Evaluation and participatory variety selection of improved highland sorghum varieties for smallholder farm at west hararghe zone

Participatory variety selection is the most important breeding program which enhanced the adoption of improved varieties through creating awareness based on smallholder farmers’ selection criteria. Several different improved sorghum varieties are released at different international and national research institutions at different times, however, the technologies were not properly addressed the farmers based on participatory, client oriented and demand driven. The experiment was carried out in West Hararghe Zone; Oromia Regional State, Ethiopia with the objective to identify and recommend the best adapted and performed improved sorghum varieties with farmers’ preference traits through continuous performance evaluation at different stages of the sorghum. A total of six sorghum varieties were evaluated in randomized complete block design in the 2019 main cropping season. Farmers have evaluated the entire experimental units using different statistical tools like direct-matrix and pair-wise ranking algorithms at maturity stage. Farmers set selection criteria to identify the superior improved varieties as compared to the local check by listing different agronomic traits like yield, biomass, seed color, seed size and biotic and abiotic resistance. The analysis of variance showed that there was highly significant difference (p<0.01) among the genotypes for all studied traits. The greatest grain yield was recorded from the variety Dibaba (11325 Kgha-1) and Jiru (10200 Kgha-1) respectively. Likewise, based on the overall farmer’s preference, Dibaba and Jiru were ranked first and second and followed by Adelle, ETS2752 and Chiro respectively. Additionally, the study revealed that participatory varietal selection is playing decisive role in gathering farmers’ perceptions, preferences, merits and shortcomings of sorghum varieties for future improvement. Hence, based on the result of the study, variety Dibaba and Jiru were recommended for multiplication and distribution to farmers through both formal and informal seed systems. Generally, the integration of plant breeders and farmers’ perceptions are used to increase the adoption rate and design a good breeding program for future improvement.

Gender-Responsive Participatory Variety Selection in Kenya: Implications for Common Bean (Phaseolus vulgaris L.) Breeding in Kenya

Participatory variety selection (PVS) is the selection of new varieties among fixed lines by farmers under different target environments. It is increasingly being used to select and promote new crop breeding materials in most African countries. A gender-responsive PVS tool was piloted in Embu and Nakuru in the first and second cropping seasons of 2019 to understand similarities and differences between men’s and women’s varietal and trait preferences for biofortified released varieties and local bean varieties (landraces). Pooled results indicate that varietal and trait preferences between men and women farmers were slightly different but followed gendered roles and division of labor. Women farmers have a higher preference for landraces compared to men due to their availability, affordability and accessibility. Preferences for bean varieties differed between men and women across the two counties. High yielding was the most prioritized trait by both men and women for Mwitemania, Nyota, and Angaza. The findings support the long-held assumption that men prefer market-oriented traits, but women have a greater range of concerns thus less market oriented than men. For Nyota, men’s preferences were shaped by market traits, while women’s preferences were based on the variety being early maturing, resistant to pests and diseases, marketable, and fast cooking. For other varieties, men preferred Mwitemania because it is high yielding, early maturing, resistant to pests and diseases, and marketable. In contrast, women preferred Mwitemania because of seed availability, fast cooking, and early maturing. However, men’s and women’s varietal preferences for Angaza were similar, with them reporting early maturing, resistance to pests and diseases, and marketability as attractive traits. Sociodemographic characteristics such as education, age, marital status, and land ownership underlined the differences in trait preferences. Despite having attractive traits for both men and women, Nyota increased drudgery, displaced women from their usual activities, and required additional inputs by women, signaling possible adoption tradeoffs. However, Nyota, Angaza, and Mwitemania can provide the opportunity to increase employment for women. This study calls for gender integration at the design stage of any breeding system to ensure men and women farmers have access to varieties they prefer for food and income generation.

Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

Trait profile of maize varieties preferred by farmers and value chain actors in northern Ghana

Plant breeders’ knowledge of precise traits preferred by variety users would accelerate varietal turnover and widen adoption of newly developed maize varieties in Ghana. The objective of this research was to provide empirical evidence of trait preferences of farmers and other actors in the maize value chain in northern Ghana, based on which research strategies for maize improvement could be formulated. Participatory rural appraisal was conducted in 2016 to determine key traits preferred by maize value chain actors across the three regions in northern Ghana. A total of 279 maize value chain actors were interviewed. Different scoring and ranking techniques were used to assess the maize traits preferred by the different actors. Participatory variety selection trials were also conducted in the Tolon, West Gonja, Binduri, and Sissala East districts in northern Ghana from 2014 to 2016. The mother-baby trial approach was used to evaluate eight hybrids with 3000 farmers. Data on yield and agronomic performance of the hybrids and farmer’s selection criteria were collected. Data analyses were performed using GenStat Edition 16 and SPSS Edition 20 statistical packages. The participatory rural appraisal method identified farmers, input dealers, traders, and processors as the primary maize value chain actors in the study areas. Trait preferences of the different actors overlapped and revolved around grain quality including nutritional value, and stress tolerance and grain yield. Results of the participatory variety selection study revealed that across districts, farmers preferred high-yielding varieties with multiple cobs per plant, white grain endosperm color, and bigger and fully filled cobs. For the first time, our holistic assessment of the trait preferences of key actors of the maize value chain in northern Ghana revealed a comprehensive list of traits, which could be used by breeders to develop varieties that may be preferred by all value chain actors in northern Ghana.

COMPARATIVE ANALYSIS OF THE CONTENT OF CATECHINS IN FRUITS OF NEW VNIISPK BREEDING APPLE VARIETIES AND IN THEIR PROCESSING PRODUCTS

Apple is a supplier of raw materials for processing as a leader in industrial horticulture. Apple preserves keep to a large extent useful properties of fresh fruits. The aim of this study was to review multi-year data of catechin content in fruits and processing products of 36 new VNIISPK breeding apple varieties. The average content of catechin in fruits of new varieties was 141.9±4.9 mg/100 g while cultivar variation was from 91 mg/100 g in Ven'yaminovskoe to 243 mg/100 g in Zaryanka (V=21.0%). The catechin content in all types of processing was lower than in fresh fruits. The catechin content of processing products remained at an average of about a third of catechin quantity in apples. The catechin content decreased in series: juice→compote→preserves of apples and jam, since their preservation is greatly influenced by the increase in temperature during the processing of raw materials (r = -0.78 *). The absence of reliable direct correlation between the initial amount of catechin in the fruits and in the processing products confirms the importance of variety selection which keep a high level of catechin during processing.

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge

We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.