Exploring the trait‐yield association patterns in different oat mega‐environments of Canada
Bibliographic record
Abstract
Abstract This article presents a graphical method to visually analyze the trait–yield association (TYA) patterns based on data from multi‐location, multi‐year crop variety trials, exemplified using oat data from trials conducted across Canada from 2017 to 2022. Each year a new set of 60–66 oat ( Avena sativa L.) breeding lines were tested in replicated yield trials at 9–11 locations, and data for yield, key agronomic and quality traits, and crown rust scores were collected at all or some of the locations. Pearson correlation coefficient was calculated between yield and each trait for each trial. The correlation coefficients from different locations and years were arranged in a TYA × trial (TYT) two‐way table. This table was subjected to singular value decomposition, and the resulting first two principal components were used to generate a TYT biplot. The TYT biplot revealed three oat mega‐environments (MEs) in Canada, consistent with the conclusion from previous ME analyses, indicating that each ME had its characteristic TYA patterns. It was found that yield was consistently and positively correlated with crown rust ( Puccinia coronata var. avenae ) resistance, test weight, kernel weight, and groat content in ME1 (the crown rust‐prone regions of Ontario); yield was correlated positively with plant height but negatively with oil content in ME2 (the northern regions of eastern Canada). Interestingly, crown rust resistance was found to contribute negatively to yield in ME2. No strong and consistent TYAs were found in ME3 (the Canadian prairies). Different types of TYAs have different uses in genotypic selection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".