Science, Irving K. Barber Faculty of (Okanagan)
Chemistry, Department of (Okanagan)
DSpace
UBCO
Jarzecki, Karine
Master of Science - MSc
University of British Columbia
Controlled environment agriculture (CEA) consists of producing crops within indoor settings rather than in open fields. This thesis explores the integration of emerging techniques and technologies in CEA through two complementary studies focused on lighting and nutrient management, testing two high-value crops: Cannabis (Cannabis sativa L.) and strawberry (Fragaria × ananassa (Duchesne ex Rozier)). My first study investigated the influence of LED light spectra on the metabolism of in vitro Cannabis in advanced growth chambers. Plantlets were exposed for seven days to one of three lighting conditions: full spectra control, red, or blue LED spectra. Meristematic tissue was then analyzed using a combination of commercial cannabinoid quantification, targeted metabolomics for plant hormones, and untargeted metabolomics for compound and pathway discovery. Blue light stimulated the accumulation of flavonoids and gibberellins, whereas red light led to increased concentrations of carboxylic acid derivatives. All treatments showed widespread amino acid conjugation. Many biosynthetic or metabolic pathways, particularly those involving tryptophan, tyrosine, and phenylalanine, were associated with shikimate and MEP/MVA pathways, connecting light to plant secondary metabolites. My second study developed and tested a proof-of-concept fertigation system for strawberries, an emerging greenhouse crop experiencing rapid market expansion in Canada. Using a Youden square experimental design, the study evaluated the performance of advanced agriculture technology, or agri-tech, sensor systems (Vivent BioSignals and Grodan GroSens) alongside conventional laboratory analysis (ICP-OES), under various fertilizer treatments delivered by an automated irrigation system (NourishLabs). The system was stable across treatments, indicating a limited capacity to modulate nutrient uptake only through fertilizers. My work established correlations between sensors and lab analyses, especially for manganese across plant, substrate, and leachate samples. Together, these studies provide insights into both the potential and the practical limitations of integrating emerging technologies into CEA.
USING CONTROLLED ENVIRONMENT AGRICULTURE TO EVALUATE LIGHT ANDNUTRIENT MANAGEMENT STRATEGIES FOR CANNABIS SATIVA L.AND FRAGARIA Ã ANANASSA (DUCHESNE EX ROZIER)byKarina JarzeckiB.Eng, University of Guelph, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Biochemistry and Molecular Biology)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)November 2025© Karina Jarzecki 2025The following individuals certify that they have read, and recommend to the College ofGraduate Studies for acceptance, a thesis entitled:Using controlled environment agriculture to evaluate light and nutrient management strategiesfor Cannabis sativa L. and Fragaria Ã ananassa (Duchesne ex Rozier)submitted by Karina Jarzecki in partial fulfillment of the requirements for the degree of Masterof Science in Biochemistry and Molecular Biology.Dr. Susan Murch, Irving K. Barber Faculty of Science, UBC OkanaganSupervisorDr. Chase Mason, Irving K. Barber Faculty of Science, UBC OkanaganSupervisory Committee MemberDr. Robert Godin, Irving K. Barber Faculty of Science, UBC OkanaganSupervisory Committee MemberDr. Nicolás Peleato, School of Engineering Faculty of Applied Science, UBC OkanaganUniversity ExamineriiAbstractControlled environment agriculture (CEA) consists of producing crops within indoor settingsrather than in open fields. This thesis explores the integration of emerging techniques andtechnologies in CEA through two complementary studies focused on lighting and nutrientmanagement, testing two high-value crops: Cannabis (Cannabis sativa L.) and strawberry(Fragaria Ã ananassa (Duchesne ex Rozier)). My first study investigated the influence ofLED light spectra on the metabolism of in vitro Cannabis in advanced growth chambers.Plantlets were exposed for seven days to one of three lighting conditions: full spectra control,red, or blue LED spectra. Meristematic tissue was then analyzed using a combinationof commercial cannabinoid quantification, targeted metabolomics for plant hormones, anduntargeted metabolomics for compound and pathway discovery. Blue light stimulated theaccumulation of flavonoids and gibberellins, whereas red light led to increased concentrationsof carboxylic acid derivatives. All treatments showed widespread amino acid conjugation.Many biosynthetic or metabolic pathways, particularly those involving tryptophan, tyrosine,and phenylalanine, were associated with shikimate and MEP/MVA pathways, connecting lightto plant secondary metabolites. My second study developed and tested a proof-of-conceptfertigation system for strawberries, an emerging greenhouse crop experiencing rapid marketexpansion in Canada. Using a Youden square experimental design, the study evaluatedthe performance of advanced agriculture technology, or agri-tech, sensor systems (ViventBioSignals andGrodanGroSens) alongside conventional laboratory analysis (ICP-OES), undervarious fertilizer treatments delivered by an automated irrigation system (NourishLabs). Thesystem was stable across treatments, indicating a limited capacity to modulate nutrient uptakeonly through fertilizers. My work established correlations between sensors and lab analyses,especially for manganese across plant, substrate, and leachate samples. Together, thesestudies provide insights into both the potential and the practical limitations of integratingemerging technologies into CEA.iiiLay SummaryControlled environment agriculture (CEA) uses technology to grow crops indoors moreefficiently. This research studied two aspects: lighting, using Cannabis in growth chambers,and nutrition, using strawberries in greenhouses. In my first study, Cannabis plants were grownunder white, red, or blue LED lights. Blue light boosted secondary metabolites like flavonoidsand gibberellins. My second study tested an automated fertilizer system for strawberries usingadvanced sensors and analytical methods. Seven different fertilizer products were tested fornutrient uptake, yield, and runoff. Biosensors gave real-time measurements of calcium, iron,manganese, and other minerals during the crop cycle. My data enables the developmentof custom fertigation systems to optimise crop growth and reduce environmental impact forCannabis and strawberry production systems.ivPrefaceChapter 2: Chapter 2 is based on the systematic literature review on Cannabis photobiologywhich I completed between December 2024 and June 2025. I collected the initial groupof articles and performed the filtering, tagging, evaluating, and interpreting of resultsgathered from the final list of 119 manuscripts. I performed the data analysis and created allvisualizations for this chapter. Dr. Susan Murch provided editorial and conceptual support.The results of this work are being prepared for publication as:Jarzecki, K. and Murch, S. J. (2025) Photobiology and plant growth regulation in Cannabis.Chapter 3: Chapter 3 is based on Cannabis cultivation work I conducted in Dr. Susan Murchâslab at the University of British Columbia Okanagan and the analytical work I conducted at SupraResearch and Development, both in Kelowna, British Columbia. I performed the experimentwork and analysis, and designed the experiment with Dr. Susan Murch who also participatedin manuscript preparation. I conducted the untargeted metabolomics work at Supra Researchand Development. Dr. Lauren Erland conducted the targeted metabolomics work at the BerryEnvironmental Resilience Research & Innovation Lab (BERRi) at the University of the FraserValley in Chilliwack, British Columbia. Paid employees conducted the routine commercialcannabinoid quantification method at Supra Research and Development. Data from this workwere presented at the 2025 joint society conference of the Canadian Society of HorticulturalScience (CSHS) and Canadian Society of Agronomy (CSA) in Kelowna, British Columbia.Chapter 4: Chapter 4 is based on work I conducted at the Plant Growth Facility (PGF) atthe University of British Columbia Okanagan as part of the funded British Columbia Centrefor Agritech Innovation (BCCAI) research project between Nourish Labs and the Universityof British Columbia Okanagan. I performed the experimental work and the data analysis.I designed the experiment with Dr. Susan Murch, who also participated in the manuscriptpreparation. ICP-OEP analysis on emitter, leachate, leaf, and fruit strawberry tissue wasconducted by Mr. Mark Button at the Fipke Laboratory for Trace Element Research (FiLTER)at the University of British Columbia Okanagan in Kelowna, British Columbia. Data from thisvwork will be presented in a final report as part of the BCCAI project deliverables.Generative artificial intelligence assisted in writing Python code used to generate data figuresin chapters 2, 3, and 4.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xxviDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xxvii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Food security and agricultural challenges for Canada 2050 . . . . . . . . . . . . 11.2 Controlled environment agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Abiotic factors in controlled environment agriculture . . . . . . . . . . . . . . . . 21.3.1 Light . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3.2 Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4 New opportunities for optimised controlled environment agriculture . . . . . . . . 51.4.1 Emerging hardware technologies . . . . . . . . . . . . . . . . . . . . . . 51.4.2 Emerging sensor technologies . . . . . . . . . . . . . . . . . . . . . . . . 51.4.2.1 Leaf measurements and technologies . . . . . . . . . . . . . . 51.4.2.2 Shoot measurements and technologies . . . . . . . . . . . . . . 61.4.2.3 Root measurements and technologies . . . . . . . . . . . . . . 71.4.2.4 Substrate measurements and technologies . . . . . . . . . . . 7vii1.4.2.5 Whole plant measurements and technologies: Plantelectrophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . 81.4.3 Emerging analytical techniques . . . . . . . . . . . . . . . . . . . . . . . 91.4.3.1 Targeted and untargeted phytochemical analysis . . . . . . . . 91.5 Hypothesis and objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Photobiology and plant growth regulation in Cannabis . . . . . . . . . . . . . . . 112.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Controlled environment agriculture and photobiology research . . . . . . 122.2.2 Light Spectra and Auxin Metabolism . . . . . . . . . . . . . . . . . . . . . 132.2.2.1 Cannabis and Auxin Metabolism . . . . . . . . . . . . . . . . . 132.2.3 Light Spectra and Cytokinin Metabolism . . . . . . . . . . . . . . . . . . 142.2.3.1 Cannabis and Cytokinin Metabolism . . . . . . . . . . . . . . . 152.2.4 Light Spectra and Abscisic Acid Metabolism . . . . . . . . . . . . . . . . 152.2.4.1 Cannabis and Abscisic Acid Metabolism . . . . . . . . . . . . . 152.2.5 Light Spectra and Ethylene Metabolism . . . . . . . . . . . . . . . . . . . 162.2.5.1 Cannabis and Ethylene Metabolism . . . . . . . . . . . . . . . . 162.2.6 Light Spectra and Salicylic Acid Metabolism . . . . . . . . . . . . . . . . 172.2.6.1 Cannabis and Salicylic Acid Metabolism . . . . . . . . . . . . . 172.2.7 Light Spectra and Jasmonate Metabolism . . . . . . . . . . . . . . . . . 172.2.7.1 Cannabis and Jasmonate Metabolism . . . . . . . . . . . . . . 182.2.8 Light Spectra and Metabolism of Gibberellins . . . . . . . . . . . . . . . . 182.2.8.1 Cannabis and Metabolism of Gibberellins . . . . . . . . . . . . 192.2.9 Light Spectra and Brassinosteroid Metabolism . . . . . . . . . . . . . . . 192.2.9.1 Cannabis and Brassinosteroid Metabolism . . . . . . . . . . . . 202.2.10 Light Spectra and Metabolism of Strigolactones and Karrikins . . . . . . 202.2.10.1 Cannabis, Strigolactones, and Karrikins . . . . . . . . . . . . . 212.2.11 Light Spectra and Indoleamine Metabolism . . . . . . . . . . . . . . . . . 212.2.11.1 Cannabis and Indoleamine Metabolism . . . . . . . . . . . . . . 212.3 Review process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.3.1 Eligibility criteria, information sources, and search strategy . . . . . . . . 22viii2.3.2 Selection process and data collection process . . . . . . . . . . . . . . . 232.3.3 Data items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.3.4 Limitations of the review . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.4 Current and emerging trends in Cannabis photobiology . . . . . . . . . . . . . . 252.4.1 Developing Cannabis research clusters . . . . . . . . . . . . . . . . . . . 262.4.2 Trends in cultivars selected for research . . . . . . . . . . . . . . . . . . 282.4.3 Range of environmental parameters . . . . . . . . . . . . . . . . . . . . . 292.5 Impact of light on Cannabis growth and development . . . . . . . . . . . . . . . 322.5.1 Visual presentation of synthesised results . . . . . . . . . . . . . . . . . . 322.5.2 Morphology and development . . . . . . . . . . . . . . . . . . . . . . . . 322.5.2.1 Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.5.2.2 Non-phytocannabinoid yield . . . . . . . . . . . . . . . . . . . . 342.5.3 Physiological and metabolic traits . . . . . . . . . . . . . . . . . . . . . . 362.5.4 Cannabinoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372.5.4.1 Cannabigerolic acid and cannabigerol . . . . . . . . . . . . . . 372.5.4.2 Cannabidiolic acid and cannabidiol . . . . . . . . . . . . . . . . 382.5.4.3 Tetrahydrocannabinolic acid and tetrahydrocannabinol . . . . . 402.5.5 Germination and rooting metrics . . . . . . . . . . . . . . . . . . . . . . . 422.6 Conclusions and open questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 Spectra-mediated changes in Cannabis sativa L. metabolism are driven bygibberellins and aromatic amino acid derivatives . . . . . . . . . . . . . . . . . . 443.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.3 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.3.1 Stock plant material and growth conditions . . . . . . . . . . . . . . . . . 