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

Jugurta Bouidghaghen, Laurence Moreau, Katia Beauchêne, Romain Chapuis, Nathalie Mangel, Llorenç Cabrera‐Bosquet, Claude Welcker, Matthieu Bogard and François Tardieu ()
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Jugurta Bouidghaghen: LEPSE, Univ Montpellier, INRAE
Laurence Moreau: Université Paris-Saclay
Katia Beauchêne: ARVALIS, 45 Voie Romaine, Ouzouer-Le-Marché
Romain Chapuis: DIASCOPE, Univ Montpellier, INRAE
Nathalie Mangel: ARVALIS, Station de recherche et d’expérimentation
Llorenç Cabrera‐Bosquet: LEPSE, Univ Montpellier, INRAE
Claude Welcker: LEPSE, Univ Montpellier, INRAE
Matthieu Bogard: ARVALIS, Chemin de la côte vieille
François Tardieu: LEPSE, Univ Montpellier, INRAE

Nature Communications, 2023, vol. 14, issue 1, 1-14

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. 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.

Date: 2023
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DOI: 10.1038/s41467-023-42298-z

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