The bud rot pathogens infecting cannabis ( <i>Cannabis sativa</i> L., marijuana) inflorescences: symptomology, species identification, pathogenicity and biological control
Bibliographic record
Abstract
Bud rot pathogens cause diseases on Cannabis sativa L. (cannabis, hemp) worldwide through pre- and post-harvest infections of the inflorescence. Seven indoor or outdoor cannabis production sites and three hemp fields were sampled for bud rot and stem canker presence during 2019–2020. Among 178 isolates recovered from diseased tissues, sequences of the ITS1-5.8S-ITS2 region of rDNA, the glyceraldehyde-3-phosphate dehydrogenase (G3PDH) gene and the heat shock 60 (HSP) gene identified the following: Botrytis cinerea (162 isolates), B. pseudocinerea (2), B. porri (1), Sclerotinia sclerotiorum (5), Diaporthe eres (3) and Fusarium graminearum (5). Pathogenicity studies conducted on fresh detached cannabis buds inoculated with spore suspensions or mycelial plugs showed that B. cinerea, S. sclerotiorum and F. graminearum were the most virulent, while B. pseudocinerea, B. porri and D. eres caused significantly less bud rot. Optimal growth of Botrytis species occurred at 15–25°C. In vitro antagonism tests showed that Bacillus spp., Trichoderma asperellum and Gliocladium catenulatum inhibited B. cinerea and S. sclerotiorum colony growth. When applied as a spray 48 h prior to B. cinerea inoculation, all biocontrol agents significantly (P < 0.01) reduced disease development on detached inflorescences. Prolific growth and sporulation of T. asperellum and G. catenulatum were observed on bud tissues. The pathogens B. porri, S. sclerotiorum, D. eres and F. graminearum are described for the first time as cannabis bud rot pathogens. Inoculum from neighbouring fields of diseased garlic, cabbage, blueberry and hay pasture, respectively, likely initiated infection of inflorescences. Several biological control agents show potential for disease reduction through competitive exclusion.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".