Prevalence of mycotoxins from silage in a small beef cattle feedlot over a storage season: a case study
Bibliographic record
Abstract
Challenges exist worldwide for the storage of animal feed, though year-long feeding of livestock is especially difficult in northern climates, including Canada. Usage of silage allows for crops to be harvested at the optimal nutritional value and stored for extended periods in customized storage systems. This case study investigated various feed management practices typical of small beef cattle feedlot farms in Southwestern Ontario, Canada. Crop samples were collected monthly and analyzed for mycotoxins and fungal secondary metabolites by liquid chromatography mass spectrometry (LC-MS/MS). Stored barley contained modest concentrations of deoxynivalenol and related compounds, as well as near trace amounts of fumonisin. Stored hay contained low concentrations of deoxynivalenol and beauvericin from the field. Hot spots of the Penicillium metabolite citrinin were detected after 7–8 months of storage in barley (39.2 ng g−1). Citrinin was also detected after 7–8 months in hay (16.3–17.5 ng g−1) along with the appearance of trace amounts of other storage toxins, including penitrem A (8.84 ng g−1), griseofulvin (34.8 ng g−1) and sterigmatocystin (14.1–261 ng g−1). This case study was a unique opportunity to use ‘citizen science’ to observe the onset of postharvest fungi and associated mycotoxins in various storage conditions allowing for the farmer to make necessary modifications before major problems begin. An improved understanding of the storage conditions that foster fungal growth and mycotoxin production in this working farm led to better agronomic practices, ultimately improving feed quality, livestock health, and profitability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".