A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions

Predicting crop performance in environments with limited field testing is challenging. Here the authors combine field experimental, DNA sequence, and weather data to predict genotypes’ future performance. They demonstrate the potential of this approach on a large dataset of wheat grain yield.

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Autores principales:	Gustavo de los Campos, Paulino Pérez-Rodríguez, Matthieu Bogard, David Gouache, José Crossa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/bc194300c8a94b7fa8b5e2c7874bcc25
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id	oai:doaj.org-article:bc194300c8a94b7fa8b5e2c7874bcc25
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spelling	oai:doaj.org-article:bc194300c8a94b7fa8b5e2c7874bcc252021-12-02T15:14:38ZA data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions10.1038/s41467-020-18480-y2041-1723https://doaj.org/article/bc194300c8a94b7fa8b5e2c7874bcc252020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18480-yhttps://doaj.org/toc/2041-1723Predicting crop performance in environments with limited field testing is challenging. Here the authors combine field experimental, DNA sequence, and weather data to predict genotypes’ future performance. They demonstrate the potential of this approach on a large dataset of wheat grain yield.Gustavo de los CamposPaulino Pérez-RodríguezMatthieu BogardDavid GouacheJosé CrossaNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Gustavo de los Campos Paulino Pérez-Rodríguez Matthieu Bogard David Gouache José Crossa A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
description	Predicting crop performance in environments with limited field testing is challenging. Here the authors combine field experimental, DNA sequence, and weather data to predict genotypes’ future performance. They demonstrate the potential of this approach on a large dataset of wheat grain yield.
format	article
author	Gustavo de los Campos Paulino Pérez-Rodríguez Matthieu Bogard David Gouache José Crossa
author_facet	Gustavo de los Campos Paulino Pérez-Rodríguez Matthieu Bogard David Gouache José Crossa
author_sort	Gustavo de los Campos
title	A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
title_short	A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
title_full	A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
title_fullStr	A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
title_full_unstemmed	A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
title_sort	data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/bc194300c8a94b7fa8b5e2c7874bcc25
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A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions

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