The Economics of Desertification, Land Degradation, and Drought

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Table 2.2—Nitrogen application rates in selected eastern and southern African countries 2002–2004 2005–2006 Change (%) kgN/ha kgN/ha Angola 1.10 1.44 30.2 Malawi 21.24 23.90 12.5 Mozambique 3.24 2.44 -24.7 Namibia 2.32 1.77 -23.8 Zambia 21.49 21.75 1.2 Uganda 0.54 0.46 -15.3 Tanzania 2.61 4.88 86.8 Kenya 11.65 13.11 12.5 Cameroon 3.13 3.45 10.3 Average 7.48 8.1 10.0 Source: FAOSTAT. Note: kgN/ha = kilograms of nitrogen per hectare. The results imply that the two-way relationship between land degradation and agronomic yield is likely to give misleading conclusions. Better results would be obtained if all major determinants of yield were included in models in order to determine the actual impact of land degradation on crop yield. Moreover, neglecting any valuation of crops or crop utilization implies neglect of economic effects. Millennium Ecosystem Assessment, 2001–2005 The Millennium Ecosystem Assessment (MA) was conducted from 2001 to 2005 in order to assess current trends in ecosystems and human well-being. The study was part of a UN effort to evaluate the impact of changes of ecosystem services on human well-being (MA 2005b; Lepers et al. 2005). The MA report, published in 2005, was based on 14 global, regional, and subregional studies, including remote sensing and other data sources, with georeferenced results compiled into a map with a spatial resolution of 10 kilometers by 10 kilometers. The period from 1980 to 2000 was not provided in all datasets; therefore, no precise information on degradation within a certain period is given (Lepers et al. 2005). Unlike GLASOD and GLADA, MA covered a broad range of ecosystem services (see Box 1.1—Ecosystem services ). MA also attempted to assess the drivers of changes to ecosystem services and their impact on human well-being, as well as providing rich ecosystem data. The MA used GLASOD, the Convention on Biological Diversity, and other available data and studies to assess land degradation, which helped to enrich its assessment by taking advantage of past studies. These aspects make the MA study informative in policy advice. The MA study points out that the drivers of changes in ecosystem services are multiple and related in a complex and interactive manner. These drivers of changes mediate and lead to trade-offs and synergetic associations. However, some general patterns could have been established as drivers of major changes. Conversion of land use types to agriculture has led to loss of biodiversity. The MA study also suffers from some weaknesses, because it does not examine the costs of land degradation or the costs and benefits of the prevention of loss of ecosystem services or the rehabilitation of degraded ecosystem services. Land Degradation in Sub-Saharan Africa The studies by Vlek, Le, and Tamene (2008, 2010) analyze land degradation in Sub-Saharan Africa, using NDVI for the same period as GLADA (1982–2003) in order to approximate NPP. The NDVI data were based on mean NDVI per year and per month. RUE was not used in this study; instead, the correlation between rainfall and NDVI was assessed to differentiate between climate-driven and 24
human-induced productivity changes. As a result, 10 percent of Sub-Saharan Africa (2.13 million square kilometers) showed a significant decline in NDVI, presumably caused by land degradation (Figure 2.11). Figure 2.11—Long-term degradation of green biomass Source: Vlek, Le, and Tamene 2008. Furthermore, the authors argued that atmospheric fertilization may be responsible for the extensive greening found in the analysis of NDVI trends (see Figure 2.12), masking underlying landdegrading processes. They selected areas with low population density, insignificant rainfall–NDVI correlation, and positive NDVI trends in order to estimate the rate of NPP improvement attributable to atmospheric fertilization. The study observed that in 17 percent of the area of Sub-Saharan Africa, land degradation was more than compensated for by atmospheric fertilization. Taking atmospheric fertilization into account, the identified degraded areas fit reasonably well with GLASOD results but do not fit the GLADA results based on RUE-adjusted NDVI (Figure 2.4). Though Bai et al. (2008b) identified a similar figure (26 percent of Sub-Saharan Africa’s land area) experiencing land degradation, a geographical overlap of the affected land areas and population density is missing. 25
Page 1 and 2: IFPRI Discussion Paper 01086 May 20
Page 3 and 4: Contents Abstract vii Acknowledgmen
Page 5 and 6: List of Figures 1.1—Conceptual fr
Page 7 and 8: ABSTRACT Attention to land degradat
Page 9 and 10: ABBREVIATIONS AND ACRONYMS ADB Asia
Page 11: SCAR Soil Conservation in Agricultu
Page 14 and 15: In the present report, the conceptu
Page 16 and 17: Framework: Confronting Action versu
Page 18 and 19: understand these arrangements in or
Page 20 and 21: Figure 1.3—Prevention, mitigation
Page 22 and 23: on assessments at the micro level (
Page 24 and 25: does not clearly distinguish betwee
Page 26 and 27: Figure 2.1—GLASOD (1991) global a
Page 28 and 29: International Earth Science Informa
Page 30 and 31: Box 2.1—Continued Normalized Diff
Page 32 and 33: Figure 2.4—Loss of annual NPP, GL