443.3.2 Experimental conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.3.3 In vitro tissue sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463.3.4 Quantification of cannabinoids . . . . . . . . . . . . . . . . . . . . . . . . 463.3.5 Targeted quantification of plant growth regulators . . . . . . . . . . . . . 483.3.5.1 Preparation of tissue extracts . . . . . . . . . . . . . . . . . . . 483.3.5.2 Chromatography and mass spectrometry . . . . . . . . . . . . . 48ix3.3.5.3 Data export and feature alignment . . . . . . . . . . . . . . . . 493.3.6 Untargeted phytochemical and pathway discovery . . . . . . . . . . . . . 493.3.6.1 Preparations of extracts . . . . . . . . . . . . . . . . . . . . . . 493.3.6.2 Chromatography and mass spectrometry . . . . . . . . . . . . . 503.3.6.3 Data export and feature alignment . . . . . . . . . . . . . . . . 503.3.6.4 Data processing and annotation . . . . . . . . . . . . . . . . . . 513.3.6.5 Multivariate analysis . . . . . . . . . . . . . . . . . . . . . . . . 513.3.6.6 Pathway analysis and mapping . . . . . . . . . . . . . . . . . . 513.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533.4.1 Cannabinoid quantification . . . . . . . . . . . . . . . . . . . . . . . . . . 533.4.2 Plant growth regulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543.4.2.1 Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543.4.2.2 Hormonomics compound and pathway discovery . . . . . . . . 543.4.2.3 Tryptophan metabolism . . . . . . . . . . . . . . . . . . . . . . 583.4.2.4 Auxins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.4.2.5 Indoleamines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.4.2.6 Melatonin conjugates . . . . . . . . . . . . . . . . . . . . . . . . 613.4.2.7 Cytokinins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.4.2.8 Abscisic acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.4.2.9 Gibberellins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643.4.3 Untargeted phytochemical and pathway discovery . . . . . . . . . . . . . 643.4.3.1 Untargeted metabolomics data quality measures . . . . . . . . 643.4.3.2 Data visualization analysis . . . . . . . . . . . . . . . . . . . . . 653.4.3.3 Significant features analysis . . . . . . . . . . . . . . . . . . . . 683.4.3.4 Functional analysis . . . . . . . . . . . . . . . . . . . . . . . . . 693.4.3.5 Pathway discovery . . . . . . . . . . . . . . . . . . . . . . . . . 693.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 844 Integration of agri-tech and analytical methods to assess nutrient managementand utilization in strawberry (Fragaria x ananassa Duchesne ex Rozier âAlbionâ) 854.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85x4.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854.3 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 864.3.1 Experimental growth room . . . . . . . . . . . . . . . . . . . . . . . . . . 864.3.2 Plant growth conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874.3.2.1 Fertigation delivery . . . . . . . . . . . . . . . . . . . . . . . . . 874.3.3 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 884.3.3.1 System design: Biosensor and sample collection overview . . . 904.3.4 Plant monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.3.4.1 Plant electrophysiology . . . . . . . . . . . . . . . . . . . . . . . 914.3.4.2 Substrate assessment . . . . . . . . . . . . . . . . . . . . . . . 934.3.4.3 Leaf and fruit tissue assessment . . . . . . . . . . . . . . . . . 944.3.5 Inductively coupled plasm a optical emission spectroscopy analysis . . . 944.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954.4.1 Autocorrelation of data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954.4.2 Significant factors from Youden analysis . . . . . . . . . . . . . . . . . . 964.4.3 Accumulation of total dissolved solids . . . . . . . . . . . . . . . . . . . . 974.4.3.1 Differences between emitter and leachate elementalcomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994.4.3.2 Elemental accumulation in leaves and berries . . . . . . . . . . 1004.4.3.3 Elemental accumulation in the substrate . . . . . . . . . . . . . 1044.4.4 Electrophysiological changes in electrical potential . . . . . . . . . . . . . 1064.4.5 Comparison of technologies . . . . . . . . . . . . . . . . . . . . . . . . . 1064.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1125 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145.1 Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145.1.1 Objective 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145.1.2 Objective 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145.1.3 Objective 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145.2 Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155.2.1 Objective 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115xi5.2.2 Objective 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155.2.3 Objective 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165.3.1 Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165.3.2 Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175.4 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175.4.1 Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175.4.2 Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1185.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Appendix A: Associated appendix for chapter 3 . . . . . . . . . . . . . . . . . . . . . 159Appendix B: Associated appendix for chapter 4 . . . . . . . . . . . . . . . . . . . . . 169xiiList of Tables1.1 Visible light divided into broad colour categories and more specific colour categories. 31.2 Summary of substrate sensor parameters alongside frequently-used methodologies. 73.1 Operational parameter for commercial UPLC method used to quantify cannabinoids. 473.2 Method performance characteristics of the commercial UPLC method used toquantify cannabinoids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473.3 Summary of hormone quantification results (pg/mg fresh weight). . . . . . . . . . . . 543.4 Matched compounds from HormonomicsDB which had a retention time (RT) matchgreater or equal to 70% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563.4 Continued: ABA refers to abscisic acidÍ¾ IAA refers to indole-3-acetic acidÍ¾Mel refersto melatonin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.5 Pathways identified with significantly different features from Functional Analysis . . 703.6 Overrepresented pathways detected using MetaboAnalystâs Pathway Analysismodule. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.1 Summary of environmental conditions (avg. Â± std. dev.) within the growth roomaveraged over the course of the experiment (2025.03.31-2025.05.16). . . . . . . . . 874.2 Elemental analysis (avg. ppm Â± std. dev.) of the seven fertilizer products used inthis experiment, once prepared in a full batch tank fill . . . . . . . . . . . . . . . . . . 894.3 Youden Square experimental design where fertilizer product was the treatmentfactor, zone was the position factor, and week was the block factor. . . . . . . . . . . 904.4 Averaged batch tank pH or EC Â± std. dev. for each fertigation treatment acrosszones. Control refers to the combination of Part A and Part B delivered to thestrawberry plants during the weekend period between treatment applications. . . . . 964.5 F( values for each parameter assessed using the Youden DOE . . . . . . . . . . . . 974.6 Total dissolved solids (TDS) Â± std. dev. reported in parts per million (ppm) for eachsample type and fertilizer product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984.7 Summary of p values as determined using Kruskal-Wallis test on elemental ppmfrom ICP-OES analysis by fertilizer treatment (Boost, Hyclean, Hygroben Microbes,Hygrozyme Enzymes, Hypochlorous Acid, Part A, Part B) . . . . . . . . . . . . . . . 99xiii4.8 Operational benefits and limitations for greenhouse management of thetechnologies and methods used in this experiment: substrate sensors (GrodanGroSens), plant electrophysiology monitoring (Vivent BioSignals), automatedfertigation (NourishLabs Irrigation), and tissue analysis (ICP-OES). . . . . . . . . . . 108A1 Putatively identified compounds alongside their KEGG ID, m/z, RT, and potentialform as identified in Functional Analysis using full spectrum control light (âcontrolâ),red light, and blue light treatment samples. . . . . . . . . . . . . . . . . . . . . . . . 161A1 Continued: Putatively identified compounds alongside their KEGG ID, m/z, RT, andpotential form as identified in Functional Analysis using full spectrum control light(âcontrolâ), red light, and blue light treatment samples. . . . . . . . . . . . . . . . . . 162A1 Continued: Putatively identified compounds alongside their KEGG ID, m/z, RT, andpotential form as identified in Functional Analysis using full spectrum control light(âcontrolâ), red light, and blue light treatment samples. . . . . . . . . . . . . . . . . . 163A1 Continued: Putatively identified compounds alongside their KEGG ID, m/z, RT, andpotential form as identified in Functional Analysis using full spectrum control light(âcontrolâ), red light, and blue light treatment samples. . . . . . . . . . . . . . . . . . 164A2 Matched compounds in the diterpenoid biosynthesis pathway through eitherMetaboAnalyst (M.A.) or HormonomicsDB (HBD) results. . . . . . . . . . . . . . . . 165A3 Matched compounds in the phenylalanine metabolism pathway. . . . . . . . . . . . 166A4 Matched compounds in the phenylpropanoid biosynthesis pathway. . . . . . . . . . 166A5 Matched compounds in the flavonoid biosynthesis pathway. . . . . . . . . . . . . . . 167A6 Matched compounds in the flavone and flavonol biosynthesis pathway. . . . . . . . 167A7 Matched compounds in the tyrosine metabolism pathway. . . . . . . . . . . . . . . . 168B1 Overview of the fertilizer products used in this experiment with details on theirmanufacturer and product purpose. . . . . . . . . . . . . . . . . . . . . . . . . . . . 169B2 Yield metrics of strawberry fruit tissue by zone and fertilizer. Control refers to thecombination of Part A and Part B delivered to the strawberry plants during theweekend period between treatment applications. . . . . . . . . . . . . . . . . . . . . 170B3 Time series analysis parameters for sensor data and strawberry yield for datacollected from zone 2, across all fertilizer treatments. . . . . . . . . . . . . . . . . . . 171xivB4 Time series analysis parameters for sensor data and strawberry yield for datacollected from zone 3, across all fertilizer treatments. . . . . . . . . . . . . . . . . . . 171B5 Time series analysis parameters for sensor data and strawberry yield for datacollected from zone 4, across all fertilizer treatments. . . . . . . . . . . . . . . . . . . 172B6 Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Boost fertilizer treatment. . . . . . . . . . . . . . . . . . . . . 172B7 Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Hyclean fertilizer treatment. . . . . . . . . . . . . . . . . . . . 173B8 Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Hygroben Microbes fertilizer treatment. . . . . . . . . . . . . 174B9 Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Hygrozyme Enzymes fertilizer treatment. . . . . . . . . . . . 175B10 Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Hypochlorous Acid fertilizer treatment. . . . . . . . . . . . . . 176B11 Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Part A fertilizer treatment. . . . . . . . . . . . . . . . . . . . . 177B12 Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Part B fertilizer treatment. . . . . . . . . . . . . . . . . . . . . 178B13 Summary of parameters calculated on electrophysical signal alongside the formulaand Python script used for each calculation using from scipy.stats importkurtosis, skew and import math packages. . . . . . . . . . . . . . . . . . . . . . . 180B14 Differences in concentration (ppm) between emitter (n = 3) and leachate (n = 3)samples collected at the end of the treatment application with the corresponding pvalue, as determined using paired t-test (α = 0.05). . . . . . . . . . . . . . . . . . . . 181B15 Individual elemental concentration (mean Â± std. dev.) in ppm by fertilizer treatmentof leaf ICP-OES data (n = 3)Í¾ note, std. dev. could not be estimated forHypochlorous Acid leaf tissue samples as only one sample was collected. . . . . . . 182B16 Individual elemental concentration (mean Â± std. dev.) in ppm by fertilizer treatmentof fruit ICP-OES data (n = 3)Í¾ note, std. dev. could not be estimated forHypochlorous Acid leaf tissue samples as only one sample was collected. . . . . . . 182B17 Summary of parameters calculated for the electrophysiogical signals extracted,denoised, and normalized from the Vivent Biosignal sensors (n = 24). . . . . . . . . 185xvList of Figures1.1 Different types of controlled environment agriculture (CEA) separated by cultivationmethod or structure type used. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1 Flow chart overview of the collection of papers for this review. . . . . . . . . . . . . . 232.2 Modified Venn diagram representing the major research themes from the Cannabisphotobiology papers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.3 Line area plots representing the major and minor research themes from theCannabis photobiology papers over time and best fitting regression model (overlaidred line). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.4 Histogram plotting frequency density of published articles onCannabis photobiologyincluded in this review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.5 Number of articles published by country as determined by the affiliation of the firstauthor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.6 A representation of the author network for Cannabis photobiology based onco-authorship on papers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.7 Word cloud of strain or cultivar names referenced in papers where font size reflectsthe frequency of the cultivar nameÍ¾ if this piece of information was omitted, thecultivar name was coded as Unnamed. . . . . . . . . . . . . . . . . . . . . . . . . . 292.8 Growing conditions described by papers in this review. . . . . . . . . . . . . . . . . . 