Monitoring of Farmland Abandonment Based on Google Earth Engine and Interpretable Machine Learning

Yameng Jiang, Yefeng Jiang (), Xi Guo, Zichun Guo, Yingcong Ye, Ji Huang and Jia Liu
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Yameng Jiang: Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Jiangxi Agricultural University, Nanchang 330045, China
Yefeng Jiang: Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Jiangxi Agricultural University, Nanchang 330045, China
Xi Guo: Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Jiangxi Agricultural University, Nanchang 330045, China
Zichun Guo: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China
Yingcong Ye: Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Jiangxi Agricultural University, Nanchang 330045, China
Ji Huang: Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Jiangxi Agricultural University, Nanchang 330045, China
Jia Liu: Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Jiangxi Agricultural University, Nanchang 330045, China

Agriculture, 2025, vol. 15, issue 19, 1-21

Abstract: In recent years, China’s hilly and mountainous areas have faced widespread farmland abandonment. However, research on farmland abandonment and its driving mechanisms in hilly and mountainous regions is limited. This study proposes a transferable methodological framework that integrates Landsat data, Google Earth Engine, a time sliding-window algorithm, and the interpretable XGBoost–Shapley Additive explanation (SHAP) model. The time sliding-window algorithm is used to robustly detect long-term land cover changes across the entire study period. The SHAP quantifies the contributions of key drivers to farmland abandonment, providing transparent insights into the driving mechanisms. Applying this framework, we systematically analyzed the spatiotemporal evolution patterns and driving factors of farmland abandonment in Ji’an City, a typical city located in the hilly and mountainous areas of southern China and ultimately developed a farmland abandonment probability distribution map. The findings demonstrate the following. (1) Methodological validation showed that the random forest classifier achieved a mean overall accuracy (OA) of 91.05% (Kappa = 0.88) and the abandonment maps achieved OA of 91.58% (Kappa = 0.83). (2) Spatiotemporal analysis revealed that farmland area increased by 13.26% over 1990–2023, evolving through three stages: fluctuation (1990–2005), growth (2006–2015), and stability (2016–2023). The abandonment rate showed a long-term decreasing trend, peaking in 1998, whereas the abandoned area reached its minimum in 2007. From a spatial perspective, abandonment was more pronounced in mountainous and hilly regions of the study areas. (3) The XGBoost–SHAP model (R 2 > 0.85) identified key driving factors, including the potential crop yield, soil properties, mean annual precipitation, population density, and terrain features. By offering an interpretable and transferable monitoring framework, this study not only advances farmland abandonment research in complex terrains but also provides concrete policy implications. The results can guide targeted protection of high-risk abandonment zones, promote sustainable land-use planning, and support adaptive agricultural policies in hilly and mountainous regions.

Keywords: farmland abandonment; driving mechanisms; hilly and mountainous areas; interpretable machine learning; time-series farmland map (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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