Effects of nitrogen fertilization, genotype and environment on the quality of oats (Avena sativa) grown in Manitoba
Bibliographic record
Abstract
Manitoba is a major producer of high quality oats (Avena sativa) destined for milling and food processing in domestic and export markets, particularly the United States. Strengthening the demand for Canadian oats requires continued improvement of oat cultivars to meet the changing needs of the agricultural and food industries. In order for plant breeders to achieve this, more information is needed regarding what factors affect variation in the milling, nutritional, functional and end-product quality of oats grown in western Canada. The objective of this study was to determine the relative effects of genotype, environment, nitrogen fertilization and their interactions on the quality of oats destined for human food. Replicated field tests were grown at each of six environments using a split plot design. Five genotypes were the main plots and four nitrogen fertilization treatments (0 to 120 kg/ha) were applied to the sub-plots. Hull content was significantly affected by a qualitative genotype-by-environment interaction, indicating the need for multiple testing sites. Growing conditions also had a strong influence on groat breakage, protein, and oil (36, 73, and 49 % of total variation respectively) but differences between genotypes remained consistent across environments. Genotype was the main factor affecting B-glucan content (78 % of total variation). Nitrogen fertilization had a greater impact on oat composition than on milling characteristics and in many cases the effect of nitrogen was dependant on the location. At sites where residual nitrogen was low (less than 36 kg/ha), fertilization resulted in increased levels of protein and B-glucan (by as much as 4.4% and 1% respectively), while oil decreased slightly (less than 1 %)...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".