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

Mahlet T Anche,Kelly R Robbins,Nicolas Morales,Michael A Gore,Daniel C Ilut,James W Clohessy,Nicholas S Kaczmar

doi:10.1007/s00122-020-03637-6

Abstract

Key messageHeritable 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 regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

Highlights

In the past few decades, advances in genotyping and computational technologies have contributed greatly to the genetic improvement in crops
It should be noted that variance estimates obtained using GBLUP are not equivalent to those obtained using pedigree information, where genetic variance from the GBLUP model in particular has been shown to have unpredictable bias; heritability estimates obtained from GBLUP were similar to those obtained when replacing G with and identity matrix, indicating these estimates are a reasonable indicator of heritable variation
In 2015 at location NYH2, lower heritability estimates were observed at the late reproductive stage for all the vegetation indices (VIs) except for Normalized Difference Vegetation Index (NDVI), where lower heritability was observed at the early reproductive stage (R1–R2)

Summary

Introduction

In the past few decades, advances in genotyping and computational technologies have contributed greatly to the genetic improvement in crops. Theoretical and Applied Genetics (2020) 133:2853–2868 cost and time-consuming nature of phenotyping (Reynolds et al 2019). This is especially true when phenotyping needs to be conducted for a large number of phenotypes in large-scale field trials over multiple geographical locations. Recent advances in multi-spectral imaging (MSI) platforms and image processing techniques have emerged, allowing for the generation of high-dimensional phenotypic data for plant breeders. Different spectral readings from MSI platforms can be combined to produce vegetation indices (VIs) that describe the crop over the growing season. In addition to NDVI, Green NDVI (GNDVI), Red Edge NDVI (NDRE), Soil-Adjusted Vegetation Index (SAVI) (Huete 1988) and Enhanced Vegetation Index (EVI) are VIs that are used to measure LAI

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