Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

TLDR

Genomic selection shortens breeding cycles and boosts genetic gain, and high‑throughput phenotyping traits such as canopy temperature and vegetation indices may further enhance pedigree and genomic prediction accuracy for traits that are hard to phenotype directly. The study tested whether incorporating aerial canopy temperature and green/red NDVI measurements as secondary traits in pedigree and genomic BLUP models could improve grain‑yield prediction accuracy in wheat. Secondary traits and grain yield were jointly modeled in a multivariate framework and compared to univariate grain‑yield models, with cross‑validation performed within and across five environments, varying replication and days‑to‑heading correction. Within environments, unreplicated secondary trait data without heading correction increased grain‑yield prediction accuracy by 56 % in pedigree and 70 % in genomic models, with smaller gains when replicated or heading‑corrected, and similar but less consistent improvements across environments, indicating that high‑throughput secondary traits can enhance early‑stage selection if validated.

Abstract

Abstract Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.