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

Roth, Lukas; Kronenberg, Lukas; Aasen, Helge; Walter, Achim; Hartung, Jens; Eeuwijk, Fred van; Piepho, Hans‐Peter; Hund, Andreas

doi:10.1101/2022.09.05.506627

Cited by 4 publications

(17 citation statements)

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“…As those two traits are to some extent genetically uncorrelated (Figure 2), an independent selection may be easy to achieve. Indeed, in Roth et al (2022b) we found evidence that for the Swiss varieties such an optimization has already happened. The reported relations of C, T and Q traits of this study are in accordance to the ones reported in Roth et al (2022b), with one exception: While for the diverse GABI wheat panel examined in Roth et al (2022b) T min was closely related to the phenology traits and , such a relation was not visible for the Swiss elite cultivar set examined in this work.…”

Section: Discussionmentioning

confidence: 76%

“…Indeed, in Roth et al (2022b) we found evidence that for the Swiss varieties such an optimization has already happened. The reported relations of C, T and Q traits of this study are in accordance to the ones reported in Roth et al (2022b), with one exception: While for the diverse GABI wheat panel examined in Roth et al (2022b), T min was closely related to the phenology traits and , such a relation was not visible for the Swiss elite cultivar set examined in this work. Consequently, the observed effects in the GABI wheat panel may be partly related to population effects, further confirming the possibility for independent selection of T min and .…”

Section: Discussionmentioning

confidence: 76%

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

Roth

Fossati

Krähenbühl³

et al. 2022

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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 GxE 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 five year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate GxE 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 GxE interactions. In contrast, the modeling of GxE 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 0.41, 0.22 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.

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 76%

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

Roth

Fossati

Krähenbühl³

et al. 2022

Preprint

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“…From the resulting point clouds, the percentile best matching manual measurements (97 th percentile, Kronenberg et al 2017), was extracted per plot as plant height estimation per time point. For the subsequent years of winter wheat experiments and for all soybean experiments, drone-based Structure-fromMotion (SfM) was used (Roth and Streit 2018; Roth et al 2022b). From the resulting point clouds, the percentile best matching manual measurements (90 th percentile, Roth and Streit 2018) per plot was extracted as plant height estimation per time point.…”

Section: Methodsmentioning

confidence: 99%

“…Nevertheless, there is evidence that phenology is related to cultivar-specific temperature responses (Kronenberg et al 2020a). Consequently, one may assume that selecting for phenology traits in breeding—such as earlier flowering in winter wheat—co-selected for temperature response (Roth et al 2022b). Indeed, we have repeatedly observed cultivar-specific temperature responses in our outdoor, high-throughput phenotyping site at ETH Zurich.…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Roth,

Binder,

Kirchgessner

et al. 2023

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SummaryPredicting plant development is a long-desired goal of crop physiology. Plant growth divides into two main, interwoven components: continuous growth, and the succession of growth stages (phenology). Growth is commonly modeled on species-level asper seresponse to temperature that is scaled to cultivar-specific intrinsic growth rates. Differences in phenology—exhibited by cultivars in different environments—are interpreted as interactions with remaining environmental covariates. We question this fundamental assumption and suspect that cultivar-specific temperature responses largely influence phenology.To gather evidence, temporally resolved plant organ tracking and field phenotyping data were collected over multiple years in winter wheat and soybean. The response of traits to temperature was examined using models of increasing complexity (ranging from linear models to hierarchical splines and neural networks). Additionally, the trait level (leaf growth, canopy development, stem elongation), the covariate level (soil temperature, air temperature) and the covariate measurement level (below/inside canopies, at a reference weather station) were varied.The cultivar-specific non-linear models explained, in contrast to thermal time, large proportions of phenology-related cultivar-by-environment interactions.We conclude that such unbiased predictions are essential in breeding and for the cultivation of crops in view of increasing heat, drought, and other adverse climatic conditions.

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

et al. 2023

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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.

show abstract

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

Cited by 4 publications

References 49 publications

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

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

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

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

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