Assessment of Selected, Major Policy Issues

336 | IAASTD Global Report improves throughout 2025 to 2050 and more so in liberalized regimes; hence both rural and urban households improve their consumption. The marginally better performance in consumption of rural poor households under AKST reassures that a more agri- culture oriented growth process lead to decline of the rural-urban consumption gap in the long run. Table 5-34 shows an improvement of per capita availability of different agricultural crops through 2025 and further till 2050. The domestic supply in agriculture is projected to grow by 2.87 an- nually to 2050, and by 4.72 to 2050. The only sector showing a decline is the “meat” sector. However, apart from “meat”, “other livestock” is expected to grow with annual growth rate of 23. The availability of non-agricultural goods in the domestic market is also expected to grow ranging from 2-5 per annum. Overall, total domestic supply is expected to grow by 4-5 every year out to 2050. The availability of goods for the domestic market indicates that domestic production along with imports remains healthy even after fulfilling demand for exports. Domestic supply of goods grows more significantly for the nonagricultural sectors and then again for the later years from 2025 through 2050. Table 5-33. Population deciles with per capita consumption expenditure changes over reference run India in ascending order. Per capita consumption Population Deciles 2000 2025 2025-1 2050 2050-1 Rupees Rural 1st Decile poorest 10 1,245 1,874 2,018 5,349 5,408 2nd Decile 1,606 2,417 2,603 6,901 6,976 3rd Decile 1,854 2,790 3,005 7,965 8,053 Poorest 30 1,571 2,364 2,545 6,748 6,822 4th Decile 2,082 3,134 3,375 8,946 9,044 5th Decile 2,310 3,476 3,743 9,922 10,031 6th Decile 2,575 3,874 4,172 11,060 11,182 7th Decile 2,879 4,333 4,666 12,368 12,504 8th Decile 3,291 4,952 5,333 14,137 14,292 9th Decile 3,954 5,949 6,407 16,984 17,170 10th Decile richest 10 6,281 9,452 10,179 26,983 27,279 All Rural 2,806 4,222 4,547 12,054 12,186 Urban 1st Decile poorest 10 1,260 1,604 2,059 4,956 5,017 2nd Decile 1,691 2,152 2,659 6,651 6,732 3rd Decile 2,010 2,559 3,145 7,907 8,004 Poorest 30 1,653 2,105 2,621 6,504 6,583 4th Decile 2,323 2,957 3,466 9,137 9,248 5th Decile 2,678 3,409 3,866 10,534 10,663 6th Decile 3,092 3,936 4,286 12,162 12,311 7th Decile 3,604 4,588 4,811 14,177 14,351 8th Decile 4,337 5,522 5,435 17,063 17,272 9th Decile 5,512 7,017 6,595 21,682 21,948 10th Decile richest 10 10,226 13,019 10,437 40,227 40,719 All Urban 3,672 4,675 4,675 14,445 14,622 Note: 1 USD = Rs. 43.3 in 2000. Source: GEN-CGE model simulations. continued Box 5-2. continued Looking Into the Future for Agriculture and AKST | 337 strengthen its net export position for these commodities. Under AKST_low_neg, on the other hand, high food prices lead to depressed global food markets and reduced global trade in agricultural commodities. Water scarcity is expected to increase considerably in the AKST_low_neg variant as a result of a sharp degrada- tion of irrigation eficiency. The irrigation water supply reli- ability index therefore drops sharply Table 5-19. Sharp increases in international food prices as a result of the AKST_low and combined variants Table 5-18 de- press demand for food and reduce availability of calories Figure 5-36. In the most adverse, AKST_low_neg variant, average daily kilocalorie availability per capita declines by 1,100 calories in sub-Saharan Africa, pushing the region be- low the generally accepted minimum level of 2,000 calories growth in SSA and LAC, and 25 in CWANA, compared to 27, 21, and 7 under the reference world. This could lead to further forest conversion into agricultural use. At the same time, rapid expansion of the livestock population un- der AKST_high requires expansion of grazing areas in SSA and elsewhere, which could also contribute to accelerated deforestation. What are the implications of more aggressive produc- tion growth on food trade and food security? Under AKST_ high, SSA cannot meet the rapid increases in food