more extreme weather events, and shifting seasons. The accelerating pace of climate change, combined with global population and income growth, threatens food security everywhere. Agriculture is vulnerable to climate change in a number of dimensions. Higher temperatures eventually reduce yields of desirable crops and tend to encourage weed and pest proliferation. Greater variations in precipitation pat- terns increase the likelihood of short-run crop failures and long-run production 2 ChapTer 1 declines. Although there might be gains in some crops in certain regions of the world, the overall impacts of climate change on agriculture are expected to be negative, particularly in the Sahelian countries, threatening regional food secu- rity. The impacts are • direct, on crops and livestock productivity domestically, • indirect, on the availability or prices of food domestically and in interna- tional markets, and • indirect, on income from agricultural production at both the farm and country levels. Roudier et al. (2011) review 16 studies on the impact of climate change on West African agriculture. Müller (2011) uses the results of Roudier et al. together with the results of Neumann et al. (2010) to point out that in addi- tion to climate change, there are existing inefficiencies in agriculture. The main points to take away from these studies, which have a lot of uncertainty built into them, are that it appears that climate change will unequivocally hurt agriculture and that right now there is room for improvements in yield with the proper investments. More generally, Hertel and Rosch (2010) provide an insightful review of various approaches to analyzing the impacts of climate change on agriculture, as do Tubiello and Rosenzweig (2008). Review of Current Regional Trends This section provides an overview of the starting point for an assessment of the potential vulnerability of West African agriculture to climate change. It looks at recent population and income developments to provide a back- drop to potential futures. Two key indicators of well-being are reviewed— under-five mortality and life expectancy at birth. The current climate situation is discussed along with the role of regional programs in supporting food security. Economic and Demographic Indicators population The population of West Africa was estimated at 291.3 million in 2008 with Nigeria accounting for half of the total (Table 1.1). West Africa’s population Overview 3 increased by about 60 percent between 1988 and 2008, and the populations in many of the countries almost doubled. In general, there is growing urbaniza- tion resulting in higher population densities in capital cities and major towns (Figure 1.1). Rapid urbanization in the region is posing a great challenge to governments in providing basic amenities for the inhabitants. Increasing unemployment, particularly of youth, is a growing concern with serious socio- political implications. Settlement areas in West Africa are linked to the current climate. Three- quarters of the population lives in the humid and subhumid zones, 20 percent in the semiarid zone (the Sahel), and 5 percent in the arid zone (ECOWAS– SWAC/OECD 2007). In all the countries, there is a general pattern of high population densities in and around urban areas. There is also a generally higher TABLE 1.1 population of west africa, annualized growth rate, and percent urban, 1988 and 2009 Total population Number (millions) percent urban Country 1988 2008 annualized growth rate (%) 1988 2008 Benin 4.5 8.66 4.62 33 41 Burkina Faso 8.37 15.21 4.09 13 20 Cape Verde 0.34 0.50 2.35 39 60 Côte d’Ivoire 11.73 20.59 3.78 39 49 Gambia 0.83 1.66 5.00 36 56 Ghana 14.17 23.35 3.24 35 50 Guinea 5.74 9.83 3.56 27 34 Guinea-Bissau 0.98 1.58 3.06 26 30 Liberia 2.24 3.79 3.46 43 60 Mali 7.30 12.71 3.71 22 32 Mauritania 1.85 3.20 3.65 38 41 Niger 7.34 14.67 4.99 15 17 Nigeria 89.05 151.32 3.50 34 48 Senegal 7.11 12.21 3.59 38 42 Sierra Leone 3.95 5.56 2.04 32 38 Togo 3.70 6.46 3.73 29 42 Totals 169.20 291.30 3.61 32 43 Source: World Development Indicators (World Bank 2009a). 4 ChapTer 1 population density along the coast. Nigeria also has a high population density in the states of Katsina, Kano, and Jigawa in the north (see Figure 1.1). Income Per capita GDP in West African states has been growing at diverse rates but invariably remains low across the region. In countries like Côte d’Ivoire, Guinea-Bissau, Liberia, Niger, and Togo, per capita GDP declined between 1988 and 2008 (Table 1.2). In most of these countries, the decline is attributed to civil wars and political unrest. Niger has suffered from adverse climatic conditions. In 2008, per capita GDP ranged from about 128 US dol- lars (US$128) in Guinea-Bissau to more than US$1,500 in Cape Verde, with all other countries having less than US$500 except Côte d’Ivoire (US$530) and Senegal (US$530). Across the region, there has been a slow decline in the share of agriculture in overall GDP (see Table 1.2). This pattern occurs in all countries as eco- nomic development progresses. Specific causes include relatively slow increase in crop productivity and production and more rapid growth in the service sec- tor, including tourism. FIGURE 1.1 Population distribution in West Africa, 2000 (persons per square kilometer) < 1 1−2 2−5 5−10 10−20 20−100 100−500 500−2,000 > 2,000 Source: CIESIN et al. (2004). OVERVIEW 5 well-being indicators (regional) Under-five mortality still remains relatively high in West Africa despite a decline in the figures between 1988 and 2008 (Table 1.3). Cape Verde has the lowest (32/1,000) under-five mortality in the region, while Sierra Leone has the highest (262/1,000). A majority of the countries have an under-five mor- tality ranging between 100 and 200. Life expectancy at birth is also generally improving across the region, with Cape Verde having the most favorable life expectancy