A stochastic process modelling of maize phyllochron enables to characterize environmental and genetic effects

Abstract

We propose a flexible statistical model for phyllochron that enables to seasonal variations analysis and hypothesis testing, and demonstrate its efficiency on a data set from a divergent selection experiment on maize.

The time between appearance of successive leaves or phyllochron enables to characterize the vegetative development of maize plants which determines their flowering time. Phyllochron is usually considered as constant over the development of a given plant, even though studies have demonstrated response of growth parameters to environmental variables. In this paper, we proposed a novel statistical approach for phyllochron analysis based on a stochastic process, which combines flexibility and a more accurate modelling than existing regression models. The model enables accurate estimation of the phyllochron associated with each leaf rank and enables hypothesis testing. We applied the model on an original maize dataset collected in fields from plants belonging to closely related genotypes originated from divergent selection experiments for flowering time conducted on two maize inbred lines. We showed that the main differences in phyllochron were not observed between selection populations (Early or Late), but rather ancestral lines, years of experimentation, and leaf ranks. Finally, we showed that phyllochron variations through seasons could be related to climate variations, even if the impact of each climatic variables individually was not clearly elucidated. All script and data can be found at https://doi.org/10.15454/CUEHO6