The Economics of Desertification, Land Degradation, and Drought

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the experience of and lessons learned from the global terrestrial observing systems (GTOS) apply to other networks and data collection efforts. Latham (2011) observed the following lessons learned by GTOS observations: • Sustainability: Some important data collection and monitoring techniques are implemented by medium- or short-term projects. Given that some important terrestrial ecosystems change slowly, however, medium- and short-term data collection efforts are likely to miss the lagged impacts of land degradation or of land management practices that prevent or mitigate land degradation. However, human and financial resources are required to maintain such long-term data collection and monitoring. Thus, a coordination of efforts (discussed below) could be one of the most important strategies for reducing data collection costs and for enhancing synergies • Coordination and interpolability: To avoid overlaps and duplication and to enhance synergies and representativeness of data collection, there is a need to harmonize and coordinate the large number of organizations collecting data in order to obtain a collection that is representative of major land use types, agroecological zones, and socioeconomic aspects. To enhance interpolation of results, sentinel sites need to be selected such that they are representative of the major ecosystems and socioeconomic characteristics. Selection of data collection sites has been largely influenced by the interest of the organizations that fund the data collection, the people involved in the data collection, and the objectives of the data collection. As a result, many important data collection efforts produce data that cannot be interpolated to the entire world. Sub- Saharan Africa and South East Asia are especially underrepresented in data collection efforts. Such coordination efforts will allow interpolation of the data at a global level, thus enhancing the global E-DLDD study. • Integrated approach: As emphasized earlier, E-DLDD will require global data that cannot be obtained using satellite imagery along. In situ data collection is required to validate satellite data and to collect data that cannot be observed remotely. Modeling is also required to determine the impacts of land degradation and of land management practices used to prevent or mitigate land degradation. • Training and capacity building: The capacity of data collection institutions in developing countries is low and requires training to ensure better data collection. New data collection methods and ever-changing global issues also require regular training, even in middle- and high-income countries, in order to enhance their capacity to collect new types of data and to use new methods and tools. • Data accessibility: There is a need to enhance data availability by increasing access to free data. Currently, there have been increasing efforts to enhance data sharing. For example, several knowledge-generation and knowledge-sharing initiatives have started in recent years, such as FAOSTAT, AQUASTAT, and TERRASTAT; the 1994 UNEP/FAO Digital Chart of the World; the “open GIS;” and others listed in Tables 7.1 and 7.2. It is also important that the data are user friendly. 124
Table 7.2—Essential climate variable monitoring programs of the terrestrial ecosystem services (part of SB1 and SB2) Metric Frequency Monitoring organizations and institutes Spatial scale River discharge Daily GTN-H In situ Water use Daily to annual FAO AQUASTAT In situ Groundwater Monthly IGRAC plus associates, NASA, ESA, DLR, WMO In situ Water level Daily to monthly GTN-H plus associates, WMO Equal Snow cover Daily to monthly GTN-H, NASA, NESDIS, NSIDC, NOAA, WWW/GOS surface synoptic network (depth), national networks Equal Glacier cover Daily to monthly GTN-G (with WGMS), GLIMS, WGI, NSIDC, IACS, ESA (GlobGlacier) Annual/multiannual Permafrost (below 0 degrees Celsius for two or more years) Daily to annual GTN-P plus associates In situ Albedo (instantaneous ratio of surface-reflected radiation flux to incident radiation flux over the shortwave spectral domain) Daily to monthly WMO, WGCV, BSRN, FLUXNET Satellite Land cover Seasonal to FAO, GTOS/GOFC-GOLD, ESA, NASA, Satellite annual IGBP, GLCN Fraction of photosynthetically active radiation (PAR) absorbed by a vegetation canopy Daily to annual In situ LAI (leaf area index) = one- Daily to annual WGCV, FLUXNET, NASA, Satellite half the total green leaf area GLOBCARBON, JRC-TIP, CYCLOPES, per unit ground surface area LANDSAF Biomass Annual to 5 years FAO Forestry, FLUXNET, ESA, national surveys Equal Fire Daily to annual ESA, NASA, WGCV, GFIMS, GOFC- GOLD, GFMC Satellite Soil carbon Annual to 5 years FAO-IIASA world soil map, FLUXNET, national surveys In situ Soil moisture Source: Author’s compilation. Daily to annual FLUXNET, WWW/GOS surface synoptic network In situ Notes: BSRN = Baseline Surface Radiation Network; CYCLOPES = computer algorithms for assessing LAI, fcover (vegetation cover fraction), and FAPAR (see Baret et al. 2009); GFIMS = Global Fire Information Management System; GLIMS = Global Land Ice Measurements from Space; GOFC = Global Observations of Forest Cover; GOLD = Global Observations of Land Cover Dynamics; GLCN = Global Land Cover Network; GTN-G = Global Terrestrial Network for Glaciers; GTN-H = Global Terrestrial Network for Hydrology; GTN-M = Global Terrestrial Network for Mountains; GTN-P = Global Terrestrial Network for Permafrost; GTN-R = Global Terrestrial Network for River Discharge; IACS = International Association of Cryospheric Sciences; IGBP = International Geosphere-Biosphere Programme; IGRAC = International Groundwater Resources Assessment Centre; IIASA = International Institute for Applied Systems Analysis; JRC-TIP = Joint Research Centre Two-Stream Inversion Package; LANDSAF = Land Surface Satellite Analysis Facility; NESDIS = National Environmental Satellite, Data, and Information Service; NSIDC = National Snow and Ice Data Center; WGCV = Working Group on Calibration and Validation; WGI = World Glacier Inventory; WGMS = World Glacier Monitoring Services. Forming the Research Team of E-DLDD The research partners for the E-DLDD study should consist of key researchers working on and from the terrestrial ecosystems depicted in the case studies. Table 7.3 gives examples of scientific partners and potential institutions to engage in the scientific process. The scientific team will conduct research in collaboration with the institutions listed in Table 7.1 and Table 7.2. The research team proposed in Table 7.3 is based on recent work in which the institutions have conducted leading research. Regional balance should also be considered, with partners from the developing world being included, as well as partners whose country is represented in the case studies. 125
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IFPRI Discussion Paper 01086 May 20
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Contents Abstract vii Acknowledgmen
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List of Figures 1.1—Conceptual fr
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ABSTRACT Attention to land degradat
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ABBREVIATIONS AND ACRONYMS ADB Asia
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SCAR Soil Conservation in Agricultu
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In the present report, the conceptu
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Framework: Confronting Action versu
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understand these arrangements in or
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Figure 1.3—Prevention, mitigation
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on assessments at the micro level (
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does not clearly distinguish betwee
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Figure 2.1—GLASOD (1991) global a
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International Earth Science Informa
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Box 2.1—Continued Normalized Diff
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Figure 2.4—Loss of annual NPP, GL
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Figure 2.7—Annual loss of NPP in
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Table 2.2—Nitrogen application ra
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Figure 2.12—Areas affected by hum
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output—which is a selection of su
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GLADIS also underlines the linkage
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Box 2.2—Continued A map on global
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Table 2.3—Global land degradation
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Table 2.3—Continued Project and d
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Table 2.4—Continued Location 10 2
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Table 2.6—Extent and severity of
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National Level Policies As discusse
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Such support has also been directed
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Migration, either as outmigration o
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The preceding discussion shows the
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Results Correlation Analysis Consis
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Figure 2.23—Relationship between
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Effects of Land Degradation On-Site
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soils and higher land productivity.
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Acknowledging the work of GLADIS, a
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health, education, security, enviro
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Box 3.1—Recent major economic ass
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An appropriate economic tool for a
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Figure 3.4—Cost of action and cos
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enefit—for example, income. This
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Figure 3.6—Ecosystem services fra
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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
Page 106 and 107: 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
Page 112 and 113: Introduction 6. CASE STUDIES Five c
Page 114 and 115: Salinity The effects of salinity on
Page 116 and 117: Third, to correctly decide which ac
Page 118 and 119: Costs of Action and Inaction We eva
Page 120 and 121: The NGOs and religious organization
Page 122 and 123: have a strong influence on NRM (And
Page 124 and 125: We estimated the cost of action (de
Page 126 and 127: Figure 6.15—Costs of action and i
Page 128 and 129: Of interest to us is the strategy t
Page 130 and 131: 7. PARTNERSHIP CONCEPT The review o
Page 132 and 133: degradation. All this should be don
Page 134 and 135: Finally, the global assessment of D
Page 138 and 139: Table 7.3—Example of E-DLDD resea
Page 140 and 141: 8. CONCLUSIONS Since the publicatio
Page 142 and 143: lowest in the region. From a socioe
Page 144 and 145: Table A.1—Land degradation assess
Page 146 and 147: Table A.2—Continued Author Region
Page 148 and 149: Table A.3—Continued Author Countr
Page 156 and 157: Table A.5—Costs of land degradati
Page 158 and 159: Figure B.1—Land use systems of th
Page 160 and 161: REFERENCES Abelson, P. 1979. Cost B
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Page 182 and 183: ———. 2010. “Assessment of L
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