302.9 Cumulative count of articles published since 2000 using various lighting technologies. 312.10 Histogram of frequency density of peak light intensities reported in papers. . . . . . 322.11 Influence of various aspects of light (spectra, intensity, and photoperiod) on plantheight, as reported in the Cannabis literature. . . . . . . . . . . . . . . . . . . . . . . 342.12 Influence of various aspects of light (spectra, intensity, and photoperiod) on plantyield. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.13 Influence of various aspects of light (spectra, intensity, and photoperiod) onspecific physiological and metabolic traits (specified in square brackets), excludingcannabinoids, as reported in the literature. . . . . . . . . . . . . . . . . . . . . . . . . 37xvi2.14 Influence of various aspects of light (spectra, intensity, and photoperiod) oncannabigerolic acid (CBGA) and cannabigerol (CBG) concentration (as denoted insquare brackets within the label), as reported in the literature. . . . . . . . . . . . . . 382.15 Influence of various aspects of light (spectra, intensity, and photoperiod) oncannabidiolic acid (CBDA) and cannabidiol (CBD) concentration (as denoted insquare brackets within the label), as reported in the literature. . . . . . . . . . . . . . 392.16 Influence of various aspects of light (spectra, intensity, and photoperiod) ontetrahydrocannabinolic acid (THCA) or tetrahydrocannabinol (THC) concentration(as denoted in square brackets within the label), as reported in the Cannabis literature. 412.17 Influence of various aspects of light (spectra, intensity, and photoperiod) ongermination or rooting metrics, as reported in the literature. . . . . . . . . . . . . . . 423.1 Spectra profiles for each of the growth chambers used in this experiment . . . . . . 453.2 Data processing flowchart used on the untargeted (left) and targeted (right)metabolomic analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533.3 Average concentration of THCA (wt %) quantified in samples per light treatment,where mean concentration Â± std. dev. of THCA was 1.13 ± 0.5 for full spectracontrol treatment, 1.06 ± 0.4 for red treatment, and 1.20 ± 0.3 for blue treatmenttissueÍ¾ differences between treatment means were not significant. . . . . . . . . . . 543.4 Compounds part of the tryptophan metabolism class detected using HormonomicsDB. 593.5 Compounds part of the auxin class detected using HormonomicsDB. . . . . . . . . . 603.6 Compounds part of the indoleamine class detected using HormonomicsDB. . . . . . 613.7 Compounds part of the melatonin conjugates class detected using HormonomicsDB. 623.8 Compounds part of the cytokinin class detected using HormonomicsDB. . . . . . . . 633.9 Compounds part of the ABA class detected using HormonomicsDB. . . . . . . . . . 643.10 Venn diagram representing features unique to, or in common between variouscombinations of, treatments for the untargeted metabolomics data . . . . . . . . . . 653.11 Principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663.12 Partial least squares discriminant analysis . . . . . . . . . . . . . . . . . . . . . . . . 673.13 Heat map of log)( peak intensities of the top 100 significant features (pairs ofmass-to-charge and retention time) for the red light treatment (top) and the bluelight treatment (bottom). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68xvii3.14 Diterpenoid biosynthesis pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.15 Diterpenoid biosynthesis pathway starting from gibberellin A53, continuing fromgibberellin A12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733.16 Tyrosine metabolism, starting from L-tyrosine. . . . . . . . . . . . . . . . . . . . . . . 743.17 Phenylalanine metabolism pathway starting from phenylpyruvate. . . . . . . . . . . 753.18 Phenylpropanoid biosynthesis pathway starting from L-phenylalanine. . . . . . . . . 763.19 Phenylpropanoid biosynthesis pathway, continuing onward from caffeic acid. . . . . 773.20 Flavonoid biosynthesis, starting from cis-3,4-leucopelargonidin. . . . . . . . . . . . . 783.21 Flavone and flavonol biosynthesis pathway starting from kaempferol. . . . . . . . . . 794.1 Photograph showing the three distinct zones set up with black plastic reservoirs withwhite plastic reservoirs underneath to catch run off. . . . . . . . . . . . . . . . . . . 864.2 Photograph showing the automated fertigation system used during the experiment.12 peristaltic pumps (top centre) can connect to twelve independent containers(bottom centre) and are used to dose reverse osmosis (RO) water (storagecontainer, left) as it fills the central batch tank (right). . . . . . . . . . . . . . . . . . . 884.3 Block diagram of the overall system, indicating key sampling points and sensormeasurement points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904.4 The active electrode (A) and the ground electrode (B) from the electrophysicalsensor inserted into the petiole and the base of the same strawberry plant,respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924.5 Differenced electrophysical signals (mV) measured every hour by zone from theVivent Biosignal devices over the course of the experiment, after noise removalthrough windowing with a step size of 40 and first-order differencing. . . . . . . . . . 934.6 Pie chart of elemental composition of strawberry fruit (left) and leaves (right) fromplants, using Part B as fertilizer as determined using ICP-OES, as an example. . . . 1004.7 Pearson correlation plots of B, Ca, Cu, Fe, K, Mg, Mn, P, S, Si, Sr, and Zn databetween emitter across fertilizer and zones, and either (Figure 4.7a) leaf or (Figure4.7b) fruit ICP-OES data (n = 7 for each element). . . . . . . . . . . . . . . . . . . . 1014.8 Scatterplots of Ca, Fe, K, Mn, and P data between manufacturer-processed Viventdata (% elemental change) and leaf ICP-OES data, averaged across zones (n = 7for each element). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102xviii4.9 Scatterplots of Ca, Fe, K, Mn, and P data between manufacturer-processed Viventdata (% elemental change) and fruit ICP-OES data, averaged across zones (n = 7for each element). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1034.10 Stacked bar graph representing elemental composition of leachate samples (n = 3)as quantified using ICP-OES by fertilizer treatment, consolidating elements < 1%into Other category. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054.11 1-layer correllogram between manufacturer-processed Vivent elemental change(%) for Ca, Fe, K, Mn, and P, and substrate electrical conductivity (EC) as measuredby Grodan GroSens sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054.12 Differenced, normalised electrophysical signals (mV) presented over the course ofa single day (0-24 hrs) by fertigation treatment averaged by zone, as collected fromthe Vivent Biosignal devices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106A1 First set of ThermoFisher Q Extractive Orbitrap MS parameters. . . . . . . . . . . . 159A2 Second set of ThermoFisher Q Extractive Orbitrap MS parameters. . . . . . . . . . 160B1 Electrophysical signals (mV) measured every hour, as for electrode #5 from theVivent Biosignal device 76331e over the course of the experiment, prior to any noisesignal removal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170B2 Differenced electrophysical signals (mV) measured every hour across zones (n =72) from the Vivent Biosignal devices over the course of the experiment, after noiseremoval through windowing with a step size of 40 and first-order differencing, foreach fertigation type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179B3 Correlogram between elemental ICP-OESmeasurements (ppm) in leaf (n = 15) andfruit (n = 15) tissue samples across all fertilizer treatments. Mo and Na were omitteddue to low frequency of detection across samples. . . . . . . . . . . . . . . . . . . . 183B4 Pearson correlation plots of B, Ca, Cu, Fe, K, Mg, Mn, P, S, Si, Sr, and Zn databetween substrate EC as measured by GroSens sensors and either (a) emitter or(b) leachate sample ICP-OES data (n = 8). . . . . . . . . . . . . . . . . . . . . . . . 184B5 Differenced, normalized electrophysical signals (mV) presented over the course ofa single day by zone averaged by fertigation treatment (n = 7), as collected from theVivent Biosignal devices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186xixB6 1-layer correllogram between parameters of differenced, normalized electrophysicalsignals (mV) (n = 24) and substrate electrical conductivity (EC) as measured byGrodan GroSens sensors (n = 24). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186B7 Pearson correlation plot between parameters of differenced, normalizedelectrophysical signals (mV) (n = 24) and the manufacturer-processed Viventdata for N, Ca, Fe, K, Mn, and P (n = 24). . . . . . . . . . . . . . . . . . . . . . . . . 187xxList of Abbreviations5HT SerotoninABA Abscisic acidABI5 Abscisic Acid Insensitive5ACS American Chemical SocietyAI Artificial intelligenceARR Arabidopsis response regulatorBAP 6-benzylaminopurine or N6-benzyladenineBCCAI BC Centre for Agritech InnovationBES1 Brassinosteroid-responsive Transcription Factor1BIN2 Brassinosteroid-insensitive2CAM Crassulacean acid metabolismCBCA Cannabichromenic acidCBD CannabidiolCBDA Cannabidiolic acidCBDVA Cannabidivarinic acidCBG CannabigerolCBGA Cannabigerolic acidCBN CannabinolCEA Controlled environment agriculturexxiCKI1 Cytokinin Independent1CKX Cytokinin oxidase/dehydrogenaseCoA Coenzyme ACOP1 Constitutively photomorphogenic1DOE Design of experimentDMAPP dimethylallyl diphosphateEC Electrical conductivityFiLTER Fipke Laboratory for Trace Element ResearchFIN219 Far-Red Insensitive219GAâ Gibberellin A*GABA γ-aminobutyric acidgph gallons-per-hourGR24 A synthetic strigolactoneHESI Heated electrospray ionizationHID High-intensity dischargeHPLC High-performance liquid chromatographyHPT1 Histidine-containing Phosphotransfer1HY5 Elongated Hypocotyl5IAA Indole-3-acetic acidIBA Indole-3-butyric acidICP-OES Inductively coupled plasma atomic emission spectroscopyxxiiIoT Internet-of-ThingsJA Jasmonic acidKEGG Kyoto Encyclopedia of Genes and GenomesLC-MS Liquid chromatography (LC)âmass spectrometry (MS)LED Light emitting diodeLOD Limit of detectionLOQ Limit of quantificationMAD Mean absolute deviationMAPE Mean accuracy percentage of errorMEL MelatoninMeOH MethanolMEP 2-C-methyl-D-erythritol-4-phosphateMSD Mean squared deviationMVA MevalonateMYB73 MYB Domain Protein73MYB77 MYB Domain Protein77m/z Mass-to-chargen.d or ND Not detectedNQ Below quantitation threshold(e)PAR (Extended) Photosynthetically active rangePC Principal componentxxiiiPCA Principal component analysisPGF Plant Growth Facility at University of British Columbia OkanaganPGR Plant growth regulatorPIF3 Phytochrome-interacting Factor3PIF5 Phytochrome-interacting Factor5PIN1 Pin-formed1PIN3 Pin-formed3PLS-DA Partial least-squares discriminant analysisPPFD Photosynthetic photon flux densityQC Quality controlqTOF Quadrupole time-of-flightRH Relative humidityRO Reverse osmosisRSD Relative standard deviationRT Retention timeRuBisCO Ribulose-1,5-bisphosphate carboxylase/oxygenaseSA Salicylic acidSPAD Soil plant analysis development, unitless proxy for chlorophyll concentrationSMAX1 SUPPRESSOR of MAX2 1 proteinTDS Total dissolved solidsTHC TetrahydrocannabinolxxivTHCA Tetrahydrocannabinolic acidTHCAS Tetrahydrocannabinolic acid synthaseTHCVA TetrahydrocannabivarinTRP TryptophanUHPLC-MS Ultra-high performance liquid chromatography-mass spectrometryUPLC Ultra-performance liquid chromatographyUV-* Ultraviolet-*UVR8 UV Resistance Locus8VIP-HESI Vacuum insulated probe-heated electrospray ionizationVMC Volumetric moisture contentZEA ZeatinxxvAcknowledgementsI would like to take this opportunity to thank everyone who played a role in my time at theUniversity of British Columbia Okanagan. First, my supervisor and mentor Dr. Susan Murch,who taught me more than just about analytical methodsÍ¾ your stories and advice came atcritical moments in my life these past two years and I am forever grateful. I would like toacknowledge my two committee members, Dr. Robert Godin and Dr. Chase Mason, for theircontinual support and direction even when I felt lost in the journey. I am a better researcherand scientist because of my incredible committee. To my fellow and past PlantSMART labmembers, I am truly indebted to you all: Pawanjit Sandhu, Daniel Gaudet, Alicia Greene,Adeyemi Adebowale, Olivia Carlson, and Barbara Hall. The words of this thesis are written inthe laughs and tears had in our student office, and I am in awe of your creativity and passionfor what you do.I would be amiss to overlook the importance of the support I received that made me believe Icould succeed in graduate school in the first place - from the University of Guelph, my mentorsDr. Ashutosh Singh P.Eng, Dr. Ping Wu P.Eng, Dr. Steffen Graether, Dr. Kim Levere, and Dr.Matthew Demers saw a potential in me I never saw. Whatever comes next for me, will havebeen made possible by you all. UoG is a world-class institution because of professors like you.As well as from my professional career - Tyson Jennett, Craig Yendrek, Tera Lewandowski,and Shaye Donald, thank you for your support.I am so appreciative of my cherished and loved ones - my parents Kazimierz and Jolanta,and my family w Polsce, my friends across the country and world who saw me at my lowestand lifted me to my highest these past years. The time spent together meant the world to me.Lastly, my partner, Jonathan Kettle, for your sympathetic ear but also for the kick-in-the-butts Ineeded to pull through. Youâre forever my sounding board and greatest supporter, and I yours.Thank you.Funding for this research was made possible by the University of British Columbia Okanagan,the Natural Sciences and Engineering Research Council of Canada, and the B.C. Centre forAgritech Innovation.xxviDedicationTo my cat, Potato, forever my little study buddy.xxviiChapter 1: Introduction1.1 Food security and agricultural challenges for Canada 2050The Canadian agriculture and agri-food sector is a critical component of the countryâs economy,employing over 2.3 million people - or 1 in 9 jobs - and