Fungicide efficacy and timing for the management of<i>Stemphylium vesicarium</i>on onion
Bibliographic record
Abstract
Stemphylium leaf blight (SLB), caused by Stemphylium vesicarium (Wallr.) E.G. Simmons, has become an important disease of onion (Allium cepa L.) in Ontario, Canada, and the northeastern USA in recent years. The disease presents as elongated lesions on the leaves and severe leaf dieback. The effect on yield is unclear, but the extensive leaf dieback limits uptake of sprout inhibitors that are applied to onion foliage prior to harvest. This can result in high losses in storage. There are no resistant commercial onion cultivars and growers apply foliar fungicides at 7–14-day intervals to manage the disease. Field trials to evaluate fungicide efficacy and disease-forecasting models were conducted at the Muck Crops Research Station, Holland Marsh, Ontario, from 2011 to 2019. Fungicide efficacy declined over the years. The disease-forecasting models reduced the number of fungicide spray applications, but none of the models, including calendar-based applications, reduced SLB severity. Seed treatments containing penflufen, combined with calendar-based fungicide applications, reduced SLB severity. Assessments of fungicide insensitivity in isolates collected locally in 2018 and 2019 demonstrated that 90% of isolates (n = 48) were insensitive to azoxystrobin (a QoI fungicide, FRAC 11) and 57% (n = 47) were insensitive to pyrimethanil (an AP fungicide, FRAC 9). Both fungicides are used extensively on onion in the Holland Marsh but are no longer effective against the population of S. vesicarium that is present in the region. Additional studies on the efficacy of seed treatments, fungicide insensitivity, and potential of biological fungicides are required.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".