Image-based phenomic prediction can provide valuable decision support in wheat breeding.

Abstract

KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.

Plant phenotyping relevance

ドローン画像から植物高・葉面積・分げつ密度を推定し、これらを用いたフェノミック選抜モデルを評価しており、形質取得と解析ワークフローが研究の中心である。

Code and data availability

The paper's Data availability statement explicitly deposits the study's datasets (phenotypic/trait data from drone-based wheat phenotyping) in the ETH Research Collection and the phenomics data processing source code in a public ETH GitLab repository. Both are paper-specific, public, and actionable.