at birth (71 years), while Guinea-Bissau, Nigeria, and Sierra Leone show an average life expectancy of only 47 years. The majority of the coun- tries have a life expectancy of between 50 and 60 years. The general decrease in under-five mortality and increase in life expectancy are due to increasing campaigns for and implementation of vaccinations against major diseases and gradual improvement in health facilities. TABLE 1.2 income of west africans (GDp per capita and share of GDp from agriculture), 1988 and 2008 GDp per capita (constant 2000 US$) Share of GDp from agriculture (%) Country 1988 2008 1988 2008 Benin 313 359 34 n.a. Burkina Faso 183 263 30 n.a. Cape Verde 839 1,632 18 8 Côte d’Ivoire 695 530 32 24 Gambia 336 374 31 29 Ghana 212 327 50 32 Guinea 335 417 23 8 Guinea-Bissau 169 128 58 55 Liberia 539 148 38 n.a. Mali 204 295 45 n.a. Mauritania 429 n.a. 33 n.a. Niger 206 180 35 n.a. Nigeria 339 487 n.a. 31 Senegal 471 530 21 15 Sierra Leone 247 262 46 43 Togo 278 245 34 n.a. Source: World Development Indicators (World Bank 2009a). Notes: GDP = gross domestic product; n.a. = not available; US$ = US dollars. 6 ChapTer 1 Widespread poverty remains a challenge. Except for Côte d’Ivoire and Mauritania, where only 40–50 percent of the population lives on less than US$2 per day, an average of about 70–80 percent of the population lives on less than US$2 per day in all the other countries in the subregion (Figure 1.2). However, the coastal areas of Ghana and several states in Nigeria, including Lagos state and the federal capital state, have 20 percent to less than 10 percent of the population living on less than US$2 per day. Climate, Land Use, and Agriculture In West Africa today, rainfall generally decreases northward from the coast (Figure 1.3). The coasts of Guinea, Sierra Leone, Liberia, and Nigeria receive the highest amount of rainfall per year (ranging from 2,500 millimeters to more than 4,000 millimeters). In the Sahelian belt, extending from Senegal through Mali, Burkina Faso, Niger, and northern Nigeria, rainfall ranges from 800 to 1,100 millimeters, while Mauritania and most parts of Mali and Niger are largely TABLE 1.3 Under-five mortality and life expectancy at birth in west africa, 1988 and 2008 Under-five mortality (deaths per 1,000) Life expectancy at birth (years) Country 1988 2008 1988 2008 Benin 184 123 54 61 Burkina Faso 206 191 50 52 Cape Verde 60 32 66 71 Côte d’Ivoire 151 127 57 57 Gambia 153 109 51 56 Ghana 120 115 58 56 Guinea 231 150 49 58 Guinea-Bissau 240 198 44 48 Liberia 205 133 49 58 Mali 250 196 48 54 Mauritania 130 119 58 64 Niger 304 176 47 57 Nigeria 230 189 47 47 Senegal 149 114 52 55 Sierra Leone 290 262 40 47 Togo 150 100 58 62 Source: World Development Indicators (World Bank 2009a). Overview 7 FIGURE 1.2 Poverty in West Africa, circa 2005 (percentage of population below US$2 per day) 0 (or no data) < 10 10−20 20−30 30−40 40−50 50−60 60−70 70−80 80−90 90−95 > 95 Source: Wood et al. (2010). Note: Based on 2005 US$ (US dollars) and on purchasing power parity value. FIGURE 1.3 Annual average precipitation in West Africa, 2000s (millimeters per year) < 50 50 to 100 100 to 200 200 to 350 350 to 500 500 to 700 700 to 900 900 to 1150 1150 to 1400 1400 to 1650 1650 to 1900 1900 to 2250 2250 to 2600 2600 to 3000 > 3000 Source: WorldClim version 1.4 (Hijmans et al. 2005). 8 CHAPTER 1 desert. As rainfall decreases, from south to north, temperature increases north- ward from the southern coast (Figure 1.4). Maximum temperatures range from 30°–33°C along the coast to 36°–39°C in the Sahel and 42°–45°C on the fringe of the desert. As precipitation declines, the agroecology of West Africa shifts from humid forest along the coast to the Guinea savanna and the Sudan savanna northward (Figure 1.5 ). As a result of shifting cultivation and indiscriminate logging, only patches of the Guinea forest that once stretched from Guinea through Nigeria now remain. The belt of savanna (mostly the Sudan savanna) that stretches from northern Senegal across Mali, Burkina Faso, Niger, and northern Nigeria is referred to as the Sahel. The Sudan savanna of the Sahel merges into the Sahara Desert in the north. In general, plantation tree crops as well as root crops dominate the humid coastal areas, while cereals become pre- dominant northward. The Sahelian region is dominated by a crop/livestock production system. The existing farming systems, including the crops and livestock, have largely adapted to the respective agroecosystems in the region. Major imbalances in these agroecosystems could be caused by changes in climate, thereby affect- ing livelihoods in the region. Dwindling forests and consequently increasing FIGURE 1.4 Annual maximum temperature in West Africa, 2000s (°C) > 45 42−45 39−42 36−39 33−36 30−33 27−30 24−27 21−24 18−21 15−18 12−15 9−12 0−5 < 0 Source: WorldClim version 1.4 (Hijmans et al. 2005). OVERVIEW 9 FIGURE 1.5 Regional land use distribution in West Africa, 2000 Tree cover, broadleaved, evergreen Tree cover, broadleaved, deciduous, closed Tree cover, broadleaved, deciduous, open Tree cover, broadleaved, needle−leaved, evergreen Tree cover, broadleaved, needle−leaved, deciduous Tree cover, broadleaved, mixed leaf type Tree cover, broadleaved, regularly flooded, fresh water Tree cover, broadleaved, regularly flooded, saline water Mosaic of tree cover/other natural vegetation Tree cover, burnt Shrub cover, closed−open, evergreen Shrub cover, closed−open, deciduous Herbacious cover, closed−open Sparse herbacious or sparse shrub cover Regularly flooded shrub or herbacious cover Cultivated and managed areas Mosaic of cropland/tree cover/other natural vegetation Mosaic of cropland/shrub/grass cover Bare areas Water bodies Snow and ice Artificial surfaces and associated areas No data Source: GLC2000 (Global Land Cover 2000) (Bartholome and Belward 2005). 