Biocontrol Activity of<i>Bacillus</i>spp. and<i>Pseudomonas</i>spp. Against<i>Botrytis cinerea</i>and Other Cannabis Fungal Pathogens
Bibliographic record
Abstract
Gray mold caused by Botrytis cinerea is one of the most widespread and damaging diseases in cannabis crops worldwide. With challenging restrictions on pesticide use and few effective control measures, biocontrol agents are needed to manage this disease. The aim of this study was to identify bacterial biocontrol agents with wide-spectrum activity against B. cinerea and other cannabis fungal pathogens. Twelve Bacillus and Pseudomonas strains were first screened with in vitro confrontational assays against 10 culturable cannabis pathogens, namely B. cinerea, Sclerotinia sclerotiorum, Fusarium culmorum, F. sporotrichoides, F. oxysporum, Nigrospora sphaerica, N. oryzae, Alternaria alternata, Phoma sp., and Cercospora sp. Six strains displaying the highest inhibitory activity, namely Bacillus velezensis LBUM279, FZB42, LBUM1082, Bacillus subtilis LBUM979, P. synxantha LBUM223, and P. protegens Pf-5, were further assessed in planta where all, except LBUM223, significantly controlled gray mold development on cannabis leaves. Notably, LBUM279 and FZB42 reduced disease severity by at least half compared with water-treated plants and prevented lesion development and/or sporulation up to 9 days after pathogen inoculation. Genomes of LBUM279, LBUM1082, and LBUM979 were sequenced de novo and taxonomic affiliations were determined to ensure nonrelatedness with pathogenic strains. Moreover, the genomes were exempt of detrimental genes encoding major toxins and virulence factors that could otherwise pose a biosafety risk when used on crops. Eighteen gene clusters of potential biocontrol interest were also identified. To our knowledge, this is the first reported attempt to control cannabis fungal diseases in planta by direct antagonism with beneficial bacteria.
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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) | 0.001 | 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".