The epidemiology and management of <i>Botrytis cinerea</i> causing bud rot on greenhouse cultivated cannabis ( <i>Cannabis sativa</i> L.)
Bibliographic record
Abstract
Botrytis cinerea Pers. causes bud rot on Cannabis sativa L. (Cannabis) inflorescences, significantly reducing yield and quality. We investigated the timing of B. cinerea spore infection during the 49-day flowering period (FP) and how environmental conditions and host genotype influence disease development. Artificial spore inoculations made at 14, 21 or 28 days of the FP resulted in the highest disease development compared to inoculations made at 7 or 35 days. Visible mycelial growth within inflorescences was observed at 33–41 days, regardless of inoculation time. The disease severity under greenhouse conditions from natural inoculum was highest during summer and fall seasons (June to November), which resulted in bud rot incidence of 1–13%, depending on the genotype; lower disease incidence occurred at other times. The highest disease was observed during September and October, which corresponded to average daily outdoor absolute humidity of 14 g m−3 and temperatures of 20–22°C. Notably, humidity and temperatures within 49-day-old inflorescences were higher by 15.4% and 2.5°C, respectively, compared to ambient conditions. These findings suggest that bud rot development is strongly influenced by environmental conditions within and outside the greenhouse, which can impact spore germination and subsequent infection. Among management practices evaluated, enhanced air circulation around inflorescences reduced bud rot incidence by 66–92%. Additionally, applications of Rootshield HC (Trichoderma harzianum, 10 g L−1) on days 14, 21 and 28 of the FP reduced disease by 47–91%. Treatments with Double Nickel (Bacillus amyloliquefaciens), LifeGard (Bacillus mycoides), Prestop (Gliocladium catenulatum) and Regalia Maxx (Reynoutria sachalinensis) provided varying levels of disease reduction.
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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 | 0.000 | 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 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".