Optimizing Photoperiod Switch to Maximize Floral Biomass and Cannabinoid Yield in Cannabis sativa L.: A Meta-Analytic Quantile Regression Approach
Dang, Department Of Chemistry, Biology, Ryerson University, Arachchige, Nishara Muthu, College Of Medicine, University Of Saskatchewan, Campbell, Lesley G.
|To find relevant articles, we searched the following search on ISI Web of Science database on February 2, 2021: “Cannabis yield photoperiod,” “Cannabis lighting,” “Cannabis flowering,” “Cannabis day length,” “Cannabis photoperiod,” “Photoperiodic hemp” (Supplementary Table S1). Our inclusion criteria required that studies of C. sativa report: (1) harvested yield as floral biomass and/or cannabinoid concentration; (2) the number of days spent under long day length lighting during the vegetative growth stage; (3) the timing of a definitive switch between long day (≥18 h of light) to short-day lighting (≤12 h of light) conditions for C. sativa (rather than a gradual change in photoperiod as might occur outdoors). Using Regression Models for Describing the Relationship Between Lighting Duration and Yield Outcomes When using long day lighting to predict yield outcomes, floral biomass is best described using linear regression models (either simple or quantile) and cannabinoid content is best described using a quadratic quantile regression model (Figures 2, 3; Tables 1, 2). Google Scholar Mills, E. (2012). doi: 10.1111/gcbb.12793 CrossRef Full Text||Google Scholar Statistics Canada (2019).|