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Determination of genetic Coefficients From Field Experiments For Ceres-Maize and Soygro Crop Growth Models
|Title:||Determination of genetic Coefficients From Field Experiments For Ceres-Maize and Soygro Crop Growth Models|
|Authors:||Ogoshi, Richard M.|
|Abstract:||Lack of genetic coefficients is a reason crop models are not widely used. A project was therefore developed to evaluate a field method to calculate genetic coefficients for crop models.|
The phenology models fi-om SOYGRO v. 5.42 and CERES-Maize v. 2.1, with the existing genetic coefficients, were tested using data for soybean and maize grown under extreme photoperiods. Identical experiments were performed at two sites on Maui Island, Hawaii, over three years. The treatment design was a factorial of photoperiods (natural, natural + 0.5 h, 14-, 17-, and 20-h) and cultivars ('Bragg', 'Evans', 'Jupiter', and 'Williams' for soybean and Pioneer hybrids X304C, 3165, 3324, 3475, and 3790 for maize). Observations included development stage dates, yield, yield components, aboveground biomass weight, soil chemical analysis, and weather. Comparisons between observed and simulated results showed that soybean and maize development was well simulated. However, soybean yield and maize growth and yield were not well simulated. Further analysis suggested that model bias and parameter uncertainty accounted for nearly equal proportions of variation in soybean grain yield, whereas most maize growth and yield variation was due to model bias.
SOYGRO and CERES-Maize genetic coefficients were calculated from the data in the above experiments. One method to recalculate genetic coefficients was to incrementally change the genetic coefficients until simulated matched observed results. Another method was performed according to the maize modeler's suggestion. The fitting method adequately established development genetic coefficients, whereas growth coefficients had similar biases as the original genetic coefficients. The explicit method did not well simulate maize growth.
Using the fitted genetic coefficient means ± standard error, a sensitivity analysis was done. The genetic coefficient error that caused the greatest variation in simulated yield and aboveground biomass was identified. The most problematic genetic coefficients and associated model routines for yield and growth was the pod production relationship to nightlength in SOYGRO and juvenile phase duration in CERES-Maize.
|Appears in Collections:||
Ph.D. - Agronomy and Soil Science|
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