Potential impacts of soil microbiota manipulation on secondary metabolites production in cannabis
Bibliographic record
Abstract
BACKGROUND: Cannabis growing practices and particularly indoor cultivation conditions have a great influence on the production of cannabinoids. Plant-associated microbes may affect nutrient acquisition by the plant. However, beneficial microbes influencing cannabinoid biosynthesis remain largely unexplored and unexploited in cannabis production. OBJECTIVE: To summarize study outcomes on bacterial and fungal communities associated with cannabis using high-throughput sequencing technologies and to uncover microbial interactions, species diversity, and microbial network connections that potentially influence secondary metabolite production in cannabis. MATERIALS AND METHOD: A mini review was conducted including recent publications on cannabis and their associated microbiota and secondary metabolite production. RESULTS: In this review, we provide an overview of the potential role of the soil microbiome in production of cannabinoids, and discussed that manipulation of cannabis-associated microbiome obtained through soil amendment interventions of diversified microbial communities sourced from natural forest soil could potentially help producers of cannabis to improve yields of cannabinoids and enhance the balance of cannabidiol (CBD) and tetrahydrocannabinol (THC) proportions. CONCLUSION: Cannabis is one of the oldest cultivated crops in history, grown for food, fiber, and drugs for thousands of years. Extension of genetic variation in cannabis has developed into wide-ranging varieties with various complementary phenotypes and secondary metabolites. For medical or pharmaceutical purposes, the ratio of CBD to THC is key. Therefore, studying soil microbiota associated with cannabis and its potential impact on secondary metabolites production could be useful when selecting microorganisms as bioinoculant agents for enhanced organic cannabinoid production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".