Isfahan University of Technology Dryland Soil Research (DLSR) 3115-9486 2 1 2025 03 01 Integrated crop and hydraulic modeling for precision irrigation: parameter estimation and sensitivity analysis (a review) 23 47 3732 10.47176/dlsr.02.01.1045 EN Meisam Rezaei Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran 0000-0003-3044-4625 Kambiz Bazargan Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran Karim Shahbazi Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran 0000-0001-7994-2559 Meysam Cheraghi Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran 0000-0001-6605-3007 Journal Article 2025 08 20 Sustainable agriculture demands innovative strategies to optimize water use amid growing climatic uncertainties, resource limitations, and to <strong>bolster the resilience of farming systems worldwide in the face of climate change</strong>. This review provides a critical synthesis of state-of-the-art modeling approaches that integrate crop growth dynamics with soil hydraulic processes to support precision irrigation management. Emphasizing the vadose zone's central role as the critical interface governing soil-plant-water interactions, the paper examines a suite of widely used, process-based crop models (e.g., WOFOST, CERES, AquaCrop, DSSAT, APSIM) alongside specialized hydrological models (e.g., HYDRUS, SWAP, SWAT). It highlights their synergistic capability to simulate the complex, nonlinear feedback between root water uptake, soil moisture dynamics, evapotranspiration, and solute transport, which is fundamental for predicting crop water requirements and responses to irrigation. The primary challenge for this approach is the accurate determination of often unknown or highly variable soil hydraulic and crop parameters. This review demonstrated that 1) Advanced inverse modeling techniques provide a powerful alternative to direct measurements by using optimization algorithms to estimate critical parameters from field data.<strong> </strong>2) Sensitivity analysis (both local and global) is indispensable for evaluating model robustness, identifying influential parameters, and mitigating calibration issues like equifinality<strong>. </strong>3) Well-calibrated, integrated models enable a robust, physically sound framework for generating site-specific irrigation schedules, moving beyond traditional homogeneous management. We also identify key challenges, including data scarcity and computational demands. To address these, we advocate for the future development of quasi-3D hybrid modeling platforms that leverage high-resolution data from easily available resources, laboratories, remote sensing, and IoT networks. This integrative approach holds significant promise for advancing next-generation precision irrigation, enhancing water use efficiency, and strengthening global agricultural resilience. https://dlsr.iut.ac.ir/article_3732_ee23e7ad9b473ad072d57aaa9b2a5222.pdf