Phenomic selection enables nursery screening for field performance in maize and pea
- 1INRAE
Abstract
Due to economical constraints, breeding programs eliminate most selection candidates at early stages, before their productivity can be evaluated in field trials. Genomic selection partially overcomes this limitation by enabling early prediction of field performance, but its implementation requires large-scale recurrent genotyping that remains inaccessible for many species and breeding programs. Phenomic selection offers a promising alternative by replacing genotyping with inexpensive high-dimensional phenotyping data. However, previous applications have focused on predicting genotypes already evaluated in field trials. We investigated whether phenomic data acquired by unoccupied aerial vehicles (UAVs) from nursery-grown genotypes can be used to predict their performance in field trials. We validated this nursery-screening approach in maize and pea, two species with contrasting biology and canopy structures, supporting its potential transferability to other species. In the most challenging scenarios, average accuracies for yield-related traits were between 0.36 for maize and 0.40 for pea. Simulations indicated that these accuracies can support substantial nursery culling while limiting the risk of discarding superior genotypes. This proof of concept demonstrates that UAV-based phenomic selection can predict the field performance of nursery-grown genotypes without genotyping the selection candidates.
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BibTeX
@misc{rincent2026phenomic,
title = {{Phenomic selection enables nursery screening for field performance in maize and pea}},
author = {R. Rincent and Q. Hamon and A. Garcia Dias and M. Ecarnot and L. Nunes and C. Bauland and P. Dai and A. Comar and G. Touzy and F. Barthes and D. Gouache and A. Le Mener and E. Goudemand and L. Moreau},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.16.757273},
url = {https://www.biorxiv.org/content/10.64898/2026.10.16.757273v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Phenomic selection enables nursery screening for field performance in maize and pea
AU - R. Rincent
AU - Q. Hamon
AU - A. Garcia Dias
AU - M. Ecarnot
AU - L. Nunes
AU - C. Bauland
AU - P. Dai
AU - A. Comar
AU - G. Touzy
AU - F. Barthes
AU - D. Gouache
AU - A. Le Mener
AU - E. Goudemand
AU - L. Moreau
PY - 2026
DA - 2026/10/16
PB - bioRxiv
DO - 10.64898/2026.10.16.757273
UR - https://www.biorxiv.org/content/10.64898/2026.10.16.757273v1
AB - Due to economical constraints, breeding programs eliminate most selection candidates at early stages, before their productivity can be evaluated in field trials. Genomic selection partially overcomes this limitation by enabling early prediction of field performance, but its implementation requires large-scale recurrent genotyping that remains inaccessible for many species and breeding programs. Phenomic selection offers a promising alternative by replacing genotyping with inexpensive high-dimensional phenotyping data. However, previous applications have focused on predicting genotypes already evaluated in field trials. We investigated whether phenomic data acquired by unoccupied aerial vehicles (UAVs) from nursery-grown genotypes can be used to predict their performance in field trials. We validated this nursery-screening approach in maize and pea, two species with contrasting biology and canopy structures, supporting its potential transferability to other species. In the most challenging scenarios, average accuracies for yield-related traits were between 0.36 for maize and 0.40 for pea. Simulations indicated that these accuracies can support substantial nursery culling while limiting the risk of discarding superior genotypes. This proof of concept demonstrates that UAV-based phenomic selection can predict the field performance of nursery-grown genotypes without genotyping the selection candidates.
ER -