EFFECT OF DIVERSE CROP SEQUENCES ON FUSARIUM HEAD BLIGHT OF WHEAT IN THE CANADIAN PRAIRIES
Bibliographic record
Abstract
Fusarium head blight (FHB) is a major disease of cereals caused by several Fusarium spp. The disease reduces wheat yield and results in the formation of mycotoxin deoxynivalenol (DON) in the grain. Fusarium head blight mitigation requires an integrated disease management approach that includes a diverse crop rotation, fungicide application, and FHB resistant varieties. In Canada, the lack of highly resistant cereal varieties, the limited effectiveness of fungicides and tight cereal rotations make managing the disease difficult. It is imperative to follow a diverse crop rotation that includes non-host crops to reduce the proliferation of FHB pathogens. My research focused on the effect of multiple host and non-host crops in a planned sequence with wheat (bread and durum). The crop sequences included four of the most common field crops in Western Canada (wheat, canola, barley, and pea), as well as maize, in a split-block design with three replicates. Maize is a major crop in North America that increases the risk of Fusarium graminearum proliferation. The experiment was conducted over three growing seasons (2018-2020) at three sites in Western Canada, durum wheat sites were Lethbridge, AB and Saskatoon, SK, while the bread wheat site was at Brandon, MB. The impact of the various crop sequences was determined by: (1) the presence of cereal diseases such as FHB and leaf spots; (2) by grain yield and quality parameters, including deoxynivalenol (DON) and protein content, and (3) the frequency of isolation of Fusarium spp. from kernels of the cereal crops. The incidence and severity of FHB was low in 2018 and 2020; the 2019 stubbles had the greatest impact on the cereals grown in 2020. High FHB severity, leaf spots and the species F. graminearum and F. poae increased in durum and bread wheat when grown after host crops compared to non-host crops. When oilseeds and pulses were included in the sequences, higher yield, TW, TKW and protein content was observed, which agrees with my hypothesis that there is a beneficial effect of diverse crop sequences on FHB mitigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".