generating about 7% of nationalGDP (Statistics Canada, 2025a). About 1.2% of national GDP is tied to crop production,providing just over 100 000 jobs on 80 000 farms across the country (Statistics Canada,2025a). This sector faces major threats amidst climate change, with more frequent extremeweather events, droughts, heat waves, and other adverse consequences, with the Canadaof 2050 expected to be hostile to agricultural production. Thus, many producers are turningto controlled environment agriculture (CEA) with its ability to control lighting, temperature,CO2 concentration, humidity, and nutrients, independent to outdoor environmental conditions(Dsouza et al., 2023).1.2 Controlled environment agricultureControlled environment agriculture (CEA) is a broad term encapsulating a wide varietyof growing systems that - partially or entirely - decouple crop production from ambientenvironmental conditions and soil. Typically CEA is categorised by the crop production methodused, or by the structure in which crop production takes place (Figure 1.1) (Shamshiri et al.,2018). Crop production methods explain how nutrients are delivered to crops in the system,where hydroponics uses nutrients suspended in water, aquaponics incorporates raising fishfor their high-nutrient waste into a hydroponics system, and aeroponics uses a mist to transfernutrients to roots (Shamshiri et al., 2018Í¾ Ting et al., 2016Í¾ Newman et al., 2023). Structuresinclude greenhouses that use or do not use supplemental lighting or rooftop venting, verticalfarms (sometimes called plant factories) with crops grown in layers stacked over each other,and growth chambers and rooms where crops sit on tabletops or shelves and are completelysealed from exterior surroundings (Wilkinson et al., 2021Í¾ Ting et al., 2016Í¾ Shamshiri et al.,2018). One method is not restricted to one structure type.CEA systems are considered to use less water, protect crops from biological hazards, decreasethe use of fertilizers, and provide nutrient-dense food year-round compared to traditional field1Figure 1.1: Different types of controlled environment agriculture (CEA) separated by cultivationmethod or structure type used.agriculture (Van Ginkel et al., 2017Í¾ Glaros et al., 2022). CEA farms can choose to producesingle or a variety of crops with similar nutrient, lighting, temperature, and relative humidityrequirements (Glaros et al., 2022). Lettuce, basil, microgreens, and tomatoes are commoncrops grown in CEA and are staple crops used in CEA research (Dsouza et al., 2023).1.3 Abiotic factors in controlled environment agriculture1.3.1 LightPlant photobiology research - or the study of how light influences plant growth and development- dominates the CEA literature landscape (Dsouza et al., 2023). Light is a complexenvironmental signal in addition to a source of energy for plants, which can act as a limitingfactor in proper crop growth and development if not considered properly (Neo et al., 2022).2Supplemental light is common in CEA, especially in high latitude countries in their wintermonths (Lanoue et al., 2024). Visible light is defined as the wavelengths of light betweenâ¼380 nm to â¼700 nm and is often further categorised based on the colour these wavelengthsappear to humans (Table 1.1) (Bilodeau et al., 2019).Table 1.1: Visible light divided into broad colour categories and more specific colour categories.Colour UV Blue Green Red Far RedUVC UVB UVA Violet Blue Green Yellow Orange RedWavelengthRange (nm) 100-280 280-320 320-400 400-450 450-520 520-560 560-600 600-625 625-700 700+A critical aspect of light is its wavelength, or spectrum, with plant photobiology primarilyfocused on spectra which fall within the photosynthetically active range (PAR) of light -between 400 nm and 700 nm - a metric which rose to prominence as a means of having astandard with which to compare action spectra between higher plant species. Photosyntheticphoton flux density (PPFDÍ¾ µmol mâ2 sâ)) is used to describe PAR light over a given area.This standardised range was determined via consensus over time and across species, withseminal papers (McCree, 1971) which showed very similar curves across plant species withpeaks at 440, 620, and 670 nm ± 10 nm, and very little detected before 400 nm and beyond700 nm. However, recent research has emerged demonstrating photosynthetic activity of farred photons (beyond 700 nm), so a new extended PAR, deemed ePAR, has been proposed(Zhen et al., 2021).An important aspect to consider in photobiology is the brightness or intensity of the (e)PARlight being used. The energy level of light is inversely proportional to its wavelength, and solight can be described by either radiometric (energy, using joules) or photometric (photon, usingmoles) units (Zheng, 2022). Using excessive light intensities can induce damage in plants, asthis abiotic stress can reduce the efficiency or fully inactivate photosystem II (photoinhibition),or generate reactive oxygen species (ROS) (Goh et al., 2012). In turn, photoinhibition canreduce plant photosynthetic activity, severely limiting its growth and development (Takahashiand Badger, 2011). Thus, lighting can and must be precisely controlled in CEA to preventextensive crop damage, loss, or yield penalties.31.3.2 NutrientsIn conventional, field agriculture, soil is the primary source for the nutrients required by plantsfor normal growth and development (Mikula et al., 2020). Chemical fertilizers are often used toovercome a nutritional deficiency in a particular area, or to replenish what has been removedwith intensive, consistent crop production over time (Mikula et al., 2020). In contrast, CEAgenerally uses soilless media (e.g. coco fibre, stone wool (Nemati et al., 2021)) which containno inherent nutritional content and often lack the capacity to build up a reserve of nutritionalcontent, so regular use of fertilizer is required (Ragaveena et al., 2021). Thus, it is possibleto apply precise amounts of nutrients, which improves nutrient use efficiency in CEA systems(Boyd et al., 2025Í¾ Ragaveena et al., 2021).Plants can uptake and consume nutrients present within a range of concentration, butthere is a risk of deficiency when nutrients are inadequate, and toxicity when nutrients areoverabundant (Bell, 2023). Nutrients are not equally necessaryÍ¾ macronutrients like N andP must be available in high quantities (g/kg), while micronutrients are obligatory in smallquantities (µg/kg-mg/kg) (Epstein, 1965Í¾ Langridge, 2022). Ideal ranges of nutrients areinfluenced by the age of the plant, the concentration of other nutrients, and abiotic factors,such as light (Mikula et al., 2020Í¾ Bell, 2023). Visual symptoms of deficiencies or toxicitiesappear at extremes, when too often the issue is irreversible and the afflicted tissue(s) cannotbe recovered (Langridge, 2022). Symptoms often look similar, and deficiencies and toxicitiescan be correlated, leading to an inaccurate diagnosis of the problem (McCauley et al., 2011).In CEA, mineral elements must be present in such a way as to be readily available for plantuptakeÍ¾ imbalances within nutrient solutions can result in precipitate formation, locking up keyelements from plant roots (Meselmani, 2023). This problem is exacerbated in closed CEAsystems, which recirculate and continually reuse the same nutrient-enriched water (sometimescalled fertigation solution). Imbalances can result in more favourable conditions for imfamouspathogens or pests in CEA, such as Fusarium spp. or Pythium spp. (Punja and Rodriguez,2018). Thus, proper application and dosage of nutrients is essential in any CEA system.41.4 New opportunities for optimised controlled environment agriculture1.4.1 Emerging hardware technologiesOptions for artificial lighting in CEA has grown rapidly in recent years. Previously, fluorescentlamps and high-pressure sodium (HPS) lights were extensively used for growth chambers andgreenhouses (Al Murad et al., 2021). These technologies have limited adjustability in theirintensity, and their spectra cannot be easily modified to suit specific crop needs (Ouzouniset al., 2015). Light emitting diodes (LEDs) - given their higher level of customization andtunability, higher energy efficiency, and lower radiation of heat - have become more popularin CEA systems (Ouzounis et al., 2015Í¾ Bilodeau et al., 2019). Thus, the new capabilities ofthese LED lighting fixtures in tandem with a drop in their cost has spurred plant photobiology(Dsouza et al., 2023).1.4.2 Emerging sensor technologiesSo-called smart technology has been rapidly developed and adopted in CEA, and includesbig data analytics, robotics, Internet of Things (IoT), and artificial intelligence (AI) (Ragaveenaet al., 2021). The incorporation of sensors can dramatically improve environmental control andlead to informed decision making. Sensors are used in four key areas in CEA, as defined byCohen et al. (2022):1. on the leaves, to assess metabolism, growth dynamics, and photosynthesisÍ¾2. on the shoot, to gauge the balance between storage and growthÍ¾3. at the roots, to determine rate of nutrient uptake and root growthÍ¾4. in the substrate, to verify material conditions for potential toxicity or deficiency.Adding to the framework of Cohen et al. (2022), new sensors have been constructed to assesswhole plant dynamics through plant electrophysiology.1.4.2.1 Leaf measurements and technologiesA basic assessment of leaves include recording their wet and dry weight as an indirectmeasure of photosynthetic capacity, as larger surface area would confer higher light-capturing5(Huang et al., 2019). Although the simplicity is an advantage for this method, it isdestructive, invasive, and time-consuming to execute. Optical methods for photosynthesismeasurement are non-destructive alternatives, and typically use three mechanisms to work:spectroscopy-based, fluorescence-based, or gas analysis-based (Espinosa-Calderon et al.,2011). Infrared spectroscopy leverages absorption of electromagnetic radiation in theinfrared region and can be easily done in situ, due to the ready availability of portableinfrared spectrometers (Espinosa-Calderon et al., 2011). Infrared gas analyzers are popularinstruments that infer new carbon dioxide exchange rate by measuring differences in carbondioxide between an enclosed sample area and the surrounding conditionsÍ¾ it can alsobe used to determine RuBisCO carboxylation, photorespiration, transpiration, and stomatalconductance (Oivukkamäki et al., 2025). Chlorophyll content and fluorescence can also beused to interpolate rate of photosynthesis, as the intensity of fluorescence emitted by an excitedstate chlorophyll is inversely related to energy used for photosynthesis (Espinosa-Calderonet al., 2011). Chlorophyll content is approximated with handheld instruments that report resultsin SPAD, but this is an empirical measurement with limited true physical meaning (Costeet al., 2010). Fluorescence is therefore a better estimation and can be measured using apulse-amplitude modulated fluorometer (Mkhabela et al., 2023), which can also be integratedinto an infra-red gas analyzer (Oivukkamäki et al., 2025) for combined readings. Hyperspectralreflectance combined with machine learning techniques on resulting data is becoming morecommon-place for measuring chlorophyll fluorescence (Min et al., 2024).1.4.2.2 Shoot measurements and technologiesShoot measurements reflect growth and storage in action. As such, most measurementsand technologies capture either instantaneous fluxes or changes over time, including someoverlap with leaf measurements. Timelapse images can be used to capture change incolour, growth, and architecture (Fiorani and Schurr, 2013), with fluorescence camerasquantifying photosynthesis status as well (Fiorani and Schurr, 2013). Isotope analyses hasbeen demonstrated in hydroponic systems for nitrogen quantification using )5c (Kalcsits andGuy, 2015) and in in vitro systems for triacylglycerol and protein quantification using )+X(Schwender and Ohlrogge, 2002).61.4.2.3 Root measurements and technologiesRate of nutrient uptake is a common limiting factor in CEA, especially in hydroponics (Samboet al., 2019). Similar measurements and technologies described previously in sections 1.4.2.1,1.4.2.2 are applicable for roots. Net ion uptake at the roots is typically determined throughcomparison of ion influx from/efflux to an external solution (Griffiths and York, 2020). Inconventional agriculture, in situ root architecture is a major obstacle as roots are obfuscated bysoilÍ¾ this is less pressing for CEA systems where roots are submerged in water or restricted to afinite volume of soilless media. Remote monitoring of root systems is advantageous to preventthe risk of contamination or disruption of root systems (Ameran et al., 2024). An electricalimpedance tomography sensor system can be used to estimate root mass (Basak and Wahid,2022) and a scanner-based system can be employed for substrate-based CEA systems toimage root system architecture over time (Adu et al., 2014).1.4.2.4 Substrate measurements and technologiesSubstrate sensors are widely used in CEA with varying integration of wireless, IoT-based,or machine learning technologies (Shamshiri et al., 2018Í¾ Dsouza et al., 2023). Forsubstrate-based CEA, sensors which detect volumetric moisture content and electricalconductivity (EC) are common, while substrate-free CEA systems (e.g. deep water culture)will incorporate sensors to measure pH and EC of the nutrient solution reservoir (Ragaveenaet al., 2021). Temperature is a frequent parameter for either type. Table 1.2 summarises typicalmethods for determining substrate sensor parameters.Table 1.2: Summary of substrate sensor parameters alongside frequently-usedmethodologies.Parameter Common Methods for DeterminationTemperature Thermistor (Yin et al., 2021)ThermometerElectrical Conductivity(Substrate)Conductivity across a specified distance(e.g. between probes) (Xu et al., 2015)Electrical Conductivity(Solution)Change in electrical charge surrounding probeafter an electrical emission (Xu et al., 2015)pH Hydrogen ion detection (Ragaveena et al., 2021)Volumetric MoistureContentTensiometers (Hutchinson et al., 2024)Dielectric capacitance sensors (Ragaveena et al., 2021Í¾ Hutchinson et al., 2024),also known as frequency domain