Changes in morpho-physiological traits and phytochemical composition of Cannabis sativa L. treated with microbial biostimulants across different substrates
Bibliographic record
Abstract
Cannabis is a versatile crop with multiple uses, making its production highly significant. Its growth is influenced by various factors, including growing medium. Beneficial microbes, as a sustainable approach, can impact plant growth, but their effects on cannabis’ chemical profile are not well understood. This study examined two substrates (pure coco fibre and potting soil containing Sphagnum peat moss and perlite) and a microbial biostimulant comprising five Bacillus strains (two B. velezensis , two B. megaterium , and one B. licheniformis ) on cannabis growth and secondary metabolites across all growth stages. The results show that biostimulant inoculation increased flower yield by 16 % in coco fiber and 12 % in potting soil compared to uninoculated controls. It also enhanced CBDA (cannabidiolic acid) and THCA (tetrahydrocannabinolic acid) concentrations by 5 % and 10 % in coco fibre and potting soil, respectively. Biostimulant-treated plants showed a 5 % increase in total terpene levels in coco fibre but not in potting soil. Regarding growing medium, cannabis grown in potting soil showed greater overall biomass accumulation (leaf, stem, and flower) and higher leaf chlorophyll concentrations during both vegetative and reproductive stages. In contrast, coco fiber promoted root growth during the vegetative stage and resulted in significantly higher cannabinoid and terpene concentrations at maturity. Hence, coco fibre may be advantageous for enhancing cannabinoid and terpenoid profiles, while potting soil is better suited for maximizing plant yield, depending on the desired production goals. These findings emphasize the critical influence of substrate type and biostimulant application, and their interaction, in optimizing cannabis cultivation practices.
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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 | 0.001 |
| 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".