Machine learning reveals mechanisms and feedstock effects on potassium and magnesium uptake by wheat in biochar-amended soil in greenhouse.

Abstract

Despite the potential of biochar to enhance crop nutrient uptake, the complex, feedstock-dependent interactions among biochar properties, soil biological processes, and plant root traits remain poorly understood. Hence, this study employed machine learning algorithms (Linear regression and Random Forest) with advanced feature selection methods (Correlation-based Feature Subset Selection Evaluator, Correlation-Based Attribute Evaluator, Principal Components Attribute Transformer, and Stepwise Regression) to predict potassium (K+) and magnesium (Mg2+) uptake by wheat (Triticum aestivum L.) roots. A greenhouse experiment was conducted for 42 days, utilizing soils amended with four biochar treatments derived from wheat stubble, wood residues, rice husk, and corn residues (25 g biochar kg⁻¹ soil). The models were trained using a comprehensive dataset of over 40 parameters from the experiment (including variables such as soil basal respiration, root cation exchange capacity, and biochar specific surface area). The results indicated that Random Forest models achieved excellent predictive performance and outperformed Linear Regression, demonstrating superior ability to capture nonlinear relationships and feature interactions. Feature selection revealed distinct mechanisms: K+ uptake was primarily driven by soil basal respiration, root adenosine triphosphate (ATP) content, and biochar surface properties (zeta potential and Brunauer-Emmett-Teller (BET) surface area), highlighting microbial mobilization and energy-dependent active transport. In contrast, Mg2+ uptake was driven predominately by biochar oxygenation, biochar Mg2+ content, root cation exchange capacity, and carboxyl groups on root cell walls (yielding a predictive CC of 0.89 and MAE of 0.71). The significance of biochar's intrinsic Mg2+ content suggests a direct nutritional contribution from the amendment itself, which is a fundamentally different pathway than the indirect mechanisms highlighted for K+. Biochar efficacy was highly feedstock-dependent, with wheat stubble and wood residue biochars outperforming rice husk and corn residue biochars. These quantitative results provide specific mechanistic insights into biochar-mediated variation of cationic nutrition and demonstrate the power of machine learning for unraveling complex rhizosphere dynamics.