Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize.

Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize.
Theor Appl Genet. 2020 Jul 01;:
Authors: Anche MT, Kaczmar NS, Morales N, Clohessy JW, Ilut DC, Gore MA, Robbins KR
Abstract
KEY MESSAGE: Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random...

Source: TAG. Theoretical and Applied Genetics - June 30, 2020 Category: Genetics & Stem Cells Authors: Anche MT, Kaczmar NS, Morales N, Clohessy JW, Ilut DC, Gore MA, Robbins KR Tags: Theor Appl Genet Source Type: research