reflectometry sensorsDissolved Oxygen Content(Solution)Iodometric titration (Langenfeld and Bugbee, 2021)Membrane diffusion (Langenfeld and Bugbee, 2021)Fluorescence quenching (Langenfeld and Bugbee, 2021)71.4.2.5 Whole plant measurements and technologies: Plant electrophysiologyStimuli detected by plants can be transmitted throughout the plant through electrical signals,such that plants can be seen as environmental biosensors (Volkov and Ranatunga, 2006).The study of detecting and interpreting these electrical signals is plant electrophysiology(Armada-Moreira et al., 2022). However, decoding these signals is complicated as the signalsare interconnected to ongoing ionic transients and plant hormone responses occurring withinthe plant (Farmer et al., 2020). Four types of electrical signals in plants have been a focusof plant electrophysiology: electric or electrical potentials (EPs), action potentials, variationpotentials also known as slow-wave potentials, and system potentials (Tran et al., 2019Í¾ Liet al., 2021). Action potentials reflect changes in ions like Ca2+ (Volkov and Ranatunga,2006Í¾ Stolarz and Dziubinska, 2017Í¾ Li et al., 2021) in response to non-damaging stimuli (Tranet al., 2019). In contrast, electric potentials change in response to changes in environmentalconditions, such as water and light (Zimmermann and Mithöfer, 2013). Daily fluctuations inEP have been observed across different plant species (Fromm and Fei, 1998Í¾ Oyarce andGurovich, 2010), suggesting that EP are connected to circadian rhythms. Souza et al. (2017)proposed the new term electrome to capture the complexity of all such signals within plants,in parallel to other terms like genome and metabolome.Signals can be detected and quantified in several different ways, such as usingmetal electrodes(Tran et al., 2019), the repurposing of aphid stylets (Fromm and Lautner, 2006), and usingclamps on animal models (Neher and Sakmann, 1976). Using electrodes, the method ofcyclic voltammetry can be deployed, which is the technique of ramping up and back downthe potential of one electrode, and recording the subsequent changes in current acrossthe other electrode throughout this cycle (Nicholson, 1965). This creates a characteristicvoltammogram, with applied potential along the x-axis and current along the y-axis, with adistinct duck shape (Elgrishi et al., 2017) that can fingerprint specific electrochemical propertyof reduction-oxidation systems (Heinze, 1984). Cyclic voltammetry has been used to determinethe profile of phenolic compounds in kiwi fruit (Jiao et al., 2018) and various wildflowers (ZlatiÄet al., 2022), and explore the antioxidant properties in leafy vegetables (Boruah, 2025). Further,methods using cyclic voltammetry were developed for determining seed viability of silver maple(Trusiak et al., 2025) and sensing strontium ions in rosemary extracts (Wahba et al., 2024).81.4.3 Emerging analytical techniquesCEA has benefited immensely through the advent of molecular biology multi-omic techniques:genomics, the study of the whole genome of an organismÍ¾ transcriptomics, the study of allRNA transcripts of an organismÍ¾ and proteomics, focused on the function, structure, andinteractions of all proteins of an organism. Next-generation sequencing (NGL) techniques,whole genome-wide association studies (GWAS), and identifying quantitative trait loci (QTLs)have become essential to breeding crops suitable for CEA systems (Riaz et al., 2025Í¾ Li andYan, 2020). Not every crop as-is can be transferred from the field into CEAÍ¾ the easiestcrops to make this transition have been leafy vegetables which do not need to undergoflowering (Dsouza et al., 2023). Transcriptomics has been used to explore fruit developmentand ripening, creating new opportunities for crops like strawberries (Yi et al., 2021), whichhave become in-demand in CEA. Physiological research questions can be addressed throughproteomics, where abiotic stress can quickly influence the proteome (Sarkar et al., 2014),as demonstrated in hydroponics systems for rice (Kim et al., 2005), lettuce (Baiyin et al.,2025), and potato (Ippel et al., 2004). Lastly, metabolomics has been particularly conduciveto researching primary and secondary metabolites and their role in stress responses (Prasharet al., 2025Í¾ Riaz et al., 2025).1.4.3.1 Targeted and untargeted phytochemical analysisMetabolomics is the study of processes involving all metabolites of an organism (Fiehn et al.,2000). The field can be subdivided into targeted and untargeted metabolomics. Targetedmetabolomics aims to detect and quantify specific metabolites in samples, using standardsand data libraries, and is commonly used in tandem with techniques described in section 1.4.3(Turi et al., 2015). Specific hypothesis-driven research can use targeted metabolomics in asimilar way to other analytical methods (Turi et al., 2015Í¾ Giebelhaus et al., 2023). Untargetedmetabolomic workflows capture all metabolites in a sample at the time of sampling, in theattempt to minimise bias. This allows for hypothesis generation (Erland et al., 2020b,aÍ¾Ye et al., 2021) and compound and pathway discovery (Murch et al., 2004). In CEAsystems, phytochemicals in kale, broccoli, and mustard have been putatively identified usingmetabolomics (Li et al., 2023) and alkaloid enrichment was discovered in response to differentLED spectra (Nagy et al., 2023).91.5 Hypothesis and objectivesThe primary objective of this research is to understand and optimise agri-technology for CEAapplications. I selected two high-value crops as my model species to evaluate under twodifferent but commonly used CEA conditions: (1) Cannabis sativa L. grown in controlledenvironment chambers, and commercial strawberry (Fragaria x ananassa Duchesne exRozier) in greenhouse hydroponics.Hypothesis: I hypothesised that crop production in CEA systems can be effectively optimisedby using innovative analytical techniques and technologies to manage the abiotic factors oflight and nutrition. I had two complimentary hypotheses, connected to my distinct experiments:Hypothesis 1 (H)): Blue and red spectra provided by LED induces plant growth regulationpathways and secondary metabolite accumulation in Cannabis.Null Hypothesis 1 (H)0): Blue and red spectra provided by LED does not affect plant growthregulation pathways and secondary metabolite accumulation in Cannabis.Hypothesis 2 (H2): Plant nutrient uptake is determined by the availability of nutrients infertigation solutions.Null Hypothesis 2 (H20): Nutrient availability does not determine uptake of nutrients in plants.10Chapter 2: Photobiology and plant growth regulation in Cannabis2.1 SynopsisThe legalization of Cannabis sp. has occurred concurrently with advancements in controlledenvironment agriculture (CEA). This chapter provides a systematic review of the availableliterature using light characteristics (spectrum, intensity, and duration) and Cannabis sp. toidentify factors influencing the endogenous plant growth regulators (PGR). The initial Webof Science survey identified 4465 publications that were screened for eligibility leaving 119manuscripts for detailed evaluation. Each publication was then evaluated for PGR functionalityand growth impact. The results of the systematic review were grouped by morphology,development, physiological and metabolic traits, cannabinoids, germination, and rooting. Keythemes that emerged from this work are: 1) plant photobiology can be explained by 10 changesin plant PGRs, 2) Cannabis photobiology research has especially expanded over the last 25years, and 3) PGR responses vary between chemovars and growing conditions, highlightingthe importance of studying the underlying plant signaling networks. By capturing the currentstate of knowledge and identifying major research themes and trends, this chapter providesa foundation for future investigations, supports the development of optimised cultivationstrategies, and identifies critical gaps in our understanding of plant signaling in Cannabisphotobiology.2.2 IntroductionThe legalization of Cannabis sativa L. for medicinal, research, and recreational purposeshas coincided with unprecedented strides in technology, instrumentation, and data analysis.This has accelerated and deepened the understanding of plant growth, development, andmorphology, beyond what was previously known from Cannabis cultivation in the informalindustry. Classification of Cannabis sativa L. and Cannabis indica Lam. were the originalbotanical identifiers for 2 species, but the terms sativa and indica have become widely usedin marketing and do not always indicate species differentiation. Over time, botanical diversityhas merged with commercial use to create a spectrum of domesticated chemotypes (Clarkeand Merlin, 2017). The terms cannabis and hemp have been used to indicate Cannabis sativa11cultivated for its psychoactive cannabinoids or for its biomass (fibre, seeds), respectively.For the purposes of this review, the term Cannabis has been used generally and does notdifferentiate between the genotypes used in the many different studies.Growing Cannabis within specifically indoor controlled environments allows for the overseeingof all required inputs, such as temperature, humidity, nutrients, and lighting (Nemati et al.,2021). Lighting options in particular have rapidly expanded, with the industry and thehorticultural industry at large pivoting from fluorescent bulbs to high-intensity discharge (HID)lamps to light emitting diodes (LED) en mass over the last decade or so (Wheeler, 2008Í¾Hansen, 2024). 17% of surveyed growers answered must be LED as their most importantfactor they consider regarding their next lighting fixture purchase, with listed price, scientificresearch supporting product development, energy efficiency, light spectrum, and light intensityas the next five most important factors to consider (Hansen, 2024).2.2.1 Controlled environment agriculture and photobiology researchOver the last century, ever improving technology in controlled environment agriculture (CEA)has allowed for highly automated, year-round, optimised growing conditions for specificcrops of interest (Davis and Burns, 2016). The heart of these advancements has stemmedfrom rapidly expanding research in plant photobiology, the field of research examining theinteraction between plants and the attributes of light: its quality (spectra) and quantity (intensityand duration) (Leister, 2023).Light is a complex environmental signal which impacts plant growth and development. Severalphotoreceptors have been characterised in plants: phytochromes, cryptochromes, ZEITLUPE(ZTL)/FLAVIN-BINDING KELCH REPEAT F-BOX1 (FKF1)/LOV KELCH PROTEIN2 (LKP2)family proteins, phototropins, and UVR8 (Higuchi and Hisamatsu, 2016Í¾ Mawphlangand Kharshiing, 2017Í¾ Pierik and Ballaré, 2021). Changes in light are mediated bythese photoreceptors, and translated into modified plant growth and development throughdownstream signaling pathways of critical plant hormones: auxins, cytokinins, abscisic acid(ABA), ethylene, salicylic acid, jasmonates, gibberellins, brassinosteroids, strigolactones, andindoleamines (Alves et al., 2021Í¾ Brini et al., 2022). Consequently, plant hormones are the12intermediaries of light and thus the crux of photobiology.2.2.2 Light Spectra and Auxin MetabolismAuxin is the primary plant hormone controlling plant growth and development and respondingto specific light signals. The most commonly studied auxin is indole-3-acetic acid (IAA).Phototropism - the ability of a plant to direct growth to or away from light - involves redistributionof auxin between cells within the plant (Went, 1935). Auxin is also involved in shootdevelopment (Sassi and Vernoux, 2013), root development (Takahashi, 2013), and branching(Holalu et al., 2021). Shoot branching in Arabidopsis was prevented with high far-red ora decrease in phytochrome B activity that increased auxin signaling (Holalu and Finlayson,2017). Clathrin and auxin work in tandem to mediate blue light-triggered phototropic bendingof the hypocotyl as clathrin promoted asymmetrical auxin distribution in Arabidopsis (Zhanget al., 2016). UV-B will repress auxin signaling through the formation of UVR8-ConstitutivelyPhotomorphogenic1 (COP1) complex that goes on to bind to Elongated Hypocotyl5 (HY5)and increases transcription of PIN-Formed1 (PIN1) and PIN-Formed3 (PIN3), which negativelyregulate auxin (Vanhaelewyn et al., 2016). When activated by UV-B, UVR8 can also interactwith the auxin-responsive transcription factors MYB Domain Proteins73 (MYB73) and MYBDomain Proteins77 (MYB77) (Yang et al., 2020). Levels of IAA displayed a persistent circadianrhythm in Characeae (Chara australis Brown) explants after exposure to continuous light(Beilby et al., 2015) indicating the ancient origins of the light-response mechanism. In tomatoleaves, green light induced IAA degradation into oxIAA while blue and red light limited thisdegradation (Kula-Maximenko et al., 2021). Many other studies and reviews have describedthe redistribution of resources and plant morphogenesis in response to light-directed auxinmetabolism (Ali et al., 2025Í¾ Han et al., 2025).2.2.2.1 Cannabis and Auxin MetabolismAuxin has been studied in the growth and development of Cannabis from rooting to floweringÍ¾while the latter explores the photoperiod attribute of light, the influence of the spectra or intensityattributes of light on these mechanisms have been minimally studied. After photoperiodicinduction, higher concentration of auxin around differentiating flower primordia influenced thesex of Cannabis as well as the degree to which the Cannabis plant flowered (Heslop-Harrison,131956). Recent characterization of auxin response factors suggest several could be necessaryfor flower development within Cannabis (Zhang et al., 2022). Auxin-related genes wereexpressed differently in Cannabis tissue which had differing phyllotaxyÍ¾ for example, anidentified ortholog to Arabidopsis Auxin Transporter-like Protein2 (LAX1) was upregulated inalternative leaves but not in opposite leaves (Hesami et al., 2024). Cannabis growers may usesynthetic auxins when propagating stem cuttings to improve rooting success (Caplan et al.,2018). Sae-Tang et al. (2024) explored the impact of far-red or blue light on Cannabis