Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices

Caixia Hu, Jie Li (), Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan and Guilong Zhang
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Caixia Hu: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Jie Li: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Yaxu Pang: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Lan Luo: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Fang Liu: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Wenhao Wu: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Yan Xu: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Houyu Li: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Bingcang Tan: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Guilong Zhang: Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China

Land, 2025, vol. 14, issue 1, 1-19

Abstract: Nitrate leaching from soil presents a significant threat to soil health, as it can result in nutrient loss, soil acidification, and structural damage. It is crucial to quantify the spatial heterogeneity of nitrate leaching and its drivers. A total of 509 observational data points regarding nitrate leaching in northern China were collected, capturing the spatial and temporal variations across crops such as winter wheat, maize, and greenhouse vegetables. A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R 2 of 0.75. However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R 2 > 0.56), which realized quantitative prediction capabilities for nitrate leaching loss concentrations. Moreover, the XGBoost model exhibited the highest fitting accuracy and the smallest error in estimating nitrate leaching losses, with an R 2 value of 0.79 and an average absolute error ( MAE ) of 3.87 kg/ha. Analyses of the feature importance and SHAP values in the optimal XGBoost model identified soil organic matter, chemical nitrogen fertilizer input, and water input (including rainfall and irrigation) as the main indicators of nitrate leaching loss. The ML-based modeling method developed overcomes the difficulty of the determination of the functional relationship between nitrate loss intensity and its influencing factors, providing a data-driven solution for estimating nitrate–nitrogen loss in farmlands in North China and strengthening sustainable agricultural practices.

Keywords: nitrate; leaching; machine learning; North China (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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