Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment

de Sousa et al. present a data-driven decentralized crop breeding approach called 3D-breeding that is geared toward improving yields for smallholder farmers. 3D-breeding is applied to a case study of durum wheat in Ethiopia, which demonstrates higher prediction accuracies for grain yield than the ex...

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Autores principales:	Kauê de Sousa, Jacob van Etten, Jesse Poland, Carlo Fadda, Jean-Luc Jannink, Yosef Gebrehawaryat Kidane, Basazen Fantahun Lakew, Dejene Kassahun Mengistu, Mario Enrico Pè, Svein Øivind Solberg, Matteo Dell’Acqua
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6e151063464f4d729fd15559a72a36b3
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spelling	oai:doaj.org-article:6e151063464f4d729fd15559a72a36b32021-12-02T15:10:45ZData-driven decentralized breeding increases prediction accuracy in a challenging crop production environment10.1038/s42003-021-02463-w2399-3642https://doaj.org/article/6e151063464f4d729fd15559a72a36b32021-08-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02463-whttps://doaj.org/toc/2399-3642de Sousa et al. present a data-driven decentralized crop breeding approach called 3D-breeding that is geared toward improving yields for smallholder farmers. 3D-breeding is applied to a case study of durum wheat in Ethiopia, which demonstrates higher prediction accuracies for grain yield than the existing model.Kauê de SousaJacob van EttenJesse PolandCarlo FaddaJean-Luc JanninkYosef Gebrehawaryat KidaneBasazen Fantahun LakewDejene Kassahun MengistuMario Enrico PèSvein Øivind SolbergMatteo Dell’AcquaNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Kauê de Sousa Jacob van Etten Jesse Poland Carlo Fadda Jean-Luc Jannink Yosef Gebrehawaryat Kidane Basazen Fantahun Lakew Dejene Kassahun Mengistu Mario Enrico Pè Svein Øivind Solberg Matteo Dell’Acqua Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
description	de Sousa et al. present a data-driven decentralized crop breeding approach called 3D-breeding that is geared toward improving yields for smallholder farmers. 3D-breeding is applied to a case study of durum wheat in Ethiopia, which demonstrates higher prediction accuracies for grain yield than the existing model.
format	article
author	Kauê de Sousa Jacob van Etten Jesse Poland Carlo Fadda Jean-Luc Jannink Yosef Gebrehawaryat Kidane Basazen Fantahun Lakew Dejene Kassahun Mengistu Mario Enrico Pè Svein Øivind Solberg Matteo Dell’Acqua
author_facet	Kauê de Sousa Jacob van Etten Jesse Poland Carlo Fadda Jean-Luc Jannink Yosef Gebrehawaryat Kidane Basazen Fantahun Lakew Dejene Kassahun Mengistu Mario Enrico Pè Svein Øivind Solberg Matteo Dell’Acqua
author_sort	Kauê de Sousa
title	Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
title_short	Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
title_full	Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
title_fullStr	Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
title_full_unstemmed	Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
title_sort	data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6e151063464f4d729fd15559a72a36b3
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Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment

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