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A geographically scaled analysis of adaptation to climate change with spatial models using agricultural systems in Africa

  • S. N. SEO (a1)
Abstract
SUMMARY

The present paper provides a geographically scaled analysis of adaptation to climate change using adoption of agricultural systems observed across Africa. Using c. 9000 farm surveys, spatial logit models were applied to explain observed agricultural system choices by climate variables after accounting for soils, geography and other household characteristics. The results reveal that strong neighbourhood effects exist and a spatial re-sampling and bootstrapping approach can remove them. The crops-only system is adopted most frequently in the lowland humid forest, lowland sub-humid, mid-elevation sub-humid Agro-Ecological Zones (AEZs) and in the highlands in the east and in southern Africa. Integrated farming is favoured in the lowland dry savannah, moist savannah and semi-arid zones in West Africa and eastern coastal zones. A livestock-only system is favoured most in the mid/high-elevation moist savannahs located in southern Africa. Under a hot and dry Canadian Climate Centre (CCC) scenario, the crops-only system should move out from the currently favoured regions of humid zones in the lowlands towards the mid-/high elevations. It declines by more than 5% in the lowland savannahs. Integrated farming should increase across all the AEZs by as much as 5%, but less so in the deserts or in the humid forest zones in the mid-/high elevations. A livestock-only system should increase by 2–5% in the lowland semi-arid, dry savannah and moist savannah zones in the lowlands. Adaptation measures should be carefully scaled, up or down, considering geographic and ecological differentials as well as household characteristics, as proposed in the present study.

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To whom all correspondence should be addressed. Email: niggol.seo@sydney.edu.au
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R. Adams , B. Mccarl , K. Segerson , C. Rosenzweig , K. Bryant , B. Dixon , R. Connor , R. Evenson & D. Ojima (1999). The economic effects of climate change on US agriculture. In The Impact of Climate Change on the United States Economy (Eds R. Mendelsohn & J. Neumann ), pp. 1854. Cambridge: Cambridge University Press.

K. Anderson & W. A. Masters (2009). Distortions to Agricultural Incentives in Africa. Washington, DC: World Bank.

L. Anselin (1988). Spatial Econometrics: Methods and Models. Dordrecht, The Netherlands: Kluwer Academic Publishers.

K. J. Beron & W. P. M. Vijverberg (2004). Probit in a spatial context: a Monte Carlo approach. In Advances in Spatial Econometrics: Methodology, Tools and Applications (Eds L. Anselin , R. J. G. M. Florax & S. J. Rey ), pp. 169192. Heidelberg, Germany: Springer-Verlag.

N. A. C. Cressie (1993). Statistics for Spatial Data. New York: John Wiley & Sons.

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The Journal of Agricultural Science
  • ISSN: 0021-8596
  • EISSN: 1469-5146
  • URL: /core/journals/journal-of-agricultural-science
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