<i>Botrytis cinerea</i>management in ornamental production: a continuous battle
Bibliographic record
Abstract
Ornamental production systems are complicated to manage due to the many species and genera that may be grown and handled together on a single production site. Ornamentals are threatened by various phytopathogenic fungi in greenhouse and field production. Among these, Botrytis cinerea is one of the most notorious pathogens of ornamentals, specifically cut flowers. B. cinerea is responsible for causing Botrytis blight disease in both pre- and post-harvest conditions. The pathogen infects leaves, stems, flowers, etc., and causes petal specking, flower blight, sepal yellowing, and peduncle bending, among other symptoms. The ability of B. cinerea to cause disease in greenhouses and fields, as well as in subsequent handling, storage, and transportation, makes this fungus an important pathogen due to its potential negative economic effects on the cut flower industry. For the management of B. cinerea, the routine application of fungicides is considered a major tool in commercial production. However, fungicide resistance, phytotoxicity, application residues, environmental concerns, and health issues have forced growers to seek alternative management approaches. In this review paper, we discuss the different approaches (classic to novel strategies) used for B. cinerea management, including chemical methods and their modes of action. The integration of new practices with existing management strategies (sanitation, nutrition, plant regulators, botanical extracts, biological control, fungicides) could provide effective results in ornamental production systems. Understanding the ecology of pathosystems, disease epidemiology and the integration of all possible management measures as a system approach may also provide adequate disease suppression in both pre- and post-harvest conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".