Maize and Sorghum Simulations with CERES‐Maize, SORKAM, and ALMANAC under Water‐Limiting Conditions

Abstract

While crop models often are tested against long‐term mean grain yields, models for aiding decision making must accurately simulate grain yields in extreme climatic conditions. In this study, we evaluated the ability of a general crop model (ALMANAC) and two crop‐specific models (CERES‐Maize and SORKAM) to simulate maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] grain yields in a dry growing season at several sites in Texas. The root mean square deviation values were 0.36 Mg ha−1 for sorghum with ALMANAC, 0.71 for sorghum with SORKAM, 0.56 for maize with ALMANAC, and 0.83 for maize with CERES‐Maize. For maize, values for coefficient of determination (r2) between measured and simulated grain yields were 0.95 for ALMANAC and 0.88 for CERES‐Maize. For sorghum, r2 values were 0.86 for ALMANAC and 0.45 for SORKAM. ALMANAC and SORKAM should be useful tools to simulate dryland sorghum in drought, as indicated by their root mean square deviation values of <0.8 Mg ha−1. The mean errors for irrigated maize were 2.0% for CERES and 6.2% for ALMANAC. For dryland maize, mean errors were 6.2% for ALMANAC and −2.2% for CERES. In CERES, simulated leaf area index (LAI) and kernel weight appeared to be overly sensitive to drought stress. Further study on the response of LAI and kernel weight to drought in CERES would be valuable. The soil, weather, and crop parameter data sets developed for this study could be useful guidelines for model applications in similar climatic regions and on similar soils.

Analysis and Modelling of the Effects of Water Stress on Maize Growth and Yield in Dryland Conditions

“…3B) that were similar to the reports for maize grown in a range of locations and conditions (e.g. Grant et al, 1989;Muchow and Sinclair, 1991;NeSmith and Ritchie, 1992a, b, c;Abrecht and Carberry, 1993;Stone et al, 2001;Xie et al, 2001;Çakir, 2004;Farré and Faci, 2006). Grain yield under RF conditions particularly in 31H50 (8.0 t ha -1 ) was in the high end of yield of raingrown maize in southeast Queensland (Birch et al, 2003b).…”

“…For instance, Cavero et al (2000) found that leaf area production in the EPICphase model and evapotranspiration in CROPWAT need to be revised to improve accuracy of prediction of yield reductions due to water stress. Xie et al (2001) found that leaf area index and kernel weight were overly sensitive to drought stress in CERES-Maize. Nouna et al (2003) subsequently revised CERES-Maize for semi-arid Mediterranean areas by introducing a new leaf area module and water stress function.…”

Crop Parameters for Modeling Sugarcane under Rainfed Conditions in Mexico

“…The model was designed to simulate a wide range of plant species in a general way that can be easily transferred to new environments and applied to different plant species [28]. The requisite input parameters for growth and development can be easily derived from field experiments.…”

Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation

“…The CSM-CERES-Maize model simulates phenological development, vegetative and reproductive plant development stages, partitioning of assimilates, growth of leaves and stems, senescence, biomass accumulation and root system dynamics. It has been used to predict grain yield and kernel numbers (Kiniry et al 1997, Asadi and Clemente 2001, Lizaso et al 2001, Tojo Soler et al 2007, the effects of ground water and precipitation (Xie et al 2001, O'Neal et al 2002 and the effect of increasing atmospheric carbon dioxide (Tubiello et al 1999). The model has performed very well for predicting corn yield.…”