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a Dep. of Agricultural and Biological Engineering, Univ. of Florida, Gainesville, FL 32611
b Dep. of Agronomy, Univ. of Florida, Gainesville, FL 32611
c Crop Science Dep., North Carolina State Univ., Raleigh, NC 27695
d Dep. of Biological and Agricultural Engineering, Univ. of Georgia, 30223
* Corresponding author (jwj{at}agen.ufl.edu)
Crop model testing in diverse environments is essential if modelers wish to make applications or extrapolations to those environments. A recent study demonstrated the effectiveness of optimization techniques for deriving cultivar coefficients for the CROPGRO-Soybean model from typical information provided by soybean performance tests. The objectives of this study were (i) to explore the extent to which cultivar coefficients developed by these approaches from crop performance tests are stable across different regions, (ii) to test the CROPGRO-Soybean model's ability to predict phenology and seed yield using cultivar coefficients that were developed in different regions, and (iii) to investigate whether 3 yr of crop performance data are adequate for developing stable genetic coefficients. A stepwise procedure was applied to derive cultivar coefficients for 10 common cultivars grown in different environments in Georgia and North Carolina. Regarding the transportability of cultivar coefficients across states, we found that the critical daylength coefficients were the most reliable cultivar traits. We found less stability of the cultivar traits that control genetic differences in seed yield potential. The estimated cultivar coefficients developed in Georgia enabled CROPGRO to predict yield and harvest maturity in North Carolina within 3.8% and 3.5 d, respectively, from the observed averages. Using the cultivar coefficients developed from North Carolina environments allowed us to simulate the actual mean yield and harvest maturity in Georgia to within 2.5% and 2.0 d. Furthermore, the model's ability to predict seed yield and maturity with cultivar coefficients developed from 3 yr of data was nearly as good as that derived from much larger data sets.
Abbreviations: DOY, day of year
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