Background: Xenobiotic contamination of agroecosystems poses increasing risks to soil ecosystem function, crop productivity, and human health, necessitating the development of scalable and mechanistically informed bioremediation strategies. Variovorax paradoxus, a metabolically versatile Gram-negative bacterium, has shown strong potential for degrading structurally diverse organic pollutants, including pesticides, herbicides, and polycyclic aromatic hydrocarbons through multiple enzymatic pathways.
Objectives: This study aimed to develop a robust machine learning (ML) framework to predict xenobiotic degradation efficiency mediated by V. paradoxus across heterogeneous contaminated agroecosystems.
Methods: An integrated dataset comprising 1,847 experimental and field observations was assembled, including pollutant physicochemical properties, soil environmental parameters, microbial community attributes, and management variables. Seven ML algorithms—multiple linear regression, support vector regression, Random Forest, Gradient Boosting Machines, Extreme Gradient Boosting (XGBoost), multi-layer perceptron neural networks, and Long Short-Term Memory networks—were trained and validated using stratified 10-fold cross-validation, with performance benchmarked against first-order kinetic degradation constants derived from laboratory experiments.
Results: XGBoost demonstrated the highest predictive accuracy (R² = 0.934, RMSE = 3.21%, MAE = 2.47%). SHAP-based feature importance analysis identified soil organic carbon content, initial pollutant concentration, and V. paradoxus inoculant density as the most influential predictors. Integration of the optimized model into a prototype decision-support tool enabled a reduction in bioremediation trial costs by 35–48% through computational pre-screening.
Conclusion: The developed ML framework provides a high-accuracy, data-driven approach for predicting and optimizing V. paradoxus-mediated bioremediation in contaminated agroecosystems, supporting more efficient planning and decision-making in sustainable agricultural management.