Isoyield Analysis of Barley Cultivar Trials in the Canadian Prairies
Bibliographic record
Abstract
Abstract Classification of test sites used for cultivar trials into groups with similar within‐group site performance and response (isoyield groups) is an important step towards identification of appropriate cultivars that are best suited for different productivity levels in farm fields. The objective of this study was to determine isoyield environments in the Canadian prairies based on the analysis of cultivar trials consolidated from individual provinces for barley ( Hordeum vulgare L.). Yield data for the analysis were taken from 324 replicated trials at 84 sites across the prairies during 1995–2003. The combined use of regression and cluster analyses of the data normalized for averaging the multi‐year unbalanced data led to a stratification of the 84 sites into 13 isoyield groups. A comparison was made of the distributions of the variability among and within groups according to three modes of grouping: isoyield groups, soil zones and agroecoregions. There was more variability among isoyield groups and correspondingly less within the groups than that among and within soil zones or agroecoreions. Similar contrasting pattern existed for the variance components involving genotype–environment interaction (GEI), although the GEI variability was generally small under all three modes of grouping. Relationships of site sensitivity (regression coefficient) and stability (coefficient of determination) with site productivity were shown to be a useful aid for selecting a subset of test sites in an effort to improve efficiency and quality of future cultivar testing. Thus, isoyield analysis should be a valuable tool for subsetting heterogeneous environments and for reducing GEI impact in cultivar testing and recommendation.
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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 | 0.004 | 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 | 0.000 |
| 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".