GGE Biplot Analysis of 12 Dioscorea rotundata Genotypes in Ghana
Bibliographic record
Abstract
To determine the effects of environment and genotypic differences on tuber yield and other related traits, 12 genotypes comprising 9 improved elite clones, two local landraces and 1 improved and released variety were evaluated for tuber yield, response to yam mosaic virus and leaf spot diseases at 16 growing environments. The multi-environment trials were conducted using randomized complete-block design with three blocks for four years in four representative agro-ecological zones (Atebubu, Kintampo, Ejura and Fumesua) in Ghana. The objective was to select high and stable yielding varieties for release as varieties in Ghana. The multi-environment data for the trials collected were subjected to combine analyses of variance using the ANOVA procedure of Statistical Tool for Agricultural Research (STAR) to determine the magnitude of the main effects and interactions. Genotype main effect and genotype by environment interaction effect (GGE) model was used to dissect the genotype by environment interaction (GEI) using the GGE biplot software (GGE biplot, 2007). GGE biplots analysis was applied for visual examination of the GEI pattern in the data set. A highly significant effects (P < 0.001) for Genotype (G), environment (E) and genotype by environment (GEI) interaction were occurred in the data set for highly significant for all the traits studied (P < 0.001), indicating genetic variability between genotypes by changing environments. This indicated changes in ranking order of the genotype performances across the test environments. The partitioning of the GGE effect for tuber yield through in GGE biplot analysis model showed that PC1 and PC2 accounted for 40.47.0% and 19.89.0% of the variation GGE sum of squares respectively for tuber yield, respectively explaining a total of 60.36% variation. Mankrong Pona was the most stable and high yielding (closest to the ideal genotype) followed by TDr95/19177. Genotypes TDr00/02472, TDr00/00539 and TDr98/00933 are desirable genotypes for further assessment on culinary characteristics and end-user assessment for release as varieties. All the four locations used for the study were highly relevant for research and development of yams. Ejura and Fumesua were the most discriminating and most representative for YMV respectively. In terms of yield, Kintampo environment was the most discriminating and Fumesua and Atebubu were the closest to ideal environment for evaluating yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".