MAIZE AND SORGHUM SIMULATION IN DIVERSE TEXAS ENVIRONMENTS

Crop models for derision making should accurately simulate grain yield s across a wide range of soils and climate regimes. This study was des igned to evaluate two models' ability to simulate plot grain yields un der diverse weather conditions and soils in Texas. The objective was t o compare measured grain yields of maize (Zea mays L.) and sorghum [So rghum bicolor (L,) Moench] with grain yields simulated by the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment C riteria) model and to compare measured maize yields with grain yields simulated by a new version of the CERES-Maize (Crop-Environment Resour ce? Synthesis) model. Using yield performance trials, both models were tested for their ability to simulate the mean yield for five years at each location and their ability to describe year-to-year variability in measured yields. Both models were tested at nine locations for maiz e and ALMANAC was tested at eight locations for sorghum. Model inputs included parameters for the soil type, planting dates, planting rates, and locally measured weather data. Mean simulated grain yield for eac h site was within 10% of the mean measured grain yield for all cases, except for CERES at Thrall, where mean simulated yield was 13% lower t han mean measured yield. When the models did not account for a signifi cant amount of the year-to-year variability in measured grain yield at a site, it was usually due to the narrow range of measured grain yiel ds. The soils, weather, and crop parameter data sets developed here ca n be useful starting points for deriving data at similar sites, giving model users examples of realistic input data.