China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This method uses a feature space of six integrals from the crops’ phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks.

A new helvolic acid derivative named helvolic acid methyl ester (1), together with two known helvolic acid compounds, helvolic acid (2) and hydrohelvolic acid (3), were isolated from the fermentation of endophytic fungus Fusarium sp. in Ficus carica leaves. Their structures were elucidated and identified by spectroscopic methods. Compounds 1–3 showed potent antifungal and antibacterial activities.

Since the 20th century, the global soybean trade has undergone major changes, shaped by rising demand, climate-related risks, and shifting international dynamics. Despite its global importance, important gaps remain in understanding the complex drivers and sustainability challenges of this transformation. This review synthesizes both direct and indirect forces reshaping trade flows, spanning market dynamics, supply chain logistics, policy shifts, and technological innovation. We examine how soybean trade expansion has impacted deforestation, inequality, and food security, and assess the responses of governments and companies to address these challenges. Finally, we provide a forward-looking perspective on the strategic pathways needed to ensure a more resilient and sustainable global soybean system. The integrated insights offered in this review can inform sustainable trade strategies and foster cross-scale policy coordination for a more resilient global agri-food system.