Multivariate analysis in the dissection of phenotypic variation of Ethiopian cultivated barley (Hordeum vulgare ssp Vulgare L.) genotypes

Efficient conservation and subsequent utilization of genetic resources are primarily dependent on the strength in the assessment of variation among geno-types. An experiment was carried out aiming at determining the extent of pheno-typic variability present in a panel of 320 barley genotypes and identifying candidate lines for further evaluation in improvement programs and successive utilization. It was conducted at two locations in Ethiopia, Aris Negelle and Holetta in the 2017/18 and 2018/19 cropping seasons. Among the best 30 lines for grain yield across all the environments, lines from farmers’ varieties constitute 73% and lines that mature in less than 85 days were identified. Based on the spike row number, the best-performing lines combined across all the environments were six-rowed types. Based on the two years’ data at Arsi Negelle the two-rowed spike type dominates, and at Holetta the six-rowed type. After principal component analysis, the first three PCs with an eigenvalue greater than one explained 70% of the variation. The correlation coefficient between grain and biomass yield was signifi-cant and though low (r = 0.38***). Significant, high, and negative correlation coef-ficient (−0.72***) was observed between 1000 kernel weight and the number of seeds per spike. A positive correlation between biomass and grain yield attracts farmers as a feed and food crop as it has also been signified in the current research. Having the improved barley gene pool largely from international sources, combining the improved materials with farmers’ varieties may minimize the existing gap between the local and improved barley gene pool

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.publishedVersio

Wheat varietal diversification increases Ethiopian smallholders’ food security: Evidence from a participatory development initiative

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. 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

FIRST EXPERIENCES WITH A NOVEL FARMER CITIZEN SCIENCE APPROACH: CROWDSOURCING PARTICIPATORY VARIETY SELECTION THROUGH ON-FARM TRIADIC COMPARISONS OF TECHNOLOGIES (TRICOT)

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

First experiences with a novel farmer citizen science approach : crowdsourcing participatory variety selection through on-farm triadic comparisons of technologies (tricot)

Rapid 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.</p

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

Replication Data for: "Data-driven decentralized breeding increases genetic gain in challenging crop production environments"

A panel of fully genotyped 400 wheat lines derived from genebank accessions in two managed fields in the Ethiopian highlands in 2012 and 2013 were evaluated. We collected phenotypic data and farmer evaluation data in this trial. For the decentralized trial, we distributed a subset of 41 genotypes as packaged sets containing incomplete blocks of three genotypes, plus one commercial variety for each farmer, following the “tricot” citizen science approach. We distributed these packages to 1,165 farmers who planted them on their farms across three regions of Ethiopia. Analyzing data from the centralized and decentralized trials in a side-by-side comparison, we evaluated if our approach can increase genetic gain in marginal crop production environments unlocking the full potential of genomics assisted breeding. For the full replication workflow please visit the GitHub repository (https://github.com/agrobioinfoservices/tricot-genomic)

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

FIRST EXPERIENCES WITH A NOVEL FARMER CITIZEN SCIENCE APPROACH: CROWDSOURCING PARTICIPATORY VARIETY SELECTION THROUGH ON-FARM TRIADIC COMPARISONS OF TECHNOLOGIES (TRICOT)

SUMMARYRapid climatic and socio-economic changes challenge current agricultural R&amp;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