Factors Influencing the Occurrence of Onion Downy Mildew (Peronospora destructor) Epidemics: Trends from 31 Years of Observational Data
Bibliographic record
Abstract
Onion downy mildew (ODM) caused by Peronospora destructor has been increasing annually in south-western Québec since the early 2000s, reaching 33% of affected onion fields in 2014. Using observational data collected over a period of 31 consecutive years, this study aimed to investigate the variations in ODM incidence and epidemic onset and identify the meteorological variables that influence its polyetic development. A logistic model was fitted to each ODM epidemic to estimate and compare the onset of epidemics on a regional basis. Results of this analysis showed that the first observation date, 10% epidemic onset (b10) and mid-time (b) were, on average, 30.4, 15.1 and 11.3 days earlier in 2007–2017 than in 1987–1996. Results of a principal component analysis suggested that regional disease incidence was mostly influenced by the precipitation regime, the final regional disease incidence the previous year, and warmer temperature during the harvest period the previous fall. Subsequently, the data were divided in three periods of 10, 10 and 11 years, and a discriminant analysis was performed to classify each year in the correct period. Using a sufficient subset of five discriminating variables (temperature and rainfall at harvest the previous fall, winter coldness, solar radiation, and disease incidence the previous year), it was possible to classify 93.5% of the ODM epidemics in the period where they belong. These results suggest that P. destructor may overwinter under northern latitudes and help to highlight the need for more research on overwintering and for the development of molecular-based tools enabling the monitoring of initial and secondary inoculum.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".