Applying GLUE for Estimating CERES-Maize Genetic and Soil Parameters for Sweet Corn Production

Sweet corn (Zea mays L.) is one of the five most valuable vegetable crops in Florida. The application of nitrogen fertilizer is necessary for farmers to reliably produce sweet corn. The use of crop simulation models can facilitate the evaluation of management practices that are profitable with minimal unwanted impacts on the environment. Before using such models in decision making, it is necessary to specify model parameters and understand the uncertainties associated with simulating variables that are needed for decision making. The generalized likelihood uncertainty estimation (GLUE) method was used to estimate genotype and soil parameters of the CERES-Maize model of the Decision Support System for Agrotechnology Transfer (DSSAT). The uncertainties in predictions for sweet corn production in northern Florida were evaluated using the existing field corn genotype coefficient and soil parameter database contained within DSSAT and field data collected during a series of experiments carried out in 2005 and 2006. Genotype coefficients (P1, P5, and PHINT) and soil parameters (SLDR, SLRO, SDUL, SLLL, and SSAT) were generated using a multivariate normal distribution that preserved the correlations between parameters. The soil parameter SLPF was not correlated with other parameters and was generated with a uniform distribution. After parameters were estimated, the CERES-Maize model correctly predicted the dry matter yields, anthesis dates, and harvest dates. The mean values of these variables were close to those measured in the field, with an average relative error of 4.4% and 2.4% for the data sets of 2005 and 2006, respectively. The calibrated CERES-Maize model simulated the temporal trend of leaf TKN concentration accurately during the early stage of the growth season, but underestimated the leaf TKN concentrations during the latter half of the season. The GLUE procedure accurately estimated soil parameters (SLLL, SDUL, and SSAT) when compared to independent measurements made in the laboratory, with an average absolute relative error of about 8.5%. The simulated time series of soil water content adequately simulated the observed soil water changes during both growth seasons for every layer. However, there were some large differences between simulated and observed soil nitrate contents. In a relevant further study, the average absolute relative error between model-predicted and field-estimated amounts of potential nitrogen leaching was 15.3%, which is much better than some reported comparable studies of nitrogen leaching modeling. In the posterior distribution of estimated parameters, the uncertainties in parameters were substantially reduced, with CV values mostly lower than 10%. The average CV value of the parameters was reduced from 27.2% in the prior distribution to 4.6% in the posterior distribution. In general, the results of this study showed that the CERES-Maize model was capable of simulating sweet corn production in northern Florida and the associated soil water content. The model can also simulate potential nitrogen leaching with acceptable accuracy. We suggest that the model can now be used to compare different management practices relative to productivity and potential nitrogen leaching outcomes.