Impact of agronomic practices onFusarium mycotoxin accumulation in maize grain
Bibliographic record
Abstract
In the Great Lakes region of North America, Gibberella ear rot (GER), caused by Fusarium graminearum, affects grain quality due to the accumulation of mycotoxins. GER severity is strongly influenced by environmental conditions; however, agronomic practices can also influence disease severity and mycotoxin accumulation. In this study, three separate small-plot experiments were conducted at Ridgetown, ON, Canada during 2019 and 2020 under an inoculated-misted system to determine Fusarium mycotoxin accumulation as affected by: (1) plant population density; (2) in-row-plant developmental variability; and (3) the effect of integrated Bt refuge genetics. In this study, DON concentrations were at least 49% higher in maize at 113,600 plants/ha compared to 79,000 plants/ha. Moreover, mycotoxin accumulation was higher in plants that were delayed developmentally in the crop row; total DON concentrations were at least 310% higher in late silked plants adjacent to early silked plants. Results of the plant population density and in-row-plant developmental variability suggest that the main driver for mycotoxin accumulation was stress induced by plant competition rather than environmental conditions; this highlights the importance of avoiding plant competitive stress as a strategy to reduce the risks of mycotoxin accumulation. In this study, there was no statistical difference in DON accumulation between the Bt component and the non-Bt component in each of the four hybrids tested; however, there was evidence that hybrids varied in susceptibility, including the Bt and non-Bt components that were paired commercially in a bag of seed maize. Reducing mycotoxins in maize requires integrated management, which includes agronomic considerations. These results indicate that mycotoxins are favoured with high plant populations and plant-to-plant variability in the row, especially in susceptible hybrids.
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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) | 0.000 | 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".