Effects of field-applied fungicides, grain moisture, and time on deoxynivalenol during postharvest storage of winter wheat grain
Bibliographic record
Abstract
Fusarium head blight, caused mainly by Fusarium graminearum Schwabe, results in major losses in wheat. In two separate field experiments, spikes of winter wheat cultivars ‘Overland’ (moderately resistant) and ‘Overley’ (susceptible) were sprayed at anthesis with the triazole fungicide Prosaro (prothioconazole + tebuconazole) or the strobilurin fungicide Headline (pyraclostrobin) or not sprayed. Following harvest, deoxynivalenol (DON) concentrations were monitored during 120 d of grain storage at 10 °C, 40% relative humidity, and 10%, 16%, or 20% grain moisture. In ‘Overland’, DON decreased significantly at P = 0.05 from an average of 3.6 to 3.0 μg g−1 in the check and decreased from 2.7 to 2.2 μg g−1 in the Prosaro treatment. DON did not significantly decrease (4.4–4.1 μg g−1) in the Headline treatment. DON concentrations did not differ between 16% (3.1 μg g−1) and 20% (3.0 μg g−1) grain moisture. In ‘Overley’, DON increased significantly from 3.1 to 3.6 μg g−1 in the check and from 2.9 to 3.5 μg g−1 in the Headline treatment, but remained the same at 2.2 μg g−1 in the Prosaro treatment. DON concentrations were not different between 16% (3.2 μg g−1) and 20% (3.1 μg g−1) grain moisture but were significantly lower (2.7 μg g−1) at 10% grain moisture. These results indicate that the effects of fungicides applied at anthesis in the field can impact DON concentrations through grain storage. Triazoles are recommended over strobilurins to achieve this extended postharvest protection from DON, and grain moisture during storage should be below the maximum safe level of 13.5% at 10 °C.
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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".