article · Reviews In Food And Agriculture
Evaluation of agronomic performance and yield traits across Bambara nut varieties using statistical methods, including Principal Component Analysis, identified Cvd-27 as a superior cultivar. Cvd-27 achieved the highest average grain yield of 1,900 kilograms per hectare, followed by Sokoto at 1,800, Torfam at 1,733, Gberangen at 1,600, and Cvd-23 at 1,550 kilograms per hectare. Across the varieties tested, traits including days to flowering, pod production, and seed weight displayed substantial genetic diversity. Principal Component Analysis highlighted that pod weight and reproductive characteristics exert significant influence over yield potential. These findings show that breeding programmes prioritising reproductive traits and seed quality can enhance Bambara nut productivity. Such targeted cultivar selection offers practical pathways to support regional food security, while future research needs to integrate disease resistance and environmental factors to improve crop adaptability.
Bambara nut is an essential crop for food security in dependent regions, yet maximising output requires identifying top-performing cultivars. Documenting yield differences and high-yielding varieties such as Cvd-27 provides clear guidance for growers seeking larger harvests. Furthermore, mapping trait diversity allows crop improvement initiatives to focus on the specific physical characteristics that drive higher productivity.
Plant breeders and agricultural extension services can use these findings to select high-yielding cultivars, particularly Cvd-27, for seed multiplication and crop improvement programmes. This work represents applied research that is tested at the variety evaluation stage. Realising full commercial potential in certified seed distribution will require further screening to assess disease resistance and crop adaptability across varying environmental conditions.
AI-generated from the published abstract. Always read the original work before citing.
This research investigates the agronomic performance of various Bambara nut (Vigna subterranea) varieties, focusing on key yield characteristics and their implications for breeding and cultivation strategies. A comprehensive analysis of agronomic traits, including days to flowering, pod production, seed weight, and overall grain yield, was conducted across several varieties using statistical methods, including Principal Component Analysis (PCA). The study identified Cvd-27 as a superior cultivar, demonstrating exceptional performance in multiple parameters, such as flowering time, pod production, and yield, with an average grain yield of 1900 kg/ha followed by Sokoto, Torfam, Gberangen, Cvd-23 with average grain yield of 1800.00kg/ha, 1733.00 kg/ha, 1600.00kg/ha, 1550.00kg/ha respectively. The analysis revealed substantial genetic diversity among varieties, underscoring opportunities for selective breeding to enhance desirable traits, particularly those linked to reproductive success and seed quality. Furthermore, the PCA results highlighted the significance of traits such as pod weight and reproductive characteristics in influencing yield potential. The findings align with existing literature and suggest that breeding programs prioritizing key agronomic traits could significantly improve productivity and contribute to food security in regions dependent on Bambara nut cultivation. Future research should consider integrating environmental factors and diseaseresistance into trait analyses to further refine breeding strategies and enhance the crop’s adaptability.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.26480/rfna.01.2025.29.37
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
