Genomic prediction of maize yield across European environmental conditions
Open accessAbstract
The development of germplasm adapted to changing climate is required to ensure food security 1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios 3-7 (genotype × environment interaction), in spite of promising results for flowering time 8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields 9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.
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Weather stations now collect high-resolution longitudinal data on environmental covariables such as temperature, soil water potential, humidity, and solar radiation for every environment within a multi-environment trial breeding program.
“Weather stations now collect high-resolution longitudinal data on environmental covariables such as temperature, soil water potential, humidity, and solar radiation for every environment within an MET breeding program (Millet et al. , 2019).”
From REML implementations of kernel-based genomic prediction models for genotype x environment x management interactions (2025) · cites this paper as Millet et al. , 2019
The available indexed passages contain bibliographic information and a dataset listing, but no discussion of weather-station measurements. Searches for weather stations, soil water potential, temperature, humidity, solar radiation, and longitudinal environmental data found no passage addressing the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredNot all covariables and timeframes in the growth cycle are relevant.
“We did not perform any feature selection, but it is well known that not all covariables and timeframes in the growth cycle are relevant (Millet et al. , 2019).”
From REML implementations of kernel-based genomic prediction models for genotype x environment x management interactions (2025) · cites this paper as Millet et al. , 2019
The available passages are bibliographic information and a dataset listing (Abstract and “2 Finished”; no page given), not a discussion of which covariables or growth-cycle periods matter. Searches for feature selection, environmental variables, and growth stages found no passage in our copy that addresses the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Genomic insights into domestication and genetic improvement of fruit crops
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- Authors
- Millet, E. J., Kruijer, W., Coupel-Ledru, A., Alvarez Prado, S., Cabrera-Bosquet, L., Lacube, S., Charcosset, A., Welcker, C., van Eeuwijk, F., & Tardieu, F.
- Affiliations
- Institut Agro Montpellier (FR)
- Wageningen University & Research (NL)
- Université Paris-Sud (FR)
- Corresponding author
- Tardieu, François
- ORCID
- Venue
- Nature Genetics
- Published
- 2019-05-20
- Volume
- 51
- Issue
- 6
- Pages
- 952-956
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W2945720445
- Cited by
- 280
- References
- 46
- Citations by year
- 2026: 30 · 2025: 38 · 2024: 42 · 2023: 43 · 2022: 49 · 2021: 49 · 2020: 25 · 2019: 4
- FWCI
- 19.90
- Type
- journal article
- Language
- en
- Open access
- green
- research.wur.nl
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- HAL (submittedVersion)
- Explore Bristol Research (submittedVersion)
- CONICET Digital (CONICET) (submittedVersion, cc-by-nc-sa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-11-INBS-0012)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- maize, grain yield prediction, gene-environment interaction, environmental drivers, sensor networks, phenotyping platforms, germplasm evaluation, climate change adaptation
- MeSH terms
- Agriculture, Edible Grain, Zea mays, Environment, Europe, Geography, Phenotype, Genome, Plant, Genomics, Genetic Association Studies, Gene-Environment Interaction
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