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• For human population, we now use new 1-km data (GRUMP, 2005). • For length of growing period, we use a layer developed from the WorldCLIM 1-km data for 2000 (Hijmans et al., 2004), together with a new “highlands” layer for the same year based on the same dataset, using similar methods as those outlined above. • Cropland and rangeland are now defined from GLC 2000, and rock and sand areas are now included as part of rangelands. • The original LGP breakdown into arid-semiarid, humid-subhumid and highland-temperate areas has now been expanded to include hyper-arid regions, defined by FAO as areas with zero growing days. This was done because livestock are often found in some of these regions in wetter years when the LGP is greater than zero (e.g. the Turkana region in north-west Kenya). • Areas in GLC 2000 defined as rangeland but having a human population density greater than or equal to 20 persons per km 2 as well as a LGP greater than 60 (which can allow cropping) are now included in the mixed system categories. The landless systems still present a problem, and are not included in version 3 of the classification. Urban areas have been left as defined by GLC 2000. Because the Seré and Steinfeld (1996) classification is livestock-based, we needed to expand the classification to include other important communities whose livelihoods are not dependent on livestock. To do this, we used the FAO farming systems classification outlined in Dixon and Gulliver (2001), which itself is based on a principal livelihoods approach and has been used to assess general trends in the poverty levels associated with each system in the coming decades. The classification itself is based on FAO data and expert knowledge, and to our knowledge is not entirely “mappable” from driver variables in global- or continental-level data sets. We created an extended systems classification by overlaying version 3 of the Seré and Steinfeld classification with the FAO classification, and from those areas that were classified as “other” (i.e., non-livestock systems), we used five new categories from the FAO system: • Coastal artisanal fishing-based systems (principal livelihoods include marine fish, coconuts, cashew, banana, yams, fruit, goats, poultry, and off-farm work); 40
• Forest-based systems (cassava, maize, beans, cocoyams); • Highland perennial-based systems (Banana, plantain, enset, coffee, cassava, sweet potato, beans, cereals, livestock, poultry, and off-farm work); • Rice-tree crop systems (rice, banana, coffee, maize, cassava, legumes, livestock, off-farm work); • Tree crop systems (cocoa, coffee, oil palm, rubber, yams, maize, off-farm work). The root crop systems and the cereal-root crop mixed systems were combined into one category, and combined this with the other areas that were still not classified. This category is referred to as “other” in the analysis below, although it should be understood to include these root-based systems. As might be expected, given the very different ways in which the two classifications were derived, there are some mismatches between them, in terms of areas that are classified inconsistently. Thus, for example, the coastal artisanal fishing system has goats and poultry (Dixon and Gulliver, 2001), although in our mapping of the Seré and Steinfeld system, these are classified as systems with no livestock. Overall, however, given the continental scale of these data sets, the matching between the two systems was found to be rather consistent, and for our purposes here, we deemed it appropriate to use the resulting map, which is shown in Figure 9. The systems are tabulated in Table 3, together with codes used in subsequent tables. We then overlaid the LGP change classes on the systems layer. The results are tabulated in Tables 4-7, for the four combinations of GCM and scenario (ECHam4+A1, HadCM3+A1, ECHam4+B1, HadCM3+B1). To summarise the data, two categories were assigned. A “2” was given to a system within a country where substantial losses (>20%) in LGP to 2050 are indicated, over at least half the geographic area of the system in that country. A “1” signifies moderate losses (5-20%) in at least half the area of the system in the country. Areas where no change is indicated are omitted from these tables, as are the (few) areas where increases in LGP are indicated; it is not that such areas are not important, but the implications are somewhat different compared with areas where loss in LGP is projected. In the A1F1 world (Tables 4 and 5) , both the HadCM3 and ECHam4 models agree on hotspots of change in the coastal systems of southern and eastern Africa, and to a more limited extent the central West African coast. Relatively large changes are projected in some 41
Page 1: Mapping climate vulnerability and p
Page 4 and 5: Citation Thornton PK, Jones PG, Owi
Page 6 and 7: egion of eastern Africa, the coasta
Page 8 and 9: Contents Executive summary 3 1 Back
Page 10 and 11: Tables Table 1. AOGCMs in the TYN S
Page 12 and 13: • Research to reduce uncertainty,
Page 14 and 15: 2 Project objectives and activities
Page 16 and 17: 3 Framework for the study The liter
Page 18 and 19: Figure 1. Representation of vulnera
Page 20 and 21: Conceptually, there is still a cons
Page 22 and 23: 4. Climate impacts in sub-Saharan A
