Machine Learning–Based Prediction of Boron Desorption in Acidic Tea-Growing Soils

In acidic tea soil, boron (B) adsorption and desorption processes are dominated by the complex relationship between soil acidity, mineralogy, and organic matter. This study investigated B adsorption–desorption behavior in five acidic tea soils (pH 3.8–5.6) col-lected from the Eastern Black Sea region of Türkiye and evaluated the potential of machine learning (ML) algorithms to predict B desorption. Laboratory batch experi-ments were conducted using five initial B concentrations, and adsorption data were interpreted using the Langmuir isotherm model. Adsorption experiments indicated that B interacted with Fe/Al-oxide-containing clay minerals, which had low but fa-vorable binding affinity, as indicated by Langmuir maximum adsorption capacities (Qmax) ranging from 46.5 to 181.8 mg kg⁻¹. Desorption experiments revealed a high degree of reversibility, particularly in soils with lower adsorption capacities, ensuring potential B leaching. To capture the non-linear relationships governing B desorption, six ML algorithms were trained on 75 data points. Among the tested models, Extreme Gradient Boosting (XGBoost) showed the highest predictive accuracy (R² = 0.963), fol-lowed by Gaussian Process Regression and Random Forest. Variable importance anal-yses consistently highlighted soil pH, organic matter content, and clay fraction as the dominant factors. The results demonstrate that integrating laboratory experiments with machine learning provides an effective framework for predicting B mobility in acidic tea soils, offering a practical tool for improving boron management strategies.