10 CHAPTER 1 savannahs could provide unfavorable conditions for farming systems suited to forest conditions, while such a situation could be good for systems that require relatively drier conditions. This could require adjustments to living conditions, including land tenure. There is, however, growing awareness of the adverse effects of deforestation in the region. Many governments are increasingly sup- porting initiatives aimed at conserving and protecting key natural resources, including forests (Figure 1.6). In general, major cities are linked within each country (Figure 1.7), a major highway runs along the coast from Côte d’Ivoire to Nigeria, and a major linkage in the north connects Senegal, Guinea, Mali, and Burkina Faso. Coastal coun- tries like Côte d’Ivoire, Ghana, Togo, Benin, and Nigeria are also linked with FIGURE 1.6 Protected areas in West Africa, 2009 Ia: Strict Nature Reserve Ib: Wilderness Area II: National Park III: National Monument IV: Habitat / Species Management Area V: Protected Landscape / Seascape VI: Managed Resource Protected Area Not applicable Not known Sources: Protected areas are from the World Database on Protected Areas (UNEP and IUCN 2009). Water bodies are from the World Wildlife Fund’s Global Lakes and Wetlands Database (Lehner and Döll 2004). OVERVIEW 11 their adjacent landlocked countries. There is a need to upgrade these interna- tional highways to facilitate regional trade. Tables 1.4–1.7 provide information on the major crops grown in West Africa. The major cereals are maize, millet, rice, and sorghum (see Table 1.4). Millet occupies the largest area among the cereal crops, followed by sorghum. Both crops are mainly produced in the Sahelian countries (Burkina Faso, Mali, FIGURE 1.7 Travel time in West Africa, circa 2000 To cities of 500,000 or more people To cities of 100,000 or more people To towns and cities of 25,000 or more people To towns and cities of 10,000 or more people Urban location < 1 hour 1−3 hours 3−5 hours 5−8 hours 8−11 hours 11−16 hours 16−26 hours > 26 hours Source: Authors’ calculations. 12 CHAPTER 1 TABLE 1.4 average harvest area of leading agricultural commodities in west africa, grains, 2005–07 (thousands of hectares) Country Maize Millet rice (paddy) Sorghum Total Benin 679 42 30 149 900 Burkina Faso 509 1,328 51 1,613 3,501 Cape Verde 31 0 0 0 31 Côte d’Ivoire 292 61 375 67 795 Gambia 37 127 23 24 211 Ghana 764 190 122 333 1,409 Guinea 403 377 781 48 1,608 Guinea-Bissau 16 29 73 20 137 Liberia 0 0 160 0 160 Mali 382 1,566 429 998 3,380 Mauritania 25 10 18 182 235 Niger 9 6,410 20 2,859 9,303 Nigeria 3,898 4,977 2,519 7,579 19,014 Senegal 167 793 93 175 1,229 Sierra Leone 60 25 1,000 22 1,107 Togo 475 69 32 221 797 Total 7,747 16,002 5,726 14,289 43,815 Source: FAOSTAT (FAO 2010). TABLE 1.5 average harvest area of leading agricultural commodities in west africa, pulses and nuts, 2005–07 (thousands of hectares) Country Beans, dry Cashew nuts Cowpeas Groundnuts Soybeans Total Benin 145 212 0 116 19 492 Burkina Faso 0 9 702 385 5 1,101 Cape Verde 0 0 0 0 0 0 Côte d’Ivoire 32 657 0 68 1 758 Gambia 0 0 0 120 0 120 Ghana 0 61 0 470 0 531 Guinea 0 3 0 210 0 212 Guinea-Bissau 0 212 2 24 0 239 Liberia 0 0 0 9 8 17 Mali 0 0 245 332 3 581 Mauritania 10 0 23 1 0 34 Niger 18 0 4,743 460 0 5,221 Nigeria 0 327 4,395 2,251 626 7,599 Senegal 0 16 187 624 0 827 Sierra Leone 0 0 0 150 0 150 Togo 188 0 0 57 0 246 Total 394 1,496 10,298 5,277 661 18,127 Source: FAOSTAT (FAO 2010). Overview 13 TABLE 1.6 average harvest area of leading agricultural commodities in west africa, root crops, bananas, and plantains, 2005–07 (thousands of hectares) Country Bananas Cassava plantains potatoes Sweet potatoes Yams Total Benin 3 175 0 0 26 185 389 Burkina Faso 0 2 0 1 7 3 12 Cape Verde 0 0 0 0 1 0 2 Côte d’Ivoire 8 339 382 0 25 723 1,476 Gambia 0 3 0 0 0 0 3 Ghana 7 797 301 0 65 299 1,469 Guinea 41 139 84 2 63 2 330 Guinea-Bissau 1 4 14 0 0 0 19 Liberia 11 85 19 0 2 2 120 Mali 4 6 0 5 13 3 31 Mauritania 0 0 0 0 2 0 3 Niger 0 5 0 2 3 0 10 Nigeria 0 3,821 464 266 1,086 3,068 8,705 Senegal 1 67 0 1 1 0 70 Sierra Leone 0 73 7 0 12 0 91 Togo 2 130 0 0 0 60 193 Total 77 5,645 1,271 277 1,307 4,346 12,922 Source: FAOSTAT (FAO 2010). TABLE 1.7 average harvest area of leading agricultural commodities in west africa, other crops, 2005–07 (thousands of hectares) Country Cocoa beans Coffee grain Seed cotton Sesame seeds Sugar cane Total Benin 0 0 225 11 1 238 Burkina Faso 0 0 483 51 5 538 Cape Verde 0 0 0 0 1 1 Côte d’Ivoire 2,151 585 247 6 25 3,015 Gambia 0 0 1 7 0 8 Ghana 1,678 10 25 0 6 1,718 Guinea 17 48 36 2 5 108 Guinea-Bissau 0 0 4 0 0 4 Liberia 17 17 0 0 26 60 Mali 0 0 320 14 5 339 Mauritania 0 0 5 73 4 82 Niger 1,110 4 513 201 57 1,884 Nigeria 0 0 44 27 7 78 Senegal 38 11 0 4 1 54 Sierra Leone 104 34 67 5 0 210 Togo 5,115 709 1,970 401 143 8,339 Total 394 1,496 10,298 5,277 661 18,127 Source: FAOSTAT (FAO 2010). 14 ChapTer 1 FIGURE 1.8 Yield (metric tons per hectare) and harvest area density (hectares) for rainfed maize in West Africa, 2000 < 0.25 MT/ha 0.25−0.5 MT/ha 0.5−1 MT/ha 1−1.5 MT/ha 1.5−2 MT/ha 2−3 MT/ha 3−5 MT/ha 5−7 MT/ha 7−9 MT/ha > 9 MT/ha < 1 ha 1−10 ha 10−30 ha 30−100 ha > 100 ha Sources: SPAM (Spatial Production Allocation Model) (You and Wood 2006; You, Wood, and Wood-Sichra 2006, 2009). Notes: ha = hectare; MT/ha = metric tons per hectare. and Niger) and northern Nigeria. Cowpeas and groundnuts are the major legumes cultivated in the region (see Table 1.5). Niger and Nigeria dominate cowpea production, while Nigeria leads the region in groundnut production, followed by Senegal, Ghana, and Niger. Cassava is the major root crop grown and consumed in West Africa (see Table 1.6). The major cash crops in the region are cocoa, coffee, and cotton (see Table 1.7). Cocoa and coffee produc- tion are confined to the humid forest areas along the coast, while cotton is pro- duced mainly in savannah regions, particularly