Increasing Inflorescence Dry Weight and Cannabinoid Content in Medical Cannabis Using Controlled Drought Stress
Bibliographic record
Abstract
Controlled application of drought can increase secondary metabolite concentrations in some essential oil-producing crops. To evaluate the effects of drought on cannabis ( Cannabis sativa L.) inflorescence dry weight and cannabinoid content, drought stress was applied to container-grown cannabis plants through gradual growing substrate drying under controlled environment. Fertigation was withheld during week 7 in the flowering stage until midday plant water potential (WP) was approximately −1.5 MPa (drought stress threshold). This occurred after 11 days without fertigation. A well-irrigated control was used for comparison. Leaf net photosynthetic rate (P n ), plant WP, wilting (leaf angle), and volumetric moisture content (VMC) were monitored throughout the drying period until the day after the drought group was fertigated. At the drought stress threshold, P n was 42% lower and plant WP was 50% lower in the drought group than the control. Upon harvest, drought-stressed plants had increased concentrations of major cannabinoids tetrahydrocannabinol acid (THCA) and cannabidiolic acid (CBDA) by 12% and 13%, respectively, compared with the control. Further, yield per unit growing area of THCA was 43% higher than the control, CBDA yield was 47% higher, ∆ 9 -tetrahydrocannabinol (THC) yield was 50% higher, and cannabidiol (CBD) yield was 67% higher. Controlled drought stress may therefore be an effective horticultural management technique to maximize both inflorescence dry weight and cannabinoid yield in cannabis, although results may differ by cannabis cultivar or chemotype.
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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".