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

Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments. This need can be addressed by combining genomics, farmers’ knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed as incomplete blocks in 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that 3D-breeding could double the prediction accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing superior productive performance across seasons. We propose this decentralized approach to leverage the diversity in farmer fields and complement conventional plant breeding to enhance local adaptation in challenging crop production environments.

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Main Authors: Sousa, Kauê de, Etten, Jacob van, Poland, Jesse A., Fadda, Carlo, Jannink, Jean-Luc, Gebrehawaryat Kidane, Yosef, Lakew, Basazen Fantahun, Mengistu, Dejene Kassahun, Pè, Mario Enrico, Solberg, Svein Øivind, Dell’Acqua, Matteo
Format: Journal Article biblioteca
Language:English
Published: Springer 2021-08-19
Subjects:data, abiotic stress, breeding, climate change, biodiversity, participatory research, plant breeding, triticum durum, wheat, estrés abiotico, mejora, cambio climatico,
Online Access:https://hdl.handle.net/10568/114893
https://doi.org/10.1038/s42003-021-02463-w
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spelling dig-cgspace-10568-1148932023-12-08T19:36:04Z Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment Sousa, Kauê de Etten, Jacob van Poland, Jesse A. Fadda, Carlo Jannink, Jean-Luc Gebrehawaryat Kidane, Yosef Lakew, Basazen Fantahun Mengistu, Dejene Kassahun Pè, Mario Enrico Solberg, Svein Øivind Dell’Acqua, Matteo data abiotic stress breeding climate change biodiversity participatory research plant breeding triticum durum wheat estrés abiotico mejora cambio climatico Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments. This need can be addressed by combining genomics, farmers’ knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed as incomplete blocks in 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that 3D-breeding could double the prediction accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing superior productive performance across seasons. We propose this decentralized approach to leverage the diversity in farmer fields and complement conventional plant breeding to enhance local adaptation in challenging crop production environments. 2021-08-19 2021-09-07T08:58:21Z 2021-09-07T08:58:21Z Journal Article de Sousa, K.; van Etten, J.; Poland, J.; Fadda, C.; Jannink, J.L.; Gebrehawaryat, Y.; Lakew, B.F.; Mengistu, D.K.; Pè, M.E.; Solberg, S.Ø.; Dell'Acqua, M. (2021) Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment. Communications Biology 4: 944. 9 p. ISSN: 2399-3642 2399-3642 https://hdl.handle.net/10568/114893 https://doi.org/10.1038/s42003-021-02463-w en https://hdl.handle.net/10568/108545 CC-BY-4.0 Open Access 9 p. application/pdf Springer Communications Biology
institution CGIAR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cgspace
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CGIAR
language English
topic data
abiotic stress
breeding
climate change
biodiversity
participatory research
plant breeding
triticum durum
wheat
estrés abiotico
mejora
cambio climatico
data
abiotic stress
breeding
climate change
biodiversity
participatory research
plant breeding
triticum durum
wheat
estrés abiotico
mejora
cambio climatico
spellingShingle data
abiotic stress
breeding
climate change
biodiversity
participatory research
plant breeding
triticum durum
wheat
estrés abiotico
mejora
cambio climatico
data
abiotic stress
breeding
climate change
biodiversity
participatory research
plant breeding
triticum durum
wheat
estrés abiotico
mejora
cambio climatico
Sousa, Kauê de
Etten, Jacob van
Poland, Jesse A.
Fadda, Carlo
Jannink, Jean-Luc
Gebrehawaryat Kidane, Yosef
Lakew, Basazen Fantahun
Mengistu, Dejene Kassahun
Pè, Mario Enrico
Solberg, Svein Øivind
Dell’Acqua, Matteo
Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment
description Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments. This need can be addressed by combining genomics, farmers’ knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed as incomplete blocks in 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that 3D-breeding could double the prediction accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing superior productive performance across seasons. We propose this decentralized approach to leverage the diversity in farmer fields and complement conventional plant breeding to enhance local adaptation in challenging crop production environments.
format Journal Article
topic_facet data
abiotic stress
breeding
climate change
biodiversity
participatory research
plant breeding
triticum durum
wheat
estrés abiotico
mejora
cambio climatico
author Sousa, Kauê de
Etten, Jacob van
Poland, Jesse A.
Fadda, Carlo
Jannink, Jean-Luc
Gebrehawaryat Kidane, Yosef
Lakew, Basazen Fantahun
Mengistu, Dejene Kassahun
Pè, Mario Enrico
Solberg, Svein Øivind
Dell’Acqua, Matteo
author_facet Sousa, Kauê de
Etten, Jacob van
Poland, Jesse A.
Fadda, Carlo
Jannink, Jean-Luc
Gebrehawaryat Kidane, Yosef
Lakew, Basazen Fantahun
Mengistu, Dejene Kassahun
Pè, Mario Enrico
Solberg, Svein Øivind
Dell’Acqua, Matteo
author_sort Sousa, Kauê de
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 Springer
publishDate 2021-08-19
url https://hdl.handle.net/10568/114893
https://doi.org/10.1038/s42003-021-02463-w
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