in Nigeria, Burkina Faso, Mali, Côte d’Ivoire, and Benin. Figures 1.8–1.11 show the distribution and yield of major cereal crops grown in West Africa. Rainfed maize is produced across the region, with major producing countries being Côte d’Ivoire, Ghana, Togo, Benin, and Nigeria (see Figure 1.8). Millet (see Figure 1.9) and sorghum (see Figure 1.10) are pro- duced mainly in the Sahel and the northernmost parts of the coastal countries. OVERVIEW 15 FIGURE 1.9 Yield (metric tons per hectare) and harvest area density (hectares) for millet in West Africa, 2000 < 0.25 MT/ha 0.25−0.5 MT/ha 0.5−1 MT/ha 1−1.5 MT/ha 1.5−2 MT/ha 2−3 MT/ha 3−5 MT/ha 5−7 MT/ha 7−9 MT/ha > 9 MT/ha < 1 ha 1−10 ha 10−30 ha 30−100 ha > 100 ha Sources: SPAM (Spatial Production Allocation Model) (You and Wood 2006; You, Wood, and Wood-Sichra 2006, 2009). Notes: ha = hectare; MT/ha = metric tons per hectare. Similar to maize production, rice production is concentrated in the coastal countries (see Figure 1.11). Rainfed cereal yields are still very low in West Africa compared to the world average and even other regions in Africa. Rice and maize yield an average of 1 metric ton per hectare, while sorghum and mil- let yield about 0.5 ton per hectare.1 Against the background of limited inputs, the predominantly resource-poor farmers are faced with such biophysical con- straints as pests and diseases, droughts, soil acidity, and nutrient-depleted and degraded soils. The threats of climate change could prove most challenging to an already overstretched production system. Regional Program on Food Security West Africa is unique in Africa in terms of the degree of regional economic inte- gration efforts. The premier institution in this regard is ECOWAS, a regional 1 All tons are metric tons. 16 CHAPTER 1 group of 15 countries founded in 1975. Its mission is to promote economic inte- gration in “all fields of economic activity, particularly industry, transport, tele- communications, energy, agriculture, natural resources, commerce, monetary and financial questions, social and cultural matters” (ECOWAS 2012). The framework for the ECOWAS Agricultural Policy (ECOWAP) was adopted by member heads of state and government in January 2005. The three major themes of the ECOWAP policy framework are 1. increasing the productivity and competitiveness of West African agriculture, 2. implementing a trade regime in West Africa, and 3. adapting the trade regime vis-à-vis countries outside the region (ECOWAS 2008). FIGURE 1.10 Yield (metric tons per hectare) and harvest area density (hectares) for sorghum in West Africa, 2000 < 0.25 MT/ha 0.25−0.5 MT/ha 0.5−1 MT/ha 1−1.5 MT/ha 1.5−2 MT/ha 2−3 MT/ha 3−5 MT/ha 5−7 MT/ha 7−9 MT/ha > 9 MT/ha < 1 ha 1−10 ha 10−30 ha 30−100 ha > 100 ha Sources: SPAM (Spatial Production Allocation Model) (You and Wood 2006; You, Wood, and Wood-Sichra 2006, 2009). Notes: ha = hectare; MT/ha = metric tons per hectare. OVERVIEW 17 In July 2005, ECOWAS drew up a regional action plan for the implemen- tation of ECOWAP and the Comprehensive Africa Agriculture Development Programme of the New Partnership for Africa’s Development in the period 2006–10 based on six priorities for joint implementation of National Agricul- tural Investment Programmes (NAIPs) and long-term Regional Agricultural Investment Programmes (RAIPs): 1. improved water management by promoting irrigation and integrated water resource management; 2. improved management of other natural resources through organized trans- humance and rangeland development, sustainable forest resources manage- ment, and sustainable fishery resources management; FIGURE 1.11 Yield (metric tons per hectare) and harvest area density (hectares) for rice in West Africa, 2000 < 0.25 MT/ha 0.25−0.5 MT/ha 0.5−1 MT/ha 1−1.5 MT/ha 1.5−2 MT/ha 2−3 MT/ha 3−5 MT/ha 5−7 MT/ha 7−9 MT/ha > 9 MT/ha < 1 ha 1−10 ha 10−30 ha 30−100 ha > 100 ha Sources: SPAM (Spatial Production Allocation Model) (You and Wood 2006; You, Wood, and Wood-Sichra 2006, 2009). Notes: ha = hectare; MT/ha = metric tons per hectare. 18 CHAPTER 1 3. sustainable agricultural development at the farm level through integrated soil fertility management, better support services for producers, and dis- semination of improved technologies; 4. development of agricultural supply chains and promotion of markets by developing the different supply chains (foodcrops, periurban agriculture, export crops, short-cycle livestock rearing, agroforestry food products, and artisanal fishing and fish farming), developing processing operations, strengthening support services for operators, and promoting national, international, and regional trade; 5. prevention and management of food crises and other natural disasters by promoting early warning systems, developing crisis management sys- tems, assisting the recovery of crisis-hit areas, and formulating mechanisms for disaster-related insurance and compensation; and 6. institution building through gender-sensitive approaches, support for capacity building in the formulation of agricultural and rural policies and strategies, long-term funding for agriculture, communication, and capacity building in steering and coordination and in monitoring and evaluation. ECOWAS recognizes the intricate relationship between agriculture and climate and therefore the potential impact of climate change on agricultural production. This is reflected in the list of priorities for NAIPs and long- term RAIPs. What is needed now is the explicit mainstreaming of climate change in these programs. It is expected that a well-integrated regional mar- ket will not only provide a much-needed pull for agricultural produce, thereby encouraging further increase in production, but will also provide an oppor- tunity to respond to regional imbalances in production as a result of climate. Therefore, since the international conference on the mitigation of