Mycorrhizal-based inoculants in the root microbiome enhanced phytocannabinoid production in medical Cannabis cultivars
Bibliographic record
Abstract
Abstract Background The root microbiome of medical Cannabisplants has been largely unexplored due to past legal restrictions in many countries. To gain insight into the microbial communities of Cannabis sativaL. cultivars with different tetrahydrocannabinol (THC) and cannabidiol (CBD) profiles, a greenhouse trial was carried out with and without inoculants added to the growth substrate. Illumina MiSeq amplicon sequencing of the bacterial 16S rDNA and fungal ITS was used to analyze the root and rhizosphere microbiome of the five cultivars. Results Plant biomass production showed higher in three of five cultivars with K2 treatments (Rhizophagus irregularis and forest microbial suspension). Blossom dry weight of THE cultivar was greater when inoculated with R. irregularis and microbial suspension than no inoculation. Twelve phytocannabinoid compounds in mature Cannabisvaried among cultivars and were affected by inoculants. For example, CBG concentration was higher in CCL cultivar in response to F treatment than other treatments; and CBGA production was higher in ECC cultivar with K1 treatments. We found microbes which were shared among cultivars, Terrimicrobium sp., Actinoplanes sp., and Trichoderma reeseiwere shared by the cultivars ECC-EUS-THE, CCL-ECC, and EUS-THE, respectively. Conclusion This study showed that inoculants influenced the production of phytocannabinoids in five Cannabis cultivars. The microbial diversity and community structure associated with Cannabisroots and rhizosphere may be useful in identifying key taxa for inclusion in Cannabis inoculants.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".