ALTERNATE APPROACH TO IMPROVE KERNEL NUMBER CALCULATION IN CERESMAIZ

Abstract

CERESMaize is a processoriented model that has been widely used to predict growth, development, and yieldof a maize crop as affected by environment, genotypes, and management. The model has performed well in predicting yieldon a large scale (field, county, region) but has had difficulty in predicting the range of variation in kernel numbers found withina field. This study was conducted to compare two approaches to describing the relationship between average interceptedphotosynthetically active radiation (IPAR) around silking and the potential number of kernels set per plant (KN). IPAR wasaveraged during a critical thermal time period between 250 growing degreedays (GDD) before and 100 GDD after silking.Equations describing a doublecurve and a linecutoff were incorporated into the CERESMaize model (Generic CERESversion 3.1) to predict potential number of kernels per plant. The potential number of kernels calculated from this averageIPAR was reduced using the water or nitrogen stress factors calculated by the model. An optimization algorithm (SimulatedAnnealing) was used to calibrate the parameters of the equations, using 105 treatments containing fieldmeasured KN. Thenew relationships were tested with 134 independent treatments from Iowa. Results indicated that the modified model withdoublecurve function gave much better predictions of kernel numbers than both the original model and the modified modelwith linecutoff, which consistently underpredicted kernel numbers per plant. The mean squared deviation between predictedand measured kernel numbers of the modified model with doublecurve function was reduced to 34% of the mean squareddeviation obtained with the unmodified model. The modified model requires no additional inputs.