Genomic prediction of maize yield across European environmental conditions.

Clicks: 431
ID: 102639
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #7 of 75 articles by views in nature genetics

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The development of germplasm adapted to changing climate is required to ensure food security. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios (genotype × environment interaction), in spite of promising results for flowering time. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields. 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.
Reference Key
millet2019genomicnature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Millet, Emilie J;Kruijer, Willem;Coupel-Ledru, Aude;Alvarez Prado, Santiago;Cabrera-Bosquet, Llorenç;Lacube, Sébastien;Charcosset, Alain;Welcker, Claude;van Eeuwijk, Fred;Tardieu, François;
Journal nature genetics
Year 2019
DOI
10.1038/s41588-019-0414-y
URL
Keywords
remote sensing sentinel-2 phenology sentinel-1 landsat-8 crop mapping urban agriculture region

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.