Improved modelling of ryegrass foliar growth

Abstract

To understand how the grassland use-value is creates, it is necessary to study and model the morphogenesis of plants. This study focuses on one part of the morphogenesis, the modelling of foliar growth using mathematical function. The aim of this study is to evaluate fitting performance of five functions on foliar growth kinetics. The five selected functions are: Logistic, Chanter, Gompertz, Hyperbola and Beta Growth function. To evaluate fitting performance of functions, data from a greenhouse experiment were used. These data are the kinetics of leaf growth of 1933 leaves from 10 genotypes under 4 light quality treatments. This experiment recreates genetic and light diversity as in field condition. The comparison of goodness of fit using criteria such as sum of squared errors (SSE), mean of squared errors (MSE) and Akaike's information criterion (AIC) shows that the Beta Growth function is the best function to model the ryegrass foliar growth.