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

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.

Data-driven, participatory characterization of farmer varieties discloses teff breeding potential under current and future climates

“…The integration of genomic, climatic, and phenotyping diversity in a participatory framework may help tailor varietal development for local adaptation. The involvement of farmers in varietal evaluation is increasingly utilized in a quantitative framework to guide breeding choices in combination with genomic data (Annicchiarico et al, 2019; de Sousa et al, 2021).…”

“…Multi-location trials are needed to capture the range of genotype by environment (G x E) interactions that influence agronomic performance of teff, which we could only partially characterize. Decentralized varietal evaluation approaches can be used to scale up the testing of teff genetic resources (de Sousa et al, 2021; van Etten et al, 2019) to better capture G x E, produce new varieties with higher local adaptation, and resulting in higher farmers’ varietal adoption. An extensive socioeconomic characterization of teff cropping, including extensive farmers interviews (Labeyrie et al, 2016), may further unveil the dynamics of teff seed exchange and associated flow of information and knowledge related to local cropping (Occelli et al, 2021).…”

“…3). Previous studies showed that farmers' appreciation is genetically determined, and may be used to perform genomic prediction (de Sousa et al, 2021) and identify genomic associations (Kidane et al, 2017).…”

“…Participatory varietal selection (PVS) approaches directly involve farmers in the evaluation of breeding materials (Ceccarelli and Grando, 2007). Previous studies shown that PVS can be combined with genomic data to identify genomic loci responsible for farmers’ appreciation (Kidane et al, 2017) and model local crop performance in farmer fields (de Sousa et al, 2021).…”

The genomic and bioclimatic characterization of Ethiopian barley (Hordeum vulgare L.) unveils challenges and opportunities to adapt to a changing climate

“…When combining genomic information with remote sensing data and global descriptors of climatic conditions, it is possible to associate genetic diversity with environmental diversity at cultivation sites, identifying genomic loci associated with local adaptation towards specific climates (Capblancq, Fitzpatrick, Bay, Exposito-Alonso, & Keller, 2020). Genomic information can then be used to speed up the development of climate-ready varieties through the application of classical or innovative approaches, including genomic selection (Scheben, Yuan, & Edwards, 2016) and decentralized breeding (de Sousa et al, 2021).…”