Maize and Sorghum Simulation in Diverse Texas Environments

Abstract

Crop models for decision making should accurately simulate grain yields across a wide range of soils and climate regimes. This study was designed to evaluate two models' ability to simulate plot grain yields under diverse weather conditions and soils in Texas. The objective was to compare measured grain yields of maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] with grain yields simulated by the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model and to compare measured maize yields with grain yields simulated by a new version of the CERES‐Maize (Crop‐Environment Resource 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 maize 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 each 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 than mean measured yield. When the models did not account for a significant 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 yields. The soils, weather, and crop parameter data sets developed here can be useful starting points for deriving data at similar sites, giving model users examples of realistic input data.

History of model development at Temple, Texas

“…The model uses light competition equations developed in The Netherlands by Spitter & Aerts (1983), as well as competition for water and nutrients. After development and testing with available crop:weed data sets (Kiniry et al, 1992), the crop-yield simulation was validated at diverse sites in the USA (Kiniry et al, 1997;Kiniry & Bockholt, 1998;Yun et al, 2001). Subsequently, the model was extended to range simulation with multiple grass species at diverse sites (Kiniry et al, 2002).…”

“…Subsequently, the model was extended to range simulation with multiple grass species at diverse sites (Kiniry et al, 2002). Parameters for common warm season grasses (Kiniry et al, 1999), improved grasses (Kiniry et al, 2007), and for mesquite and juniper (Kiniry & Bockholt, 1998) were developed to allow their simulation. The model has also been demonstrated as a valuable tool to assess switchgrass production for biofuels at diverse sites in the USA (Kiniry et al, 1996.…”

A Review of Crop Growth Simulation Models as Tools for Agricultural Meteorology

“…The crop growth models can be used to predict crop performance in regions where the crop has not been grown before or not grown under optimal conditions. Such applications are of value for regional development and agricultural planning in developing countries [12]. It can be developed at various levels of complexity.…”

Simulating the Productivity of Desert Woody Shrubs in Southwestern Texas

“…The ALMANAC model is a process-oriented plant model that effectively simulates growth of a wide range of plant species [63,64]. Strength of ALMANAC is its capability to accurately simulate competition for light, nutrients, and water for several plant species [65].…”