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A crop coefficient –based water use model with non-uniform root distribution

Robert C. Schwartz, Alfonso Domínguez, José J. Pardo, Paul D. Colaizzi, R. Louis Baumhardt, Jourdan M. Bell
Agricultural water management 2020 v.228 pp. 105892
Zea mays, corn, crop coefficient, crop models, crops, deficit irrigation, evapotranspiration, grain yield, growing season, heat stress, irrigation management, plant available water, prediction, production functions, rooting, runoff, soil profiles, stress response, uncertainty, water stress
Uncertainties in the estimation of evapotranspiration (ET¹) using the crop coefficient (Kc)-reference ET method arise for deeply rooted crops and severe water stress. We expanded upon the crop coefficient–based model by modifying plant available water via a nonuniform root distribution that limited deep water extraction using daily estimated soil profile water contents. The model was calibrated to predict maize (Zea mays L.) ET over a wide range in crop water deficits. In addition, maize grain yield was calibrated with model-predicted ET using a multiplicative water production function. The calibrated model with optimized crop and stress response coefficients predicted actual maize ET for a wide range in water deficits with a daily and growing season prediction root mean square error (RMSE) of 1.16 mm d⁻¹ and 25.6 mm, respectively. A nonuniform root distribution functioned similarly to stress response coefficients reducing soil water extraction deeper in the profile with a resultant 18% reduction in the prediction RMSE compared with the optimized stress response conventionally used with the Kc approach. The largest uncertainties in predicted crop ET resulted from an underestimation of runoff and an overestimation of crop water use during stress-induced early senescence. Measured and predicted soil water contents averaged over the entire rooting depth agreed closely, however root water extraction was overestimated deeper in the profile. Calibration of the water production function using data exhibiting a wide range in measured grain yield resulted in a RMSE of 2.1 Mg ha⁻¹. Including an additive high temperature stress response expression improved the calibration. Because of the limited input requirements and robustness over a wide range in crop water stress levels, the model would be suitable for evaluating deficit irrigation strategies.