cuttings,finding that far-red improved rooting, with neither far-red nor blue light significantly affectingendogenous auxin concentrations within leaf tissue after 21 days.2.2.3 Light Spectra and Cytokinin MetabolismCytokinin elevates Arabidopsis response regulators (ARRs) (DâAgostino et al., 2000) in asimilar mechanism as red light (Sweere et al., 2001). Sweere et al. (2001) used burstsof far-red to reverse the elevation in AAR4 induced after exposure to red light, implicatingphytochrome B involvement. Phytochrome A regulates the function of Cytokinin Independent1(CKI1), which is involved in skotomorphogenesis (Dobisova et al., 2017). Cytokinin plays aprotective role under high light stress conditions, as a reduction in cytokinin content leads toa decrease in the D1 protein which is part of the photosystem II complex (Cortleven et al.,2014). HY5 can be regulated through cytokinin inhibition of COP1 that then inhibits HY5(Lau and Deng, 2010). Vandenbussche et al. (2007) hypothesised that cytokinins suppressedCOP1âs ability to degrade HY5Í¾ HY5 protein also increased in response to both cryptochromeand cytokinin pathways. Within pepper (Capsicum annuum L.) seedlings, trans-zeatin washighest under white light, and higher under blue light than red light conditions (Nie et al.,2023). Red light increased expression of the cytokinin transduction protein Histidine-containingPhosphotransfer1 (HPT1) in Pinus sylvestris L. seedlings (Pashkovskiy et al., 2021). Carabelliet al. (2007) hypothesise that part of the shade avoidance response could involve reducingcytokinin levels via cytokinin oxidase/dehydrogenase (CKX) responses to low red:far-redspectra.142.2.3.1 Cannabis and Cytokinin MetabolismThe published research is mostly comprised of studies that assessed the exogenousapplication to stimulate de novo organogenesis in tissue culture. For example, supplemental6-benzylaminopurine (BAP) induced shoot organogenesis (Richez-Dumanois et al., 1986Í¾Ioannidis et al., 2020Í¾ Stephen et al., 2023). Cannabis seedlings sprayed with 120 mg Lâ) BAPexhibited increased plant height and fresh weight increased by 21.5% and 45.7% respectivelyarising from the upregulation of genes associated with photosynthesis and oxidoreductase,antioxidant, and peroxidase activity in the leaves (Yu et al., 2024). Explants grown on mediacontaining the synthetic cytokinin thidiazuron had increased growth rate in tissue culture(Piunno et al., 2019). The cytokinin kinetin applied to Cannabis plants was found to have afeminizing effect, alongside auxin and ethylene (Galoch, 1978). Notably, meta-topolin has beenused to stimulate root formation in Cannabis (Lata et al., 2016), unlike other crops (Werbroucket al., 1996).2.2.4 Light Spectra and Abscisic Acid MetabolismSeveral light-mediated plant processes involve ABA metabolism, such as stomatal opening,root development, and germination (Mahapatra et al., 2025). ABA acts as a suppressor ofseed germination during dark periods, but loses this ability when exposed to light (Finkelsteinet al., 2002). Light signaling transcription factors influence the expression of Abscisic AcidInsensitive5 (ABI5), a pivotal part of ABA signaling (Mahapatra et al., 2025). ABA signalingoccurs at night through the collective binding of phytochrome-interacting factor proteins (PIFs)to the ABI5 promoter, positively upregulating ABI5 protein levels (Qi et al., 2020). HY5 hasalso been shown to bind to the ABI5 promoter (Bhagat et al., 2021). Germination is preventedunder unfavorable conditions through the regulation of ABA by COP1 (Yadukrishnan et al.,2020). Shu et al. (2018) provides an overview of when ABA has both promoted and suppressedflowering time and transition, though ABA is typically seen as gibberellinâs opposite with respectto flowering (Shu et al., 2015).2.2.4.1 Cannabis and Abscisic Acid MetabolismStudies in Cannabis have shown that exogenous ABA treatment both increased THCconcentration in female Cannabis leaves and flowers (Mansouri et al., 2009b), and decreased15THC and cannabidiol (CBD) (Mansouri and Asrar, 2012), depending on the cultivar andexperimental design. ABA application was shown to mitigate salt stress and damage (Gaoet al., 2018). Genes for ABA biosynthesis have been identified and were upregulated inCannabis plants exposed to drought stress (Yang et al., 2024). ABA stored in Cannabis seedsis rapidly metabolised during germination (Hou et al., 2023).2.2.5 Light Spectra and Ethylene MetabolismSeedlings exposed to ethylene during periods of darkness have been known to display adistinct triple response morphology: a pronounced apical hook with a short and thickenedhypocotyl (Guzmán and Ecker, 1990)Í¾ this response does not occur under light conditions(Harkey et al., 2019). Goeschl et al. (1967) reported the negative effect of a burst of red-lightexposure on ethylene concentrations within pea (Pisum sativum L.) seedlings, which waslessened when there was a period of far-red light following red light, which is typical for aphytochrome-controlled mechanism. Pea phytochrome A and B mutants had high levels ofethylene and their phenotype was rescuedwith the addition of an ethylene biosynthesis inhibitor(Foo et al., 2006). Under red light conditions, phytochrome B was found to induce degradationof PIF5, whereas under dark conditions an overabundance of PIF5 increased the concentrationof ethylene (Khanna et al., 2007). The impact on ethylene production can vary despite similarlighting conditions across different plant species, tissue, or developmental phase being tested(Rodrigues et al., 2014).2.2.5.1 Cannabis and Ethylene MetabolismSexual expression of Cannabis has long been manipulated through the use of ethyleneprecursors (e.g. Ethephon) or specific enzyme inhibitors (e.g. silver thiosulfate). Feminizationof male plants using a low dosage of Etheponwas reversed using silver nitrate or cobalt chloridecitepram1982. More recently, Ethepon was shown to increase the number of female flowersas well as alter their cannabinoid profile (Ruzgas et al., 2024). THC content in leaves wasincreased by Ethephon treatment during the vegetative stage of crop growth (Mansouri et al.,2016). Transcriptomics studies have demonstrated differentially expressed genes associatedwith ethylene signaling pathways (Adal et al., 2021). Orthologs part of the Yang cycle ofethylene biosynthesis described in Arabidopsis were identified in Cannabis (Monthony et al.,162024).2.2.6 Light Spectra and Salicylic Acid MetabolismSalicylic acid mediates plant responses to pathogens (Kurepin et al., 2010) and light intensity(Mateo et al., 2006). Mateo et al. (2006) described an increase in the operating efficiencyof photosystem II after the higher light and foliar salicylic acid treatment in Arabidopsis. Insunflowers (Helianthus annuus L.) salicylic acid was increased by increased light intensity, butwas not affected by different red:far-red ratios (Kurepin et al., 2010). An interaction betweenred light and salicylic acid was described in tomato (Solanum lycopersicum L.) exposed topathogen stress (Pseudomonas syringae pv. Tomato) (Yang et al., 2015). UV-C exposureincreased the level of salicylic acid in leaves in tobacco (Nicotiana tabacum L.) (Yalpani et al.,1994).2.2.6.1 Cannabis and Salicylic Acid MetabolismExogenously applied salicylic acid alone or in combination with γ-aminobutyric acid (GABA)or increased expression of genes involved in THC biosynthesis (Jalali et al., 2019Í¾Mirzamohammad et al., 2021). Salicylic acid application increased tetrahydrocannabinolic acid(THCA) and cannabidiolic acid (CBDA) in leaves (Garrido et al., 2022) and increased CBDconcentration in adventitious roots (Wang et al., 2024). Interestingly, increases in THCASgene expression and trichome density were not observed in the study (Garrido et al., 2022).Supplementation of culture media with salicylic acid did not increase cannabinoid content inglandular trichomes suggesting disconnect between salicylic acid-induced gene and proteinresponses and cannabinoid metabolism (Dimopoulos et al., 2025).2.2.7 Light Spectra and Jasmonate MetabolismJasmonate signaling has been implicated in UV-B damage repair as well asphotomorphogenesis (Ruan et al., 2019). In Brassica oleracea var. italica, UV-B exposureincreased the expression of jasmonate genes (Mewis et al., 2012). Under low red:far-red ratiolight, jasmonate sensitivity was found to be reduced in Arabidopsis (Cerrudo et al., 2012).A low red:far-red ratio was found to suppress root nodule formation in Lotus japonicus andjasmonate-responsive genesÍ¾ by comparison, applying exogenous jasmonate improved nodule17formation under these light conditions (Suzuki et al., 2011). A key enzyme in the processof forming jasmonate-isoleucine, FIN219, has been shown to be involved in far-red lightsignaling (Chen et al., 2018) and acts antagonistically to phytochrome chromophore-mediatedsignaling for photomorphogenesis (Chen et al., 2018). An Arabidopsis cryptochrome 2 mutantdid not have the same increase in jasmonate and salicylic acid as was observed in the wildtype control when both were exposed to 2 hours of green light during the dark period (Satoet al., 2015). In the same experiment, the equivalent treatment using blue light did increasejasmonate and salicylic acid in the cryptochrome 2 mutant (Sato et al., 2015).2.2.7.1 Cannabis and Jasmonate MetabolismMethyl jasmonate applied to Cannabis cuttings was found to decrease height while increasingtrichome density, total CBD concentration, and total THC concentration in the inflorescencesand leaves (Hahm et al., 2024). Similarly, Wang et al. (2024) also reported an increase intotal CBD in adventitious roots in response to methyl jasmonate. Total cannabinoid content(Apicella et al., 2022Í¾ Welling et al., 2023), as well as THCA, tetrahydrocannabivarin (THCVA),cannabichromenic acid (CBCA), CBDA, cannabidivarinic acid (CBDVA), and cannabigerolicacid (CBGA) (Welling et al., 2023) all increased in Cannabis leaves after exogenous methyljasmonate application during the flowering stage, though Oultram et al. (2024) observed nochange in cannabinoid content under a jasmonate treatment. Huang et al. (2024) suggests thatglandular trichome formation may be regulated bymethyl jasmonate. There was an associationbetween jasmonate related compounds with sexual expression and cannabinoid content infemale flowers (Welling et al., 2021). Jasmonate also improved the structure of Cannabisphloem fibres by inducing higher area coverage of secondary phloem fibres and higher lignincontent in seedling hypocotyls (Behr et al., 2018).2.2.8 Light Spectra and Metabolism of GibberellinsMechanistically, activation of phytochrome A and B increased levels of gibberellin synthesisgenes and GA4 within Arabidopsis seeds during germination (Oh et al., 2007) and light actsas an inhibitor of GA4 accumulation and reduces gibberellin signaling after germination duringphotomorphogensis (Sun, 2008). In the absence of gibberellin, DELLA proteins can sequesterPIF3, causing the negatively impacted photomorphogensis (Li et al., 2016). UV-B exposure will18downregulate gibberellins, leading to elongation of lateral roots and the hypocotyl (Dai et al.,2024). Under far-red light, cucumber (Cucumis sativus L.) seedlings had accumulated higherconcentrations of gibberellins (GA+, GA4, GA7) alongside increased biomass and elongatedleaves and internodes (Li et al., 2024). In Lolium temulentum, levels of gibberellins increased inresponse to an increase in photoperiod associated with flowering and the reponse was inhibitedby Paclobutrazol (King et al., 2006).2.2.8.1 Cannabis and Metabolism of GibberellinsIn Cannabis, the application of gibberellic acid increases the thickness of Cannabis fibres (Ataland Sethi, 1961Í¾ Stant, 1963). Exogenous application of GA+, GA4, GA7, or GA1 induced maleflowers on female Cannabis plants (Mohan Ram and Jaiswal, 1972). Exogenous application ofgibberellin decreases THC content in 4-week-old plant leaves (Masoud et al., 1973), in femaleflowers and leaves (Mansouri et al., 2009a), and inmature female flowers at weekly applications(Oultram et al., 2024). Mansouri et al. (2011) reported an increase in both THC and CBD inleaves at the highest dose of gibberellin tested. Seed germination rate has been shown toimprove (Du et al., 2022Í¾ Langa et al., 2024Í¾ Yang et al., 2024) and decrease (Islam et al.,2022b) after gibberellin treatment.2.2.9 Light Spectra and Brassinosteroid MetabolismLight and brassinosteroids have opposite effects in the morphogenesis of seedlings (Lin et al.,2021). Mutant type plants in either light signaling or brassinosteroids will demonstrate similarphenotypes (Lin et al., 2021). A G-protein (AGB1) has been shown to regulate brassinosteroidsignaling through its interaction with a Brassinosteroid-responsive Transcription Factor1(BES1) (Zhang et al., 2018). AGB1 itself interacts with cryptochrome 1 via blue light (Lianet al., 2018), acting as a mediator between brassinosteroids and blue light. Additionally, redlight through the direct binding of phytochrome B to Brassinazole Resistant1 (Dong et al., 2020)or through phytochrome A and B directly interacting with Brassinosteroid-insensitive2 (BIN2)(Zhao et al., 2022), and blue light via cryptochrome 1 interacting with BIN2 (He et al., 2019)have been shown to reduce the signaling of brassinosteroids. Two transcription factors ofbrassinosteroid genes were shown to physically interact with UVR8 in Arabidopsis exposed toUV-B (Liang et al., 2018).192.2.9.1 Cannabis and Brassinosteroid MetabolismTwo studies have specifically examined brassinosteroids in Cannabis metabolism. Putativelybrassinosteroid-related transcripts were found to be enriched by â¼2 fold in Cannabis fibroustissues, including a potential brassinosteroid receptor (De Pauw et al., 2007). Some genesfor homeodomain zipper (HD-Zip) family transcription factors which were detected in Cannabiswere postulated to be involved in brassinosteroid signaling as well (Ma et al., 2022).2.2.10 Light Spectra and Metabolism of Strigolactones and KarrikinsUnder low light conditions with a low red:far-red ratio or high planting density, plants will deployshade-avoidance responses like growing tall or growing wider leavesÍ¾ these shade conditionsdeactivate phytochrome B (Finlayson et al., 2010). Arabidopsis mutants of phytochrome B(phyB) will express shade-avoidance responses under high red light (Finlayson et al., 2010).Strigolactone mutants max1, max2, rms3, and rms4 remain short and grow rounded leavesunder shade conditions (Brewer et al., 2013). Under low red:far-red conditions, double mutantsfor both strigolactones and phytochrome B still grew tall, suggesting that the phytochrome Bresponses are upstream to or repressing the strigolactone pathway (Finlayson et al., 2010).The nuclear localization of COP1, which represses light signaling, was heavily diminished byan application of a synthetic strigolactone, GR24 (Tsuchiya et al., 2010). Further, this effectof GR24 occurred after a 24-hr period just like light, and both inhibit COP1 (Tsuchiya et al.,2010). Jia et al. (2014) suggest that phytochrome A, phytochrome B, and cryptochrome 1were necessary for proper GR24 function.Karrikins show a high degree of similarity to strigolactones, with respect to compound structure,signaling pathway, and function in plant growth and development (Janssen and Snowden,2012Í¾ Meng et al., 2017). Karrikin 1 was shown to upregulate several genes associated withlight signaling as well as to induce HY5 in Arabidopsis seedlings (Nelson et al., 2010). In thesame study, hypocotyl elongation was also improved through the use of karrikins. Lee et al.