Response of oat grain yield and quality to nitrogen fertilizer and fungicides
Bibliographic record
Abstract
Abstract Recently agronomists and producers have expressed interest in combining higher nitrogen (N) rates with a fungicide application even when disease intensity is low. The objective of this study was to evaluate the effect of fungicide application and N rates on grain yield and oat quality ( Avena sativa L.). The experimental design was a split plot with fungicide (none, pyraclostrobin, propiconazole + trifloxystrobin) as the main plot, and eight N rates as sub‐plots (5, 20, 40, 60, 80, 100, 120, and 140 kg ha −1 ). This study was conducted in 2012 and 2013 at two locations in Saskatchewan, Melfort and Indian Head. Disease intensity was very low for crown rust ( Puccinia coronata ) and low‐to‐moderate for all other foliar diseases, and with no large effect on grain yield and quality. No interaction between fungicide and N was observed. A curvilinear increase in grain yield occurred as the N rate increased from 5 to 140 kg ha −1 . Increasing the N rate caused a small linear decrease in test weight. At a low oat price, $130 t −1 , the N rate that maximized economic return was sensitive to N fertilizer price. As the crop price increased the optimum N rate was100 kg ha −1 . In conclusion, our results indicate that using an N rate of 100 kg ha −1 provided the most consistent economic returns when the crop price is between $162 and $194 t −1 . There is no beneficial interaction between fungicide and N for growers using higher N rates at low disease intensity and resistant genotypes.
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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".