Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton

This study introduces a novel methodology for predicting cotton yield by integrating machine learning (ML) with mechanistic soil modeling. This hybrid approach enhances yield prediction by combining data-driven ML techniques with soil process modeling. Using the developed yield model, yield curves for various nitrogen (N) levels can be constructed to identify the optimal N dose that maximizes yield. Estimating cotton N requirements is crucial, as growers often apply excessive N, exceeding the amount needed for maximum yield. By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). Additionally, the RF model exhibited high sensitivity to N fertilization, ranking N as the most influential variable in feature importance analysis. Furthermore, phosphorus (P) availability in the soil model was found to be a significant factor influencing the RF yield model, highlighting P’s crucial role in cotton growth and productivity.