Effect of pydiflumetofen onGibberella ear rot andFusarium mycotoxin accumulation in maize grain
Bibliographic record
Abstract
In Ontario, Canada,Fusarium graminearum Schwabe causesGibberella ear rot (GER) in maize, resulting in the accumulation of mycotoxins, mainly deoxynivalenol (DON), DON-3-glucoside (DON-3G) and zearalenone (ZEN) in infected kernels. Fungicides can be an important tool for managing GER and DON and otherFusarium mycotoxins in maize. Until recently, all fungicides available to growers were triazoles, thus no resistance management strategy through fungicide use was possible. In this study, a novel carboxamide fungicide active ingredient (pydiflumetofen) was evaluated against conventional triazole fungicides and mixtures for: (1) effectiveness on mycotoxins (2) optimal application timing; and (3) efficacy of application, with and without an insecticide, under natural and inoculated-misted conditions. The best timing for fungicide application was at full silk, resulting in the highest reduction of GER symptoms and lowest accumulation ofF. graminearum mycotoxins in harvested grain. DON and DON-3G concentrations were reduced by at least 50% with a fungicide application at full silk. Fungicide treatments did not affect fumonisin concentrations in grain. Pydiflumetofen (94 g active ingredients (AI)/ha) and fungicides containing pydiflumetofen (75-94 g AI/ha) were similar to standard triazole fungicides (prothioconazole at 200 g AI/ha and metconazole at 90 g AI/ha) for reducing GER andF. graminearum mycotoxins under misted-inoculated plots and commercial field conditions; as a result, we expect pydiflumetofen to be competitive with triazole-only chemistries in the marketplace, which should delay the onset of fungicide resistance.
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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".