Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach

Huiting Yan, Hao Chen, Fei Wang () and Linjing Qiu
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Huiting Yan: College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, China
Hao Chen: College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, China
Fei Wang: College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, China
Linjing Qiu: Department of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Land, 2025, vol. 14, issue 1, 1-18

Abstract: 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 2 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.

Keywords: cropland; non-agriculturalization; machine learning; spatiotemporal pattern; driving mechanism (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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