demand through domestic production alone. As a result, imports of both cereals and meats increase compared to the refer- ence run, by 137 and 75, respectively Figure 5-34 and 5-41. Under AKST_high, ESAP would also increase its net import position for meats and cereals, while NAE would Table 5-34. Total domestic supply of goods and services, India, reference run and trade liberalization variant. Base = 2000 2025 2025-1 2050 2050-1 Unit Rs. 10 million Annual Growth Rice 170,095 1.7 0.62 2.91 2.79 Wheat 50,853.5 4.62 4.1 4.96 4.86 Maize 5,556.32 4.48 4.32 4.6 4.48 Other coarse grains 8,833.8 4.53 4.16 5.6 6.36 Pulses 21,635.1 4.59 4.28 5.03 4.93 Potatoes 7,036.53 4.59 4.27 5.12 5.28 Other crops 230,682 1.83 4.66 4.22 4.36 Oilseeds and edible oils 133,039 1.14 1.14 2.44 2.46 Meat 39,045.7 4.59 4.2 2.27 2.08 Fishing 21,015 4.6 4.08 1.54 1.9 Other livestock 115,019 4.63 4.2 5.19 5.34 Total Agriculture 802,810 2.87 2.3 3.79 3.88 Fertilizers 34,902.5 2.49 3.26 1.13 0.81 Other manufacturing 1,458,410 2.59 2.71 1.58 1.58 Other services 1,248,214 2.7 2.89 1.4 0.89 Total Nonagriculture 2,741,526 2.64 2.8 1.5 1.35 Grand Total 3,544,336 2.69 2.69 2.28 2.24 Note: 1 USD = Rs. 43.3 in 2000. Source: GEN-CGE model simulations. Box 5-2. continued 338 | IAASTD Global Report Table 5-16. Assumptions for highlow agricultural investment variants. Parameter changes for growth rates 2050 REFERENCE RUN 2050 High AKST variant 1 2050 Low AKST variant 2 GDP growth 3.06 per year 3.31 per year 2.86 per year Livestock numbers and yield growth Base model output numbers growth 2000-2050 Livestock: 0.74yr Milk: 0.29yr Increase in numbers growth of animals slaughtered by 20 Increase in animal yield by 20 Reduction in numbers growth of animals slaughtered by 20 Reduction in animal yield by 20 Food crop yield growth Base model output yield growth rates 2000-2050: Cereals: yr: 1.02 RT: yr: 0.35 Soybean: yr 0.36 Vegetables: yr 0.80 Sup-tropicaltropical fruits: 0.82yr Increase yield growth by 40 for cereals, RT, soybean, vegetables, ST fruits sugarcane, dryland crops, cotton Increase production growth of oils, meals by 40 Reduce yield growth by 40 for cereals, RT, soybean, vegetables, fruits sugarcane, dryland crops, cotton Reduce production growth of oils, meals by 40 Source: Authors. Table 5-17. Assumptions for highlow agricultural investment combined with highlow Investment in other AKST-related factors irrigation, clean water, water management, rural roads, and education. Parameter changes for growth rates 2050 BASE 2050 High AKST combined with other services 3 2050 Low AKST combined with other services Low 4 GDP growth 3.06 per year 3.31 per year 2.86 per year Livestock numbers growth Base model output numbers growth 2000-2050 Livestock: 0.74yr Milk: 0.29yr Increase in numbers growth of animals slaughtered by 30 Increase in animal yield by 30 Reduction in numbers growth of animals slaughtered by 30 Reduction in animal yield by 30 Food crop yield growth Base model output yield growth rates 2000-2050: Cereals: yr: 1.02 RT: yr: 0.35 Soybean: yr 0.36 Vegetables: yr 0.80 Sup-tropicaltropical fruits: 0.82yr Increase yield growth by 60 for cereals, RT, soybean, vegetables, ST fruits sugarcane, dryland crops, cotton Increase production growth of oils, meals by 60 Reduce yield growth by 60 for cereals, RT, soybean, vegetables, fruits sugarcane, dryland crops, cotton Increase production growth of oils, meals by 60 Irrigated area growth apply to all crops 0.06 Increase by 25 Reduction by 25 Rain-fed area growth apply to all crops 0.18 Decrease by 15 Increase by 15 Basin efficiency Increase by 0.15 by 2050, constant rate of improvement over time Reduce by 0.15 by 2050, constant rate of decline over time Access to water Increase annual rate of improvement by 50 relative to baseline level, subject to 100 maximum Decrease annual rate of improvement by 50 relative to baseline level, constant rate of change over time Female secondary education Increase overall improvement by 50 relative to 2050 baseline level, constant rate of change over time unless baseline implies