Abandoned cropland is a great waste of land resources, which not only affects the stability of food production, but also wastes limited cropland resources. China has large areas of abandoned cropland, and accurately extracting the large-scale abandoned cropland is a prerequisite for the government to implement reclamation policies and estimate the food production potential of abandoned cropland. China has implemented the Grain for Green Project (GGP) since 1980, but most research has not considered its impact on the extraction of abandoned cropland. Therefore, a method was proposed to identify abandoned cropland excluding the regions of GGP, and to analyze the spatio-temporal characteristics and potential of abandoned cropland in China from 1991 to 2018. GGP potential and food production potential of abandoned cropland were discussed. The results showed that 26.47 million hectares of cropland has been abandoned in the past 28 years (including cropland that has been reclaimed). Under the positive influence of the government’s food security policy, the abandoned cropland has decreased in recent years, but the total area is still high. The abandonment mainly occurred in the Jiangnan Hills, the Hilly Regions of Fujian and Guangdong Province, the North China Mountains, and Eastern Inner Mongolia. The higher the elevation and the steeper the slope, the higher the abandonment rate. In addition, the urban-rural income gap significantly affected the abandonment rate. This study drew the spatial and temporal distribution map of abandoned cropland in China with a spatial resolution of 30 m considering GGP. It provides an important basis for formulating reclamation and GGP policy.

Climate change and human activities are reshaping the structure and function of terrestrial ecosystems, particularly in vulnerable regions such as agro-pastoral ecotones. However, the extent to which climate change impacts vegetation growth in these areas remains poorly understood, largely due to the modifying effects of human-induced land cover changes on vegetation sensitivity to climatic variations. This study utilizes satellite-derived vegetation indices, land cover datasets, and climate data to investigate the influence of both land cover and climate changes on vegetation growth in the agro-pastoral ecotone of northern China (APENC) from 2001 to 2022. The results reveal that the sensitivity of vegetation productivity, as indicated by the kernel Normalized Difference Vegetation Index (kNDVI), varies depending on the land cover type to climate change in the APENC. Moreover, ridge regression modeling shows that pre-season climate conditions (i.e., pre-season precipitation and temperature) have a stronger positive impact on growing-season vegetation productivity than growing season precipitation and temperature, while the effect of vapor pressure deficit (VPD) is negative. Notably, the kNDVI exhibits significant positive sensitivity (p < 0.05) to precipitation in 34.12% of the region and significant negative sensitivity (p < 0.05) to VPD in 38.80%. The ridge regression model explained 89.10% of the total variation (R² = 0.891). These findings not only emphasize the critical role of both historical and contemporary climate conditions in shaping vegetation growth but also provide valuable insights into how to adjust agricultural and animal husbandry management strategies to improve regional climate adaptation based on climate information from previous seasons in fragile regions.

Estimating the spatiotemporal variations in natural grassland carrying capacity is crucial for maintaining the balance between grasslands and livestock. However, accurately assessing this capacity presents significant challenges due to the high costs of biomass measurement and the impact of human activities. In this study, we propose a novel method to estimate grassland carrying capacity based on potential net primary productivity (NPP), applied to the source area of the Nujiang River and Selinco Lake on the Tibetan Plateau. Initially, we utilize multisource remote sensing data—including soil, topography, and climate information—and employ the random forest regression algorithm to model potential NPP in areas where grazing is banned. The construction of the random forest model involves rigorous feature selection and hyperparameter optimization, enhancing the model’s accuracy. Next, we apply this trained model to areas with grazing, ensuring a more accurate estimation of grassland carrying capacity. Finally, we analyze the spatiotemporal variations in grassland carrying capacity. The main results showed that the model achieved a high level of precision, with a root mean square error (RMSE) of 4.89, indicating reliable predictions of grassland carrying capacity. From 2001 to 2020, the average carrying capacity was estimated at 9.44 SU/km², demonstrating a spatial distribution that decreases from southeast to northwest. A slight overall increase in carrying capacity was observed, with 65.7% of the area exhibiting an increasing trend, suggesting that climate change has a modest positive effect on the recovery of grassland carrying capacity. Most of the grassland carrying capacity is found in areas below 5000 m in altitude, with alpine meadows and alpine meadow steppes below 4750 m being particularly suitable for grazing. Given that the overall grassland carrying capacity remains low, it is crucial to strictly control local grazing intensity to mitigate the adverse impacts of human activities. This study provides a solid scientific foundation for developing targeted grassland management and protection policies.