A review of non-fungicidal approaches for the control of apple scab
Bibliographic record
Abstract
Apple scab is the single most important disease of apple in Canada and the most costly to control. Failure to control apple scab results in severe yield losses and a reduction in market value of harvested fruits. Currently, the strategy to control apple scab relies on multiple applications of fungicides. These sprays are a significant cost to growers and the indirect environmental impact may be substantial. Reliance on fungicides can be reduced by the integration of non-fungicidal control measures that include genetic, physical, and biological approaches. Recommendations for chemical control of apple scab are often based on the highly susceptible cultivar McIntosh. This was justified because up to 60 to 70% of orchards in northeastern North America were planted with this cultivar. Today, the situation is changing as more and more growers are planting new cultivars, some of which are less susceptible to apple scab. A change in the recommendations to account for cultivar susceptibility could result in a reduction in the number of fungicide applications required. In addition, there has been long-standing research on resistant cultivars, but none of the scab-resistant cultivars have been widely accepted. However, with new molecular techniques to identify and locate the resistance genes, there is potential for progress on this front. Furthermore, fungicide applications for primary infections can be delayed if the number of ascospores is reduced after fall treatments that include leaf shredding, the application of biological control agents, or urea. These methods are preventive and can be integrated into existing management programs. However, the integration of all or some of these methods is more complex than simply the use of fungicides. Nevertheless, integrated management of apple scab may prove more sustainable on a long-term basis, mainly because it does not depend on the use of a single method. Hence the risk of the development of fungicide resistance in the pathogen population is reduced.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".