International Journal of Environment and Climate Change

Efficient management of agricultural water resources is increasingly important under climate variability and freshwater scarcity. Accurate estimation of crop water requirements (CWR) supports crop productivity, irrigation scheduling and water-use efficiency. Conventional approaches, including crop-coefficient, Penman–Monteith and soil-water-balance methods, remain widely used but can require extensive field observations and may have limited capacity to represent dynamic soil–crop–atmosphere interactions. Crop simulation models provide a process-based digital framework that integrates weather, soil, crop and management information to simulate crop growth, evapotranspiration, yield and water productivity under different environmental and management conditions. This review synthesises the roles of widely used models, including DSSAT, APSIM, AquaCrop, CROPWAT and CropSyst, in estimating crop water requirements and supporting irrigation management. Applications across maize, wheat, rice, cotton and other crops show that these models can evaluate irrigation schedules, deficit-irrigation strategies, water-saving practices and potential responses to future climate conditions. Model performance, however, depends on appropriate input data, local calibration and validation. The review also identifies opportunities to strengthen crop simulation through integration with hydrological information, remote sensing, artificial intelligence and modules for biotic stresses. Overall, crop simulation models provide useful decision-support tools for evaluating water-management options and improving the evidence base for sustainable agricultural water-resource planning.