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

Author

Listed:
  • Jugurta Bouidghaghen

    (LEPSE, Univ Montpellier, INRAE
    ARVALIS, Chemin de la côte vieille)

  • 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)

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.

Suggested Citation

  • Jugurta Bouidghaghen & Laurence Moreau & Katia Beauchêne & Romain Chapuis & Nathalie Mangel & Llorenç Cabrera‐Bosquet & Claude Welcker & Matthieu Bogard & François Tardieu, 2023. "Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42298-z
    DOI: 10.1038/s41467-023-42298-z
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    2. Yusuke Toda & Hitomi Wakatsuki & Toru Aoike & Hiromi Kajiya-Kanegae & Masanori Yamasaki & Takuma Yoshioka & Kaworu Ebana & Takeshi Hayashi & Hiroshi Nakagawa & Toshihiro Hasegawa & Hiroyoshi Iwata, 2020. "Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-21, June.
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