Biological management of Fusarium head blight and mycotoxin contamination in wheat
Bibliographic record
Abstract
Fusarium head blight (FHB), caused by Gibberella zeae is a harmful disease of wheat. To manage FHB and mycotoxin contamination in wheat, field experiments were conducted from 2007 to 2008 to evaluate a total of 20 selected bioagents for their ability to inhibit perithecial production of G. zeae and for the control of FHB and deoxynivalenol (DON) contamination, in comparison with the registered fungicide Folicur (tebuconazole). All 20 bioagents significantly reduced the perithecial production compared to the untreated control. Clonostachy rosea strain ACM941 was the most effective treatment, reducing the production of perithecia by 63.7% in 2007 and 67.5% in 2008. These effects were significantly better than Folicur fungicide, which reduced perithecial production by 30.4% and 20.5%, for 2007 and 2008, respectively. When sprayed on to wheat heads, seven of the 20 bioagents significantly reduced the FHB index, one reduced Fusarium damaged kernels (FDK), and six reduced DON content in grains in 2007. ACM941 was the only treatment that significantly reduced FHB index, FDK, and DON, by 46.4%, 29.0% and 28.7%, respectively. Among the six bioagents and three formulated products evaluated in two separate field trials in 2008, ACM941 and its formulated product ACM941-CU were the only treatments that significantly reduced FHB index, FDK, and DON. The treatments reduced FHB index by 30.8% and 31.4%, FDK by 17.8% and 43.8%, and DON by 30.8% and 37.1%, for ACM941 and ACM941-CU, respectively. These effects were less marked than those of the Folicur fungicide that reduced FHB index by 98.8%, FDK by 94.2%, and DON by 92.1%. Results of this study suggest that ACM941 is a promising bioagent against G. zeae and may be used as a control measure in organic farming and in an integrated FHB and DON management program for wheat production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".