Application of random regression models to model growth curve in Maize
using phenotypes derived from multi-spectral images
Abstract
Vegetation indices (VIs) derived from multi-spectral imaging (MSI) can
be used to collect non-destructive phenotypes that could be used to
better understand development curves and interactions with environmental
factors throughout the growing season. To investigate the amount of
variation present in VIs derived from MSI and their relationship with
important end-of-season traits, genetic and residual (co)variances for
the VIs and their genetic and residual correlations with grain yield and
grain moisture were estimated using maize data collected as part of the
Genomes to Fields (G2F) initiative. One of the VIs considered in this
study was normalized difference vegetation index (NDVI). In addition to
NDVI, cumulative NDVI (cNDVI) was used as a phenotype to explore methods
to simultaneously fit multiple phenotypes from MSI collected throughout
the growing season. The potential of random regression models were
investigated using either linear Splines or Legendre polynomial
functions. Low to moderately high heritability estimates (0.10 – 0.35)
was observed for NDVI values at each of the time points within years,
indicating that there exists a reasonable amount of genetic variation.
Moreover, strong genetic and residual correlations were found between
grain yield and NDVI. Finally, it was found that using random regression
with either of the functions converged using all time points and show a
potential to be used as an alternative to multi-trait models.