# **3. Results**

The estimated multinomial regressions are reported in Table 3. The omitted AEZ is the desert. The coefficients for the climate, soils, and elevation variables are shown for a weighted and unweighted regression. The weighted regression weights districts by the square root of area. Precipitation and temperature clearly play a significant role in determining the probability of AEZs. In the unweighted regressions, there is a significant temperature effect in every AEZ except mid and high elevation humid forest and lowland dry savannah. In the weighted regressions, there is no temperature effect in these same three AEZs as well as in mid-elevation dry and moist savannah. In the unweighted regressions, there is a significant precipitation effect in every regression. In the weighted regressions, precipitation is significant in all but high elevation humid forest, mid elevation moist savannah, mid elevation sub-humid forest, and lowland dry savannah. Despite the fact that many AEZs are labeled by elevation, elevation is not always significant. Elevation is not significant in identifying many mid-elevation AEZs or in the lowland dry savannah and lowland semi-arid AEZs. The interaction terms between elevation and climate capture a subtle effect across some AEZs. Elevation and summer temperature often have a negative and significant interactive effect. Controlling for elevation and climate, places that are both higher and hotter during the summer are relatively more likely to be desert. Several lowland AEZs have a positive interaction effect between precipitation and elevation. Controlling for climate and elevation, places that are both higher and wetter are more likely to be lowland savannah or humid forest AEZs. Finally, although we examined the explanatory power of detailed soil data, no soil variables were jointly significant, and consequently soils were dropped from the model.

The estimated models in Table 3 correlate strongly with the present AEZ distribution. The overall fit of the unweighted model is high as measured by the pseudo-R squared 0.62 and the log likelihood function value of -1611. Similarly, the weighted model has a log likelihood value of -1138.3 with a pseudo-R squared of 0.70. These estimates suggest that both models do a good job of capturing the distribution of AEZs currently observed.

Figure 1 compares the observed distribution of AEZs relative to the predicted distribution given current climate and the weighted model. Both maps are similar. However, observed high humid forests in Central Africa are predicted to be low to mid elevation humid forests and some observed lowland dry savannahs in southwest Africa are predicted to be moist savannahs. Except for these differences, the predicted AEZs look like the observed AEZs.

We now predict how future climate scenarios might affect the distribution of AEZs, the amount of cropland, and cropland net revenues in Africa. For exposition purposes we use two 2100 scenarios, although the analysis could just as easily have been replicated for any other time-frame. The actual predicted climate changes in each country vary. We used the predicted absolute change in temperature and percentage change in precipitation from the 2000-2010 decade to the 2090-2100 decade from each climate model. District changes in temperature are calculated by adding the predicted country temperature changes to current