vulnerabil- ity to climate change in natural, economic, and social systems in West Africa, held in Burkina Faso in January 2007, ECOWAS has elaborated a subregional program of action to mitigate vulnerability to climate change in West Africa. The program of action emphasizes efforts to stem soil degradation on the one hand and, on the other, to foster technical and institutional synergies for cli- mate adaptation in the region. Overview 19 TABLE 1.8 Summary statistics for assumptions on west africa’s population and per capita GDp used in the iMpaCT model, 2010 and 2050 2050 Category/country 2010 Optimistic Baseline pessimistic population (millions) Benin 9,212 19,402 21,982 24,744 Burkina Faso 16,287 36,189 40,830 45,757 Cape Verde 513 595 703 822 Côte d’Ivoire 21,571 37,845 43,373 49,350 Gambia 1,751 3,292 3,763 4,270 Ghana 24,333 39,660 45,213 51,163 Guinea 10,324 21,131 23,975 27,025 Guinea-Bissau 1,647 3,147 3,555 3,990 Liberia 4,102 7,730 8,841 10,040 Mali 13,323 24,941 28,260 31,792 Mauritania 3,366 5,304 6,061 6,873 Niger 15,891 52,568 58,216 64,156 Nigeria 158,259 254,129 289,083 326,395 Senegal 12,861 22,814 26,102 29,620 Sierra Leone 5,836 10,904 12,446 14,100 Togo 6,780 11,481 13,196 15,054 income per capita (2000 US$) Benin 373 2,539 1,397 149 Burkina Faso 340 2,579 1,428 791 Côte d’Ivoire 710 6,265 3,401 1,536 Gambia 412 3,162 1,724 750 Ghana 543 4,975 2,724 988 Guinea 162 2,140 835 683 Guinea-Bissau 697 5,234 2,876 1,456 Liberia 85 1,594 394 347 Mali 417 3,818 2,108 1,122 Niger 221 1,671 637 559 Nigeria 344 2,491 1,364 684 Senegal 678 5,602 3,055 1,362 Sierra Leone 337 2,566 1,410 378 Togo 309 2,653 1,438 660 Sources: Computed from GDP data from the World Bank Economic Adaptation to Climate Change project (World Bank 2010), from the Millennium Ecosystem Assessment (2005) reports, and from population data from the United Nations (UNPOP 2009). Notes: 2010 income per capita is for the baseline scenario. GDP = gross domestic product; IMPACT = International Model for Policy Analysis of Agricultural Commodities and Trade (International Food Policy Research Institute); US$ = US dollars. 20 ChapTer 1 Scenarios for the Future Population and Income Scenarios All scenarios for the future, described further in Chapter 2, include a signifi- cant increase in the population of West African countries except for Cape Verde by 2050 (Table 1.8). In the pessimistic scenario, populations of all coun- tries in the region with the exception of Cape Verde will more than double. A similar outcome occurs in the baseline scenario for all countries except Cape Verde and Nigeria. In the optimistic scenario, the population doubles only in Benin, Burkina Faso, and Niger. Income per capita does not improve signifi- cantly in the pessimistic scenario and could even decline in the case of Benin (see Table 1.8). However, in the optimistic scenario income per capita in 2050 could range from US$1,594 for Liberia to US$6,265 for Côte d’Ivoire. Climate Change Scenarios and Their Effects on Agriculture The rainfall scenarios used in this monograph are generally dissimilar (Figures 1.12 and 1.13).2 There is similarity in the predictions for reduction in rain- fall in the southern parts of Ghana, Togo, Benin, and Nigeria, but the CSIRO A1B scenario has a greater reduction in precipitation than the MIROC A1B scenario.3 The CSIRO A1B scenario predicts no change to as much as 100 millimeters per year decline in the Sahelian region and an increase in pre- cipitation along the coast of Sierra Leone and most parts of Liberia, while the MIROC A1B scenario predicts an increase in precipitation in the Sahelian region (50–100 to 100–200 millimeters per year) and severe drought in Liberia (a decline of 200–400 millimeters per year). As indicated earlier, a substantial change in climate could require adjustments for which resource- poor farmers lack the essential means. Heavy and persistent rainfall in hitherto dry areas of the Sahel could cause an increase in diseases and pests that live- stock in those areas are not adapted to. On the other hand, a marked decrease in rainfall in hitherto wet regions like Liberia could cause significant changes in the growing conditions that may require changes in the farming system with 2 See Chapter 2 for details on how these scenarios were produced. 3 CSIRO and MIROC are acronyms for two of the general circulation models (GCMs) discussed in this book. CSIRO is a climate model developed at the Australia Commonwealth Scientific and Industrial Research Organisation. MIROC is the Model for Interdisciplinary Research on Climate, developed at the University of Tokyo Center for Climate System Research. The A1B sce- nario is a greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources. Overview 21 FIGURE 1.12 Change in average annual precipitation in West Africa, 2000–2050, CSIRO A1B (millimeters) < −400 −400 to −200 −200 to −100 −100 to −50 −50 to 50 50 to 100 100 to 200 200 to 400 > 400 Source: Authors’ calculations based on Jones, Thornton, and Heinke (2009). Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; CSIRO = climate model developed at the Australia Commonwealth Scientific and Industrial Research Organization. FIGURE 1.13 Change in average annual precipitation in West Africa, 2000–2050, MIROC A1B (millimeters) < −400 −400 to −200 −200 to −100 −100 to −50 −50 to 50 50 to 100 100 to 200 200 to 400 > 400 Source: Authors’ calculations based on Jones, Thornton, and Heinke (2009). Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; MIROC = Model for Interdisciplinary Research on Climate, developed at the University of Tokyo Center for Climate System Research. 