Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China
The growing global population and increasing agricultural demands have made nitrogen fertilizers essential for modern agriculture. However, nearly 50% of applied nitrogen fertilizers are lost to the environment, causing pollution and greenhouse gas (GHG) emissions. Biochar-based fertilizers (BBFs),...
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MDPI AG
2025-05-01
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| author | Yuan Zeng Sujuan Chen Yunpeng Li Li Xiong Cheng Liu Muhammad Azeem Xiaoting Jie Mei Chen Longjiang Zhang Jianfei Sun |
| author_facet | Yuan Zeng Sujuan Chen Yunpeng Li Li Xiong Cheng Liu Muhammad Azeem Xiaoting Jie Mei Chen Longjiang Zhang Jianfei Sun |
| author_sort | Yuan Zeng |
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| description | The growing global population and increasing agricultural demands have made nitrogen fertilizers essential for modern agriculture. However, nearly 50% of applied nitrogen fertilizers are lost to the environment, causing pollution and greenhouse gas (GHG) emissions. Biochar-based fertilizers (BBFs), combining biochar with chemical fertilizers, enhance nutrient efficiency, boost crop yields, and reduce N<sub>2</sub>O emissions. However, comprehensive field studies on BBF impacts remain limited. This study uses a global dataset of BBF field experiments to build predictive models with three machine learning algorithms for crop yields and N<sub>2</sub>O emissions, and to assess BBFs’ potential to increase yields and mitigate emissions in China’s major crops. The artificial neural network (ANN) model outperformed random forest (RF) and support vector machine (SVM) in predicting N<sub>2</sub>O emissions (R<sup>2</sup>: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R<sup>2</sup>, EF: 0.98–0.99). Variable importance analysis revealed that BBF C/N and BBF N/Mineral N explained 4.25% and 3.95% of yield variation, and 3.19% and 0.55% of N<sub>2</sub>O emission variation, respectively. BBFs could increase China’s major crop yields by 4.3–5.0% and reduce N<sub>2</sub>O emissions by 3.7–6.3%, based on simulations. Challenges like high costs and limited adaptability persist, necessitating optimized production, standardized protocols, and expanded trials. |
| format | Article |
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| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-c958bc1dc69648edb3db166d1a1e6fb82025-08-20T01:57:01ZengMDPI AGAgronomy2073-43952025-05-01155123810.3390/agronomy15051238Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in ChinaYuan Zeng0Sujuan Chen1Yunpeng Li2Li Xiong3Cheng Liu4Muhammad Azeem5Xiaoting Jie6Mei Chen7Longjiang Zhang8Jianfei Sun9Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaInstitute of Soil and Fertilizer & Resources and Environment, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, ChinaInstitute of Eco-environmental Research, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaState Key Laboratory for Ecological Security of Regions and Cities, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaThe growing global population and increasing agricultural demands have made nitrogen fertilizers essential for modern agriculture. However, nearly 50% of applied nitrogen fertilizers are lost to the environment, causing pollution and greenhouse gas (GHG) emissions. Biochar-based fertilizers (BBFs), combining biochar with chemical fertilizers, enhance nutrient efficiency, boost crop yields, and reduce N<sub>2</sub>O emissions. However, comprehensive field studies on BBF impacts remain limited. This study uses a global dataset of BBF field experiments to build predictive models with three machine learning algorithms for crop yields and N<sub>2</sub>O emissions, and to assess BBFs’ potential to increase yields and mitigate emissions in China’s major crops. The artificial neural network (ANN) model outperformed random forest (RF) and support vector machine (SVM) in predicting N<sub>2</sub>O emissions (R<sup>2</sup>: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R<sup>2</sup>, EF: 0.98–0.99). Variable importance analysis revealed that BBF C/N and BBF N/Mineral N explained 4.25% and 3.95% of yield variation, and 3.19% and 0.55% of N<sub>2</sub>O emission variation, respectively. BBFs could increase China’s major crop yields by 4.3–5.0% and reduce N<sub>2</sub>O emissions by 3.7–6.3%, based on simulations. Challenges like high costs and limited adaptability persist, necessitating optimized production, standardized protocols, and expanded trials.https://www.mdpi.com/2073-4395/15/5/1238biochar-based fertilizersN<sub>2</sub>Ocrop yieldgreenhouse gas reductionmachine learning models |
| spellingShingle | Yuan Zeng Sujuan Chen Yunpeng Li Li Xiong Cheng Liu Muhammad Azeem Xiaoting Jie Mei Chen Longjiang Zhang Jianfei Sun Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China Agronomy biochar-based fertilizers N<sub>2</sub>O crop yield greenhouse gas reduction machine learning models |
| title | Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China |
| title_full | Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China |
| title_fullStr | Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China |
| title_full_unstemmed | Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China |
| title_short | Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China |
| title_sort | using machine learning to assess the effects of biochar based fertilizers on crop production and n sub 2 sub o emissions in china |
| topic | biochar-based fertilizers N<sub>2</sub>O crop yield greenhouse gas reduction machine learning models |
| url | https://www.mdpi.com/2073-4395/15/5/1238 |
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