Developing genetic coefficients for crop simulation models with data from crop performance trials

Successful uses of crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as so on as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CROPGRO-Soybe an model from typical information provided by crop performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coeffi cients and site-specific soil traits for use in crop models, and (iii) to e xplore the extent to which our approach allowed the crop model to reproduce observed genotype X environment (GE) interactions, cultivar ranking, and y ear-to-year yield variability. Crop performance tests typically record harv est maturity date, seed yield, seed size, height, and lodging. A stepwise p rocedure using data on 11 cultivars grown st five sites in Georgia over 4 t o 10 yr efficiently decreased the root mean square error (RMSE) between obs erved and predicted data. For 'Stonewall', a maturity group VII cultivar, t he RMSE of 769 kg ha(-1) between the actual and modeled seed yield, estimat ed initially by means of the existing general maturity group coefficients, was reduced to 404 kg ha(-1). For the same cultivar, the initial RMSE of 5. 3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity gr oup coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving us eful information on site characteristics and cultivar traits, our approach has enabled CROPGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype X environment interactions. Across all environme nts, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.