Knowledge-Based Integrated Management of Botrytis Bunch Rot of Grapevine Caused by <i>Botrytis cinerea</i> in a Northern Climate
Bibliographic record
Abstract
In this study, we evaluated the efficacy of control methods against Botrytis bunch rot (BBR) and explored the integration of several control methods. Among the biological control agents, Aureobasidium pullulans (Botector) demonstrated the best efficacy, with an average reduction in BBR of 57% (86% reduction in BBR for the synthetic fungicides). With the exception of leaf removal at the fruit set stage (71 on the BBCH scale) on one row side only, leaf removal on both row sides at stage 71 and on one and two row sides at the early flowering stage (63 on the BBCH scale) significantly reduced the percent BBR at harvest (23.6, 21.8, and 15.1%, respectively) compared with the control without leaf removal (48.2%). Of the 10 BBR management programs, the programs that kept the BBR below the economic threshold of 5% bunch area diseased at harvest were, for the susceptible cultivar Seyval Blanc, trash management combined with leaf removal at stage 63 on both row sides combined with application of synthetic fungicides at fixed intervals (3.9%), synthetic fungicides applied based on risk of BBR (4.3%), or application of Botector according to the risks of BBR (4.5%). With respect to Vidal Blanc, a moderately susceptible cultivar, for these management programs, the severity of BBR at harvest was 4.5, 4.7, and 4.9%, respectively. The results showed that by using integrated BBR management it is possible to maintain the disease below 5% without synthetic fungicides or with a reduced number of applications. [Formula: see text] Copyright © 2025 His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".