Predicting and Mapping the Phosphorus Adsorption Maximum and Phosphorus Adsorption Affinity Constant at Regional Scale

ABSTRACT Insight into the variation of the soil phosphorus (P) adsorption maximum (Q max ) and the P adsorption affinity constant (K L ) is crucial for accurately assessing the dynamics of P availability, P uptake and P leaching in agricultural systems at regional scale. Data on the variation in soil P adsorption characteristics, derived from traditional batch experiments, combined with data on soil properties affecting them, such as pH, clay and organic matter content, can be used to assess the influence of soil properties on P adsorption characteristics. However, current studies are limited to explaining the variation in Q max using linear models, focusing on either noncalcareous or calcareous soils. This study aims to (1) identify the soil properties governing both Q max and K L for a combination of noncalcareous and calcareous soils, including nonlinear and interaction effects; and (2) create spatial maps depicting the variations in both soil P adsorption characteristics at the regional scale (two typical Chinese counties). We leveraged 83 data points of both Q max and K L from 16 publications with main soil properties affecting P adsorption, that is, pH and the content of soil organic matter (SOM), clay and oxalate extractable Fe and Al (Fe OX and Al OX ), to develop predictive models for soil P adsorption. General linear regression (GLM) and extreme gradient boosting (XGB) models were used to unravel the relationships between soil properties and P adsorption characteristics. The XGB model outperformed GLM model, explaining more than 80% of the variations in both Q max and K L in noncalcareous and calcareous soils, while the GLM model explained 52% for Q max and only 21% for K L . Key drivers influencing Q max were found to be Fe OX , Al OX and pH, while clay and pH played significant roles in explaining the variability in K L . When applying these models at the county level using county‐level inventory data, noncalcareous soils generally exhibited higher P sorption capacity and binding energy than calcareous soils. To enhance the accuracy of soil P sorption predictions and guide sustainable P fertiliser use, regional mapping of Fe OX and Al OX content is essential.