Data-driven decentralized breeding increases genetic gain 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 marginal production environments. This challenge can be addressed by combining genomics, farmers’ knowledge, and environmental analysis into a data-driven decentralised approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralised trial distributed as incomplete blocks in 1,165 farmer-managed plots across the Ethiopian highlands with a benchmark representing genomic selection applied to conventional breeding. We found that 3D-breeding could double the accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing consistent yield advantages across seasons and locations. We propose this decentralised approach to leverage the diversity in farmers’ fields and change the paradigm of plant breeding for local adaptation in challenging crop production environments.
We thank all farmers who evaluated the genotypes in both centralized and decentralized trials. This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. This work was supported by the Doctoral School for Agrobiodiversity at Scuola Superiore Sant’Anna and by The Nordic Joint Committee for Agricultural and Food Research (grant num. 202100-2817).