greater subject to 100 maximum Decrease overall improvement by 50 relative to 2050 baseline level, constant rate of change over time unless baseline implies less Source: Authors. Looking Into the Future for Agriculture and AKST | 339 Figure 5-31. Cereal feed, food and other demand projections, 2050, alternative AKST variants. Source: IFPRI IMPACT model simulations. Figure 5-32. Sources of cereal production growth, High_AKST variant, by IAASTD region. Source: IFPRI IMPACT model simulations. Figure 5-33. Sources of cereal production growth, Low_AKST variant, by IAASTD region. Source: IFPRI IMPACT model simulations. Figure 5-34. Cereal trade in 2050, alternative AKST variants, IAASTD regions. Source: IFPRI IMPACT model simulations. Figure 5-35. Meat trade 2050, alternative AKST variants, IAASTD regions. Source: IFPRI IMPACT model simulations. respectively, under the more aggressive AKST and support- ing service variations Figure 5-38. On the other hand, if in- vestments slow down more rapidly, and supporting services degrade rapidly then absolute childhood malnutrition levels could return or surpass 2000 malnutrition levels with 189 million children in 2050 under the AKST_low_neg variation and 126 million children under the AKST_low variation. What are the implications for investment under these alternative policy variants? Investment needs for the group of developing countries for the alternative AKST variants have been calculated following the methodology described in Rosegrant et al. 2001 Figure 5-39. Investment re- quirements for the reference run for key investment sec- tors, including public agricultural research, irrigation, rural roads, education and access to clean water are calculated at US1,310 billion see also Tables 5-16 and 5-17 for changes in parameters used. As the igure shows, the much better outcomes in developing-country food security achieved un- der the AKST_high and AKST_High_pos variants do not require large additional investments. Instead they can be achieved at estimated investment increases in the ive key investment sectors of US263 billion and US636 billion, respectively. and thus also below the levels of the base year 2000. Calorie availability together with changes in complementary service sectors can help explain changes in childhood malnutrition levels see Rosegrant et al., 2002. Under the AKST_high and AKST_high_pos variants, the share of malnourished children in developing countries is expected to decline to 14 and 8, respectively, from 18 in the reference world and 27 in 2000 Figure 5-37. This translates into abso- lute declines of 25 million children and 55 million children, 340 | IAASTD Global Report lingo-cellulosic bioenergy sources becomes available, since these sources offer more CO 2 reductions and use less land per unit of energy. However, this second generation bioen- ergy is not expected to become available within the coming 10 to 15 years UN-Energy, 2007. To explore the bioenergy potential under the IAASTD reference case, the procedure of De Vries et al. 2007 is followed in which the potential for bioenergy is deined as the amount of bioenergy that could be produced from 1 abandoned agricultural land and 2 40 of the natural grass areas see Appendix. Under these assumptions, the technical potential in 2050 is around 180 EJ in the absence of residues mainly from USA, Africa, Russia and Central Asia, South East Asia and Oceania. Obviously this number is very uncertain—and depends, among other factors, on 1 agricultural yields for food production, 2 yields and conversion rates for bioenergy, 3 restrictions in supply of bioenergy to reduce biodiversity damage, and 4 uncer- tainty in water supply. The potential supply from residues is also very uncertain and estimates range from very low num- bers to around 100 EJ. In the reference projection, a poten- tial supply of 80 EJ is assumed. Until 2050, in this scenario the overall impact of bioenergy on biodiversity is negative, given the direct loss of land for nature versus the long-term gain of avoided climate change SCBDMNP, 2007.

