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. Abstract 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$$\times$$ × 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$$\times$$ × 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$$\times$$ × E interactions. In contrast, the modeling of G$$\times$$ × 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.

Mixing things up! Identifying early diversity benefits and facilitating the development of improved variety mixtures with high throughput field phenotyping

“…Furthermore, in order to gain representative data, plot size could be varied according to the respective trait (Rebetzke et al, 2014). Estimation of yield is usually done in relatively large plots (1.25 × 6 m) while other traits such as canopy cover, plant height or plant indices can be readily measured in micro-plots (e.g., 1.25 × 1.75 m) or even single rows (Anderegg et al, 2020(Anderegg et al, , 2021Kronenberg et al, 2021;Roth et al, 2023). Using micro-plots or single rows, the number of evaluation units on a specific area can be a multiple (e.g., 3-4x) compared to a situation where yield plots are the measurement units.…”

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase

“…In brief, the QMER method determines the time point at which the elongation rate exceeds or falls short of by a certain threshold of the maximum elongation rate. The threshold was determined based on a wheat growth simulation ( Roth et al , 2021 ) and empirical data for wheat ( Roth et al , 2023 ) and soybean ( Roth et al , 2022a ) to 1/4, thus named ‘quarter of maximum elongation rate’ (QMER). Final height (PH max ) was calculated as the median of the top 24 spline predictions after the estimated stop of growth ( Fig.…”

“…Phenology is driven by environmental (E) and genotype (G) characteristics and corresponding interactions, and therefore is a result of G×E. In contrast, temperature response traits are only to a limited extent affected by—but are rather drivers of—G×E ( Roth et al , 2022b , 2023 ). Describing such responses directly in the breeding nursery may allow breeders to predict the phenotypic performance in new unseen environments ( Poorter et al , 2010 ).…”

From neglecting to including cultivar-specificper setemperature responses: Extending the concept of thermal time for plant development modeling

“…Furthermore, we found that the differences in the stem elongation phase of winter wheat were related to the breeding origin of cultivars (Roth et al 2022b) and allow a 'phenomic prediction/selection' for yield (Roth et al 2023).…”

“…We found cultivar-specific differences in the temperature response in the early canopy development of winter wheat (Grieder, Hund, and Walter 2015; Nagelmüller et al 2016), as well as in the stem elongation phase of winter wheat (Kronenberg et al 2020a; Roth, Piepho, and Hund 2022) and soybean (Friedli et al 2016). Furthermore, we found that the differences in the stem elongation phase of winter wheat were related to the breeding origin of cultivars (Roth et al 2022b) and allow a ‘phenomic prediction/s-election’ for yield (Roth et al 2023).…”