Page 34 and 35: Figure 2.7—Annual loss of NPP in
Page 38 and 39: Figure 2.12—Areas affected by hum
Page 40 and 41: output—which is a selection of su
Page 42 and 43: GLADIS also underlines the linkage
Page 44 and 45: Box 2.2—Continued A map on global
Page 46 and 47: Table 2.3—Global land degradation
Page 48 and 49: Table 2.3—Continued Project and d
Page 50 and 51: Table 2.4—Continued Location 10 2
Page 52 and 53: Table 2.6—Extent and severity of
Page 54 and 55: National Level Policies As discusse
Page 56 and 57: Such support has also been directed
Page 58 and 59: Migration, either as outmigration o
Page 60 and 61: The preceding discussion shows the
Page 62 and 63: Results Correlation Analysis Consis
Page 64 and 65: Figure 2.23—Relationship between
Page 66 and 67: Effects of Land Degradation On-Site
Page 68 and 69: soils and higher land productivity.
Page 70 and 71: Acknowledging the work of GLADIS, a
Page 72 and 73: health, education, security, enviro
Page 74 and 75: Box 3.1—Recent major economic ass
Page 76 and 77: An appropriate economic tool for a
Page 78 and 79: Figure 3.4—Cost of action and cos
Page 80 and 81: enefit—for example, income. This
Page 82 and 83: Figure 3.6—Ecosystem services fra
Page 84 and 85: Replacement Cost Approaches The rep
Page 86 and 87:
Box 3.2—Measuring land degradatio
Page 88 and 89:
land degradation and conservation m
Page 90 and 91:
Flooding and Aquifer Recharge Richa
Page 92 and 93:
year (Diao and Sarpong 2007). Sonne
Page 94 and 95:
Bringing together the different cos
Page 96 and 97:
This section discusses the most imp
Page 98 and 99:
governing land use patterns (Leeman
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promotion of community forest manag
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Box 4.3—Reducing emissions from d
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Soil Nutrient Depletion Soil nutrie
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that the yields of salt-tolerant wh
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Figure 5.3—Forest area as a perce
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Figure 5.5—Per capita water stora
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Introduction 6. CASE STUDIES Five c
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Salinity The effects of salinity on
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Third, to correctly decide which ac
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Costs of Action and Inaction We eva
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The NGOs and religious organization
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have a strong influence on NRM (And
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We estimated the cost of action (de
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Figure 6.15—Costs of action and i
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Of interest to us is the strategy t
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7. PARTNERSHIP CONCEPT The review o
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degradation. All this should be don
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Finally, the global assessment of D
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the experience of and lessons learn
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Table 7.3—Example of E-DLDD resea
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8. CONCLUSIONS Since the publicatio
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lowest in the region. From a socioe
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Table A.1—Land degradation assess
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Table A.2—Continued Author Region
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Table A.3—Continued Author Countr
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Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Table A.5—Costs of land degradati
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Figure B.1—Land use systems of th
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REFERENCES Abelson, P. 1979. Cost B
Page 162 and 163:
Benin S., E. Nkonya, G. Okecho, J.
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Clark, E. H. 1985. The Off-Site Cos
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De Jager a., D. Onduru and C. Walag
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Foster, V., and C. Briceño-Garmend
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Holden, S. and H. Lofgren. 2005. As
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Lapar, M. L., and S. Pandey. 1999.
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Nachtergaele, F., M. Petri, R. Bian
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Pender, J. L. 2009. “Food Crisis
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Sauer, J., and H. Tchale. 2006. Alt
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Tan, Z. X., R. Lal, and K. D. Wiebe
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———. 2010. “Assessment of L
Page 187 and 188:
RECENT IFPRI DISCUSSION PAPERS For
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The Economics of Desertification, Land Degradation, and Drought

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