Spatiotemporal Evolution and Differentiated Spatial Governance of Slope-Classified Cultivated Land Fragmentation in Rapid Urbanization: Machine Learning-Driven Insights from Guangdong Province

Abstract

Rapid urbanization exerts immense pressure on cultivated land. Among these, slope-classified cultivated land (referring to cropland categorized by slope gradients) is especially vulnerable to fragmentation due to its ecological fragility, challenging utilization, and critical role in soil conservation and sustainable agriculture. This study explores the spatiotemporal dynamics and driving mechanisms of slope-classified cultivated land fragmentation (SCLF) in Guangdong Province, China, from 2000 to 2020. Using multi-temporal geospatial data, machine learning interpretation, and socioeconomic datasets, this research quantifies the spatiotemporal changes in SCLF, identifies key drivers and their interactions, and proposes differentiated protection strategies. The results reveal the following: (1) The SCLF decreased in the Pearl River Delta, exhibited “U-shaped” fluctuations in the west and east, and increased steadily in northern Guangdong. (2) The machine learning interpretation highlights significantly amplified synergistic effects among drivers, with socioeconomic factors, particularly agricultural mechanization and non-farm employment rates, exerting dominant influences on fragmentation patterns. (3) A “core–transitional–marginal” protection framework is proposed, intensifying the land use efficiency and ecological resilience in core areas, coupling land consolidation with green infrastructure in transitional zones, and promoting agroecological diversification in marginal regions. This research proposed a novel framework for SCLF, contributing to cultivated land protection and informing differentiated spatial governance in rapidly urbanizing regions.