Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model
Cotton is the world’s most widely cultivated fiber crop and holds great significance in Xinjiang. However, unsuitable planting environments can hinder farmer income and result in a substantial waste of agricultural resources.This study explores suitability of cotton planting areas in Xinjiang to red...
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Elsevier
2025-06-01
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| Series: | Resources, Environment and Sustainability |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266691612500012X |
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| author | Yonglin Jia Yi Li Asim Biswas Jiayin Pang Xiaoyan Song Guang Yang Zhen’an Hou Honghai Luo Xiangwen Xie Javlonbek Ishchanov Ji Chen Juanli Ju Kadambot H.M. Siddique |
| author_facet | Yonglin Jia Yi Li Asim Biswas Jiayin Pang Xiaoyan Song Guang Yang Zhen’an Hou Honghai Luo Xiangwen Xie Javlonbek Ishchanov Ji Chen Juanli Ju Kadambot H.M. Siddique |
| author_sort | Yonglin Jia |
| collection | DOAJ |
| description | Cotton is the world’s most widely cultivated fiber crop and holds great significance in Xinjiang. However, unsuitable planting environments can hinder farmer income and result in a substantial waste of agricultural resources.This study explores suitability of cotton planting areas in Xinjiang to reduce agricultural inputs and pollution. The goal is to promote sustainable agricultural development by considering both climate change and soil fertility, factors often overlooked in previous research. We analyzed climate change trends in Xinjiang and used machine learning-transfer component analysis to build a transferable coupling model for total nitrogen (TN) and soil organic carbon (SOC) indicators, resulting in a cotton suitability zoning that accounts for climate and soil fertility factors. Xinjiang has seen an overall increase in cumulative temperature and rainfall, with southern Xinjiang showing the most significant rise (4.02% in temperature and 16.26% in rainfall). The random forest model (RF) outperformed multivariate linear regression (MLR) and support vector machines (SVM) in predicting soil fertility indicators (TN: R2=0.80, SOC: R2=0.77). The RF-TCA coupling model enhanced adaptability, with better performance in TN prediction compared to SOC. The Xinjiang cotton suitability zoning, based on meteorological and soil data, indicates a northward shift in suitable cotton planting areas in northern Xinjiang, while southern Xinjiang continues to maintain a substantial number of suitable planting zones. Notably, the disparity in suitability between the two regions has been narrowing over time. The research offers valuable insights for optimizing cotton planting locations, enhancing resource efficiency, and promoting sustainable development in Xinjiang. |
| format | Article |
| id | doaj-art-7d987c5d7cee4cbcaa2ce886999244fd |
| institution | DOAJ |
| issn | 2666-9161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Resources, Environment and Sustainability |
| spelling | doaj-art-7d987c5d7cee4cbcaa2ce886999244fd2025-08-20T03:21:52ZengElsevierResources, Environment and Sustainability2666-91612025-06-012010020010.1016/j.resenv.2025.100200Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning modelYonglin Jia0Yi Li1Asim Biswas2Jiayin Pang3Xiaoyan Song4Guang Yang5Zhen’an Hou6Honghai Luo7Xiangwen Xie8Javlonbek Ishchanov9Ji Chen10Juanli Ju11Kadambot H.M. Siddique12College of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi, 712100, PR ChinaCollege of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi, 712100, PR China; College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang, 832003, PR China; Institute of Soil Fertilizer and Agricultural Water Saving, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, PR China; Corresponding author at: College of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi, 712100, PR China.School of Environmental Sciences, University of Guelph, Guelph Ontario, N1G 2W1, CanadaThe UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001, Australia; School of Biological Sciences, The University of Western Australia, Perth, WA 6001, AustraliaCollege of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi, 712100, PR ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang, 832003, PR ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang, 832003, PR ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang, 832003, PR ChinaInstitute of Soil Fertilizer and Agricultural Water Saving, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, PR ChinaTashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent 100000, UzbekistanDepartment of Civil Engineering, The University of Hong Kong, 999077, Hong Kong, PR ChinaCollege of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas at Ministry of Education, Northwest A&F University, Yangling, Shaanxi, 712100, PR ChinaThe UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001, AustraliaCotton is the world’s most widely cultivated fiber crop and holds great significance in Xinjiang. However, unsuitable planting environments can hinder farmer income and result in a substantial waste of agricultural resources.This study explores suitability of cotton planting areas in Xinjiang to reduce agricultural inputs and pollution. The goal is to promote sustainable agricultural development by considering both climate change and soil fertility, factors often overlooked in previous research. We analyzed climate change trends in Xinjiang and used machine learning-transfer component analysis to build a transferable coupling model for total nitrogen (TN) and soil organic carbon (SOC) indicators, resulting in a cotton suitability zoning that accounts for climate and soil fertility factors. Xinjiang has seen an overall increase in cumulative temperature and rainfall, with southern Xinjiang showing the most significant rise (4.02% in temperature and 16.26% in rainfall). The random forest model (RF) outperformed multivariate linear regression (MLR) and support vector machines (SVM) in predicting soil fertility indicators (TN: R2=0.80, SOC: R2=0.77). The RF-TCA coupling model enhanced adaptability, with better performance in TN prediction compared to SOC. The Xinjiang cotton suitability zoning, based on meteorological and soil data, indicates a northward shift in suitable cotton planting areas in northern Xinjiang, while southern Xinjiang continues to maintain a substantial number of suitable planting zones. Notably, the disparity in suitability between the two regions has been narrowing over time. The research offers valuable insights for optimizing cotton planting locations, enhancing resource efficiency, and promoting sustainable development in Xinjiang.http://www.sciencedirect.com/science/article/pii/S266691612500012XAgricultural sustainabilityCottonPrecision agricultureSuitable zoning |
| spellingShingle | Yonglin Jia Yi Li Asim Biswas Jiayin Pang Xiaoyan Song Guang Yang Zhen’an Hou Honghai Luo Xiangwen Xie Javlonbek Ishchanov Ji Chen Juanli Ju Kadambot H.M. Siddique Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model Resources, Environment and Sustainability Agricultural sustainability Cotton Precision agriculture Suitable zoning |
| title | Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model |
| title_full | Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model |
| title_fullStr | Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model |
| title_full_unstemmed | Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model |
| title_short | Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model |
| title_sort | evaluation of cotton planting suitability in xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model |
| topic | Agricultural sustainability Cotton Precision agriculture Suitable zoning |
| url | http://www.sciencedirect.com/science/article/pii/S266691612500012X |
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