A review of integrated crop growth and hydrological models for sustainable agriculture in the midwest: Challenges and opportunities

The U.S. Midwest, a global hub for corn–soybean production, is becoming increasingly vulnerable to climate variability, nutrient losses, and the unsustainable use of surface and groundwater resources. This review examines how agro-hydrological modeling can help address these challenges and support sustainable agriculture in the region. We evaluate major crop models (e.g., DSSAT, APSIM, AquaCrop, EPIC) and hydrological models (e.g., SWAT, DRAINMOD, MODFLOW, VIC), applied separately and in combination, and summarize their use in assessing water quality and quantity, managing irrigation and drainage, and quantifying nutrient losses. Case studies using coupled systems, including DRAINMOD–DSSAT, APEX–HUMUS–SWAT, FEST-C coupled with EPIC–WRF–CMAQ–SWAT, and DSSAT–MODFLOW, show that integrated frameworks can simultaneously evaluate yields, evapotranspiration (ET), tile drainage, groundwater trends, and nitrate loads, thereby providing stronger decision support than stand-alone models. However, broader application in the Midwest remains constrained by the limited availability of field-scale calibration and validation data, complex calibration, equifinality, and simplified representations of key processes (such as tile hydraulics, capillary rise, and socio-economic feedbacks). Recent advances in artificial intelligence (AI), remote sensing (RS), and data assimilation (DA) offer new opportunities but are not yet widely incorporated into Midwest-specific decision-support systems. Overall, this review clarifies the limitations and added value of integrated crop–hydrological modeling and outlines priorities for more reliable, decision-oriented tools for the Corn Belt and Great Lakes regions. • Reviews integrated crop–hydrologic models for Midwest sustainability. • Evaluates DSSAT, APSIM, EPIC, AquaCrop, SWAT, MODFLOW, and related frameworks. • Highlights the benefits of integrated models for ET, nitrate loss, and irrigation management. • Shows integrated models improve decision support over stand-alone tools. • Identifies key gaps and future research needs, including AI, remote sensing, and data assimilation.