activity. Satellite based estimates provide a spatially explicit and unbiased picture of the distribution of croplands and can be updated frequently at relatively low cost. However, alternative mapping approaches using different data and algorithms often result in different depictions of cropping patterns in terms of both location and density (Giri et al. 2005; Hannerz & Lotsch 2006).

Due to these discrepancies, several sources of remote sensing-based land cover maps were used to control for the effect of their quality on cropland–climate models. These data sources include land cover data developed at Boston University (Friedl et al. 2002) based on the National Aeronautics and Space Administration (NASA) Moderate Imaging Spectroradiometer (MODIS) instrument, the Global Land Cover Facility (GLCF) data archive from the University of Maryland (Hansen et al. 2000), the 2000 Global Land Cover inventory (GLC2K) produced by the European Union’s Joint Research Center (Mayaux et al. 2003), and the Global Land Cover Characterization (GLCC) created by the United States Geological Survey (Loveland et al. 1991).

Each of these sources provides categorical labels of land use (e.g. croplands, cropland mosaic, forest, shrublands) at the scale of one square kilometer. The GLCC data has the highest categorical detail with 197 land cover categories for the African continent, many of which capture agricultural land use. The information on agricultural land use provided by the GLCC data was used by the LandScan project at the Oak Ridge National Laboratory (ORNL) and International Food Policy Research Institute (IFPRI) as the basis for estimating agricultural extent at the landscape level (Dobson et al. 2000; Wood et al. 2000). Lastly, the Center for Sustainability and Global Environment (SAGE) at the University of Wisconsin inter-calibrated remote sensing based estimates of cropland extent from GLCC with available agricultural statistics at the national and sub-national level (Ramankutty & Foley 1998). For this analysis, these six sources of land use data – MODIS, GLCF, GLC2K200, ORNL, IFPRI, and SAGE – were reprocessed using a consistent definition of land use and the aggregate extent and share of croplands was calculated for each grid cell.

The sub-regional breakdown of cropland extent and cropland share shown in Table 1 illustrates the discrepancies in estimates of cropland extent from different sources of land cover data and highlights the importance of accounting for the effect of these estimates in climate change impact analyses. For instance, the estimates of cropland extent for West Africa range from 1.52 million hectares (MODIS) to 12.20 million hectares (GLC2K) and from .78 million hectares (MODIS) to 10.77 million hectares (ORNL) for southern Africa. Similar disagreement is observed when assessing the sub-regional distribution of cropland share. While some approaches fail to account for sparsely cropped areas, others tend to identify crops reasonably well in mixed land use systems, but to overestimate the actual share of crops.

# **3. Explaining variations in land use patterns**

For large geographic regions the suitability of land for cropping is largely determined by ecological and physical parameters (Ramankutty et al. 2002; Woodward & Williams 1987). Thus, to explain current land use in Africa an empirical modeling approach is chosen that relates cropping patterns to a set of explanatory variables. For the purpose of this study, land use patterns are modeled using a set of such variables (seasonal climate patterns, soil and terrain properties), and it is assumed that socio-economic variables play a secondary role in 5