Mapping phosphorus sorption and availability in California vineyard soils using an ensemble of machine learning models

Abstract

AbstractSpatial variability of soil P is tied to pedogenic state factors and management practices in cultivated soils. The distribution of P availability and sorption was predictively mapped in the Napa and Lodi American Viticulture Areas in California. We tested three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGB), and Cubist, as well as two super learner ensembles of base models, model stacking and model averaging. Pedons (n = 141) were analyzed for Olsen P and phosphorus‐sorption index (PSI), aggregated by depth weighted average (0–30 cm and 30–100 cm) and intersected with rasters of environmental predictors to model Olsen P and PSI. Base models (RF, XGB, and Cubist) performed well for PSI prediction (R2 = .68–.73), but less well for Olsen P (R2 = .46–.56). For ensembles, model averaging was selected for PSI at 0–30 cm (R2 = .77) and model stacking was selected for PSI at 30–100 cm (R2 = .74). For Olsen P, model averaging was selected for 0–30 cm (R2 = .42), and model stacking for 30–100 cm (R2 = .52). Predictions (30‐m) highlight regional trends in P‐sorption capacity and Olsen P reflective of differences in pedogenic controls on P dynamics. Predictions were strong for PSI, and less robust for Olsen P. Fe/Al‐(hydr)oxides control P sorption in weathered soils, whereas management influences Olsen P. Because the spatial variability of Fe/Al‐(hydr)oxides is tied to pedogenic state factors, P‐sorption capacity lends itself to environmental correlation mapping owing to the pedological underpinnings of digital soil mapping.