22 ChApteR 1 FIGURE 1.14 Changes in yields (percent), 2000–2050, from the DSSAT crop model, maize (rainfed), CSIRO A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy source; CSIRO = climate model developed at the Australia Commonwealth Scientific and Industrial Research Organisation; DSSAT = Decision Support Software for Agrotechnology Transfer. FIGURE 1.15 Changes in yields (percent), 2000–2050, from the DSSAT crop model, maize (rainfed), MIROC A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; MIROC = Model for Interdisciplinary Research on Climate, developed at the University of Tokyo Center for Climate System Research. OVERVIEW 23 regard to crops and livestock composition and management. The real issue is the inability of resource-poor farmers to react appropriately and fast enough. Crop models using the CSIRO and the MIROC general circulation mod- els’ climate outputs predict a general decrease in maize yields of 5–25 percent of baseline in most parts of the countries along the southern coast of West Africa and a yield gain of 5–25 percent in the Sahel (Figures 1.14 and 1.15). Both models also have a loss in baseline area in the northernmost parts of Mali, Burkina Faso, and Nigeria. Based on both the CSIRO and the MIROC climate outcomes in the A1B SRES (Special Report on Emissions Scenarios) scenario, sorghum yields will decline by 5–25 percent across West Africa, with greater reductions in parts of Togo, Benin, and adjacent areas of Ghana and Nigeria (Figures 1.16 and 1.17). Both climate scenarios also have a loss in baseline area in the Sudan savanna from Senegal to Nigeria. However, the MIROC scenario has a greater reduc- tion than the CSIRO scenario. Rainfed rice yields are predicted to decrease by 5–25 percent in most parts of Côte d’Ivoire, Ghana, and Togo based on both the CSIRO and the MIROC models (Figures 1.18 and 1.19) and in Nigeria as well based on the FIGURE 1.16 Changes in yields (percent), 2000–2050, from the DSSAT crop model, sorghum (rainfed), CSIRO A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; CSIRO = climate model developed at the Australia Commonwealth Scientific and Industrial Research Organisation; DSSAT = Decision Support Software for Agrotechnology Transfer. 24 CHAPTER 1 FIGURE 1.17 Changes in yields (percent), 2000–2050, from the DSSAT crop model, sorghum (rainfed), MIROC A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; DSSAT = Decision Support Software for Agrotechnology Transfer; MIROC = the Model for Interdisciplinary Research on Climate, developed at the University of Tokyo Center for Climate System Research. FIGURE 1.18 Changes in yields (percent), 2000–2050, from the DSSAT crop model, rice (rainfed), CSIRO A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; CSIRO = climate model developed at the Australia Commonwealth Scientific and Industrial Research Organisation; DSSAT = Decision Support Software for Agrotechnology Transfer. OVERVIEW 25 FIGURE 1.19 Changes in yields (percent), 2000–2050, from the DSSAT crop model, rice (rainfed), MIROC A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; DSSAT = Decision Support Software for Agrotechnology Transfer; MIROC = Model for Interdisciplinary Research on Climate, developed at the University of Tokyo Center for Climate System Research. FIGURE 1.20 Changes in yields, 2000–2050, from the DSSAT crop model, groundnuts (rainfed), CSIRO A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; CSIRO = climate model developed at the Australia Commonwealth Scientific and Industrial Research Organisation; DSSAT = Decision Support Software for Agrotechnology Transfer. 26 CHAPTER 1 FIGURE 1.21 Changes in yields, 2000–2050, from the DSSAT crop model, groundnuts (rainfed), MIROC A1B 2000 old area lost Yield loss > 25% of 2000 Yield loss 5−25% Yield change within 5% Yield gain 5−25% Yield gain > 25% 2050 new area gained Source: Authors’ estimates. Notes: A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; DSSAT = Decision Support Software for Agrotechnology Transfer; MIROC = Model for Interdisciplinary Research on Climate, developed at the University of Tokyo Center for Climate System Research. CSIRO model (Figure 1.20). Both models also have an increase in rice yield in the Sahelian belt, while baseline area will be lost in Mali and Niger. Both the CSIRO A1B and the MIROC A1B results show a decline in rainfed groundnut yield across West Africa, but the impact will be relatively less in the Mano River Union countries of Guinea, Liberia, and Sierra Leone (see Figures 1.20 and 1.21). However, both models show certain areas of the northern parts of Côte d’Ivoire, Ghana, Burkina Faso, and Nigeria with an increase in yield of 5–25 percent. In this regard, the MIROC model is more positive than the CSIRO, though more area is lost in the MIROC model, so in that sense it is less positive. In general, both climate models (CSIRO and MIROC) indicate declin- ing rains along the coasts of Nigeria, Togo, Benin, Ghana, and Côte d’Ivoire, while there is either increased rainfall (MIROC) or slight dryness or wetness in the Sahel (CSIRO). This outcome seems to be related to the relatively higher prevalence of yield gain for both rice and maize in the Sahel compared to the more pronounced yield loss in the coastal areas. The increase in wetness in the Sahel may suggest an unfavorable condition for drought-tolerant and -adapted sorghum, with yield loss under increasingly wet conditions. This sce- nario suggests that farmers could face various predicaments and the need to adapt to conditions they are not used to. OVERVIEW 27 TABLE 1.9 Global commodity prices, 2010 and 2050 (2000 US$ per metric ton) 2050 Pessimistic Baseline Optimistic Crop Model price, 2010 Min Max Min Max Min Max Maize 111 209 265 216 272 200 253 Millet 341 305 327 291 307 267 283 Rice 239 433 441 378 388 323 328 Sorghum 145 184 193 184 193 175 184 Wheat 146 218 252 222 254 206 236 Source: Based on analysis conducted for Nelson et al. (2010). Notes: The minimum (min) and maximum (max) price increases arise from the differences in the climate model effects on yields. US$ = US dollars. TABLE 1.10 Maize changes in West