# **1.  Introduction**

The growing evidence from the Intergovernmental Panel on Climate Change (IPCC) that climate will change as greenhouse gases accumulate (IPCC 2007) has added urgency to the need to understand the consequences of warming. Initial studies of climate change, using a variety of methods, identified Africa as one of the most vulnerable locations on the planet to climate change because it is already hot and dry, a large fraction of the economy is tied to agriculture, and the farming methods are relatively primitive (Pearce et al. 1996; Tol 2002; Mendelsohn and Williams 2004). The livelihoods and welfare of hundreds of millions of Africans depend on how climate change will affect African agriculture.

There have been predominantly two different methods used to measure the economic impact of climate change on African agriculture: the crop simulation approach and the Ricardian approach. The crop simulation approach uses the direct effect of climate change on individual crops (see Rosenzweig and Parry 1994; Parry et al. 2004). These studies reveal that the yields of the major grains grown in Africa would fall precipitously with warming. The Ricardian approach measures the relationship between net revenues from crops and climate using cross sectional evidence (Kurukulasuriya et al. 2006; Kurukulasuriya and Mendelsohn 2008). These studies also find that hot and dry climate scenarios would reduce crop net revenues in Africa. The Ricardian studies, however, generally estimate smaller damages than the crop simulation models. One explanation for this difference is the handling of adaptation. The Ricardian model captures endogenous adaptation, measures that farmers actually take to adjust to climate change. This adaptation is efficient (it makes the farmer better off). In contrast, the crop simulation studies examine only exogenous measures arbitrarily added by the researcher that are not necessarily efficient responses to climate change.

This paper relies on an entirely different approach to measure climate impacts. The study uses AgroEcological Zones (AEZs) as the cornerstone of the analysis. FAO established AEZs as a method of measuring crop productivity (FAO 1978). The zones were intended to capture the length of the growing season taking into account soil moisture. Although longer growing seasons are not always better, the AEZ system does do a good job of dividing a heterogeneous landscape into a set of homogeneous zones. Other factors that determine productivity such as status of soils, drainage, and crop type are reflected in the AEZ classification. In this analysis, we rely on the FAO classification of every district in Africa using this AEZ methodology<sup>3</sup> (FAO 2003). More recent work by FAO in this area includes the Land Use Systems of the World<sup>4</sup> and Globcover<sup>5</sup> initiatives.

> 3 FAO and IIASA applied the AEZ methodology for the whole world (Fischer et al. 2002), not only for Africa.

> 4 This data is developed in the framework of the LADA project (Land degradation Assessment in Drylands) by the Land Tenure and Management Unit of the Food and Agriculture Organization of the United Nations and is copyright of FAO/UNEP GEF.

> 5 GlobCover is an European Space Agency led initiative in partnership with JRC, EEA, FAO, UNEP, GOFC-GOLD and IGBP. The GlobCover project has developed a service capable of delivering global