Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach
Cropland is a critical component of food security. Under the multiple contexts of climate change, urbanization, and industrialization, China’s cropland faces unprecedented challenges. Understanding the spatiotemporal dynamics of cropland non-agriculturalization (CLNA) and quantifying the contributio...
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MDPI AG
2025-01-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/14/1/190 |
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| author | Huiting Yan Hao Chen Fei Wang Linjing Qiu |
| author_facet | Huiting Yan Hao Chen Fei Wang Linjing Qiu |
| author_sort | Huiting Yan |
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| description | Cropland is a critical component of food security. Under the multiple contexts of climate change, urbanization, and industrialization, China’s cropland faces unprecedented challenges. Understanding the spatiotemporal dynamics of cropland non-agriculturalization (CLNA) and quantifying the contributions of its driving factors are vital for effective cropland management and the optimal allocation of land resources. This study investigated the spatiotemporal dynamics and driving mechanisms of CLNA in Shaanxi Province (SP), a major grain-producing region in China, from 2001 to 2020, using geospatial statistical analysis and machine learning techniques. The results showed that, between 2001 and 2020, approximately 17,200.8 km<sup>2</sup> of cropland (8.4% of the total area) was converted to non-cropland, with a pronounced spatial clustering pattern. XGBoost-SHAP attribution analysis revealed that among the 15 selected driving factors, precipitation, road network density, rural population, population density, grain yield, registered population, and slope length exerted the most significant influence on CLNA in SP. Notably, the interaction effects between these factors contributed more substantially than the individual factors. These findings highlight the pronounced regional disparities in CLNA across SP, driven by a complex interplay of multiple factors, underscoring the urgent need to implement water-saving agricultural practices and optimize rural land-use planning to maintain the dynamic balance of cropland and ensure food security in the region. |
| format | Article |
| id | doaj-art-c33d5deba0704841b8b2e47c116c955b |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-c33d5deba0704841b8b2e47c116c955b2025-01-24T13:38:16ZengMDPI AGLand2073-445X2025-01-0114119010.3390/land14010190Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning ApproachHuiting Yan0Hao Chen1Fei Wang2Linjing Qiu3College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, ChinaDepartment of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaCropland is a critical component of food security. Under the multiple contexts of climate change, urbanization, and industrialization, China’s cropland faces unprecedented challenges. Understanding the spatiotemporal dynamics of cropland non-agriculturalization (CLNA) and quantifying the contributions of its driving factors are vital for effective cropland management and the optimal allocation of land resources. This study investigated the spatiotemporal dynamics and driving mechanisms of CLNA in Shaanxi Province (SP), a major grain-producing region in China, from 2001 to 2020, using geospatial statistical analysis and machine learning techniques. The results showed that, between 2001 and 2020, approximately 17,200.8 km<sup>2</sup> of cropland (8.4% of the total area) was converted to non-cropland, with a pronounced spatial clustering pattern. XGBoost-SHAP attribution analysis revealed that among the 15 selected driving factors, precipitation, road network density, rural population, population density, grain yield, registered population, and slope length exerted the most significant influence on CLNA in SP. Notably, the interaction effects between these factors contributed more substantially than the individual factors. These findings highlight the pronounced regional disparities in CLNA across SP, driven by a complex interplay of multiple factors, underscoring the urgent need to implement water-saving agricultural practices and optimize rural land-use planning to maintain the dynamic balance of cropland and ensure food security in the region.https://www.mdpi.com/2073-445X/14/1/190croplandnon-agriculturalizationmachine learningspatiotemporal patterndriving mechanism |
| spellingShingle | Huiting Yan Hao Chen Fei Wang Linjing Qiu Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach Land cropland non-agriculturalization machine learning spatiotemporal pattern driving mechanism |
| title | Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach |
| title_full | Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach |
| title_fullStr | Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach |
| title_full_unstemmed | Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach |
| title_short | Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach |
| title_sort | dynamics of cropland non agriculturalization in shaanxi province of china and its attribution using a machine learning approach |
| topic | cropland non-agriculturalization machine learning spatiotemporal pattern driving mechanism |
| url | https://www.mdpi.com/2073-445X/14/1/190 |
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