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

Authors: DE SOUSA, KAUE - Bioversity International; VAN ETTEN, JACOB - Bioversity International; POLAND, JESSE - Kansas State University; FADDA, CARLO - Bioversity International; Jannink, Jean-Luc; GEBREHAWARYAT, YOSEF - Bioversity International; LAKEW, BASAZEN - Institute Of Life Science, Scuola Superiore Sant'Anna; MENGISTU, DEJENE - Bioversity International; PE, MARIO - Institute Of Life Science, Scuola Superiore Sant'Anna; DELL'ACQUA, MATTEO - Institute Of Life Science, Scuola Superiore Sant'Anna; SOLBERG, SVEIN - Inland Northwest Research Alliance, Inra

Submitted to: Communications Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/16/2021
Publication Date: 8/19/2021
Citation: De Sousa, K., Van Etten, J., Poland, J., Fadda, C., Jannink, J., Gebrehawaryat, Y., Lakew, B.F., Mengistu, D.K., Pe, M.E., Dell'Acqua, M., Solberg, S. 2021. Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment. Communications Biology. https://doi.org/10.1038/s42003-021-02463-w.
DOI: https://doi.org/10.1038/s42003-021-02463-w

Interpretive Summary: Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security for 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 variety evaluation approach. We tested this idea by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed across 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that decentralized evaluation could double the prediction accuracy of the benchmark. Decentralized evaluation 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.

Technical Abstract: 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.