Evaluation of epidemics and weather-based fungicide application programmes in controlling anthracnose fruit rot of day-neutral strawberry in outdoor field and protected cultivation systems
Bibliographic record
Abstract
Anthracnose fruit rot caused by Colletotrichum acutatum is an economically important disease of strawberry in Ontario. Three years of experiments (2009–2011) were conducted at the University of Guelph’s Cedar Springs Research Station in Blenheim, ON to understand anthracnose fruit rot epidemics in outdoor field and protected production systems and to evaluate different fungicide spray programmes for disease control in day-neutral strawberry. Weather-based fungicide timing programmes were compared with calendar spray programmes in two day-neutral cultivars, ‘Seascape’ and ‘Albion’. Incidence of disease in high-tunnels was very low in all 3 years, even in fungicide non-sprayed plots, indicating that cultivation of day-neutral strawberry in high-tunnels could be an alternative strategy for controlling anthracnose fruit rot with minimal use of fungicides. In outdoor fields, disease incidence was greatly influenced by leaf wetness duration, rainfall and temperature. The use of a weather-based model to determine the timing of fungicide treatments reduced the number of sprays and was as effective as a calendar-based spray at 7-day intervals to reduce the disease and increase marketable fruit yield. Rotating fungicides with different modes of actions (pyraclostrobin, myclobutanil and boscalid + pyraclostrobin) was more effective in reducing disease than regular sprays of captan. The outcomes of this research will be useful to develop decision support tools and select proper fungicides and cultivation systems to manage anthracnose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".