Relationships of phenotypic stability measures for genotypes of three cereal crops
Bibliographic record
Abstract
Multi-environment trial (MET) data are required to obtain stability performance parameters as selection tools for effective genotype evaluation. The main objective of this study was to investigate the interrelationships among nine phenotypic stability methods using grain yield from three sets of cereal experiments [15 durum wheat (Triticum turgidum var. durum) genotypes × 12 environments; 20 bread wheat (T. aestivum L.) genotypes × 18 environments; and 13 barley (Hordeum vulgare L.) genotypes × 18 environments]. The experiments were conducted in representative rain-fed areas of Iran in collaboration with the International Center for Agricultural Research in the Dry Areas (ICARDA). The combined ANOVA for environments (E), genotypes (G) and G × E interaction was highly significant (P < 0.01) for each set of data, suggesting differential genotypic responses and the need for stability analysis. The inter-relationships among the parameters and their association with mean yield based on Spearman’s rank correlation were determined in each of the three cereal experiments. Highly significant correlations were found between several of the stability measures indicating that several of the statistics probably measure similar aspects of phenotypic stability for these crop species. The AMMI stability value (ASV), variance of regression deviation (S2di) and Wricke’s ecovalence (W2i) were consistently and highly correlated with each other over these crops and, therefore, could be used if selection is to be based primarily on stability. The superiority index (Pi) and geometric adaptability index (GAI), which are related to the dynamic concept of stability showed significant correlation with mean yield over these crops, suggesting Pi and GAI would be the best methods for ranking genotypes across environments. The coefficient of variation (CV), regression coefficient (bi), yield reliability index (Ii), and environmental variance (S2x) showed inconsistent relationships with either the static or dynamic concepts of stability over these crops. The correlation analysis provided a good description of static and dynamic concepts of stability for interpreting the G × E interaction and verified that the groups of stability methods (dynamic vs. static) discriminated genotypes in different fashions in these crops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".