(2019) showed that an Arabidopsis karrikin receptor mutant (ply2) influenced the expressionof genes involved in light signaling. SUPPRESSOR of MAX2 1 (SMAX1) proteins, which areregulators of karrikin signaling, will accelerate DELLA protein degradation (Kim et al., 2022).202.2.10.1 Cannabis, Strigolactones, and KarrikinsResearch to date has not fully investigated the potential roles of karrikins or stringolactones inCannabis systems. Future work may lead to new understandings important for optimization ofCannabis production systems.2.2.11 Light Spectra and Indoleamine MetabolismTwo indoleamines, melatonin and serotonin, have emerged as a new class of plant hormoneswhich play a role in light perception and signaling (Murch and Erland, 2021). Serotoninsupplementation performed the same time as a short duration of red light exposure on maizeleaves can modulate the polyphosphoinositide cycle (Chandok and Sopory, 1994). Melatonincontent increased under red light within Glycyrrhiza uralensis roots, when compared to blueor white light (Afreen et al., 2006). Afreen et al. (2006) also found elevated melatonin inroots under 3 days of high intensity UV-B exposure. Tan et al. (2007) showed that levelsof melatonin and its oxidative metabolite N)-acetyl-N2-formyl-5-methoxykynuramine followeda diurnal rhythm and were elevated under higher light intensity in water hyacinth. Exogenousmelatonin and serotonin applied onto Chenopodium rubrum L. seedlings resulted in reducedflowering (KoláÅ et al., 2003). Further, exogenous melatonin delayed the flowering of appletrees grown in the field and in vivo melatonin levels were lowest before flowering occurred(Zhang et al., 2019a). Additionally, Zhang et al. (2019a) found that sub-cultured apple leaveshad higher levels of melatonin under blue and far-red light, in contrast to red. Tomato plantsgrown under a combination of red and blue light had higher melatonin content within their fruitduring the ripening process than under blue, red, or white light (Yan et al., 2021b)Í¾ the samepattern was observed in rice seedlings exposed to cadmium (Hwang et al., 2020). Hwanget al. (2020) also reported significant reduction in melatonin content in phytochrome A andphytochrome B mutants under the mixed red and blue light treatment. In Chara australis,exogenousmelatonin increased photosystem II quantum yield, suggesting that melatonin playsa protective role (Lazar et al., 2013).2.2.11.1 Cannabis and Indoleamine MetabolismOloumi et al. (2022) found that adding melatonin to the fertigation solution in a Cannabisproduction system reduced the effects of abiotic stresses induced by high levels of zinc and21copper.2.3 Review process2.3.1 Eligibility criteria, information sources, and search strategyThe goal of this systematic review was to understand the dynamic and rapidly changinglandscape of photobiology research in Cannabis specifically and to reexamine the researchin the context of plant hormone-mediated growth and development. Cannabis lends itself wellas a model plant for conducting photobiology research, given that Cannabis: can be easilygrown in a variety of different ways (Nemati et al., 2021), has high economic value in countriesor areas where it is legalised (Statistics Canada, 2023), and responds with a high degree ofplasticity to varying light conditions (Rodriguez-Morrison et al., 2021). As such, a systematicliterature review mirroring the PRISMA 2020 protocol (Page et al., 2021) was conducted tocapture the full scope of Cannabis photobiology research from its humble beginnings in theearly 20tr century to the end of 2024 (Figure 2.1). An initial Web of Scienceâ¢ query of plantAND photobiology produced n = 2159 results. The keywords were then mined for the 100most commonly occurring terms using a Python script. This list was further narrowed downto 20 terms by combining morphological variants, consolidating specific colours under theterm spectra, and retaining terms which explicitly referred to core concepts in the field withoutrequiring additional context. For example, broader terms like solar, nanoparticles, and stresswere excluded because of their likelihood of returning irrelevant articles. The three terms withthe highest occurrence frequency were photo* (453), spectr* (300), and light (188). Thesesearch terms were combined into a single query on Web of Scienceâ¢ of ALL = (cannabis AND((spectr*) OR (photo*) OR (light))) with a publication date range of 1900-01-01 to 2024-12-31,producing the starting paper list of n = 4465 (Figure 2.1).22Figure 2.1: Flow chart overview of the collection of papers for this review. A Web of Scienceâ¢query of ALL = (cannabis AND ((spectr*) OR (photo*) OR (light))) with a publication date rangeof 1900-01-01 to 2024-12-31 was used to generate the initial list of n = 4465 papers. The finalconsolidated list was 119 articles.2.3.2 Selection process and data collection processThe papers collected from theWeb of Scienceâ¢search were exported and aggregated into oneprimary Excelâ¢ spreadsheet. Excelâ¢ filter functions were used to efficiently remove papersuntil there were n = 512 papers left for manual review. Abstracts were first manually reviewedfor applicability, then the full article was reviewed. Throughout the selection process, n = 8papers were included which were missed during the initial search term selection, and a furtherrelevant n = 2 papers were included which were published during the selection process, tocreate the final n = 119 list. Of these, n = 104 were original research articles while n = 15 werereview articles.232.3.3 Data itemsThe final list of papers spanned the years 1925 to 2025. Many papers published before 1960lacked some specific details such as cultivar used, planting density, environmental conditionsduring the experiment. Each publication was carefully evaluated for the following data: articletitle, author full names, year published, type of growing system, type of journal article, affiliationof first author, journal name, spectra treatment used, cultivar or variety name, growing media,planting density, experimental cultivation area, duration of propagation/vegetative/reproductivestage of growth, environmental conditions, peak light intensity, number of plants per treatment,fertigation composition/pH/EC used, drying conditions. Broader categories from thesecollected data were then created: indoor or outdoor growing conditions, research theme,type of Cannabis cultivated based on function, vegetative or reproductive growth stage,spectrum of interest. Where certain data were not explicitly outlined in the methods, they wereinterpolated when there was sufficient information available (e.g. planting density, but numberof plants and growing area dimensions were described). Numerical data were converted toconform to common units to allow for comparison, such as converting reported light intensityto photosynthetic photon flux density (PPFD, µmol mâ2 sâ)). When such interpolation orcalculation was not possible, the data were coded as missing and excluded from graphs ortabulation for that specific analysis.2.3.4 Limitations of the reviewNon-English manuscripts or manuscripts that were not scientific articles, reviews, bulletins,conference proceedings, or proceedings papers were excluded frommy process. Records andanecdotal reports from the informal Cannabis breeding and grower cultures were not included.Careful consideration was made to include the appropriate manuscripts that focused on otheraspects of Cannabis cultivation, e.g. plant density, which could be extrapolated or tied to lightintensity or spectra. Papers describing outdoor cultivation ofCannabiswere incorporated whendifferent geographic locations were assessed, as these locations would have unique lightingconditions due to elevation, longitude, and latitude. Research which focused exclusively onphotoperiod insensitive Cannabis, or autoflowers, was excluded (Toth et al., 2022).242.4 Current and emerging trends in Cannabis photobiologyMajor and minor research topics emerged when reviewing the literature (Figure 2.2). The 3major themes broadly clustered around light intensity, spectra, and photoperiod. For thesepapers, the experimental treatments used and then assessed are differing PPFD levels (e.g.Rodriguez-Morrison et al. (2021)), LEDs with differing wavelengths (e.g. Westmoreland et al.(2021)), or differences when the flowering photoperiod is 12 hours or 13 hours long (e.g. Ahrenset al. (2023)). 4 minor themes were then identified. For instance, lighting technology, wherelight systems were compared directly (e.g. Reichel et al. (2021)), and day cycle, where a24-hour period was the focus. One minor theme, lighting strategy (Arora and Yun, 2023) whichimplemented distinct lighting treatments at different growth stages, did not overlap with a majortheme. The rate of publishing for papers covering one of the three major research themes hasrapidly increased since 2010 (Figure 2.3). The Spectra theme is the fastest growing.Figure 2.2: Modified Venn diagram representing the major research themes from the Cannabisphotobiology papers (n representing number of papers with that theme). No paper was foundthat combined research with light intensity, spectra, and photoperiod all together.25Figure 2.3: Line area plots representing the major and minor research themes from theCannabis photobiology papers over time and best fitting regression model (overlaid red line).Themes were counted separately for papers with multiple themes. Day cycle and lightingstrategy were omitted each contained low number of papers (n < 3).2.4.1 Developing Cannabis research clustersSignificant advancements in the field of Cannabis photobiology have been made over the last25 years (Figure 2.4) and across many countries (Figure 2.5). The top 5 countries were,in descending order: United States of America (n = 22), Canada (n = 19), Israel (n = 8),Australia (n = 7), and China (n = 7). Unsurprisingly, countries which have published Cannabisphotobiology research all have some degree of legalization in some capacity (Kumar et al.,2021Í¾ Farrelly et al., 2023Í¾ Sun, 2023), aside from Iran which has all Cannabis cultivationcriminalised (Shamabadi and Akhondzadeh, 2024).26Figure 2.4: Histogram plotting frequency density of published articles on Cannabisphotobiology included in this review, with the left sub panel between 1900-1999 and the rightsub panel between 2000-2024 (with some articles being postdated to 2025). Total number ofarticles within each bin are labeled above their respective bin.Figure 2.5: Number of articles published by country as determined by the affiliation of the firstauthor. The top 5 countries were, in descending order: United States of America (n = 22),Canada (n = 19), Israel (n = 8), Australia (n = 7), and China (n = 7).Several affiliations of the first author appeared multiple times, suggesting potential emergingresearch clusters to follow the development of Cannabis photobiology: the University ofGuelph (n = 13), the Volcani Center (n = 8), Wageningen University and Research (n = 4),McGill University (n = 4), and n = 3 for each from Kangwon National University, University ofHohenheim, and Utah State University. All authors were aggregated for each paper and thenplotted to show the network of authorship (Figure 2.6).27Figure 2.6: A representation of the author network for Cannabis photobiology based onco-authorship on papers. Each node is an author and each edge connection between nodesrepresents their co-authorshipÍ¾ authors with fewer edges would have a higher node degreethan authors with many edges. Fewer than 6 edges were filtered from the dataset prior toplotting the graph. Authors with high co-authorship are positioned on an outer circumference.Last authors are shown with a red perimeter around their node.2.4.2 Trends in cultivars selected for researchBroadly, the literature can be separated into two groupings: papers which used Cannabiscultivated for psychoactive cannabinoids, or papers which used Cannabis grown for its fibre,seed, or non-psychoactive cannabinoids. Canada was the country with the highest number ofpapers utilizing the former at n = 18, followed by the US (n = 6) and Israel (n = 2). For the latter,the US had n = 13 papers compared to Chinaâs n = 7 and Australiaâs n = 5. Most of photobiologyresearch used Cannabis cultivars grown for fibre, seed, or non-psychoactive cannabinoids,influenced by the wave of lifting regulations through the 1970-1980s (Siracusa et al., 2019).Classifications were assigned based on either direct terminology used by the paper (e.g. hemp,drug-type), or by inference from the reported detected secondary metabolite(s) (e.g absenceof psychoactive cannabinoidâ9-tetrahydrocannabinol (THC). The distinction was only unclearin n = 2 papers.28Digging deeper into cultivation selection, Figure 2.7 visualises the diversity of cultivars usedacross the selected papers. Notably, it was the absence of any specification