Amid persistent global food security challenges, the efficient utilization of cultivated land resources has become increasingly critical, as optimizing Cultivated Land Utilization Efficiency (CLUE) is paramount to ensuring food supply. This study introduced a cultivated land utilization index (CLUI) based on Fractional Vegetation Cover (FVC) to assess the spatiotemporal variations in Henan Province’s CLUE. The Theil–Sen slope and the Mann–Kendall test were used to analyze the spatiotemporal variations of CLUE in Henan Province from 2000 to 2020. Additionally, we used a genetic algorithm optimized Artificial Neural Network (ANN) and a particle swarm optimization-based Random Forest (RF) model to assess the comprehensive in-fluence between topography, climate, and human activities on CLUE, in which incorporating Shapley Additive Explanations (SHAP) values. The results reveal the following: (1) From 2000 to 2020, the CLUE in Henan province showed an overall upward trend, with strong spatial heterogeneity across various regions: the central and eastern areas generally showed decline, the northern region remained stable with slight increases, the western region saw significant growth, while the southern area exhibited complex fluctuations. (2) Natural and economic factors had notable impacts on CLUE in Henan province. Among these factors, population and economic factors played a dominant role, whereas average temperature exerted an inhibitory effect on CLUE in most parts of the province. (3) The influenced factors on CLUE varied spatially, with human activity impacts being more concentrated, while topographical and climatic influences were relatively dispersed. These findings provide a scientific basis for land management and agricultural policy formulation in major grain-producing areas, offering valuable insights into enhancing regional CLUE and promoting sustainable agricultural development.

Forests are composed of landscape spatial units (patches) of different sizes, shapes, and characteristics. The forest landscape pattern and its trends are closely related to resistance to disturbance, restoration, stability, and the biodiversity of the forest landscape and directly influence the benefits and sustainable exploitation of forest landscape resources. Therefore, forest landscape patterns and the driving forces have increasingly attracted the attention of researchers. The present study analyzed the spatial and temporal dynamics of woodland landscape patterns in typical hilly mountainous areas in southern China using ArcGIS, landscape pattern index, and morphological spatial pattern analysis. Meanwhile, a logistic regression model was used to analyze the drivers of woodland change in Anyuan County from three aspects: natural, geographic location, and socio-economic conditions. The total area of woodland decreased during the 10-year study period, with a net decrease of 4959.27 ha, mainly due to conversion into cultivated land, garden land, and construction land. Patch density, edge density, and aggregation index of woodlands increased over time, indicating enhanced fragmentation, stable and complex patch edges, and increased patch connectivity. Conversely, the highest patch index values exhibited decreasing trends, indicating decreases in the dominant patch type. Morphological spatial pattern analysis results showed that the core area was dominant and the islet area increased over time, which also indicates enhanced fragmentation. Forest landscape change is the result of environmental change, ecological processes, and human disturbance, with geographical location and social economy having greater influences on forest landscape change. Human activities such as navel orange cultivation, returning cultivated land to forest, and land occupation for construction were the major factors driving woodland change. The results provide reference that could facilitate forest management and sustainable forest resource utilization.

With the acceleration of urbanization in agricultural areas and the continuous changes in land-use patterns, the transformation of agricultural land presents complexity and dynamism, which puts higher demands on precise monitoring. And most existing monitoring methods are constrained by limited spatial and temporal resolution, high computational demands, and challenges in distinguishing complex land cover types. These limitations hinder their ability to effectively detect rapid and subtle land use changes, particularly in areas experiencing rapid urban expansion, where their shortcomings become more pronounced. To address these challenges, this study presents a multimodal deep learning framework using a temporal semantic segmentation change detection (TSSCD) model optimized with ant colony optimization (ACO) to detect and analyze agricultural land conversion in Zhengzhou City, a major grain-producing area in China. This model utilizes Landsat 7/8 imagery and Sentinel-2 satellite imagery from 2003 to 2023 to capture the spatiotemporal transformation of cropland driven by urban expansion, infrastructure development, and population changes over the last two decades. The optimized TSSCD model achieves superior classification accuracy, with the kappa coefficient improving from 0.871 to 0.892, spatial F1 score from 0.903 to 0.935, and temporal F1 score from 0.848 to 0.879, indicating its effectiveness in identifying complex land-use changes. The significant spatiotemporal variation characteristics of agricultural land conversion in Zhengzhou City from 2003 to 2023 were revealed through the TSSCD model, with transformations initially concentrated near Zhengzhou’s urban core and expanding outward, particularly to the east and north. These results highlight the effectiveness of remote sensing and deep learning techniques in monitoring agricultural land conversion.

With China’s urbanization process and changes in rural family structures, the abandonment of farmland in scattered villages within hilly mountainous regions is becoming an increasingly serious issue, restricting the improvement of land use efficiency. This study analyzes the basic characteristics and variations in abandoned farmland by conducting surveys and interviews with peasants in a scattered village in southern China. Using the Heckman two-stage model, we perform empirical analysis on the factors influencing farmland abandonment, addressing potential sample selection bias. The findings show the following: peasants with better health and higher education levels are more likely to transition to non-agricultural occupations which contributes to an increased abandonment of farmland. However, larger and more integrated land parcels, along with favorable farming conditions, help reduce abandonment. Additionally, rural land transfer and agricultural subsidies are important factors that enhance farmland utilization and mitigate abandonment. These results provide a reference for addressing the abandonment of farmland and improving both the farming environment and social policies in rural villages.

Non-grain production of cultivated land (NGPCL) threatened food security. Therefore, scholars have begun study this area in China and other countries, but most of the studies have focused on large scales, and few studies have focused on plot scale analysis. This study presents an analytical framework to shed light on the causes of NGPCL in the hilly mountainous regions of southern China. First, we categorized NGPCL into severe damage class and slight damage class according to the degree of damage of NGPCL to cultivated soils and the difficulty of restoring food production capacity. Then, we revealed the characteristics of spatial differentiation and causes of NGPCL in the southern hilly areas by using methods such as binary logistic regression model and spatial correlation analysis. Finally, the results in the study showed that: (1) the overall NGPCL areal in 2020 was 11288.46 hm², accounts for 38.14%, of which the areas of NGPCL in the minor damage class and the serious damage class were 27.32% and 10.82%, respectively. (2) The spatial differentiation of NGPCL was obvious, which showed a clustered distribution pattern, with NGPCLs of the minor damage class clustered at high values in the topographically flat areas of the east-central zone, and NGPCLs of the severe damage class clustered at high values in the vicinity of the central urban area. (3) The levels of NGPCL for each type were significantly correlated with the three dimensions of natural, locational, and socio-economic factors, while topography, cultivated land infrastructure conditions and the degree of centralized and contiguous cultivated land were the important drivers of spatial differentiation of NGPCL. This paper reveals the distribution and influencing factors of NGPCL at the plot scale, that can provide theoretical reference and categorized governance suggestions for NGPCL governance in similar regions in China and even in the world.