Improving soil pH prediction and mapping using anthropogenic variables and machine learning models
This study evaluates the impact of anthropogenic activities on soil pH prediction in China's Huang-Huai-Hai Plain using four machine learning models (RF, LightGBM, XGBoost, SVM). By incorporating five anthropogenic variables (fertilization, population density, heat flux, urbanization, road dens...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2482699 |
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| _version_ | 1849340306967756800 |
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| author | Daocheng Li Erlong Xiao Yingxin Xia Xingyu Liang Mengxin Guo Lixin Ning Jun Yan |
| author_facet | Daocheng Li Erlong Xiao Yingxin Xia Xingyu Liang Mengxin Guo Lixin Ning Jun Yan |
| author_sort | Daocheng Li |
| collection | DOAJ |
| description | This study evaluates the impact of anthropogenic activities on soil pH prediction in China's Huang-Huai-Hai Plain using four machine learning models (RF, LightGBM, XGBoost, SVM). By incorporating five anthropogenic variables (fertilization, population density, heat flux, urbanization, road density) alongside 24 environmental factors, model performance improved significantly (R² up to 0.56 for RF). Key findings: (1) Fertilization and population density were the most influential human factors; (2) Precipitation remained the dominant predictor overall. The results highlight human activities' substantial role in soil pH variation, supporting precision agriculture and sustainable land management in intensively cultivated regions. |
| format | Article |
| id | doaj-art-28dce28f38824ef7aa257a70b33e5d64 |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-28dce28f38824ef7aa257a70b33e5d642025-08-20T03:43:57ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2482699Improving soil pH prediction and mapping using anthropogenic variables and machine learning modelsDaocheng Li0Erlong Xiao1Yingxin Xia2Xingyu Liang3Mengxin Guo4Lixin Ning5Jun Yan6School of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaSchool of Information Science and Engineering, Shandong Agricultural University, Taian, ChinaThis study evaluates the impact of anthropogenic activities on soil pH prediction in China's Huang-Huai-Hai Plain using four machine learning models (RF, LightGBM, XGBoost, SVM). By incorporating five anthropogenic variables (fertilization, population density, heat flux, urbanization, road density) alongside 24 environmental factors, model performance improved significantly (R² up to 0.56 for RF). Key findings: (1) Fertilization and population density were the most influential human factors; (2) Precipitation remained the dominant predictor overall. The results highlight human activities' substantial role in soil pH variation, supporting precision agriculture and sustainable land management in intensively cultivated regions.https://www.tandfonline.com/doi/10.1080/10106049.2025.2482699Soil pHdigital soil mappinganthropogenic variablesHuang-Huai-Hai plainmachine learning models |
| spellingShingle | Daocheng Li Erlong Xiao Yingxin Xia Xingyu Liang Mengxin Guo Lixin Ning Jun Yan Improving soil pH prediction and mapping using anthropogenic variables and machine learning models Geocarto International Soil pH digital soil mapping anthropogenic variables Huang-Huai-Hai plain machine learning models |
| title | Improving soil pH prediction and mapping using anthropogenic variables and machine learning models |
| title_full | Improving soil pH prediction and mapping using anthropogenic variables and machine learning models |
| title_fullStr | Improving soil pH prediction and mapping using anthropogenic variables and machine learning models |
| title_full_unstemmed | Improving soil pH prediction and mapping using anthropogenic variables and machine learning models |
| title_short | Improving soil pH prediction and mapping using anthropogenic variables and machine learning models |
| title_sort | improving soil ph prediction and mapping using anthropogenic variables and machine learning models |
| topic | Soil pH digital soil mapping anthropogenic variables Huang-Huai-Hai plain machine learning models |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2482699 |
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