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Optimizing Photoperiod Switch to Maximize Floral Biomass and Cannabinoid Yield in Cannabis sativa L.: A Meta-Analytic Quantile Regression Approach.

ABSTRACT: Cannabis sativa L. is an annual, short-day plant, such that long-day lighting promotes vegetative growth while short-day lighting induces flowering. To date, there has been no substantial investigation on how the switch between these photoperiods influences yield of C. sativa despite the tight correlation that plant size and floral biomass have with the timing of photoperiod switches in indoor growing facilities worldwide. Moreover, there are only casual predictions around how the timing of the photoperiodic switch may affect the production of secondary metabolites, like cannabinoids. Here we use a meta-analytic approach to determine when growers should switch photoperiods to optimize C. sativa floral biomass and cannabinoid content. To this end, we searched through ISI Web of Science for peer-reviewed publications of C. sativa that reported experimental photoperiod durations and results containing cannabinoid concentrations and/or floral biomass, then from 26 studies, we estimated the relationship between photoperiod and yield using quantile regression. Floral biomass was maximized when the long daylength photoperiod was minimized (i.e., 14 days), while THC and CBD potency was maximized under long day length photoperiod for ~42 and 49-50 days, respectively. Our work reveals a yield trade-off in C. sativa between cannabinoid concentration and floral biomass where more time spent under long-day lighting maximizes cannabinoid content and less time spent under long-day lighting maximizes floral biomass. Growers should carefully consider the length of long-day lighting exposure as it can be used as a tool to maximize desired yield outcomes.

SUBMITTER: Dang M

PROVIDER: S-EPMC8786113 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Dataset Information

Optimizing Photoperiod Switch to Maximize Floral Biomass and Cannabinoid Yield in Cannabis sativa L.: A Meta-Analytic Quantile Regression Approach.

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