Spatially Explicit Soil Acidification under Optimized Fertilizer Use in Sub-Saharan Africa

Abstract

Acidic soils (pH −1 yr−1) has no effect on soil acidification in Sub-Saharan Africa (SSA). However, the effect of optimized fertilizer use on soil acidification (H+) in SSA crops remains unknown. This study intended to predict the spatial variation of H+ caused by optimized fertilizer use using data from 5782 field trials in SSA cropland. We used ensemble machine learning to predict spatial variation (H+) after measuring the inputs and outputs of major elements and their effect on H+ production. The results revealed that H+ ranged spatially from 0 to 16 keq H+ ha−1 yr−1. The most protons (H+) were produced by cassava, banana, and Irish potatoes systems with 12.0, 9.8, and 8.9 keq H+ ha−1 yr−1, respectively. The results of the 10-fold cross validation for the soil acidification model were a coefficient of determination (R2) of 0.6, a root mean square error (RMSE) of 2.1, and a mean absolute error (MAE) of 1.4. Net basic cation loss drives soil acidification under optimized fertilizer application and climate covariates had a higher relative importance than other covariates. Digital soil mapping can produce soil acidification maps for sustainable land use and management plans.

Keywords

• digital soil mapping
• ensemble machine learning
• optimized fertilizer use
• soil acidification
• soil pH