Nitrogen management strategies on plant growth and severities of <i>Sclerotinia</i> stem rot of canola in eastern Canada
Bibliographic record
Abstract
Effective nitrogen (N) management strategies are important for ensuring a balance between optimizing plant growth and minimizing disease damage. A field experiment was conducted for three years to (i) assess the effects of N fertilizer application on the growth and seed yield of canola and severities of Sclerotinia stem rot (SSR), and (ii) to determine a reasonable N-rate for optimizing plant growth and minimizing the loss from SSR in eastern Canada. The experiment was designed with factorial combinations of eight N treatments and two canola hybrids. All N treatments reduced canola emergence with increasing preplant N application rates above 100 kg ha−1, but had a positive impact on plant height, fresh weight, dry weight, and seed yield. The development of SSR showed differential responses to N application rates. Of all the treatments, the split application (50 kg N ha−1 at preplant plus 100 kg N ha−1 side-dressed at the 6-leaf stage) increased canola growth and often produced the highest or similar seed yields to those of equivalent N rate applied as preplant. At the 150 kg ha−1 N rate, no severe development of SSR was observed in either preplant-only or split application. Overall, this study demonstrates that the split-N management strategy (50 + 100 kg ha−1) maintained a balance between enhancing plant growth and mitigating the negative impacts of SSR on canola.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".