The influence of nitrogen, phosphorus and potash fertilizer application on oat yield and quality
Bibliographic record
Abstract
Western Canada has become one of the key production areas for oat (Avena sativa L.) in North America. Limited information is available regarding fertilizer management strategies to optimize yield and quality in this environment. An experiment was conducted at two locations in southern Manitoba in 2000, 2001 and 2002 to assess the impact of factorial combinations of N (0, 40, 80, 120 kg N ha-1 as urea), P (0, 13, 26 kg P ha-1 as monoammonium phosphate), and KCl (0, 33 kg K ha-1) on the growth, yield and quality of AC Assiniboia oat. Low to moderate N rates significantly increased yield, with optimum relative yield achieved with a plant-available N supply of approximately 100 kg N ha-1. Increasing N rate also increased lodging and reduced test weight, kernel weight and kernel plumpness, suggesting that optimal N management must balance yield improvement against reductions in grain quality. Phosphorus application increased yield in 2 of 6 site-years, but had no overall effect on quality. Application of KCl resulted in small increases in yield (88 kg ha-1), kernel weight and kernel plumpness on moderate to high K soils, which were not likely to provide a significant economic benefit. The lack of consistent interactions among N, P, and KCl suggests that these nutrients may be managed individually. Key words: Oat, nitrogen, phosphorus, potassium chloride, yield, quality
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".