Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field

Abstract

Abstract Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. We show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allowed translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits matched between indoor and field conditions. Genomic prediction resulted in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguished genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for the diversity of future climates.

Plant phenotyping relevance

ロボット化した屋内フェノタイピングによる適応形質の高スループット測定と、圃場条件への推定・検証が研究の中心であるため。

Code and data availability

The supplied blocks describe indoor PhenoArch phenotyping, UAV imaging processed by Hiphen, and G-BLUP genomic prediction, but contain no data availability statement, deposited phenotype datasets, author analysis code, or trained model checkpoints. The two URLs present (Lepse/M3P platform page and Hiphen company site)

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