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Vernonia amygdalina is an African traditional leafy vegetable which is a source of income for many households involved in its production in West and Central Africa. The species possesses several medicinal and nutritional properties. Despite its importance, there is limited information on farmers’ access to seed, seed production practices, seed trade, and related constraints. This research aimed at investigating farmers’ seed access and management practices. A non-probabilistic snowball method was used to sample and interview 164 bitter leaf seed sector actors from urban and peri-urban areas in Benin. Results indicated that vegetable farming is mainly practiced by men and it was the main occupation for 94.47% of the farmers interviewed. A significant association was found between the location and farmers’ experience (p-value = 0.01358). A diversity of Vernonia amygdalina morphotypes were found. Sixty-three percent of the farmers surveyed use seeds as planting material, 29% use purchased seedlings and only 7% use stem cuttings for Vernonia amygdalina production. Most of the seeds used (74%) were from farmer-saved seeds, harvested from isolated plants during production. The main constraints to V. amygdalina production reported by farmers were the unavailability of seeds (41% of respondents), low germination percentage (18%) and low seed longevity (12%). Findings from this study can inform solutions to improve farmers’ access to high-quality seeds and enhance V. amygdalina vegetable production.

Seed saving is crucial to ensure seed and food security, especially in developing countries. In Ghana, about 90% of pearl millet farmers utilise farmer-saved seed for production. Such seeds usually have a low germination rate, and may carry important seed-borne pathogens. In this study, the quality of farmer-saved seeds and the potential of botanicals to control seed-borne fungi were examined. A structured questionnaire was administered to 120 farmers from Garu, Bongo and Kassana-Nankana districts in Ghana. During the survey, 60 samples of farmer-saved seed were obtained for seed quality evaluation. Aqueous extracts of neem (Azadirachta indica) and citrus (Citrus sinensis) were prepared as seed treatments compared with a control. Fourteen (14) fungal species were associated with the seed samples, but the four dominant ones were Curvularia spp. (62%), Talaromyces spp. (53%), Aspergillus spp. (52%) and Exserohilum spp. (42%). The application of citrus extract inhibited fungal incidence (15–31.7%) compared to neem extract (33.3–93.3%) and the control (96.7–98.3%). The application of citrus extract resulted in a higher (74.7–82.8%) field germination rate compared to neem extract (62.3–73.2%) and the control (65.5–69.2%). The citrus extract was the most efficient treatment in reducing the incidence of fungi and enhancing the seed germination and vigour of pearl millet.

With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data. This study undertook an extensive analysis of 907 elite winter barley (Hordeum vulgare L.) lines across multiple environments from two breeding companies. Six genomic prediction models were evaluated to demonstrate the effect of using pedigree and marker information individually and in combination, as well their interactions with the environment. Each model was evaluated using three cross-validation schemes that allows the prediction of newly developed lines (lines that have not been evaluated in any environment), prediction of new or unobserved years, and prediction of newly developed lines in unobserved years. The results showed that the best prediction model depends on the cross-validation scheme employed. In predicting newly developed lines in known environments, marker information had no advantage to pedigree information. Predictions in this scenario also benefited from including genotype-by-environment interaction in the models. However, when predicting lines and years not observed previously, marker information was superior to pedigree data. Nonetheless, such scenarios did not benefit from the addition of genotype-by-environment interaction. A combination of pedigree-based and marker-based information produced a similar or only marginal improvement in prediction ability. It was also discovered that combining populations from the different breeding programs to increase training population size had no advantage in prediction.