Developing Genetic Coefficients for Crop Simulation Models with Data from Crop Performance Trials
- T. Mavromatis *a,
- K.J. Booteb,
- J.W. Jonesa,
- A. Irmaka,
- D. Shindec and
- G. Hoogenboomd
- a Dep. of Agricultural and Biological Engineering, Univ. of Florida, Gainesville, FL 32611
b Dep. of Agronomy, Univ. of Florida, Gainesville, FL 32611
c Tropical Research and Education Center, Univ. of Florida, Homestead, FL 33031
d Dep. of Biological and Agricultural Engineering, Univ. of Georgia, 30223
Successful uses of crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CROPGRO–Soybean model from typical information provided by crop performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site-specific soil traits for use in crop models, and (iii) to explore the extent to which our approach allowed the crop model to reproduce observed genotype × environment (GE) interactions, cultivar ranking, and year-to-year yield variability. Crop performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For `Stonewall', a maturity group VII cultivar, the RMSE of 769 kg ha−1 between the actual and modeled seed yield, estimated 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 group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CROPGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype × environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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