Use of the CERES-Maize model in genetic coefficient estimation and simulation of yield and yield components

In corn production the primary objective is to produce maximum yield, however, many factors interact that can reduce yields. Crop models can be used as management tools to maximize returns to the producer and help manage resources. Crop models need to accurately predict yield and yield components to be accepted on a large scale.; The first objective of this research was to evaluate the use of a fitted quadratic response surface for estimating genetic coefficients and to determine the optimal number of observations for estimating CERES-Maize model parameters, P1, which describes the period from emergence to the end of the juvenile stage in GDD8, and P2, the photoperiod sensitivity coefficient (d/hr). The optimal P1 and P2 combination produced the minimum sum of squares between predicted and measured silking date. Results were indistinguishable from estimates obtained with pre-existing software, only required 30 minutes for program execution, and thus eliminated the need for time consuming iterative search methods.; Estimation of P1 and P2 was conducted using 20 sets each of 10, 20, 30, 40, and 70 silking date observations. The number of observations did not affect the estimation of P1 or P2, but reduced the standard deviation of P1. Use of estimated P1 and P2 accounted for only 47% to 53% of the variability in measured silking date and, therefore, number of observations may not be the critical factor in the estimation of genetic coefficients.; The second objective was to evaluate simulated leaf number using CERES-Maize 3.51 and to compare four equations for simulation of kernel number and yield under varying management and weather conditions. Leaf numbers were overpredicted for 5 of 8 hybrids. None of the three equations improved kernel number prediction compared to the current of CERES-Maize 3.51. Kernel numbers were generally overpredicted at low kernel numbers and underpredicted at high kernel numbers. A similar trend was observed for yields.; The CERES-Maize model capability of simulating evapotranspiration and water stress effects on yield and yield components was evaluated. Data were taken from a planting date and irrigation experiment conducted in 1999 and 2000 at the Sandyland Experiment Field, St. John, KS. Experimental design was a modified strip-strip split plot with 4 replications. Ears were sampled during the grain filling period to calculate kernel growth rate and at maturity final kernel number and yield were determined. The model overestimated yield and yield components in 1999 and underestimated them in 2000. The model was unable to mimic the effects of water stress in 1999 and imposed too much water stress in 2000.