Africa under the baseline scenario, 2010 and 2050 2050 2010 Yield (MT/ha) Area (thousands of ha) Production (MT) Country Yield (MT/ha) Area (thousands of ha) Production (MT) Min Max Min Max Min Max Benin 1.08 748 810 1.87 2.08 886 929 1,660 1,911 Burkina Faso 1.41 458 646 2.20 2.61 408 424 900 1,105 Côte d’Ivoire 1.11 745 824 1.98 2.09 787 825 1,601 1,661 Gambia 1.93 16 31 2.55 2.73 17 18 43 48 Ghana 1.52 825 1,255 2.44 2.59 945 990 2,311 2,538 Guinea 1.15 138 159 2.14 2.29 161 168 344 386 Guinea-Bissau 1.90 16 31 2.03 2.15 18 19 37 41 Mali 1.39 381 531 2.31 2.61 304 313 703 803 Niger 0.78 4 3 1.57 1.69 1 2 2 3 Nigeria 1.29 4,696 6,070 1.74 1.90 4,405 4,829 7,664 9,181 Senegal 1.98 132 263 2.76 2.90 144 151 398 439 Sierra Leone 1.92 10 20 2.98 3.10 10 11 30 33 Togo 1.11 477 531 1.78 2.01 318 334 567 661 Source: Based on analysis conducted for Nelson et al. (2010). Notes: The minimum (min) and maximum (max) price increases arise from the differences in the climate model effects on yields. ha = hectares; MT = metric tons. 28 CHAPTER 1 Regional Agricultural Outcomes Scenarios reflecting the prices of major foodcrops are presented in Table 1.9. World market prices for maize, rice, sorghum, and wheat are predicted to increase in all scenarios, while the price of millet will be less in 2050 than in 2010. In 2050, prices for millet, rice, sorghum, and wheat will be higher in the pessimistic scenario than in the optimistic scenario as the higher populations of the pessimistic scenario combine with lower income to increase demand for these crops. The production of maize (Table 1.10), millet (Table 1.11), and sorghum (Table 1.12) is predicted to increase in West Africa by 2050. The area under cultivation of both millet and sorghum will increase, while the area under cul- tivation of maize will decrease. The productivity of all three crops is assumed to increase due to increased use of inputs under improved management practices and assuming the availability of improved varieties. It is, however, TABLE 1.11 Millet changes in West Africa under the baseline scenario, 2010 and 2050 2050 2010 Yield (MT/ha) Area (thousands of ha) Production (MT) Country Yield (MT/ha) Area (thousands of ha) Production (MT) Min Max Min Max Min Max Benin 0.75 48 36 2.21 2.35 80 85 180 198 Burkina Faso 0.83 1,369 1,142 2.34 2.62 1,669 1,760 3,992 4,539 Côte d’Ivoire 0.55 95 52 1.65 1.75 143 152 237 267 Gambia 1.29 102 132 2.85 2.92 156 166 447 485 Ghana 0.78 211 166 1.68 1.74 333 354 562 616 Guinea 0.77 19 14 2.45 2.54 16 17 40 44 Guinea-Bissau 1.49 31 46 2.77 2.93 48 52 134 151 Mali 0.67 1,726 1,149 2.17 2.54 2,067 2,204 4,641 5,408 Niger 0.46 5,964 2,737 1.23 1.51 6,190 7,915 9,188 10,570 Nigeria 1.31 5,555 7,299 3.12 3.23 5,580 5,895 17,727 19,010 Senegal 0.51 831 425 1.39 1.42 1,267 1,358 1,758 1,922 Sierra Leone 1.14 8 9 2.52 2.61 8 8 20 22 Togo 0.81 62 51 1.82 1.93 78 83 145 160 Source: Based on analysis conducted for Nelson et al. (2010). Notes: The minimum (min) and maximum (max) price increases arise from the differences in the climate model effects on yields. ha = hectares; MT = metric tons. OVERVIEW 29 important to note that despite the projected increases in yields of these crops, the maximum yields are far lower than the potential for these crops and even lower than the yields currently obtained in developed countries. For example, although developed countries now typically get over 5 tons per hect- are, the highest projected yield for maize is only 3.10 tons per hectare (in Sierra Leone), whereas the highest yields for millet (Nigeria) and for sorghum (Gambia) are below 4 tons per hectare. Against the possible limitations of cli- mate, the low use of fertilizers could partly account for the low productivity of these crops. Compared with a current total world consumption of fertilizers of approximately 150 million tons and an application rate of 100 kilograms per hectare of arable land, Africa south of the Sahara (SSA) is stranded at 6 kilo- grams per hectare. The projected annual growth rate in SSA before 2030 is a dismal 1.9 percent per year (FAO 2003). This situation calls for a holistic strategy including the development of appropriate production technologies as TABLE 1.12 Sorghum changes in West Africa under the baseline scenario, 2010 and 2050 2050 2010 Yield (MT/ha) Area (thousands of ha) Production (MT) Country Yield (MT/ha) Area (thousands of ha) Production (MT) Min Max Min Max Min Max Benin 0.90 211 190 1.96 2.06 375 385 739 787 Burkina Faso 1.02 1,594 1,632 1.86 2.08 1,952 1,981 3,638 4,109 Côte d’Ivoire 0.62 107 67 1.25 1.29 167 171 210 219 Gambia 1.64 25 41 3.51 3.59 41 42 144 151 Ghana 0.95 369 352 2.04 2.09 631 647 1,290 1,342 Guinea 0.63 10 6 1.40 1.43 8 8 11 12 Guinea-Bissau 0.99 27 27 1.97 2.05 45 46 89 93 Mali 0.86 983 846 2.70 3.03 1,142 1,176 3,142 3,517 Niger 0.46 2,329 1,075 1.19 1.42 2,724 3,360 3,847 4,241 Nigeria 1.15 8,412 9,675 2.04 2.13 9,947 10,145 20,336 21,617 Senegal 0.79 188 149 1.74 1.77 315 323 550 571 Sierra Leone 1.91 10 20 2.83 2.88 14 14 39 41 Togo 1.16 236 274 2.32 2.45 321 329 747 803 Source: Based on analysis conducted for Nelson et al. (2010). Notes: The minimum (min) and maximum (max) price increases arise from the differences in the climate model effects on yields. ha = hectares; MT = metric tons. 30 CHAPTER 1 well as enabling farmers to access vital inputs required for improved productiv- ity and production under climate change conditions. Vulnerability Outcomes In the optimistic scenario, the number of malnourished children decreases for all the countries in West Africa except Niger (Table 1.13). In the pessimis- tic scenario, the number increases in all countries except Guinea-Bissau and Senegal. The results are mixed in the baseline scenario. It is important to keep in mind that although in some cases the absolute number of malnourished children increases, in most cases this still