An Intelligent Fertilization Decision Model for Cereal Crops Integrating Explainable Ensemble Learning and Hybrid Optimization: A Case Study in Wensu County, Xinjiang, China

Optimizing fertilizer management is crucial for increasing crop yields while reducing environmental impact. However, traditional methods rely on extensive field trials, which are costly and limit their scalability. To overcome these limitations, this study developed data-driven yield prediction models (YPM) for wheat, rice, and maize by integrating multiple feature selection and machine learning algorithms with explainable ensemble learning, namely stacking regression (SR) and voting mean (VM). The optimal YPM was subsequently combined with the hybrid optimization strategy to construct an intelligent fertilization decision model (IFDM), and the economic–environmental benefits were subsequently evaluated. The best-performing models were SHAP-SR for wheat and rice and GBM-SR for maize, achieving R2 values of 0.79, 0.69, and 0.67, and RMSEs of 681.69, 725.35, and 1091.49 kg ha−1, respectively. Based on the IFDM, the recommended application ranges for nitrogen (N), phosphorus (P2O5), and potassium (K2O) were as follows: for wheat, 122.1–256.3, 45.4–98.2, and 30.6–60.7 kg ha−1; for rice, 170.8–261.2, 55.1–91.4, and 40.6–98.5 kg ha−1; and for maize, 157.5–293.4, 84.2–156.4, and 30.1–62.7 kg ha−1. Simulation-based evaluation suggested that adopting these recommendations could potentially increase average yields by 9.2–12.4% and enhance economic–environmental benefits by 32.86–97.73% across the three crops. This study indicates that coupling interpretable ensemble learning with a hybrid optimization strategy can support efficient decision-making for field-scale fertilization and provides a data-driven and cost-effective approach for precision fertilization, with potential applicability to arid agricultural regions under similar agro-ecological conditions.