Nitrogen Fertilizer Complements Breeding in Improving Yield and Quality of Milling Oat
Bibliographic record
Abstract
A four‐level nitrogen (N) fertilizer study was conducted for nine diverse oat ( Avena sativa L.) cultivars at three geographically diverse locations in Canada in 2013 and 2014 to study the effects of N fertilizer on grain yield and important quality parameters of milling oat. Analysis of variance and biplot analysis were used to interpret the multifactor, multitrait data. The findings are: (i) N fertilizer up to 150 kg ha −1 led to simultaneous and statistically significant improvement of grain yield, milling quality (groat content and proportion of undehulled kernels), and compositional quality (β‐glucan, protein, and oil concentrations), despite some N × genotype and N × environment interactions. (ii) β‐Glucan and oil concentrations were much more strongly determined by genotype than by N fertilizer; groat content and proportion of undehulled kernels were slightly more strongly determined by genotype than by N fertilizer; protein was similarly determined by genotype and N fertilizer; and grain yield was much more determined by N fertilizer than by genotype. (iii) Nitrogen fertilizer effectively increased the yield of high‐β‐glucan, low‐yielding cultivars but had a limited (though statistically significant) effect in improving the β‐glucan levels of cultivars that are low in β‐glucan. (iv) Cultivars differed in the extent of response to N fertilizer, so it is necessary to develop cultivar‐specific N management plans for different cultivars. It is proposed to use N fertilizer to improve yield (and, to a lesser extent, quality parameters) to complement breeding prioritizing superior quality (high β‐glucan in particular) and lodging resistance.
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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 | 0.001 | 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 | 0.001 | 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".