Comparative Analysis of Genotype x Environment Interaction Techniques in West African Okra, (Abelmoschus caillei, A. Chev Stevels)
Bibliographic record
Abstract
West African okra occurs in wild and unselected variants in Nigeria but farmers desire stable and high-yielding cultivars. Twenty-five West African okra genotypes from diverse geographical backgrounds were evaluated in five different environments for stability of performance. Performance was measured by number of days to 50% flowering, number of pods per plants, number of seeds per pod, plant height at maturity and seed yield per plant. A regression method, Additive main effects and Multiplicative Interaction (AMMI) and Genotype main effect and genotype x environment Interaction (GGE) were employed in the evaluation. Joint regression and AMMI analyses showed significant (P< 0.01) G x E interaction with respect to seed yield, and both identified NGAE-96-0060 and NGAE-96-0063 as stable genotypes. The AMMI and GGE biplot analyses are more efficient than the Eberhart and Russell analysis. The GGE biplot explains higher proportions of the sum of squares of the GxE interaction and is more informative with regards to environments and cultivar performance than the AMMI analysis. GGE-biplot models showed that the five environments used for the study belonged to three mega-environments with environment 2 (Upland, 2007) being the most representative and most desirable of all. The GGE results also confirmed NGAE-96-0063 as being stable with NGAE-96-04 as the most stable. NGAE-96-04 was identified as most superior genotype in terms of yield and stability of performance and could be recommended for cultivation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".