Early fungicide treatment reduces blackleg on canola but yield benefit is realized only on susceptible cultivars under high disease pressure
Bibliographic record
Abstract
Blackleg [Leptosphaeria maculans (Desm.) Ces. & de Not.] is the most widespread disease of canola (Brassica napus L.) on the Canadian prairies. It has a noticeably increased incidence in recent years possibly due to shifts in the pathogen population and erosion in cultivar resistance. This study was conducted to assess foliar-applied fungicides for mitigating the risk of blackleg. Field trials were conducted at five locations in the Black or Dark-Brown Soil Zones for four years using the susceptible canola ‘Westar’. The fungicides pyraclostrobin (Headline®), azoxystrobin (Quadris®), propiconazole (Tilt®), and azoxystrobin + propiconazole (Quilt®) were applied at the 2–4 leaf stage against early infection. For comparisons, pyraclostrobin was also applied at bolting of ‘Westar’ and at the 2–4 leaf stage of two resistant cultivars (‘43E01’, ‘45H29’). These early treatments, except propiconazole, significantly reduced the mean disease incidence (MDI) and disease severity index (DSI) on ‘Westar’, relative to untreated control, reducing the impact of disease on yield by 16.5–26.9%. Late application of pyraclostrobin at bolting was ineffective. Two-application treatments, with pyraclostrobin at the 2–4 leaf stage and propiconazole at the bolting stage, or vice versa, provided no further efficacy or yield benefit relative to the early application of pyraclostrobin alone. None of the treatments showed substantial disease reduction or yield benefit in two low-disease station years (MDI <30%). On the resistant cultivars, pyraclostrobin reduced MDI and DSI, but showed no yield benefit. As resistant canola cultivars are used commonly on the prairies, a routine application of foliar fungicide is not recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".