RESUMO
Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers' priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot's recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers' preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.
RESUMO
Common bean (Phaseolus vulgaris L.) has two major origins of domestication, Andean and Mesoamerican, which contribute to the high diversity of growth type, pod and seed characteristics. The climbing growth habit is associated with increased days to flowering (DF), seed iron concentration (SdFe), nitrogen fixation, and yield. However, breeding efforts in climbing beans have been limited and independent from bush type beans. To advance climbing bean breeding, we carried out genome-wide association studies and genomic predictions using 1,869 common bean lines belonging to five breeding panels representing both gene pools and all growth types. The phenotypic data were collected from 17 field trials and were complemented with 16 previously published trials. Overall, 38 significant marker-trait associations were identified for growth habit, 14 for DF, 13 for 100 seed weight, three for SdFe, and one for yield. Except for DF, the results suggest a common genetic basis for traits across all panels and growth types. Seven QTL associated with growth habits were confirmed from earlier studies and four plausible candidate genes for SdFe and 100 seed weight were newly identified. Furthermore, the genomic prediction accuracy for SdFe and yield in climbing beans improved up to 8.8% when bush-type bean lines were included in the training population. In conclusion, a large population from different gene pools and growth types across multiple breeding panels increased the power of genomic analyses and provides a solid and diverse germplasm base for genetic improvement of common bean.
RESUMO
Common bean (Phaseolus vulgaris L.) is important in African diets for protein, iron (Fe), and zinc (Zn), but traditional cultivars have long cooking time (CKT), which increases the time, energy, and health costs of cooking. Genomic selection was used to predict genomic estimated breeding values (GEBV) for grain yield (GY), CKT, Fe, and Zn in an African bean panel of 358 genotypes in a two-stage analysis. In Stage 1, best linear unbiased estimates (BLUE) for each trait were obtained from 898 genotypes across 33 field trials in East Africa. In Stage 2, BLUE in a training population of 141 genotypes were used in a multivariate genomic analysis with genome-wide single nucleotide polymorphism data from the African bean panel. Moderate to high genomic heritability was found for GY (0.45 ± 0.10), CKT (0.50 ± 0.15), Fe (0.57 ± 0.12), and Zn (0.61 ± 0.13). There were significant favorable genetic correlations between Fe and Zn (0.91 ± 0.06), GY and Fe (0.66 ± 0.17), GY and Zn (0.44 ± 0.19), CKT and Fe (-0.57 ± 0.21), and CKT and Zn (-0.67 ± 0.20). Optimal contributions selection (OCS), based on economic index of weighted GEBV for each trait, was used to design crossing within four market groups relevant to East Africa. Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032. Genomic selection with OCS will accelerate breeding of high-yielding, biofortified, and rapid cooking African common bean cultivars.
