Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils
Soil salinization is one of the major environmental problems facing the world at present, and its negative impact on agricultural production and ecological balance is increasingly prominent. In this study, the BP neural network algorithm was applied to build a prediction model of plant productivity...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/28/e3sconf_eppct2025_02024.pdf |
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| author | Mao Zhenxuan Chen Kun Liu Qiang Xu Mengjiao |
| author_facet | Mao Zhenxuan Chen Kun Liu Qiang Xu Mengjiao |
| author_sort | Mao Zhenxuan |
| collection | DOAJ |
| description | Soil salinization is one of the major environmental problems facing the world at present, and its negative impact on agricultural production and ecological balance is increasingly prominent. In this study, the BP neural network algorithm was applied to build a prediction model of plant productivity in salt-affected soils improved by biochar, and the internal mechanism of biochar application affecting plant growth in salt-affected soils was deeply revealed. The results showed that the nitrogen content of biochar (SHAP = 0.08) had the most significant positive effect on vegetation productivity. The pH value of biochar (SHAP = 0.06) and the amount of biochar applied (SHAP = 0.06) showed a certain negative effect. This study not only provides a solid theoretical basis for the biochar restoration of salt-affected soils, but also provides important technical support for the sustainable management practice of salt-affected soils, and has important scientific value and practical significance for promoting the ecological restoration of salt-affected soils. |
| format | Article |
| id | doaj-art-6007f0b5079c4ecb80af71ec57f6a6fd |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-6007f0b5079c4ecb80af71ec57f6a6fd2025-08-20T03:54:07ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016280202410.1051/e3sconf/202562802024e3sconf_eppct2025_02024Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soilsMao Zhenxuan0Chen Kun1Liu Qiang2Xu Mengjiao3Sanya Oceanographic Institution, Ocean University of ChinaInstitute of Coastal Environmental Pollution Control, Ministry of Education Key Laboratory of Marine Environment and Ecology, College of Environmental Science and Engineering, Ocean University of ChinaInstitute of Coastal Environmental Pollution Control, Ministry of Education Key Laboratory of Marine Environment and Ecology, College of Environmental Science and Engineering, Ocean University of ChinaSanya Oceanographic Institution, Ocean University of ChinaSoil salinization is one of the major environmental problems facing the world at present, and its negative impact on agricultural production and ecological balance is increasingly prominent. In this study, the BP neural network algorithm was applied to build a prediction model of plant productivity in salt-affected soils improved by biochar, and the internal mechanism of biochar application affecting plant growth in salt-affected soils was deeply revealed. The results showed that the nitrogen content of biochar (SHAP = 0.08) had the most significant positive effect on vegetation productivity. The pH value of biochar (SHAP = 0.06) and the amount of biochar applied (SHAP = 0.06) showed a certain negative effect. This study not only provides a solid theoretical basis for the biochar restoration of salt-affected soils, but also provides important technical support for the sustainable management practice of salt-affected soils, and has important scientific value and practical significance for promoting the ecological restoration of salt-affected soils.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/28/e3sconf_eppct2025_02024.pdf |
| spellingShingle | Mao Zhenxuan Chen Kun Liu Qiang Xu Mengjiao Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils E3S Web of Conferences |
| title | Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils |
| title_full | Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils |
| title_fullStr | Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils |
| title_full_unstemmed | Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils |
| title_short | Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils |
| title_sort | machine learning based assessment of the impact of biochar amendment on plant productivity in salt affected soils |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/28/e3sconf_eppct2025_02024.pdf |
| work_keys_str_mv | AT maozhenxuan machinelearningbasedassessmentoftheimpactofbiocharamendmentonplantproductivityinsaltaffectedsoils AT chenkun machinelearningbasedassessmentoftheimpactofbiocharamendmentonplantproductivityinsaltaffectedsoils AT liuqiang machinelearningbasedassessmentoftheimpactofbiocharamendmentonplantproductivityinsaltaffectedsoils AT xumengjiao machinelearningbasedassessmentoftheimpactofbiocharamendmentonplantproductivityinsaltaffectedsoils |