Ensemble Machine Learning Predicts Flooding- and Organic Matter-Induced Micronutrient Dynamics in Calcareous Soils

Flooding and farmyard manure (FYM) application trigger complex, non-linear redox reactions that govern micronutrient availability in calcareous soils, yet predictive modelling of these dynamics using machine learning (ML) remains largely unexplored, and the present study was designed to address this gap. To this end, seven supervised ML algorithms—Ridge Regression, Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Cubist—were compared under a unified nested cross-validation scheme to predict DTPA-extractable Fe, Mn, Cu, and Zn concentrations in a flooding incubation experiment comprising 10 contrasting calcareous soils (Entisol, Mollisol, Inceptisol, Vertisol) from the Atabey Plain (Isparta, Türkiye), two FYM doses, and five flooding durations (n = 100). Under Leave-One-Out Cross-Validation (LOO-CV), rule- and tree-based ensemble methods consistently outperformed linear and neural network models, with Cubist achieving the best performance for Fe (R2 = 0.812) and Mn (R2 = 0.915), XGBoost for Cu (R2 = 0.929), and GBM for Zn (R2 = 0.919). However, a stricter leave-one-soil-out (LOSO) validation with grouped inner cross-validation revealed that this accuracy is element-specific in its transferability: Mn predictions remained robust on previously unseen soils (R2cv = 0.739) and Fe moderate (R2cv = 0.412), whereas Cu and Zn did not generalise beyond the soils used for training, indicating that their high within-soil accuracy reflects soil-specific rather than transferable structure. SHAP analysis revealed that flooding duration was the dominant predictor of Fe and Mn availability, amorphous Fe oxide content was the primary driver for Cu, and plant-available phosphorus (Olsen-P) was the principal feature for Zn. These findings demonstrate that combining ensemble ML with SHAP interpretability enables element-specific, cross-soil-validated and mechanistically interpretable prediction of micronutrient dynamics under varying redox and organic amendment conditions, while highlighting cross-soil transferability as a critical consideration for deploying such models in calcareous agroecosystems.