Morphological Variation in Selected Accessions of Bambara Groundnut (Vigna subterranea L. Verdc) in South Africa
Bibliographic record
Abstract
Bambara groundnut (Vigna subterranea L. Verdc) is an underutilized crop in the African continent. It is a drought tolerant crop and fixes atmospheric nitrogen. Bambara groundnut is primarily grown for the protein content of its seeds and is mainly produced by small scale farmers at subsistence level. The objective of the study was to assess the morphological variation of landraces of bambara groundnut in South Africa. Thirty accessions of bambara groundnut were evaluated for their variability in agronomic and morphological traits. The field experiment was conducted at ARC-VOPI in Roodeplaat research farm during the 2014/2015 summer cropping season. The field trial was arranged as a complete randomized block design with three replications. Eighteen quantitative traits were recorded to estimate the level of genetic variability among accessions. The analysis of variance revealed significant differences among the phenotypic traits evaluated. The UPGMA cluster analysis based on the quantitative traits produced four distinct groups of genotypes and a singleton. Genotypes SB11-1A, SB19-1A, SB12-3B and Bambara-12 were found to possess good vegetative characters and are recommended for use as suitable parents when breeding cultivars for fodder production. Desirable yield and yield-related traits were identified in B7-1, SB4-4C, SB19-1A, Bambara-12 and SB16-5A and are recommended as suitable parental lines for bambara groundnut grain production improvement. The phenotypic characters therefore provide a useful measure of genetic variability among bambara genotypes and will enable the identification of potential parental materials for future breeding programs in South Africa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".