Urbanization and regional geomorphological and agricultural conditions have caused rural hollowing out and a large number of farms to be abandoned, which is a big obstacle to food security and rural industrial development. Sichuan Province is representative of national major grain producing areas and high-quality cultivated protected land in China. This paper reveals the spatial heterogeneity of farmland abandonment in the Sichuan Province. In total, 455 random sample plots were investigated to explore spatial heterogeneity of abandoned farmland in Sichuan Province. In each sample plot, farmland and abandoned farmland patches were mapped. The rate of paddy field abandonment (RPFA), the rate of non-irrigated farmland abandonment (RNFA), and the rate of farmland abandonment (RFA) were calculated as indicators of farmland abandonment. Our results reveal the spatial heterogeneity of abandoned farmland in Sichuan Province, and the clustering characteristics of spatial cold- and hot spots of the RFA. The findings from this study may have importance in rural infrastructure construction and the implementation of a rural revitalization strategy, as well as in making policies to curb abandonment of farmland. The spatial heterogeneity of farmland abandonment revealed in this study may contribute to sustaining food security and farmland protection in China.

Research investigates the transformations in production–living–ecological spaces (PLES) across diverse scenarios and their ecological effects, with the aim of offering advice for environmental preservation and long-term growth in Sichuan Province. Utilizing the PLUS model, we simulated the PLES configuration in Sichuan Province for the year 2030 and subsequently evaluated its ecological impacts using an ecological effect assessment model. The findings reveal that: (1) population and GDP are key drivers of the expansion of Industrial-Production Spaces (IMPS), Urban-Living Spaces (ULS), and Rural-Living Spaces (RLS), whereas altitude has a crucial influence on shaping the expansion of Agricultural-Production Spaces (APS), Forest-Ecological Spaces (FES), Grassland-Ecological Spaces (GES), Water-Ecological Spaces (WES), and Other-Ecological Spaces (OES); (2) significant changes in PLES are observed in Sichuan Province by 2030 across four scenarios, with notable distinctions between the production priority scenario and the other three; (3) variations in ecological quality exist among the four scenarios concerning PLES; (4) the reasons behind better or worse ecological conditions differ across scenarios. The research demonstrates that the PLUS model can effectively simulate PLES in Sichuan Province under multiple scenarios for 2030, offering various potential development pathways and their corresponding ecological effects, thereby aiding in the selection of optimal development pathways.

Serious farmland abandonment in hilly areas, and the resolution of commonly used satellite-borne remote sensing images are insufficient to meet the needs of identifying abandoned farmland in such regions. Furthermore, addressing the problem of identifying abandoned farmland in hilly areas with a certain level of accuracy is a crucial issue in the research of extracting information on abandoned farmland patches from remote sensing images. Taking a typical hilly village as an example, this study utilizes airborne multispectral remote sensing images, incorporating various feature factors such as spectral characteristics and texture features. Aiming at the issue of identifying abandoned farmland in hilly areas, a method for extracting abandoned farmland based on the OVR-FWP-RF algorithm is proposed. Furthermore, two machine learning algorithms, Random Forest (RF) and XGBoost, are also utilized for comparison. The results indicate that the overall accuracy (OA) of the OVR-FWP-RF, Random Forest, and XGboost classification algorithms have reached 92.66%, 90.55%, and 90.75%, respectively, with corresponding Kappa coefficients of 0.9064, 0.8796, and 0.8824. Therefore, by combining spectral features, texture features, and vegetation factors, the use of machine learning methods can improve the accuracy of identifying ground objects. Moreover, the OVR-FWP-RF algorithm outperforms the Random Forest and XGboost. Specifically, when using the OVR-FWP-RF algorithm to identify abandoned farmland, its producer accuracy (PA) is 3.22% and 0.71% higher than Random Forest and XGboost, respectively, while the user accuracy (UA) is also 5.27% and 6.68% higher, respectively. Therefore, OVR-FWP-RF can significantly improve the accuracy of abandoned farmland identification and other land use type recognition in hilly areas, providing a new method for abandoned farmland identification and other land type classification in hilly areas, as well as a useful reference for abandoned farmland identification research in other similar areas.

Given that the increasing non-agricultural conversion of cultivated land (NACCL) endangers food security, studying the spatial and temporal variation characteristics and driving mechanisms of NACCL in Sichuan Province can offer a scientific foundation for developing local farmland preservation measures and controlling further conversion. Guided by the theoretical framework of land use transition, this study utilizes land use datasets spanning multiple periods between 2000 and 2023. Comprehensively considering population scale factors, natural geographical factors, and socioeconomic factors, the county-level annual NACCL rate is calculated. Following this, the dynamic evolution and underlying driving forces of NACCL across 183 counties in Sichuan Province are examined through temporal and spatial dimensions, utilizing analytical tools including Nonparametric Kernel Density Estimation (KDE) and the Geographical Detector model with Optimal Parameters (OPGD). The study finds that: (1) Overall, NACCL in Sichuan Province exhibits phased temporal fluctuations characterized by “expansion—contraction—re-expansion—strict control,” with cultivated land mainly being converted into urban land, and the differences among counties gradually narrowing. (2) In Sichuan Province, the spatial configuration of NACCL is characterized by the expansion of high-value agglomerations alongside the dispersed and stable distribution of low-value areas. (3) Analysis through the OPGD model indicates that urban construction land dominates the NACCL process in Sichuan Province, and the driving dimension evolves from single to synergistic. The findings of this study offer a systematic examination of the spatiotemporal evolution and underlying drivers of NACCL in Sichuan Province. This analysis provides a scientific basis for formulating region-specific farmland protection policies and supports the optimization of territorial spatial planning systems. The results hold significant practical relevance for promoting the sustainable use of cultivated land resources.

Scarcity of cultivated land resources and socioeconomic expansion have diversified cultivated land utilization modes. This research takes 183 county administrative units across Sichuan Province as research samples to build a three-dimensional (production, living, ecological) evaluation framework for cultivated land multifunctionality. Entropy weight method, multifunctionality index and sensitivity model are applied to quantify the spatiotemporal evolution of cultivated land functions across 2013–2023, and to detect the response intensity of multifunctionality to cultivated land quantity variation. Results indicate the following: (1) During 2013–2023, Sichuan’s cultivated land production function (PF) maintained stability, showing a contiguous east-high–west-low spatial pattern. The living function (LF) experienced moderate growth with scattered high–low values but shared identical spatial differentiation rules; the ecological function (EF) declined slightly with a decelerated decreasing rate in the late research phase, and its high- and sub-high-value areas agglomerated in the Eastern Sichuan Basin and mountainous area of Southwest Sichuan. (2) Provincial cultivated land area slightly increased initially then declined, yielding a slight total reduction while the overall spatial layout remained consistent. (3) Comparison between 2013–2018 and 2018–2023 reveals that production function sensitivity decreased sharply, LF sensitivity fell moderately, and ecological function sensitivity climbed upward. Investigations into spatiotemporal evolution and sensitive responses of cultivated land multifunctionality deliver vital theoretical and practical implications for mitigating conflicts between regional cultivated land protection and exploitation, as well as advancing efficient land resource allocation and coordinated promotion of multifunctions.