Productivity effects of single plant growth promoting rhizobacterium inoculation on Cannabis sativa L. morphological development and flower yield
Bibliographic record
Abstract
The legalization of cannabis and awareness of its end-use applications has resulted in the renewed consumer demand. An important challenge is achievement of high yield with minimum input for indoor production. The beneficial phytomicrobiome as a sustainable approach gives its potential and ability to enhance plant growth has already been evaluated for a range of plants. This study evaluated three individual plant growth promoting rhizobacterium (PGPR, Bacillus sp., Mucilaginibacter sp. and Pseudomonas sp.) on cannabis (cv. CBD Kush) cuttings’ root development and subsequent plant growth. Our hypothesis was that the PGPR would improve rooting speed of cuttings, yield attributes, and physiological variables. When compared to control (mock inoculation with MgSO4), inoculation with PGPR increased root length at vegetative stage and enhanced flower fresh weight by 5.13%, 6.94% and 11.45%, inoculating with Bacillus sp., Mucilaginibacter sp. and Pseudomonas sp. respectively. While the plant height, node number, branch number and leaf area treated with PGPR was rarely different from control. Throughout growth (vegetative and reproductive), inoculation with Pseudomonas sp. resulted in the greatest increase in photosynthetic rate. Future research should investigate the effects of PGPR on the cannabinoid profile and the effect of various cell densities at inoculation, or different inoculation timings.
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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".