SUMMARYRapid climatic and socio-economic changes challenge current agricultural R&D capacity. The necessary quantum leap in knowledge generation should build on the innovation capacity of farmers themselves. A novel citizen science methodology, triadic comparisons of technologies or tricot, was implemented in pilot studies in India, East Africa, and Central America. The methodology involves distributing a pool of agricultural technologies in different combinations of three to individual farmers who observe these technologies under farm conditions and compare their performance. Since the combinations of three technologies overlap, statistical methods can piece together the overall performance ranking of the complete pool of technologies. The tricot approach affords wide scaling, as the distribution of trial packages and instruction sessions is relatively easy to execute, farmers do not need to be organized in collaborative groups, and feedback is easy to collect, even by phone. The tricot approach provides interpretable, meaningful results and was widely accepted by farmers. The methodology underwent improvement in data input formats. A number of methodological issues remain: integrating environmental analysis, capturing gender-specific differences, stimulating farmers' motivation, and supporting implementation with an integrated digital platform. Future studies should apply the tricot approach to a wider range of technologies, quantify its potential contribution to climate adaptation, and embed the approach in appropriate institutions and business models, empowering participants and democratizing science.

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.

Background Evaluation of the extent of genetic variation within and between the populations of crop genetic resources are of paramount importance in any breeding program. An experiment aimed at assessing the extent of variation among barley lines and the degree of association between hordein polypeptide and agronomic traits was hence executed. Methods Field experiment was conducted in six environments between 2017–2019 involving 19 barley lines. Hordein bands were separated using vertical Sodium Dodecyl Sulphate Poly- acrylamide Gel Electrophoresis (SDS-PAGE). Results The analysis of variance revealed significant variation among lines and wider range units were observed for the agronomic traits. The line (Acc# 16,811–6) was superior, producing the highest grain yield (2.97 ton ha−1) across environments, 3.6 ton ha−1 at Holleta, and 1.93 ton ha−1 at Chefedonsa. At Arsi Negelle a different line Acc# 17146–9 was the highest yielding (3.15ton ha−1). SDS-PAGE-based analysis of barley lines separated 12 hordein bands between C (four bands) and B (eight bands) subunits. Interestingly bands 52, 46a, and 46b were uniquely conserved in the four naked barley lines (Acc#16809–14,16956–11, 17240–3, 17244–19). A considerably high proportion of genetic diversity within the populations than among the populations could be a repercussion of high gene flow which substantiates the longstanding and dominant informal seed exchange system among the farmers. The significant positive association between grain yield and band 50 evocates the expression of this allele may code for higher grain yield. The negative association between days to maturity and band 52 perhaps stipulates earliness in barely lines upon the manifestation of the band. Band 52 and 60 appeared to be associated with more than one agronomic trait (days to maturity and thousand kernel weight; grain filling period and grain yield respectively) and could be the result of pleiotropic characteristics of the genes residing in these banding regions. Conclusion The barley lines exhibited substantial variation for hordein protein and agronomic traits. However, imparted the need for the implementation of decentralized breeding as a consequence of genotype-by-environment interaction. Significant hordein polypeptide and agronomic traits association advocated the utilization of hordein as a protein marker and perhaps consider them in the parental line selection.

This study assesses the impact of a participatory development program called Seeds For Needs, carried out in Ethiopia to support smallholders in addressing climate change and its consequences through the introduction, selection, use, and management of suitable crop varieties. More specifically, it analyzes the program’s role of boosting durum wheat varietal diversification and agrobiodiversity to support higher crop productivity and strengthen smallholder food security. The study is based on a survey of 1008 households across three major wheat-growing regional states: Amhara, Oromia, and Tigray. A doubly robust estimator was employed to properly estimate the impact of Seeds For Needs interventions. The results show that program activities have significantly enhanced wheat crop productivity and smallholders’ food security by increasing wheat varietal diversification. This paper provides further empirical evidence for the effective role that varietal diversity can play in improving food security in marginal environments, and also provides clear indications for development agencies regarding the importance of improving smallholders’ access to crop genetic resources.

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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