Modeling biomass accumulation in maize kernels

• A comparison of models for description of maize kernel growth was conducted.
• The Gompertz function can be used to describe the accumulation of biomass.
• A variance assumption was used to account for the heterogeneity of residuals.
• Autocorrelation was not present due to the experimental design.
• Differences in kernel growth were detected between hybrids and environments.

The use of nonlinear functions provides a concise measure of a variety of physiological traits associated with growth and development that would otherwise be difficult to observe. Maize kernel growth and development is a complex process, and the description of the processes involved benefits from the application of a nonlinear function to the process over growing degree days (GDD), a measure of heat accumulation over a period of development. The objective of this study was to compare and contrast canonical models of growth and development to determine which provided the best description of maize kernel biomass accumulation. Observations of kernel dry weights starting shortly after pollination through maturity were regressed onto a measure of thermal time. Observations from differing maize hybrids taken in two years with significantly different weather patterns were used to construct the model. Of the four nonlinear functions described, the Weibull and Gompertz functions were found to describe the pattern of biomass accumulation best. The Gompertz function was selected to describe kernel growth based on information criteria, biological interpretation of the parameters, and computational ease. Tests of autocorrelation and homogeneity of errors determined that the errors were heteroscedastic but autocorrelation was not an issue. The application of a variance function, which models the residuals as a function of the variance, was used to account for heteroscedastic errors. While the Gompertz function was selected as the best fit for use with this data set, it is suggested that future studies use the selection process described herein to determine the most appropriate function.