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

The climate crisis is impacting agroecosystems and threatening food security of millions of smallholder farmers. Understanding the potential for current and future climatic adaptation of local crop agrobiodiversity may guide breeding efforts and support resilience of agriculture. Here, we combine a genomic and climatic characterization of a large collection of traditional barley varieties from Ethiopia, a staple for local smallholder farmers cropping in challenging environments. We find that the genomic diversity of barley landraces can be partially traced back to geographic and environmental diversity of the landscape. We employ a machine learning approach to model Ethiopian barley adaptation to current climate and to identify areas where its existing diversity may not be well adapted in future climate scenarios. We use this information to identify optimal trajectories of assisted migration compensating to detrimental effects of climate change, finding that Ethiopian barley diversity bears opportunities for adaptation to the climate crisis. We then characterize phenology traits in the collection in two common garden experiments in Ethiopia, using genome-wide association approaches to identify genomic loci associated with timing of flowering and maturity of the spike. We combine this information with genotype-environment associations finding that loci involved in flowering time may also explain environmental adaptation. Our data show that integrated genomic, climatic, and phenotypic characterizations of agrobiodiversity may provide breeding with actionable information to improve local adaptation in smallholder farming systems.

Chickpea production restored through upscaling crowdsourcing winner varieties and planting date adjustments in the Ada'a district, East Shoa zone, Ethiopia.

Chickpea is an important cash crop for Ada'a farmers as it does for farmers in Ethiopia and elsewhere in the world. Its production, however, has been dwindling due to biotic and abiotic stresses. According to participant farmers from Ada'a district, the production of chickpea in some Kebeles of Ada'a such as Gubasaye has been abandoned because of root rot and foliar diseases such as fusarium wilt. This paper presents the evaluation of upscaled varieties' performance assessed by metric data as well as through beneficiary farmers' self-assessment data. Recognizant to the problem, five varieties of chickpea tested in the Goro district of the Southwest Shoa zone, were introduced as part of the upscaling of crowdsourcing winner crop varieties in Ethiopia. Crowdsourcing is an approach of outsourcing variety evaluation, selection, and dissemination to volunteer crowds of farmers. The introduction of the winner varieties and adjustment of the planting time was found effective in the Ada'a district. Higher grain yield was obtained from the upscaled winner varieties in the range of 2.4-2.53 t/ha, with slight variations over varieties. Habru variety showed slightly higher performance than the others. Survey participant farmers have reported an increase in GY due to growing the winner varieties compared with varieties they used to grow before and gained higher annual income due to higher productivity, market demand of the upscaled varieties, and premium market price with 6-25 Ethiopian birr (ETB) per kilogram of sold grain of these varieties. High productivity is attributed to the genetic potential of the varieties, their response to farm management, and better adaptation to the local growing conditions. Participant farmers perceived that their livelihood has been improving because of the adoption of the upscaled varieties' productivity and market demand. The annual income of participant farmers is estimated to be 2500 to 181,000 ETB for growing the winner varieties. The results indicate that upscaling pre-tested chickpea varieties and delaying their planting time to early September are effective mechanisms for reducing yield loss to fusarium wilt and root rot diseases. It can be inferred that using the crowdsourcing approach for variety evaluation and selection for upscaling is a robust approach to improve the adoption and dissemination of improved agricultural technologies.

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.