Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario
Bibliographic record
Abstract
Superior crop cultivars must be identified through multi-environment trials (MET) and on the basis of multiple traits. The objectives of this paper were to describe two types of biplots, the GGE biplot and the GT biplot, which graphically display genotype by environment data and genotype by trait data, respectively, and hence facilitate cultivar evaluation on the basis of MET data and multiple traits. Genotype main effect plus genotype by environment interaction effect (GGE) biplot analysis of the soybean [Glycine max (L.) Merr.] yield data for the 2800 crop heat unit area of Ontario for MET in the period 1994-1999 revealed yearly crossover genotype by site interactions. The eastern Ontario site Winchester showed a different genotype response pattern from the three southwestern Ontario sites in four of the six years. The interactions were not large enough to divide the area into different mega-environments as when analyzed over years, a single cultivar yielded the best in all four sites. The southwestern site, St. Pauls, was found to always group together with at least one of the other three sites; it did not provide unique information on genotype performance. Therefore, in future cultivar evaluations, Winchester should always be used but St. Pauls can be dismissed. Applying GT biplot to the 1994-1999 multiple trait data illustrated that GT biplots graphically displayed the interrelationships among seed yield, oil content, protein content, plant height, and days to maturity, among other traits, and facilitated visual cultivar comparisons and selection. It was found that selection for seed yield alone was not only the simplest, but also the most effective strategy in the early stages of soybean breeding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".