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
The current study was aimed at identifying mega-environments in Ghana and evaluating adaptability of superior sweetpotato [Ipomoea batatas (L.) Lam.] genotypes from a targeted breeding effort. Three sets of genotypes were evaluated in multi-environment trials (MET). Twelve sweetpotato varieties were evaluated across nine environments representing the main agro-ecological zones in Ghana. MET analysis was conducted using a stage-wise approach with the genotype × environment (G × E) table of means used as a starting point to model the G × E interaction for sweetpotato yield. Emphasis was given to the genetic correlation matrix used in a second-order factor analytic model that accommodates heterogeneity of genetic variances across environments. A genotype main effect and G × E interaction of storage root yield explained 82% of the variation in the first principal component, and visualized the genetic variances and discriminating power of each environment and the genetic correlation between the environments. Two mega-environments, corresponding to northern and southern trial sites, were delineated. Six breeding lines selected from the south and eight breeding lines selected from the north were tested and compared to two common check clones at five locations in Ghana. A Finlay-Wilkinson stability analysis resulted in stable performances within the target mega-environment from which the genotypes were selected, but predominantly without adaptation to the other region. Our results provide a strong rationale for running separate programs to allow for faster genetic progress in each of these two major West African mega-environments by selecting for specific and broad adaptation.