CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA Africa Land Cover 2000), (c) suitable area (extracted from the FAO/IIASA global crop suitability surfaces), and (d) irrigated land area (from the global map of irrigation, Siebert and others 2001).

The model algorithm begins with an initial estimate of crop area and production by location derived from available production statistics and corresponding biophysical suitability maps. It then uses a crossentropy regression to create a new allocation of crop area and production that satisfies a set of constraints yet minimizes the differences between the initial and final allocations. These constraints are:

- The sum of allocated crop areas/production is equal to existing statistical data.

- The actual agricultural area from the satellite image is the upper limit.

- The sum of allocated crop area cannot exceed the suitable area for the particular crop.

- The sum of allocated irrigated areas cannot exceed the area in the Africa map of irrigation.

The model produces estimates for four production systems: rain-fed/high-input, rain-fed/low-input, irrigated, and subsistence<sup>4</sup> for each of the following 20 crops:

- Six cereals: wheat, rice, maize, barley, millet, sorghum

- Seven pulses: potato, sweet potato and yam, cassava, plantain and banana, soybean, beans, other pulses

- Five fibers: sugarcane, sugar beets, coffee, cotton, other fibers

- Two others: groundnuts, other oil crops

In section 5, we present regression results for crop production for four crop groups: maize, cereals (wheat, rice, maize, barley, millet, and sorghum), cash crops (coffee, cotton, groundnuts), and the total of the 20 crops. In all cases, crop production is measured in U.S. dollars, using the median of the dollar price crop indices across Sub-Saharan Africa (see table A.2). Each crop or crop group is also disaggregated into three different production systems (rain-fed/high-input, rain-fed/low-input, including subsistence, and irrigated).

# Transport networks and travel times

We constructed several road-connectivity measures, including distance to roads and travel time. We used UNEP road data<sup>5</sup> to calculate distance to the nearest type-1 (motorway/major road), type-2 (all weather/improved), and type-3 (partially improved/earth) roads.<sup>6</sup> For travel-time measures, we used

> 4 In our analysis presented below, we combine low-input/rain-fed and subsistence systems together, since they share similar characteristics and because estimates of the location of subsistence crop areas were generated based on the distribution of population using assumptions of low-input/rain-fed technology. See You and others (2007a, 2006).

> 5 The United Nations Environment Programme (UNEP) road map was created as part of the fourth version of the Africa Population Database by Andy Nelson. The input road maps were from Digital Chart of the World (Environmental Systems Research Institute, ESRI, 1993) and Michelin Travel Publications (2004, Michelin Travel Assistance Services).

> 6 Using spatial analysis tools in ESRI ArcGIS software, we calculate travel time from each point (pixel) to the nearest city of a specified size in Sub-Saharan Africa. These travel time raster data are exported to a standard-format database and then merged with the SPAM data sets for analysis. Note, however, that these estimates depend greatly

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