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MODELING

Evaluating CROPGRO-Soybean Performance for Use in Climate Impact Studies

^a Dep. of Geogr., Univ. of South Carolina, Columbia, SC 29208
^b Natl. Cent. for Atmos. Res., P.O. Box 3000, Boulder, CO 80303
^c Dep. of Agric. and Biol. Eng., Univ. of Florida, Gainesville, FL 32611
^d USDA-ARS, Coastal Plains Soil, Water, and Plant Res. Cent., 2611 W. Lucas St., Florence, SC 29501
^e Dep. of Biol. and Agric. Eng., Univ. of Georgia, Athens, GA 30602

^* Corresponding author (greg.carbone{at}sc.edu)

Received for publication September 17, 2001. Researchers frequently use crop simulation models to estimate the impacts of climate change on agricultural production. While most models used for this purpose have been validated thoroughly at the research plot level, few studies have evaluated them for multiple years and sites and with inputs commonly used in climate impact studies. Here, we examine how well CROPGRO-Soybean performs across space and time using cultivar coefficients provided with the model, estimates of solar radiation, and soil inputs that were estimated from readily available soil surveys. Modeled yield for three maturity groups was compared with that observed from 8 to 23 yr at eight agricultural experiment stations in the southeastern United States. The model was evaluated with respect to its ability to replicate the mean and standard deviation of observed yield. The mean squared deviation (MSD), weighted according to the number of years at each station, was 0.42 (Mg ha^-1)². The model simulated mean yield and the magnitude of interannual yield variability very well. The component of MSD related to the pattern of interannual variability contributed most to MSD. Our results support the use of crop models in studies that require accurate simulation of the temporal mean and variance of yields.

Abbreviations: LCS, lack of correlation weighted by the standard deviations • MSD, mean squared deviation • SB, squared bias • SD, standard deviation • SDSD, squared difference between standard deviations

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