Sensitivity of<i>Rhizoctonia solani</i>to Succinate Dehydrogenase Inhibitor and Demethylation Inhibitor Fungicides
Bibliographic record
Abstract
Soybean seedling diseases are caused by Rhizoctonia solani and can be managed with seed-applied fungicides that belong to different chemistry classes. To provide a benchmark for assessing a decline in sensitivities to these fungicide classes, R. solani isolates collected prior to 2001 were evaluated for their sensitivities to succinate dehydrogenase inhibitor (SDHI) (penflufen and sedaxane) and demethylation inhibitor (DMI) fungicides (ipconazole and prothioconazole). The effective concentration of each fungicide that reduced mycelial growth by 50% (EC50) was determined in vitro and compared with those of isolates recovered after 2011 from soybean plants with damping off and hypocotyl and root rot symptoms across different soybean-growing regions in the United States and Canada. All isolates, regardless of collection date, were extremely sensitive (EC50< 1 μg/ml) to the SDHI fungicides but were either extremely sensitive or moderately sensitive (1 ≤ EC50≤ 10 μg/ml) to the DMI fungicides. For all four active ingredients, variation in sensitivities was observed within and among the different anastomosis groups composing both isolate groups. Isolates collected after 2011, which also had varying in vitro sensitivities, were further evaluated for in vivo sensitivity to the four fungicides in the greenhouse. In vitro fungicide sensitivity did not always coincide with fungicide efficacy in vivo because all isolates tested, regardless of in vitro sensitivity, were effectively controlled by the application of the seed treatment fungicides in the greenhouse. Overall, our results indicate no shift in sensitivity to the fungicide classes evaluated, although considerable variability in the sensitivities of the two groups of isolates examined was present. Based on this research, continued monitoring of fungicide sensitivities of R. solani populations should occur to determine whether sensitivities become further reduced in the future.
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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) | 0.001 | 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".