Spectral-genomic chain-model approach enhances the wheat yield component prediction under the Mediterranean climate.

Abstract

In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high-yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic-by-environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho-physiological and yield-related traits. The results indicated that a multi-environment model outperformed a single-environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral-based estimated spike number was utilized as a secondary trait in a multi-trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral-genomic chain-model workflow, which utilizes spectral-based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.

Plant phenotyping relevance

UAV搭載ハイパースペクトル反射から小穂数という植物形質を機械学習で推定する手法と、そのスペクトル形質を用いた再利用可能なワークフローが研究の中心であるため。

Code and data availability

The article reports UAV hyperspectral phenotyping, XGBoost SPN prediction, and GBLUP chain-model analyses, but no public deposit of phenotype datasets, hyperspectral imagery, or author analysis code is stated. The data availability statement only points to the paper and its supplementary materials, and the only URL (ar

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