Agricultural drought poses a critical constraint to food security and regional sustainable development, particularly in the Huang-Huai-Hai Plain, a major grain-producing region characterized by high spatial heterogeneity in drought risk. Previous studies have demonstrated that the Crop Water Stress Index (CWSI) outperforms traditional meteorological indices in detecting agricultural droughts in various regions. However, there is limited research specifically focusing on its spatiotemporal dynamics and the complex relationships with environmental factors, particularly in the Huang-Huai-Hai Plain. To fill this gap, this study first estimated CWSI using remote sensing evapotranspiration data and systematically assessed the spatiotemporal dynamics of agricultural drought in the Huang-Huai-Hai Plain from 2005 to 2020. Then, an integrated analytical framework that combines Local Indicators of Spatial Association (LISA) with Random Forest (RF) modeling has been proposed to identify primary environmental drivers. Results revealed a general downward trend in CWSI over the study period, with drought hotpots primarily concentrated in the central plains and along the eastern foothills of the Taihang Mountains. LISA identified four distinct spatial cluster types and revealed significant spatial associations between CWSI and six environmental variables. The major driving factors of CWSI included vegetation conditions (NDVI), land surface temperature (LST), rainfall, and temperature-related factors (SAT, DSR), with LST and SAT exhibiting the strongest correlations with CWSI in multiple regions. Among these, LST and SAT exhibited strong positive correlations with CWSI in multiple regions. By integrating spatial clustering and variable importance analysis, we found that agricultural drought patterns are shaped by interacting environmental factors, with region-specific dominant mechanisms. This study provides a novel analytical framework that bridges remote sensing, spatial statistics, and machine learning, offering valuable insights and tools for drought monitoring and attribution at regional scales.

Grain supply and demand affect regional food security; however, the drivers are often unclear, making precise forecasting and policymaking challenging. This study used Central Asia as a case to integrate Partial Least Squares Structural Equation Modeling (PLS‐SEM) with particle swarm optimization least squares support vector machine (PSO‐LSSVM) to separately identify the drivers of grain supply and demand and enhance prediction accuracy. We analyzed the interannual variations in the production, import/export volumes, consumption, and inventory of wheat, rice, barley, maize, and other grains in Central Asia (1992–2019). We then decoupled the factors affecting wheat production and consumption using PLS‐SEM and made predictions by integrating PLS‐SEM with the PSO‐LSSVM. The results showed that grain supply and demand across Central Asia, primarily driven by wheat production and consumption, declined and later recovered, with a turning point between 1995 and 1998. Kazakhstan exports 44% of its wheat, whereas other countries heavily depend on imports. In Central Asia, the path coefficients ( r ) of the wheat area and yield on total production were 0.36 and 0.77, respectively, whereas in Kazakhstan, they were 0.37 and 0.81, respectively. Climate and cultivation factors indirectly affect production through wheat yield, whereas yield and consumption influence production through area. Economic growth increased wheat consumption, whereas urban population growth decreased it. In Kazakhstan, wheat exports reduced consumption ( r = −0.23) but boosted the economy ( r = 0.33), a pattern that was not observed in Central Asia. The coupling model of PLS‐SEM and PSO‐LSSVM enhanced the prediction accuracy of wheat yield, reducing the error by 10.21% in Central Asia and 32.8% in Kazakhstan. This study offers a novel approach to decouple the driving factors of grain production and consumption and predicts crop yields in regions with limited data availability.

Water use efficiency (WUE), as an important metric for ecosystem resilience, has been identified to play a significant role in the coupling of carbon and water cycles. The farming–pastoral ecotone of Northern China (FPENC), which is highly susceptible to drought due to water scarcity, has long been recognized as an ecologically fragile zone. The ecological restoration projects in China have mitigated land degradation and maintain the sustainability of dryland. However, the process of greening in drylands has the potential to impact water availability. A comprehensive analysis of the WUE in the FPENC can help to understand the carbon absorption and water consumption. Using gross primary production (GPP) and evapotranspiration (ET) data from a MODerate resolution Imaging Spectroradiometer (MODIS), alongside biophysical variables data and land cover information, the spatio-temporal variations in WUE from 2003 to 2022 were examined. Additionally, its driving force and the ecosystem resilience were also revealed. Results indicated that the annual mean of WUE fluctuated between 0.52 and 2.60 gC kgH₂O⁻¹, showing a non-significant decreasing trend across the FPENC. Notably, the annual averaged WUE underwent a significant decline before 2012 (p < 0.05), and then showed a slight increased trend (p = 0.14) during the year afterward (i.e., 2013–2022). In terms of climatic controls, temperature (Temp) and soil volumetric water content (VSWC) dominantly affected WUE from 2003 to 2012; VPD (vapor pressure deficit), VSWC, and Temp showed comprehensive controls from 2013 to 2022. The findings suggest that a wetter atmosphere and increased soil moisture contribute to the decline in WUE. In total, 59.2% of FPENC was shown to be non-resilient, as grassland occupy the majority of the area, located in Mu Us Sandy land and Horqin Sand Land. These results underscore the importance of climatic factors in the regulation WUE over FPENC and highlight the necessity for focused research on WUE responses to climate change, particularly extreme events like droughts, in the future.

Understanding the implications of dietary consumption and food trade for water resources is essential for sustainable development in water-scarce regions. In this study, we quantified the dietary virtual water use (DVWU) and trade flows associated with food production and consumption across the upstream, midstream, and downstream regions of the Yellow River Basin (YRB), with explicit consideration of urban–rural dietary differences. We then evaluated how virtual water transfers in the food trade affect regional water stress. The results revealed that, in 2020, grain-based diets accounted for 28.32% more DVWU than animal-based diets in the YRB. Upstream residents had the highest per capita DVWU (312.3 m³), primarily due to higher intake of water-intensive foods and feed. Rural diets were dominated by grain-based virtual water (60.64%), whereas urban diets relied more on animal sources (53.43%). The food trade led to a net outflow of 60.7 BCM of virtual water from the YRB, primarily from the downstream area, resulting in a 53.64% overall increase in the water stress index (WSI) across the basin. Although upstream regions experienced a 24% decrease in WSI due to virtual water inflow, downstream areas experienced an 80% increase, highlighting the spatial mismatch between food production and water availability. This study provides evidence-based insights for integrated food–water management and water-efficient food system planning in response to increasing water stress in the YRB and other arid regions of China.

Reduced precipitation combined with extensive irrigation are the primary causes of groundwater overexploitation in semi-arid agricultural regions, leading to crises in water, food, and ecology. Government policies aim to mitigate groundwater depletion, yet its effects are challenging to quantify. We developed a 1×1 km monthly Agriculture-Groundwater (AG) model framework for the Bashang Plateau in northern China to assess the impact of water policies on groundwater. The framework innovation achieves the distributed dynamic input of land use, climate, and water consumption components. Results found that water use during 2015–2018 decreased by 8.6 million m³/year relative to 2012-2014, with groundwater levels stabilizing and no reduction observed in the yields of major crops. This outcome was mostly influenced by reduced irrigation usage and increased precipitation. The AG model demonstrated that the contributions of increased precipitation, efficient irrigation, and cropping pattern adjustments to reduction in groundwater extraction are 12.06, 5.07, and 21.88 million m³/year, respectively, contributing to 27.41%, 21.48%, and 51.11% of groundwater level recovery. Enhanced government interventions in coming decades will enhance regional developmental stability against increasing climate change uncertainties.