The Economics of Desertification, Land Degradation, and Drought

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GLADIS also underlines the linkage between population pressure and land degradation, especially the strong relationship between poverty and land degradation. Using the database on global subnational infant mortality rates (IMR) and population density from CIESIN, we can get information on this linkage through the LDII (Figure 2.17). Figure 2.17—Land Degradation Impact Index (LDII), GLADIS Source: Nachtergaele et al. 2010. Referring to Nachtergaele et al. (2010), 42 percent of the very poor live in degraded areas, as compared with 32 percent of the moderately poor and 15 percent of the nonpoor. Moreover, poverty was emphasized for being notably a rural problem, which is definitely not a new finding. The GLADIS approach, with its WebGIS and the combination of biophysical as well as socioeconomic determinants, offers a broad but not exact view of global hot spots of land degradation. GLADIS emphasizes a close link between land degradation and poverty (Blaikie and Brookfield 1987; Barbier 1997, 2000; Duraiappah 1998) (see Box 2.2). 15 GLADIS is not amenable to the global or national level, and the authors warn against using any information for policy strategies on the national or local level. The global scale generalizes the analysis; in addition, in some even-larger areas, such as Argentina, 16 it does not correctly represent the status of land degradation in a region. Results contradict other regional studies, such as the ones by Vlek, Le, and Tamene (2008, 2010). The GLADIS approach is limited due to the availability of global data with sufficient detail and resolution. For example, the analysis on “Trends of Agricultural and Forestry Production” (axis on economic determinants) contains a trend for national agricultural production, which takes into account livestock plus cropped agriculture for 1990 and 2003 by FAOSTAT (FAO Statistics), and a trend for forestry trends, which compares data of 1990 and 2006. “This gives rough estimates of trends in production over the 15-year period” (Nachtergaele et al. 2010: 40) but does not illustrate an exact calculation, even if the earliest date corresponds. 15 Infant mortality and child malnutrition both are proxies for poverty and welfare of an area (CIESIN 2010 http://sedac.ciesin.columbia.edu/povmap/methods_global.jsp).Within the GLADIS approach, a CIESIN global poverty dataset on infant mortality rates on a national level was given as an indicator for poverty. Data from the Demographic and Health Surveys, the Multiple Indicator Cluster Surveys, and the national Human Development Reports were incorporated in this dataset. 16 According to a presentation at the Technical LADA Meeting, September 6–14, 2010 in Wageningen, Netherlands; by the LADA Argentina Team. Available at www.fao.org/nr/lada/index.php?option=com_docman&task=doc_download&Itemid=165&gid=622&lang=en. 30
Box 2.2—Socioeconomic determinants of land degradation according to GLADIS Within the GLADIS approach, six axes were used to get a broad and interdisciplinary view of land degradation. The Economic Indicators for Ecosystems consist of the Economic Production Status of Ecosystems, the Livestock Economic Status, the Forest Economic Status, and Trends of Agricultural and Forestry Production. Within the Economic Production Status for agricultural land, two variables were used: an average crop yield for the year 2000—estimated in Global Agro-Ecological Zones (GAEZs) 17 —and a statistical database, mainly from FAOSTAT, on major agricultural crops. The average crop yield was measured in the international Geary-Khamis price 18 of 2000/01. Within the resource database, rainfed areas were distinguished from irrigated areas. The Livestock Economic Status includes information on climate regions (polar, desert, tropical, boreal, subtropical, and temperate), as well as on regional weighted averages of cattle and small ruminants shown in a livestock intensity map, according to Nachtergaele and Petri (2008). 19 Data on 2006 national forest GDP (Lebedys 2008) was the input for the global map on Forest Economic Status. A classification value from 0 (low) to 100 (high) for the year 2006, incorporating the country gross value, which was added to the forest sector, indicates the value of this index. Within the analysis maps, the axis inputs were calculated and combined to depict both land degradation status and land degradation process. Land degradation trends include trends on national agricultural production analyzed with livestock and crop data from 1990 and 2003, taken from FAOSTAT data. Moreover forestry trends were taken into account by comparing data on forestry production in U.S. dollars from 1990 and 2006 (Lebedys 2008). The Economic Production Status analysis for agricultural land reveals high economic output in countries such as the Netherlands, Belgium, the United Kingdom, France, and Germany relative to many developing countries, which are situated in drylands and which have low economic output. The land degradation trend analysis shows an increasing trend of crop and livestock production in Sub-Saharan Africa, Brazil, and several parts of Southeast Asia. A low trend is also clearly seen in Central and Eastern Europe. The overall trends of agricultural and forest production depict high values (an increasing trend) in Brazil, Sub-Saharan Africa, and the eastern parts of China, and low trends in Norway, Japan, and Indonesia. These results should be seen as being related to the input factors that concentrate on agricultural production, livestock, and forest areas. In general, drylands (such as arid and semiarid Sub-Saharan Africa) have a lower agricultural output and only limited forest areas; therefore, they of course show low values in the status analysis. The trend analysis takes into account different time series—agricultural production of 1990 and 2003, and forestry trends comparing a dataset of 1990 and 2006. It should be questioned whether this analysis is still representative because of the different periods used, even if the starting points of the time series are the same. Global accessibility, tourism, protected areas, and the Human Development Index (HDI) are building the sixth axes on Social and Cultural Provisions of Ecosystems. 17 According to Fischer, Van Velthuizen, and Nachtergaele (2000:4), the GAEZs provide “a standardized framework for the characterization of climate, soil, and terrain conditions relevant to agricultural production.” Therefore several determinants, such as the length of the growing period and the latitudinal thermal climates, were incorporated in this approach. Moreover, the GAEZs depict limitations in climate, including soil and terrain resources, which do have an impact on crops. 18 The Geary-Khamis price is an average price method that “entails valuing a matrix of quantities using a vector of international prices. The vector is obtained by averaging national prices across participating countries after they have been converted to a common currency with purchase power parities (PPP) and real final expenditures above the basic heading” (UN 1992: http://unstats.un.org/unsd/methods/icp/ipco_htm.htm). 19 The livestock intensity map is based on a map of Global Livestock Production Systems (Thornton et al. 2002) and data on cattle and small ruminants (Wint & Robinson 2007). 31
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 36 and 37: Table 2.2—Nitrogen application ra
Page 38 and 39: Figure 2.12—Areas affected by hum
Page 40 and 41: output—which is a selection of su
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
Page 100 and 101:
promotion of community forest manag
Page 102 and 103:
Box 4.3—Reducing emissions from d
Page 104 and 105:
Soil Nutrient Depletion Soil nutrie
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that the yields of salt-tolerant wh
Page 108 and 109:
Figure 5.3—Forest area as a perce
Page 110 and 111:
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
Page 116 and 117:
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
Page 150 and 151:
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
Page 168 and 169:
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:
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