Fungicide Sensitivity Monitoring of <i>Alternaria</i> spp. Causing Leaf Spot of Sugarbeet (<i>Beta vulgaris</i>) in the Upper Great Lakes
Bibliographic record
Abstract
Alternaria leaf spot (ALS), caused by Alternaria spp., can occur wherever sugarbeet is grown. Infection by Alternaria spp. and disease management has historically been considered a minor issue in sugarbeet production in the United States. An increase of both incidence and severity in 2016 of ALS high enough to cause yield loss has been observed in Michigan. With a renewed need to consider potential management of this disease, the sensitivity was determined for populations of Alternaria spp. to three classes of fungicides currently labeled for management of leaf spot on sugarbeet, including demethylase inhibitor (DMI), quinone outside inhibitor (QoI), and organo-tin fungicides. Leaves with symptoms of ALS were sampled from sugarbeet fields in east-central Michigan and southwestern Ontario, Canada. Monoconidial isolates were obtained to determine sensitivity to each fungicide class above. A spiral gradient dilution method was used to estimate the fungicide effective concentration (in milligrams per liter) that caused a 50% inhibition of fungal growth in vitro for all isolates. Significant temporal shifts were detected in the frequencies of sensitivity phenotypes to DMI and QoI but not organo-tin fungicides from 2016 through 2017. Individual isolates of Alternaria spp. were recovered with cross-resistance to DMI and multiple resistance to DMI, QoI, and triphenyltin hydroxide fungicides. To our knowledge, this is the first report of a fungus other than Cercospora beticola with resistance to organo-tin fungicides. Fungicide sensitivity monitoring indicates that an effective integrated disease management approach combining fungicide efficacy trials and monitoring pathogen biology is essential for developing effective resistance management recommendations.
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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 | 0.000 | 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 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".