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.
The climate crisis is impacting agroecosystems of the global South, threatening the food security of millions of smallholder farmers. Understanding the effect of current and future climates on crop agrobiodiversity may guide breeding efforts and adaptation strategies to sustain the livelihoods of farmers cropping in challenging conditions. Here, we combine a genomic and climatic characterization of a large collection of traditional barley varieties from Ethiopia, key to food security in local smallholder farming systems. We employ data-driven approaches to characterize their local adaptation to current and future climates and identify barley genomic regions with potential for breeding for local adaptation. We used a sequencing approach to genotype at high-density 436 barley varieties, finding that their genetic diversity can be traced back to geography and environmental diversity in Ethiopia. We integrate this information in a genome-wide association study targeting phenology traits measured in common garden experiments as well as climatic features at sampling points of traditional varieties, describing 106 genomic loci associated with local adaptation. We then employ a machine learning approach link barley genomic diversity with climate variation, estimating barley genomic offset in future climate scenarios. Our data show that the genomic characterization of traditional agrobiodiversity coupled with climate modelling may contribute to the mitigation of the climate crisis effects on smallholder farming systems.
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