Machine learning prediction and driver analysis of nitrate leaching in field and vegetable cropping systems of Northwest China
2026-05-31 AGRICULTURAL WATER MANAGEMENT 2026 329(卷), null(期), (null页)
JCR分区:
影响因子:
Intensive agriculture in arid and semi-arid Northwest China leads to severe nitrate leaching, posing a serious threat to groundwater quality and ecosystem health. To investigate its driving factors, we applied four machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN)-to a synthesis of 43 published studies from the region. Results revealed that vegetable systems (44.75 kg ha(-1) season(-1)) leached 67% more nitrate than field crop systems (26.76 kg ha(-1) season(-1)). Among the models, XGBoost achieved the best performance (R-2 >= 0.75). SHAP analysis further identified irrigation and nitrogen input as the primary drivers, while soil organic matter played a key modulating role, particularly in vegetable systems. Nitrate leaching increased significantly when irrigation rates exceeded 300 mm or when nitrogen inputs surpassed 200 kg ha(-1) for crops and 680 kg ha(-1) for vegetables. These findings provide an empirical, data-driven foundation for agroecological risk assessment and support the design of precision nitrogen management practices in water-limited farming systems.
相关推荐
- Multi-objective calibration of APSIM Next Generation using NSGA-II for simulating spring wheat yield and soil N2O emissions under nitrogen gradients: A site-specific case study on the Loess Plateau [2026-05-31]
- Summer fallow drives leaching of legacy nitrogen in cereal land converted from old orchard [2026-05-31]
- From micro-expansion to macro-buckling: Failure mechanism and stability criterion of cement-stabilized gravel bases under water-thermal-salt coupling [2026-05-31]
- Impact of agricultural mechanization on ecological efficiency of cultivated land use in the Yellow River Basin [2026-05-31]
- Degradable versus inert microplastics: Effects on soil organic carbon persistence via microbial drivers in an agroecosystem [2026-05-31]