for which we have found international price data. We then aggregate the potential production value of each crop per ha by the cropping pattern observed in each of the communities, or close to them. We use the share of each crop in the cropped area of each EAs as weights to aggregate the potential crop values into the overall AEP.

Two limitations need to be considered when using the AEPs. First, they reflect international prices for three very recent years, and therefore are AEPs for today. When we analyze the influence of the AEP on current farming systems variables such as cropping intensity, value of production or input use, current prices are appropriate. However when we analyze the impact of AEP on population density and road investments, the AEP for a prolonged past period should be used, with which the current AEP is only correlated. Second, the AEP calculations for each region in a country are based on the current cropping pattern observed in each EA, rather than a past cropping pattern, for which the data set contains no data. In addition, as cropping patterns respond to prices, technical change and market conditions, the AEP calculated today would not be the same as an AEP calculated at an earlier or later date. Even if the yield potentials do not change, our AEP variable, while comparable across country at each point of time, is not unchanging.

From the coordinates of the community to be matched to the geographic units of the GAEZ data, we calculate the central point of each of the EAs. We select the corresponding grid cell from the IASA-FAO data set as well as the adjacent grid cells. We average across such geographic units by weighing the values for the adjacent grid cells by their distance from the central point of the households of the EA for which we calculate the AEP.

In the analysis that follows we assume that today’s agro-ecological potential is highly correlated with the agro-ecological potential over the past. Because the crop yield estimates that have been used in computing AEP include the known impacts of soil degradation, today’s estimates are possible a slight underestimation of past AEPs. However, much of the AEP is explained by innate characteristics of the soils that have not changed and a relatively stable climate over the past. We conclude that past AEP would be highly correlated with current AEP and that the current AEP variable captures the impact of past AEP as well. This is an important assumption, as migration, fertility, infrastructure investment decisions were made over a long historical period, and we therefore should capture the impacts of AEP in the past. This also applies to the intensification variables, although they can change much more rapidly than population density and infrastructure. Therefore for the intensification variables the errors in variables possibly associated with small changes in AEP over the past are less severe.

# **Population pressure**

In this paper we measure population pressure in two ways, starting with the traditional measure of population pressure (persons/sqkm). However, the raw population density is not a true measure of rural population pressure, as it does not account for the vast differences in agro8