Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 551174
Title Genomic prediction of maize yield across European environmental conditions
Author(s) Millet, Emilie J.; Kruijer, Willem; Coupel-Ledru, Aude; Alvarez Prado, Santiago; Cabrera-Bosquet, Llorenç; Lacube, Sébastien; Charcosset, Alain; Welcker, Claude; Eeuwijk, Fred van; Tardieu, François
Source Nature Genetics 51 (2019). - ISSN 1061-4036 - p. 952 - 956.
DOI https://doi.org/10.1038/s41588-019-0414-y
Department(s) Mathematical and Statistical Methods - Biometris
Biometris
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2019
Abstract

The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3–7 (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields9,10. 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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