Assessing Soil and Water Assessment Tool Plant Stress Algorithms Using Full and Deficit Irrigation Treatments
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2019 by the American Society of Agronomy 5585 Guilford Road, Madison, WI 53711 USA All rights reserved. Decreased groundwater levels of the Ogallala Aquifer have increased interest in simulating crop responses to deficit irrigation strategies to evaluate the sustainable irrigation management for profitable crop production. However, the ability of widely used simulation models to accurately represent crop responses to deficit irrigation is not thoroughly evaluated. Therefore, the objective of this research was to evaluate the efficacy of the plant stress algorithms in Soil and Water Assessment Tool (SWAT) to simulate corn (Zea mays L.) responses to deficit irrigation treatments. Results showed simulated corn leaf area index (LAI), biomass, and yield under full irrigation scenarios matched measured data reasonably well at two study sites. However, clear reductions in model performance statistics for corn LAI simulations were found under the deficit irrigation scenarios for both sites (Nash-Sutcliffe efficiency [NSE] <0.49; percent bias [PBIAS] >14%). Additionally, considerable overestimation of yield occurred in the deficit irrigation scenarios for both sites (PBIAS >30% in most years). The unsatisfactory results from simulations of both LAI and yield under the deficit irrigation scenarios suggested potential deficiencies of the plant stress algorithms in SWAT. Two apparent limitations of the plant stress algorithms were (i) the equation for computing actual plant growth factor using a singular stress factor, determined by the maximum value of four plant stress factors of water, temperature, nitrogen, and phosphorus, and (ii) the computed actual plant growth factor only adjusting potential daily accumulations of LAI rather than modifying the shape of the LAI development by adjusting related parameters.