Risk of Spreading Soft Rot Through Cutting Dips Against Whiteflies in Greenhouse-Grown Poinsettia
Bibliographic record
Abstract
Dipping is a quick and cost-effective technique to reduce pest infestations on unrooted cuttings of greenhouse ornamental crops. Large numbers of cuttings are immersed in an insecticidal treatment, e.g., biopesticides and/or insecticidal soap, before they are stuck in the growing medium and rooted. This research investigated the risk of cross-contamination of poinsettia cuttings with Pectobacterium carotovorum subsp. carotovorum, a potentially devastating pathogen causing soft rot, through the dipping process. Sampling at four commercial greenhouses showed that P. carotovorum subsp. carotovorum was present in all dip suspensions during and after processing poinsettia cuttings; concentrations up to 1 × 10 5 CFU/ml were detected. A laboratory experiment determined that P. carotovorum subsp. carotovorum-infected cuttings could contaminate clean dip suspensions to similar levels. These results indicated that there is potential for disease transfer during dipping. The potential for cross-contamination of healthy cuttings was evaluated by immersing poinsettia cuttings in dip suspensions artificially inoculated with P. carotovorum subsp. carotovorum (from 1 × 10 3 to 1 × 10 7 CFU/ml). Disease incidence increased as P. carotovorum subsp. carotovorum concentrations in the dip suspension increased and the variety ‘Prestige Red’ was more susceptible than ‘Freedom White.’ However, even at the highest P. carotovorum subsp. carotovorum concentration of 1 × 10 7 CFU/ml, the proportion of diseased cuttings was low at 6% for var. ‘Freedom White,’ but higher at 21% for var. ‘Prestige Red.’ We conclude that P. carotovorum subsp. carotovorum transfer among unrooted poinsettia cuttings through the dipping process is relatively low although some varieties are sensitive to high levels of inoculum. Even so, strict sanitation practices are still important to prevent build-up of inoculum in the dip treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".