represents a drop in the proportion of children who are malnourished because the populations will increase signifi- cantly between now and 2050. TABLE 1.13 Number of malnourished children in West Africa, 2010 and 2050 (thousands) 2050 Pessimistic Baseline Optimistic Country 2010 Min Max Min Max Min Max Benin 423 741 794 520 554 375 404 Burkina Faso 1,047 1,439 1,462 1,159 1,180 866 887 Côte d’Ivoire 740 851 904 500 541 222 254 Gambia 48 59 60 28 29 6 7 Ghana 836 977 1,057 620 683 365 417 Guinea 420 526 555 312 334 127 145 Guinea-Bissau 102 92 96 65 68 0 2 Liberia 312 365 384 295 310 61 72 Mali 7,817 8,410 8,720 6,325 6,596 4,338 4,587 Nigeria 884 915 946 605 631 313 337 Niger 1,398 2,821 2,846 2,485 2,506 1,757 1,776 Senegal 449 388 400 169 178 13 21 Sierra Leone 242 450 462 227 236 108 116 Togo 254 274 296 168 185 80 94 Source: Based on analysis conducted for Nelson et al. (2010). Notes: Min (minimum) represents the smallest projected number from the simulations based on the CSIRO A1B, CSIRO B1, MIROC A1B, and MIROC B1 climate model/scenario combinations. Max (maximum) represents the largest of the four simulated values. A1B = greenhouse gas emissions scenario that assumes fast economic growth, a population that peaks midcentury, and the development of new and efficient technologies, along with a balanced use of energy sources; B1 = greenhouse gas emissions scenario that assumes a population that peaks midcentury (like the A1B), but with rapid changes toward a service and information economy and the introduction of clean and resource-efficient technologies; CSIRO = climate model developed at the Australia Commonwealth Scientific and Industrial Research Organisation; MIROC = Model for Interdisciplinary Research on Climate, developed at the University of Tokyo Center for Climate System Research. OVERVIEW 31 Adaptation and Means of Implementation The challenges to the agricultural sector and its stakeholders from a chang- ing climate are growing and pose a serious threat to the welfare of people in the region, particularly farmers. Although higher temperatures are likely, the magnitude of the increase is uncertain, and the effects will differ across the region depending on which climate scenario eventually occurs. Precipitation outcomes are even more uncertain. The general consequences of drought and excess availability of water on the physiology and productivity of crops are largely known. However, the effects of changes in climate on the limits of tol- erance of existing varieties as well as the possible emergence of diseases and pests will be a real challenge. Consequently, possible avenues for adaptation must include dealing with drought, floods, high temperatures, waterlogging, new and increasing inci- dence of plant pests and diseases, a shorter growing season, and associated human health concerns, such as malaria and sleeping sickness in the Sahel due to wetter conditions favorable to mosquitoes and tsetse flies. Selection and breeding of appropriate varieties will be crucial in any adaptation venture. Developing appropriate management practices of such varieties is imperative. Against the background of the debate on the relative emphasis on adap- tation and mitigation, it is worth noting that in many instances the best bets for improved agricultural production and sustainable management of natu- ral resources also have considerable mitigation potential (see Nin-Pratt et al. 2011 for an analysis of the best opportunities for productivity research). The existing traditional farming practices in West Africa, notably shifting cultiva- tion, burning as a means of clearing, inefficient use of paddies, indiscriminate plowing of lands, and so on, offer significant avenues for improvement that will also reduce the production of greenhouse gasses and conserve the natu- ral resource base needed by farmers in the region. The World Bank (2009b) reported that although Africa accounts for only 4 percent of global carbon dioxide emissions, more than 60 percent of its emissions are due to deforesta- tion and land degradation. Therefore, crop intensification, minimum tillage, and agroforestry coupled with designation and maintenance of protected for- ests will go a long way toward carbon sequestration as well as conserving and improving the natural resource base. 32 ChapTer 1 Finance, Technology, and Capacity Building The majority of farmers in West Africa are resource poor. In addition to bio- physical constraints to their farming pursuits, lack of access to funds as well as markets severely limits their ability to break out of the vicious circle of poverty. In view of their scale of production, targeted subsidies coupled with micro- credit with practical and reasonable collateral requirements will go a long way toward enabling small-scale farmers to acquire vital inputs required for boosting production. In addition, access to payments for carbon credits will encourage farmers to join hands in the global effort to meet the challenges of climate change. It is critical that appropriate technologies be available for farmers to effec- tively undertake adaptation and mitigation measures. There is also a need for appropriate awareness raising to inform mostly illiterate farmers about how to efficiently use technologies as well as to ensure that they are aware of their rights and are able to negotiate for benefits. In this regard, CORAF, with a mandate to coordinate agricultural research in West and Central Africa, has developed a strategic framework to guide climate change research in its man- date region, including West Africa. The strategy identifies priority research areas for climate change adaptation and mitigation and provides for capacity building, knowledge management, and partnerships in the context of an IAR4D (Integrated Agricultural Research for Development) approach. 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