|MAREK, THOMAS - Agrilife Research|
|XUE, QINGWU - Agrilife Research|
|Evett, Steven - Steve|
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/13/2017
Publication Date: 7/14/2017
Citation: Marek, G.W., Marek, T.H., Xue, Q., Gowda, P., Evett, S.R., Brauer, D.K. 2017. Simulating evapotranspiration (ET) yield response of selected corn varieties under full and limited irrigation in the Texas High Plains using DSSAT-CERES-Maize. Transactions of the ASABE. 60(3):837-846.
Interpretive Summary: Water scarcity due to drought and groundwater depletion has led to an increased number of modeling studies aimed at evaluating crop response to limited irrigation. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used crop growth model. However, the ability DSSAT to represent crop response and water balance under limited irrigation is not well studied. Therefore 6 scientists from ARS and Texas A&M AgriLife compared simulated and measured plant growth values for corn grown in the Texas Panhandle under full and limited irrigation. Results showed that DSSAT overestimated corn growth, yield, and crop water use (ET) under limited irrigation. These results are of interest to agronomist, plant physiologists and crop modelers because they demonstrated the weakness of the current model to simulate corn growth under less than ideal growing conditions.
Technical Abstract: Water scarcity due to drought and groundwater depletion has led to increased interest in deficit irrigation strategies that reduce irrigation requirements while maintaining profitable yields. This has resulted in an increase in the number modeling studies aimed at evaluating crop response to limited irrigation strategies. However, ability of widely used crop simulation models to accurately represent responses to limited irrigation is not thoroughly evaluated. The primary objective was to determine the efficacy of DSSAT-CERES-Maize (version 18.104.22.168) to simulate LAI, crop evapotranspiration (ET), and yield response to full (100%) and limited (75% & 50%) irrigation regimes for two corn varieties. Comparisons of simulated and measured data from full and limited irrigation treatments of two drought tolerant corn hybrids (DuPont Pioneer AQUAmaxTM P1151HR and Pioneer 33D49) grown in the Texas Panhandle in 2013 and 2014 were evaluated in this study. Simulated in-season daily crop ET values for P1151HR grown in 2013 were also compared to those measured by precision large weighing lysimeters at Bushland, TX. Additionally, a comparison of simulated and measured soil water content (SWC) within the root zone was also performed for P1151HR grown in 2013. Simulated LAI for fully irrigated treatments approximated measured values reasonably well although manipulation of plant genetic parameters failed to match measured LAI during the period between maximum LAI and the beginning of crop senescence in the 50% irrigation treatments. Similarly, simulated yield values approximated measured values for the fully irrigated treatments while considerable overestimation of yield occurred in limited irrigation treatments for both varieties. However, consistent overestimation of both LAI and yield for the limited irrigation treatments suggests a functional relationship between LAI and yield. Further, DSSAT overestimated crop ET for fully irrigated P1151HR by 16% and by 40% for limited irrigation treatments in 2013 as compared to measured lysimeter values. Corresponding underestimations of SWC were also observed in neutron probe measurements for both treatments. Overestimation of ET and yield and corresponding underestimation of SWC in limited irrigation treatments were mainly due to overestimation of LAI in those treatments indicating a potential deficiency in the water stress algorithms. Additional comparisons of agronomic and lysimeter-based water balance data are needed to corroborate findings in this study. Further investigation into the calculation of reference evapotranspiration (ETo), crop coefficients, and water stress functions in DSSAT are needed in order to provide suggestions for model improvement.