What agronomic practices can reduce the effect of Fusarium head blight on durum wheat in Saskatchewan?
Bibliographic record
Abstract
Fusarium head blight (FHB) has become an important disease of cereals in moist regions of western Canada. This disease has played an important role in contributing to lower grain yields and substantial downgrading of durum wheat (Triticum turgidum L. var. durum). The objective of this study, conducted at three locations on the Canadian prairie, two in Saskatchewan and one in Manitoba, from 2001 to 2003, was to determine the effect of seeding density, nitrogen supply, fungicide treatment, and durum wheat cultivar on FHB development, grain quality, grade protection and economic return. A four-way factorial design was used with two seed densities (150 and 300 viable seeds m-2), two nitrogen rates (75 and 100% of recommended rate), three cultivars (AC Avonlea, AC Morse and AC Navigator), and four fungicide treatments (no application, Tilt at flag leaf, Folicur at anthesis and Tilt at flag leaf followed by Folicur at anthesis). Increasing the seed density decreased FHB at 4 out of the 7 site year when FHB occurred, however increasing the seed density tended to increase leaf disease severity. The application of Folicur did not affect Fusarium levels. The application of Tilt and /or Folicur decreased leaf disease at 6 out of 9 site years and affected yield at 5 out of 9 site years. There was no consistent effect from nitrogen or cultivar.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".