Biological control of <i>Fusarium oxysporum</i> causing damping-off and <i>Pythium myriotylum</i> causing root and crown rot on cannabis ( <i>Cannabis sativa</i> L.) plants
Bibliographic record
Abstract
Damping-off, crown rot, and root rot caused by Fusarium oxysporum and Pythium spp. are important diseases that reduce the growth of cannabis plants and cause mortality. There are currently limited management options available to producers in the absence of registered chemical fungicides. The comparative efficacy of five biological-control agents in reducing disease severity was evaluated in this study. Rhapsody® ASO (Bacillus subtilis), Stargus® (Bacillus amyloliquefaciens), Lalstop® (Gliocladium catenulatum, Prestop®), RootShield® Plus WP (Trichoderma harzianum and Trichoderma virens), and Asperello® (Trichoderma asperellum) were applied to cannabis cuttings in rockwool blocks followed 48 hr later by inoculation with a F. oxysporum spore and mycelial suspension. Disease was rated 7, 14 and 21 days post-inoculation. The most effective treatments were Lalstop, Rootshield, Asperello and Stargus, which provided a significant (P < 0.05) reduction (30 to 56.3%) in mean disease severity 14 days post-inoculation compared to the Fusarium inoculated control. Application of Rootshield, Asperello and Lalstop resulted in endophytic colonization of cannabis cuttings when assessed after 2 and 7 days. For Pythium biocontrol, Rootshield Plus WP, Asperello, and Lalstop were applied as a drench to rooted plants 7 days before inoculation with P. myriotylum. Based on disease severity ratings and plant measurements in two experiments, the most effective treatments were Rootshield and Lalstop. Plants receiving Lalstop or Rootshield had significantly (P < 0.05) higher fresh root weights compared to the pathogen only treatment. These results indicate that several biological control agents can protect cannabis plants from infection by Fusarium and Pythium spp. through pre-emptive colonization.
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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".