Skip to main content
ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #373602

Research Project: Resilient Management Systems and Decision Support Tools to Optimize Agricultural Production and Watershed Responses from Field to National Scale

Location: Grassland Soil and Water Research Laboratory

Title: Simulation of dryland maize growth and evapotranspiration using DSSAT-CERES-MAIZE model

item Menefee, Dorothy
item RAJAN, NITHYA - Texas A&M University
item CUI, SONG - Middle Tennessee State University
item SCHNELL, RONNIE - Texas A&M University
item WEST, JASON - Texas A&M University

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/28/2020
Publication Date: N/A
Citation: N/A

Interpretive Summary: Evapotranspiration (ET) was simulated using the DSSAT system for a dryland corn crop in Texas. Simulated ET was compared to ET measured using a eddy covariance system. Both of DSSAT’s internal ET calculation methods were used, Priestly-Taylor and FAO-56. The modeling effort was a mixed success with the FAO-56 method producing more consistent results.

Technical Abstract: Evapotranspiration (ET) is a crucial component of plant water use and ecosystem energy balance. Given the dependence of agricultural practices on water use and availability, accurate simulation of ET is critical for using modeling results to assist with agricultural decision-making. This study aims to simulate ET using a crop modeling system and compare that to ET measured in-situ using an eddy covariance system for a dryland corn (Zea mays) crop in Texas. An eddy covariance system was established in the center of the study site in early 2017. The Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate ET over three growing seasons (2017 – 2019) using the CERES-MAIZE model. Both of DSSAT’s internal potential ET methods, FAO-56 and Priestly-Taylor were utilized and compared to each other. The simulation was also run with both of the soil types present at the field location (Vertisol and Inceptisol) in succession. Most of the simulations tended to underestimate actual ET, with this effect being more pronounced in the Priestly-Taylor models. The underestimation was greatest in the 2019 growing season. The average nRMSE for the ET model across all three years was 0.36 for the Priestly-Taylor models and 0.35 for the FAO-56 models. Simulation results were affected by soil type, indicating the need for caution when simulating areas with multiple soil (contrasting) soil types present. The mixed simulation results (particularly compared to those seen in studies of irrigated crops) indicate a greater need to improve ET estimation capabilities in dryland systems.