A degree-day model to initiate fungicide spray programs for management of grape powdery mildew [<i>Erysiphe necator</i>]
Bibliographic record
Abstract
Powdery mildew, caused by Erysiphe necator, is the most important grape disease in Quebec, Canada. Based on the premise that the production of secondary inoculum is a key factor in powdery mildew development, a model based on degree-day accumulation was developed and validated as a tool to initiate a calendar-based fungicide program. The Richard%rsquo;s model was used to describe the proportion of seasonal airborne inoculum as a function of degree-days (base 6 °C) accumulated since the Eichhorn-Lorenz grape phenological stage 7 (2%ndash;3 fully expanded leaves). The model explained 91% of the variation in proportion of seasonal airborne inoculum and 96% when validated against independent observations. Reliability of the model to time the initiation of a standard fungicide spray program was validated in experimental vineyards from 2004 to 2007. The following management schemes were compared: (1) no fungicides (control); (2) fungicides applied at fixed intervals starting at the 3%ndash;4 leaves growth stage; (3) a fungicide spray program initiated based on the degree-day model; and (4) a fungicide spray program initiated based on both the degree-day model and airborne inoculum concentration. Depending on years and cultivars, the use of the model reduced the number of fungicide sprays by 40% to 55%. The degree-day model could be used as a component of a risk management system for grape powdery mildew to estimate the need for fungicide sprays before bloom or to time the initiation of a fungicide spray program.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".