The detailed spatial predictions of soil nutrient densities by Hengl et al. [18] form the basis of our analysis. Their approach is based on the compilation of a geo-referenced **dataset** of soil samples across Africa from the past 50 years, together comprising a **dataset** of around 55,000 unique point estimates. Hengl et al. [18] use machine learning to estimate variation in soil nutrient densities as a function of an array of geophysical/climatic variables. These estimates are subsequently used to predict soil nutrient densities in all grid cells for which no soil samples exist. Fig 1 shows such extrapolations for zinc and manganese concentrations. Such predictions, for grid cells of 250m by 250m, form the basis for the soil factors F ! used in our analysis. We aggregate and average the soil **data** to grid cells of 5km by 5km as much of the other **data** used is not available on such a detailed scale. All **data** ( Table 2) are an estimate of the nutrient content (macro-and micronutrients, as well as organic matter content) in the topsoil (30 cm) and are available for 813,704 point estimates across Sub-Saharan Africa [18].