Previous Crop Sequences Effect on Fusarium Head Blight of Cereals in the Prairies
Bibliographic record
Abstract
Fusarium head blight (FHB) is a disease of concern across the Canadian prairies; low crop diversity within rotations increases disease risk. Approximately 60% of the area seeded to annual crops in Alberta, Manitoba and Saskatchewan consists of wheat and canola. The present study focusses on the effect of previous crop sequences on the severity of FHB of cereals across the prairies. From 2018 to 2020, six locations were seeded with a core set of five crops including wheat, barley, canola, pea, and maize; at some sites, a sixth crop was included such as lentil. Each year, yield, crop quality and FHB severity were recorded; also, Fusarium spp. were isolated and identified from cereal kernels. Several Fusarium spp. caused FHB among cereal crops and were associated with host crops. The experiment consisted of a factorial arrangement in a split block design. The diversity criteria were established by using groups A, B, and C. Where A is the crop sequences that included cereals, pulses and oilseeds in the rotation. Treatment B, consisted of cereals and pulses, or cereals and oilseeds; while C, consisted only of cereals. This year data from Saskatoon shows that the diversity criteria played an important role in the proportion of the various Fusarium spp.. Fusarium spp. shows a significant difference between treatments and the frequency of F. graminearum isolated was similar in sequences with only cereals and cereal with pulses/ oilseeds, but both differed from a crop sequence that include three-different crops. The lack of crop diversity across western Canada is a risk factor for future disease outbreaks. Link to Video Presentation: https://youtu.be/zbUD6Lp7-cg
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".