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

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. Here, 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 allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under 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・整合性評価が研究の中心であり、単なる生物学的実験のルーチン測定ではない。

Code and data availability

The paper's Data availability statement deposits its phenotypic/genotypic datasets (diversity panel, genetic progress panel, recent hybrids panel) on Recherche Data Gouv with three public DOIs. These are paper-specific public phenotype datasets directly reproducing the study's measurements. The PhenoArch platform page,