Wheat plays a very important role in China’s agriculture. The wheat grain yields are affected by the growing period that is determined by temperature, precipitation, and field management, such as planting date and cultivar species. Here, we used the CSM-CERES-Wheat model along with different Representative Concentration Pathways (RCPs) and two global circulation models (GCMs) to simulate different impacts on the winter wheat that caused by changing climate for 2025 and 2050 projections for Guanzhong Plain in Northwest China. Our results showed that it is obvious that there is a warming trend in Guanzhong Plain; the mean temperature for the different scenarios increased up to 3.8 °C. Furthermore, the precipitation varied in the year; in general, the rainfall in February and August was increased, while it decreased in April, October and November. However, the solar radiation was found to be greatly reduced in the Guanzhong Plain. Compared to the reference year, the results showed that the number of days to maturity was shortened 3–24 days, and the main reason was the increased temperature during the winter wheat growing period. Moreover, five planting dates (from October 7 to 27 with five days per step) were applied to simulate the final yield and to select an appropriate planting date for the study area. The yield changed smallest based on Geophysical Fluid Dynamics Laboratory (GFDL)-CM3 (−6.5, −5.3, −4.2 based on RCP 4.5, RCP 6.0, and RCP 8.5) for 2025 when planting on October 27. Farmers might have to plant the crop before 27 October.

Freshwater scarcity has increased the demand for precision irrigation, and root water uptake (RWU) models are now essential for field-scale water management. Conventional macroscopic models estimate uptake capacity from root length density. While computationally efficient, these frameworks overlook root topology, xylem resistance, and rhizosphere heterogeneity and often fail to simulate compensatory uptake under heterogeneous soil moisture such as partial root-zone drying. Hydraulic architecture theory addresses this gap by representing roots as conductance-based networks, in which compensatory uptake arises from physical principles. Though fully resolved 3D models capture microscale rhizosphere processes, practical implementation is often limited by high parameter demands and computational overhead. Recent advances in root imaging, stochastic modeling, upscaling, and machine learning guided by physical principles have eased bottlenecks in simulation across scales. Belowground digital twins open further opportunities for precision irrigation. This review charts the evolution of RWU models and evaluates recent progress in parameterization and upscaling, with the aim of guiding drought-tolerant breeding and water-saving irrigation design. An illustrative case study demonstrates the practical consequences of the modeling choice for irrigation design.

Since the Industrial Revolution, the increasing emissions of greenhouse gases have posed unprecedented challenges to sustainable human development. As one of the most vital terrestrial ecosystems, farmland ecosystems play an irreplaceable role in balancing carbon emissions and absorption, attracting growing scholarly attention. Taking Jiangsu Province, one of China's major grain-producing regions, as the study area, this research integrates the Slacks-Based Measure (SBM) model, the entropy-weighted method, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to analyze the spatiotemporal evolution of farmland carbon effects-including carbon emissions, carbon absorption, and net carbon sequestration-during 2011-2021. Furthermore, a Grey Prediction Model was employed to forecast the carbon effects of 13 cities over the next 12 years. The results show that Jiangsu's farmland carbon emission efficiency exhibited an overall upward trend with fluctuations, with an average value of 0.76. The multi-year mean fitting degrees of resource input and agricultural output were relatively low, at 0.426 and 0.358, respectively, with substantial intercity differences. The average coupling coordination degree between resource input and agricultural output was 0.66, indicating a primary coordination state. The constructed GM (1,1) model achieved a qualification rate exceeding 73.80%, demonstrating its reliability for predicting farmland carbon effects. Forecasts suggest a potential weakening of the province's agricultural carbon sink effect, with the net carbon sequestration in 2033 expected to decline by 15.55% compared with the maximum value during the observation period. This study reveals the spatiotemporal characteristics and potential evolution patterns of farmland carbon effects, providing theoretical support for region-specific agricultural emission reduction policies and promoting the sustainable development of efficient, low-carbon agriculture.

Developing context-specific drought regionalization is crucial for targeted risk management, as drought evolves as a cascading hazard driven by complex land–atmosphere interactions rather than isolated climatic anomalies. However, conventional regionalization frameworks remain largely static and fail to capture the dynamic propagation from meteorological forcing to agricultural impacts. To address this limitation, we developed a framework that links continuous drought dynamics to discrete drought events, enabling identification of propagation patterns and their associated environmental mechanisms across the Loess Plateau, China. By integrating run theory, dimensionality reduction, clustering, and explainable machine learning, we identified three distinct drought propagation regimes: Propagation Blocked, Disaster Amplified, and Response Desensitized zones. At the regional scale, eco-hydrological factors, particularly vegetation productivity and soil moisture dynamics, showed the strongest attribution signals for differentiating drought propagation regimes. However, regime-specific environmental associations differed substantially: (i) propagation blockage was associated with terrain–vegetation interactions; (ii) disaster amplification was associated with low ecological productivity and declining soil moisture; and (iii) response desensitization was associated with intensive agricultural activities and relatively favorable soil moisture conditions, which may partly buffer vegetation responses to thermal and meteorological stress and create apparent resilience that may mask underlying hydrological vulnerability. SHAP analysis further indicated that topography and thermal conditions were strongly associated with broad-scale differentiation, while eco-hydrological conditions showed stronger associations with local regime-specific responses. Anthropogenic activities may also be associated with altered drought propagation pathways and potential risks of unsustainable water use. These findings highlight drought as a dynamic propagation process rather than a static hazard and provide a basis for targeted drought management strategies.