<i>Bacillus</i> species in biofertilizer formulations inhibit the development of fusarium root and stem rot on cannabis ( <i>Cannabis sativa</i> L.) plants and reduce <i>Fusarium oxysporum</i> populations in growing medium
Bibliographic record
Abstract
Fusarium root and stem rot (FRS), caused by Fusarium oxysporum, is a significant disease affecting cannabis (Cannabis sativa L.) production, causing plant mortality during propagation and reducing development at later cultivation stages. With no registered fungicides and few evaluated alternatives, management options remain limited. We assessed the ability of Bacillus spp. formulated in two commercial biofertilizers – Tarantula® and Piranha® – containing Bacillus spp. and other beneficial microbes, to reduce the development of FRS on rooted cuttings of two cannabis genotypes. The products were applied to the roots 1 week prior to pathogen inoculation, and disease evaluations were made weekly over a 6-week period. Tarantula® significantly reduced FRS development at 4 and 6 weeks after application. Cannabis genotype influenced the degree of biocontrol achieved, as the more susceptible genotype displayed a higher level of infection. The effect of Tarantula® was investigated on pathogen growth and survival in two brands of coco coir growing media. These were inoculated with F. oxysporum 1 week after treatment, followed by serial dilutions and plating, after which colony-forming units were counted. Reduced pathogen populations were observed 1 week after incubation. Additionally, the extent of endophytic colonization by Bacillus spp. was explored in cannabis stems over a 3-week period following application. Endophytic colonization was observed for a 5–10 cm distance on cannabis stems following Tarantula® application. These results demonstrate the potential for Bacillus-based biofertilizer products to be used in cannabis cultivation for pre-emptive disease management and to reduce pathogen survival in growing media.
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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".