Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices
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 l...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/1/69 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588146350489600 |
|---|---|
| author | Caixia Hu Jie Li Yaxu Pang Lan Luo Fang Liu Wenhao Wu Yan Xu Houyu Li Bingcang Tan Guilong Zhang |
| author_facet | Caixia Hu Jie Li Yaxu Pang Lan Luo Fang Liu Wenhao Wu Yan Xu Houyu Li Bingcang Tan Guilong Zhang |
| author_sort | Caixia Hu |
| collection | DOAJ |
| description | 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<sup>2</sup> of 0.75. However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R<sup>2</sup> > 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<sup>2</sup> value of 0.79 and an average absolute error (<i>MAE</i>) 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. |
| format | Article |
| id | doaj-art-8ea53e5736e0491f9dec7e0f13243fbd |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-8ea53e5736e0491f9dec7e0f13243fbd2025-01-24T13:37:46ZengMDPI AGLand2073-445X2025-01-011416910.3390/land14010069Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural PracticesCaixia Hu0Jie Li1Yaxu Pang2Lan Luo3Fang Liu4Wenhao Wu5Yan Xu6Houyu Li7Bingcang Tan8Guilong Zhang9Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaAgro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, ChinaNitrate 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<sup>2</sup> of 0.75. However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R<sup>2</sup> > 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<sup>2</sup> value of 0.79 and an average absolute error (<i>MAE</i>) 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.https://www.mdpi.com/2073-445X/14/1/69nitrateleachingmachine learningNorth China |
| spellingShingle | Caixia Hu Jie Li Yaxu Pang Lan Luo Fang Liu Wenhao Wu Yan Xu Houyu Li Bingcang Tan Guilong Zhang Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices Land nitrate leaching machine learning North China |
| title | Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices |
| title_full | Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices |
| title_fullStr | Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices |
| title_full_unstemmed | Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices |
| title_short | Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices |
| title_sort | deep learning driven insights into nitrogen leaching for sustainable land use and agricultural practices |
| topic | nitrate leaching machine learning North China |
| url | https://www.mdpi.com/2073-445X/14/1/69 |
| work_keys_str_mv | AT caixiahu deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT jieli deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT yaxupang deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT lanluo deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT fangliu deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT wenhaowu deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT yanxu deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT houyuli deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT bingcangtan deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices AT guilongzhang deeplearningdriveninsightsintonitrogenleachingforsustainablelanduseandagriculturalpractices |