Page 24 and 25: Table 1. AOGCMs in the TYN SC 2.0 d
Page 26 and 27: We then prepared similar climate gr
Page 28 and 29: Mapping changes to the probability
Page 30 and 31: confidence in the ability of that G
Page 32 and 33: Figure 5. Length of growing period
Page 34 and 35: Figure 6. Coefficient of variation
Page 36 and 37: Figure 7 (B). Percentage changes in
Page 38 and 39: Figure 7 (D). Percentage changes in
Page 40 and 41: Figure 8 (B). Percentage changes in
Page 44 and 45: Figure 9. Farming/livelihood system
Page 46 and 47: Table 4. Country-by-system breakdow
Page 48 and 49: latter, the highland systems in sou
Page 50 and 51: Table 6. Country-by-system breakdow
Page 52 and 53: Table 8. Positive LGP changes to 20
Page 54 and 55: Figure11. Number of growing seasons
Page 56 and 57: 4.3 Uncertainties in the analysis T
Page 58 and 59: 5. Poverty and vulnerability The se
Page 60 and 61: Table 9. Vulnerability indicators u
Page 62 and 63: datasets (more information on this
Page 64 and 65: political process, civil liberties
Page 66 and 67: the major development issue facing
Page 68 and 69: scenarios. For this synthesis, the
Page 70 and 71: Table 11. Rotated component (or “
Page 72 and 73: Table 12. Synthesis of possible reg
Page 74 and 75: climate change-vulnerability hotspo
Page 76 and 77: contain components that draw far mo
Page 78 and 79: 6.1 A survey of information needs o
Page 80 and 81: unit; the International Red Cross;
Page 82 and 83: predominantly in southern Africa. I
Page 84 and 85: Table 13. Institutional preparednes
Page 86 and 87: particularly so for government and
Page 88 and 89: 6.2 Prospects for a decision suppor
Page 90 and 91: Figure 14. Stages of impact assessm
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An ex ante impact assessment might
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A second issue relates to the setti
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A second key point that these resul
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References AchutaRao K, Covey C, Do
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Food and Agriculture Organisation o
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JRL (2005). GLC 2000 (Global Land C
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Roeckner E, Oberhuber J M, Bacher A
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Acronyms ACTS ASARECA CGIAR DFID EN
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Appendix 2. Candidate vulnerability
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Appendix 3. Country by system CV of
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Appendix 4 List of contacted instit
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112 Note 1. Indicators of Adaptive
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Box 1: Source: IPCC, 2001 1. Adapti
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irrigation and quality of infrastru
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Irrigation rate is measured by net
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moisture, especially in southern, N
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Bryman, A and D. Cramer . 1997. Qua
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Note 2. South-South Cooperation (pr
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• Effective policy frameworks in
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vulnerable countries in 12 selected
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130 Note 3. Climate Change and Heal
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impacts (developing nations). These
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most relevant for the case of HIV/A
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emerging infectious diseases; use o
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Within the Sub-Saharan region, the
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geographical shifts and yield reduc
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142 Note 4. The climate, developmen
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Figure 2 Percentage of undernourish
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The Africa Environment Outlook desc
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Figure 5 Total affected in hydro-me
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Figure 5 Present and future freshwa
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Turner B L, Kasperson R E, Matson P
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Note 5. The Sub-Saharan Africa Chal
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Several useful lessons were learned
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Note 6. The ASARECA priority settin
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Figure 1. Development domains and a
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Note 7. The SLP’s food-feed impac
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Figure 2. Domains in Kenya with hig
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Note 8. The SAKSS poverty targettin
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168 Note 9. Simulating regional pro
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Applications (ICASA). The results f
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