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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...

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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
Language:Inglés
Published: Springer 2021
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
Summary: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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