Light Quality Impacts Vertical Growth Rate, Phytochemical Yield and Cannabinoid Production Efficiency in Cannabis sativa
Bibliographic record
Abstract
Light is one of the most crucial parameters for enclosed cannabis (Cannabis sativa) production, as it highly influences growth, secondary metabolite production, and operational costs. The objective of this study was to investigate and evaluate the impact of six light spectra on C. sativa (‘Babbas Erkle Cookies’ accession) growth traits and secondary metabolite (cannabinoid and terpene) profiles. The light spectra evaluated included blue (430 nm), red (630 nm), rose (430 + 630 nm, ratio 1:10), purple (430 + 630 nm, ratio 2:1), and amber (595 nm) LED treatments, in addition to a high-pressure sodium (HPS, amber-rich light) treatment as a control. All the LED light treatments had lower fresh mean inflorescence mass than the control (HPS, 133.59 g plant−1), and monochromatic blue light yielded the least fresh inflorescence mass (76.39 g plant−1). Measurement of Δ9-tetrahydrocannabinol (THC) concentration (%) and total yield (g plant−1) showed how inflorescence mass and THC concentration need to be analyzed conjointly. Blue treatment resulted in the highest THC concentration (10.17% m/m), yet the lowest THC concentration per plant (1.44 g plant−1). The highest THC concentration per plant was achieved with HPS (2.54 g plant−1). As with THC, blue light increased cannabigerol (CBG) and terpene concentration. Conversely, blue light had a lesser impact on cannabidiol (CBD) biosynthesis in this C. sativa chemotype. As the combined effects of the light spectrum on both growth traits and secondary metabolites have important ramifications for the industry, the inappropriate spectral design could cause a reduction in cannabinoid production (20–40%). These findings show promise in helping producers choose spectral designs that meet specific C. sativa production goals.
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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) | 0.002 | 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".