Most modeling to forecast climate change impacts on crop yields uses average weather data adjusted for forecasted national variations. Using average data ignores both the inherent variability of weather and its effect on crop yield, and hence food security, as the climate changes. A methodology has been developed and tested that allows outputs from global circulation models (GCMs) to be downscaled and applied to point simulation models (Jones and Thornton, 2001). It is possible to model and map the impacts of climate change on poor farmers at the subnational scale by using different GCM scenarios of climate change; land-use change scenarios; crop, livestock, and tree production models; and maps of the distribution of the world's poor and their crop and livestock resources.

Figure 1.1 shows an example of this approach. It predicts the maize caloric deficit in Southern Africa caused by both climate change and population growth. The subnational level of resolution, as well as the easily interpretable nature of such maps, makes this alternative more useful to policymakers than country-level resolution, single-factor assessments.

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