Effects of genotype and environment on the physiochemical properties of Canadian oat varieties
Bibliographic record
Abstract
BACKGROUND: The relationship between oat grain composition and physical attributes as influenced by oat genotype and Canadian growing environments was investigated. Thirty Canadian oat (Avena sativa L.) genotypes, grown in three Canadian growing locations (Brandon, Manitoba; Portage la Prairie, Manitoba; and Lacombe, Alberta) over 2 consecutive years (2020-2021), were analyzed. RESULTS: Analysis of variance showed that the protein, total starch, and amylose content were significantly affected by genotype, environment, and their interaction. A principal component analysis bi-plot illustrated that protein and total starch had an inverse relationship and were more affected by growing year, whereas amylose content had a negligible influence. The majority of genotypes were stable across environments but some genotypes, like CDC Morrison, were more influenced by different environments. Correlation analysis suggested that drought-like conditions early in the growing season generated oat genotypes that favored the accumulation of protein, β-glucan, and oil. CONCLUSION: The results provide detailed information regarding the relationship between important oat chemical and physical traits and different growing environments, which can assist breeders to improve characteristics to obtain high-quality oat grains and thus high-quality end products. © 2025 His Majesty the King in Right of Canada and The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".