Survey of foliar diseases in faba bean ( <i>Vicia faba</i> L.), and pathogenicity of fungi associated with diseases, on the Canadian Prairies
Bibliographic record
Abstract
A survey of 103 commercial faba bean (Vicia faba L.) fields was conducted in Alberta and Saskatchewan from 2017 to 2019 to determine the incidence and severity of foliar diseases and to identify associated fungi. Foliar disease incidence was high (>75%) in both Alberta and Saskatchewan in all three years and reached 100% in Alberta in 2019. However, disease severity was low (<2.0 on a 1–5 scale) in both provinces, except in Alberta in 2019 when average disease severity was 3.2. A total of 4,057 fungal isolates were recovered from leaf samples exhibiting foliar leaf spot and blight symptoms. Based on culture morphology and internal transcribed spacer (ITS) sequence similarity, fungal genera were identified as Botrytis (11.1%), Alternaria (43.3%), Fusarium (6.5%), and Stemphylium (24.4%). Sclerotinia and Colletotrichum constituted 4.7%. Polymerase chain reaction using species-specific primers was employed to amplify DNA to distinguish between B. fabae and B. cinerea. The abundance of B. fabae (88.2%) dominated over B. cinerea (11.8%) causing faba bean chocolate spot. Pathogenicity tests on whole plants revealed both Botrytis species induced chocolate spot symptoms, while other associated fungal isolates caused disease symptoms different from chocolate spot upon inoculation. However, it was very difficult to distinguish between symptoms caused by Stemphylium and Botrytis on field samples. Information from this survey is important for understanding the diversity and abundance of pathogens and for developing management practices of foliar diseases of faba bean in the prairies.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".