5.4.4 Focus on bioenergy Among the renewable sources, bioenergy deserves special

attention energy from crops, lingo-cellulosic products and timber byproducts. Currently, bioenergy is the only alter- native to fossil fuels that is available for the transport sector. Studies of the potential conirm that the production of liq- uid fuels from biomass could meet the demand in the global transport sector. Bioenergy can also be used to produce elec- tricity and heat. Large-scale application of biomass as an energy source will mean that in the short term bioenergy will primarily be derived from speciic crops that are culti- vated for energy production sugar cane, maize, oil crops. The eventual contribution from biomass greatly depends on the expectations of extracting energy from lingo-cellulosic products both woody and non-woody products, like pop- lar and grass. The large-scale cultivation of biomass for energy applications can mean a considerable change in fu- ture land use, and could compete with the use of this land for food production. Other aspects of sustainability, such as maintaining biodiversity and clean production methods, also play a role here see Chapters 3, 4 and 6. Under sce- narios in which agricultural land could become available as a result of rapid yield improvement and slow popula- tion growth, bioenergy potential is considerably higher than in land-scarce scenarios. Results for bioenergy can become more positive when the second generation bioenergy the Figure 5-36. Average daily calorie availability per capita, projected 2050, selected regions, AKST variants. Source: IFPRI IMPACT model simulations. Figure 5-37. Malnourished children under alternative AKST variants in developing countries. Source: IFPRI IMPACT model simulations. Figure 5-38. Malnourished children under alternative AKST variants in developing countries. Source: IFPRI IMPACT model simulations. Figure 5-39. Investment requirements, alternative AKST variants, developing countries. Source: IFPRI IMPACT model simulations. Looking Into the Future for Agriculture and AKST | 341 large cities in water-short areas, such as MENA, Central Asia, India, Pakistan, Mexico, and northern China. Water for en- ergy, i.e., hydropower and crop production for biofuels, will further add to the pressure on water resources. Third, signs of severe environmental degradation because of water scar- city, overabstraction and water pollution are apparent in a growing number of places Pimentel et al., 2004; MA, 2005; Khan et al., 2006; CA, 2007 with often severe consequenc- es for the poor who depend heavily on ecosystems for their livelihoods Falkenmark et al., 2007. Lastly, climate change may exacerbate water problems particularly in semiarid ar- eas in Africa were the absolute amount of rain is expected to decline, while seasonal and interannual variation increas- es Wescoat, 1991; Rees and Collins, 2004; Alcamo et al., 2005; Barnett et al., 2005; Kurukulasuriya et al., 2006. 5.4.5 The scope of improving water productivity The reference run foresees a substantial increase in water consumption in agriculture, and particularly in non-agricul- tural sectors. This may be reason for concern. First, already more than a billion people live in river basins character- ized by physical water scarcity CA, 2007. In these areas water availability is a major constraint to agriculture. With increased demand for water, existing scarcity will deepen while more areas will face seasonal or permanent shortages. Second, competition for water between sectors will inten- sify. With urbanization, demand for water in domestic and industrial sectors will increase between 2000 and 2050. In most countries water for cities receives priority over wa- ter for agriculture by law or de facto Molle and Berkoff, 2006, leaving less water for agriculture, particularly near Table 5-19. Irrigation water supply reliability, projected to 2050, reference run and AKST variations. Reference AKST_high_pos AKST_low_neg Region Percent North America and Europe NAE 64 72 60 East-South Asia and Pacific ESAP 56 66 51 Central-West Asia and North Africa CWANA 46 52 39 Latin America and Caribbean LAC 83 86 75 sub-Saharan Africa SSA 87 92 85 Developed Countries 66 74 62 Developing Countries 56 65 51 World 58 67 53 Source: IFPRI IMPACT model simulations. Table 5-18. Selected international food prices, projected to 2050, reference run and AKST variations. Reference run AKST- high AKST_low AKST_high_pos AKST_low_neg Food US per metric ton Beef 2,756 -23 36 -31 63 Pork 1,164 -29 48 -40 84 Sheep goat 3,079 -24 36 -34 60 Poultry 1,434 -34 62 -46 114 Rice 245 -46 105 -62 232 Wheat 173 -53 173 -68 454 Maize 114 -67 311 -81 882 Millet 312 -59 204 -72 459 Sorghum 169 -57 200 -70 487 Other coarse grains 104 -74 545 -86 1952 Soybean 225 -31 56 -43 106 Source: IFPRI IMPACT model simulations.