In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.

As the transition zone between urban and rural, the metropolis fringe is an area where various functions permeate and compete fiercely with each other. Understanding land use functions (LUFs) and their relationships are crucial for both urban and rural sustainable development. In this study, we established a conceptual framework of land use multifunctions in the urban fringe and proposed an improved evaluation method to quantify LUFs at the grid scale. The bivariate spatial autocorrelation method was used to explore the trade-offs among LUFs. Taking Qingpu District in Shanghai as a case study, we found that LUFs displayed pronounced spatiotemporal heterogeneity. The economic- and social-dominated functional trade-off mainly occurred in the east part of Qingpu, whereas the ecological function dominated in the west. Human preference and corresponding policies were the key factors leading to these trade-offs. Additionally, land use function zoning was proposed to resolve existing conflicts. These findings can provide scientific information for efficient land use management in the metropolis fringe.

The international food trade is beneficial for enhancing global food security but also raises issues such as global cropland redistribution, land use efficiency, and environmental problems. While current studies have examined the impacts of the international food trade on these issues, its long-term effects on global cropland use efficiency remain unclear, especially when considering different crops and countries. Utilizing the international trade theory and the principle of virtual cropland, this study explores the relationship between international food trade and global cropland use efficiency from 2000 to 2022. The results illustrate that the global crop trade surged by 142%, outpacing the 102% increase in virtual cropland trade, which was attributed to crop yield enhancements. By 2022, the global virtual cropland trade encompassed 10.7% of the total croplands, with China emerging as the foremost importer, particularly due to soybean imports. Notably, the global crop trade led to substantial cropland savings and higher cropland use efficiency, totaling 1244.9 million hectares (Mha) between 2000 and 2020. These gains were largely attributed to the superior yields of major crop-exporting countries. Despite these gains, socio-economically vulnerable countries face significant challenges, potentially compromising their food security amidst the complexities of the global trade dynamics.