Agro-ecological distribution of the phenotypic diversity of aerial yam (Dioscorea bulbifera L.) in Cameroon using multivariate analysis: prospect for germplasm conservation and improvement
Bibliographic record
Abstract
Abstract Aerial yam (Dioscorea bulbifera L.) is a crop of great economic importance and an excellent candidate for improving food security in developing countries. Understanding the genetic variability of any crop species is a decisive step for its improvement and requires characterization and evaluation of available germplasm. The objectives of this study were to determine the extent of genetic variability, estimate the association between agromorphological traits and clustering among 57 genotypes of aerial yam from three distinct agro-ecological zones in Cameroon using multivariate analysis. Thirty nine characters (23 qualitative and 16 quantitative) were used for the study. Significant differences in genetic diversity indices were found. Accessions from the bimodal humid forest zone (Na = 2.08, He = 0.27) showed significantly lower diversity compared to both western highland (Na = 2.30, He = 0.34) and humid monomodal forest zones (Na = 2.57, He = 0.32). Means values of most quantitative traits also showed significant differences between agro-ecological zones. Batingla-3 and Bawouwoua-1 had important bulbil yield, reaching 3500 g / plant. Significant associations were found between many traits. The use of the Unweighted Pair Group Method with Arithmetic Mean allowed the distribution of the 57 genotypes into six distinct clusters with the clustering pattern not showing any parallelism with location sites or agro-ecological zones. Mahalanobis D2 statistics revealed the highest inter-cluster distance between cluster II and VI. Accessions of these clusters are potential parents for future breeding programs. This study showed that aerial yam from Cameroon has an enormous wealth of traits variation, indicating huge potential for its genetic improvement through selection and hybridization.
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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".