ArticleNature communications2023
Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bridging the bench-to-field gap: a quantitative multi-omics and CRISPR/Cas9 framework for climate-resilient agriculture.Transgenic research · 2026Review
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Synthetic-augmented multimodal deep learning fuses dual-angle RGB images and phenology to unlock genotype-informative canopy structural trait in wheat.Plant phenomics (Washington, D.C.) · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Spectral response of gross primary production to in situ canopy light absorption coefficient of chlorophyll.Photosynthesis research · 2025Article
- High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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