The original **data** provides information on particles per million, which is converted to kilograms per hectare as discussed in Berkhout et al. [10] This **data** considers Sub-Saharan Africa as all land mass below a latitude of 28 degrees north. Hengl et al. [18] also provide estimates for phosphorus (P) and sulfur (S). As the fit of these estimates is very low (R 2 of 0.11 and 0.10 respectively) these elements are not included in this analysis. Altogether 10 variables are thus available for inclusion in (1). We use factor analysis to reduce the dimensionality of this **dataset**. If one or more variables co-vary strongly, the estimation of (1) is affected by multi-collinearity. In addition, factor analysis is likely to reduce the impact of measurement error underlying the variables in Table 2. Moreover, an understanding of which soil nutrients co-vary is insightful information in itself. In fact, strong communal variation between certain soil variables may stem from the fact that certain geological or soil formation processes affect soil nutrient densities similarly.