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Publication - Dr Aude Coupel-Ledru

    Genomic prediction of maize yield across European environmental conditions

    Citation

    Millet, EJ, Kruijer, W, Coupel-Ledru, A, Prado, SA, Cabrera-Bosquet, L, Lacube, S, Charcosset, A, Welcker, C, van Eeuwijk, F & Tardieu, F, 2019, ‘Genomic prediction of maize yield across European environmental conditions’. Nature Genetics, vol 51., pp. 952-956

    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.

    Full details in the University publications repository