of cultivar, whichwas coded as Unnamed, that was the most frequently occurring (n = 9). When present, Finolawas the most commonly named cultivar, being used in n = 6 papers. Some papers referredto only the geographic source when describing their plant (e.g. THC strain of Mexican origin,Vogelmann et al. (1987)), which was coded as country + strain.Figure 2.7: Word cloud of strain or cultivar names referenced in papers where font size reflectsthe frequency of the cultivar nameÍ¾ if this piece of information was omitted, the cultivar namewas coded as Unnamed.2.4.3 Range of environmental parametersCannabis photobiology research took place under many growing conditions, spanning bothoutdoor and indoor environments (Figure 2.8). Indoor environments were more heavily usedthan outdoor, and were broadly categorised as grow room, greenhouse, growth chamber,tissue culture, glasshouse, and other (e.g. glass jars (Vogelmann et al., 1987), grow tents(Wei et al., 2021). By comparison, 71% and 41% of Cannabis Business Times 2024 (Hansen,2024) survey respondents reported using indoor or outdoor facilities, respectively, with afurther 48% using greenhouses with or without supplemental lighting. Future research shouldconsider utilizing greenhouses or glasshouses to better reflect the increasing number ofcommercial Cannabis growers adopting these structures. Many papers used a combination ofgrowing conditions, typically divided between the stock plants and the subsequent propagatedplants. n = 10 papers used grow rooms which were specifically mentioned to be in commercial29facilities, suggesting increasing collaboration between industry and academia. Growingconditions could not be determined in n = 2 papers.Figure 2.8: Growing conditions described by papers in this review, with the major distinctions ofindoor (84%), outdoor (8%), both (8%), or unspecified (coded asMissing, 1%) contained withinthe inner circle, and their respective subcategories aligned along the circumference. Papersusing multiple growing conditions contributed one count per condition.The type of lighting technology used in Cannabis photobiology research has changed rapidlysince 2000 (Figure 2.9). LEDs are widely used as their spectra, intensity, and photoperiod canbe easily changed, allowing researchers the opportunity to explore various questions. Severalpapers have compared lighting technologies directly (Jenkins and Livesay, 2021Í¾ Namdar et al.,2019Í¾ Campbell et al., 2019). Some papers compare lighting fixtures without standardizing lightintensity or placement of fixtures, making it difficult to assess the main effects (e.g. Wei et al.(2021)).30Figure 2.9: Cumulative count of articles published since 2000 using various lightingtechnologies, with LED on the left and all other lighting technologies on the right. The Othercategory grouped together less commonly used technologies (e.g. plasma).The peak light intensities used in these papers were converted into PPFD and plotted in ahistogram to assess the frequency density, separating data from tissue culture growing systemsfrom non-tissue culture growing systems as the former conventionally uses low light intensities(Figure 2.10). For tissue culture growing systems, the median and average peak light intensitywere 50.4 µmol mâ2 sâ) and 117.2 µmol mâ2 sâ), respectively. For all other growing systems,the median and average peak light intensity were 520.0 µmol mâ2 sâ) and 636.1 µmol mâ2 sâ),respectively. In contrast, over the past decade, commercial Cannabis facilities have adoptedhigher lighting intensities (> 800 µmol mâ2 sâ)) than those reported in the papers selected inthis review (Hansen, 2024). Therefore, future studies could align more closely with both lightingtechnologies and lighting intensities used commercially.31Figure 2.10: Histogram of frequency density of peak light intensities reported in papers,expressed as photosynthetic photon flux density (PPFD, µmol mâ2 sâ)), showing tissue culturegrowing systems only on the right and all other systems on the left. Total number of articleswithin each bin are labeled above their respective bin. Reported peak light intensities wereconverted into PPFD where possible, excluding a total of n = 29 review papers or papers withinsufficient data.2.5 Impact of light on Cannabis growth and development2.5.1 Visual presentation of synthesised resultsA coordinate pair was assigned to each depending on whether the paper reported anincreasing, decreasing, or neutral effect on the response measure based on the light attributebeing investigated. If the response measure increased with an increase in this attribute, thenthe paper would be plotted at (1, 0)Í¾ conversely, if the response measure decreased with anincrease in this attribute, the coordinate pair would be (-1, 0). If there was found no effect, thenthe coordinate pair was (0, 1). Effect due to spectra was further categorised by the colour of lightbeing assessed. Coordinate pairs were grouped together by their aspect of light, to make visualcomparisons easier. Lastly, coordinate pairs were exploded vertically to help with legibility, anddo not reflect severity or degree of impact on the response measure by the attribute.2.5.2 Morphology and development2.5.2.1 HeightCannabis plant height can be manipulated by various aspects of light (Figure 2.11). Forexample, papers reported taller plants in treatments with higher proportions of far-red32(Campbell et al., 2019Í¾ Pepe et al., 2021), where far-red inclusion led to a height increase inseedlingsÍ¾ (Kotiranta et al., 2024Í¾ Reichel et al., 2024Í¾ Kotiranta et al., 2025), where far-redinclusion led to increases in mature plants. These responses are aligned with known signs ofshade avoidance response induced by far-red light (Tan et al., 2022). In one study, green lightdid not change Cannabis plant height significantly (Reichel et al., 2021).Increasing the photoperiod was correlated to increased plant height, especially in studieswhich focused on outdoor cultivation across different sowing dates, thus extending thevegetative period (e.g. (Lisson and Mendham, 2000Í¾ Sengloung et al., 2009Í¾ Hall et al.,2013)). Peterswald et al. (2023) investigated short-day photoperiods and reported increasedplant height across three different Cannabis cultivars with an additional 2-4 hours of light.Gibberellins are known regulators of plant height and are likely involved in these observations.Supporting a link between photoperiod and gibberellin activity, Alter et al. (2024) reportedincreased internode length, and up to a 300% rise in gibberellin A4 content in the shoot apicesof Cannabis grown under a longer photoperiod. There was also a measured decrease in IAAcontent in the same samples.Other attributes exhibited conflicting effects on plant height, which may, in part, reflectunderlying hormonal differences among studies. For instance, blue light has been reportedto increase height in mature Cannabis plants (Cheng et al., 2022, 2023) and in in vitro plantlets(Puttharak et al., 2024). Separately, blue light was shown to decrease (Namdar et al., 2019Í¾Pepe et al., 2021Í¾ Morello et al., 2022), or produce no significant effect (Reichel et al., 2021Í¾Westmoreland et al., 2021Í¾ Kotiranta et al., 2024) on height. Kong and Zheng (2024) proposedseveral factors that may account for these discrepancies, including potential interactions withlight intensity. Sato et al. (2015) demonstrated blue light mechanisms that elevate endogenousjasmonate and salicylic acid levels as a potential interpretation of studies reporting a reductionin Cannabis height. Rodriquez-Morrison et al. (2021) reported a reduction in plant height underUV-B exposure which may be attributable to the downregulation of gibberellin pathways.33Figure 2.11: Influence of various aspects of light (spectra, intensity, and photoperiod) on plantheight, as reported in the Cannabis literature.2.5.2.2 Non-phytocannabinoid yieldThe term yield was used in different publications to refer to a variety of different physiologicalresponses such as: dry or fresh inflorescence weight, whole plant biomass, stem or fibreweight, or whole seedling weight. To account for this variability, a unified yield category wasdefined (Figure 2.12) to encompass all non-chemical, biomass components, though findingson leaf mass were excluded due to its limited economic relevance for Cannabis cultivation.The literature strongly suggests that increasing light intensities will increase Cannabis yield,at times linearly (Potter and Duncombe, 2012Í¾ Eaves et al., 2020Í¾ Wei et al., 2021Í¾ Colladoet al., 2024Í¾ Moher et al., 2022Í¾ Konvalina et al., 2024). Excessive light can act as a stress inplants, leading to reduced efficiency, production of reactive oxygen species (ROS) or damageof photosystem II (photoinhibition) (Goh et al., 2012).34Plant hormones shown to protect or improve photosystem II efficiency, including melatoninin Chara australis (Lazar et al., 2013), salicylic acid in Arabidopsis (Mateo et al., 2006) andHelianthus annuus L. (Kurepin et al., 2010), and cytokinin in Arabidopsis (Cortleven et al.,2014), need further study in Cannabis. Two papers investigated the effects of far-red light inCannabis (Kotiranta et al., 2024, 2025), and reported increased dry weight inflorescence yield(Kotiranta et al., 2024) and no increase in inflorescence yield (Kotiranta et al., 2025). Studiesinvestigating the effects of UV radiation on yield in Cannabis include Abedini et al. (2024),who found a 25-75% increase in yield with increasing UV-A/UV-B supplemented lighting incell suspension cultures. Three papers evaluated UV light in Cannabis plants and reporteddecreased yield (Jenkins, 2021Í¾ Rodriquez-Morrison et al., 2021Í¾ Westmoreland et al., 2023).Two studies found no significant differences in Cannabis yield as a result of applying UV-Aalone or together with UV-B (Llewellyn et al., 2022Í¾ Kotiranta et al., 2024).Figure 2.12: Influence of various aspects of light (spectra, intensity, and photoperiod) on plantyield. Here, yield was the common term used, which could refer to dry or fresh inflorescenceweight, whole plant biomass, stem or fibre weight, or whole seedling weight, depending on thecontext of the study.352.5.3 Physiological and metabolic traitsTwenty-two reported physiological measurements and metabolic traits in their experiments(Figure 2.13). Four manuscripts did not detect significant differences in measured physiologicalor metabolic traits. Photosynthetic rate (µmol CO2 mâ2 sâ)) is a limiting factor in crop yield(Karthick et al., 2024). Photosynthetic rate was increased in Cannabis response to increasingblue (Islam et al., 2021aÍ¾ Cheng et al., 2023), far-red (Reichel et al., 2022), or light intensity(Lam et al., 2022). Several papers report effects on total chlorophyll, chlorophyll a, and/orchlorophyll b. Chlorophyll content increased in response to blue light (Islam et al., 2021a,2022aÍ¾ Cheng et al., 2022) while red light was also identified to enhance total chlorophyll(Codesido et al., 2023) and chlorophyll b (Islam et al., 2021b) concentration. Changes inproline content is an indication of a mounting stress response in plants (Renzetti et al., 2025).In Cannabis, proline was found to decrease under blue light (Aasim et al., 2024) (comparedto the white light treatment) and green light (Islam et al., 2022a) (compared to other red:bluetreatments). Additionally, proline content increased with red and with blue (Cheng et al.,2023), and UV (Abedini et al., 2024). No studies examine light intensity and its impact onproline content, though light intensity can act as a stressor (refer to section 2.5.2.2). Prolinelevels within plants can be mediated both through ABA-dependent and ABA-independentpathways (Pál et al., 2018).Varga et al. (2024) found that phenols, flavonoids, and total flavanol content decreased inCannabis in response to blue light whereas Livadariu et al. (2019) found flavonoid concentrationincreased. Similarly, blue light increased total phenolic content in buckwheat sprouts (Thweet al., 2014), rice (Lakshmanan et al., 2015), and soybean microgreens (Zhang et al., 2019b).Flavonols have been put forward as modulators of auxin signaling and transport by impactingthe activity of PIN proteins, or through scavenging of reactive oxygen species (ROS) (Brunettiet al., 2018). In particular, quercetin-derived flavonols could be involved in stomatal closurein both an ABA-dependent and blue-light dependent manner via phototropin involvement(Hayashi and Kinoshita, 2011)Í¾ in turn, the biosynthesis of various flavonols has been shownto be promoted through the use of ABA (Brunetti et al., 2018).36Figure 2.13: Influence of various aspects of light (spectra, intensity, and photoperiod)on specific physiological and metabolic traits (specified in square brackets), excludingcannabinoids, as reported in the literature. NCER refers to Net CO2 exchange rate.2.5.4 Cannabinoids2.5.4.1 Cannabigerolic acid and cannabigerolSeven publications evaluated the effects of light on cannabigerol (CBG) or its acidic precursor,CBGA (Figure 2.14). Treatments with higher proportions of blue light (broad spectrum or1:1 R:B LEDs) led to higher CBGA content across three cultivars (Danziger and Bernstein,2021), and Qian et al. (2023) found that red light had elevated CBGA content in seedlings after96 hours of exposure. Both Llewellyn et al. (2022) and Konvalina et al. (2024) explored theimpact of light intensity, and both found no impact on CBG. In Cannabis clones, exogenousmethyl jasmonate was shown to increase CBGA (Welling et al., 2023) whereas exogenous37cytokinin did not have an impact (Oultram et al., 2024). However, Oultram et al. (2024) didobserve a reduction of CBGA and CBG with exogenous gibberellic acid in their final week ofsamplingCannabis inflorescences. The highest dosage of ethylene used by Bafort et al. (2024)increased CBGA and total CBG concentration in mature inflorescences. Exposure to blue lightfor 24 hours in young Cannabis seedlings resulted in the highest concentration of CBGA (Qianet al., 2023).Figure 2.14: Influence of various aspects of light (spectra, intensity, and photoperiod) oncannabigerolic acid (CBGA) and cannabigerol (CBG) concentration (as denoted in squarebrackets within the label), as reported in the literature.2.5.4.2 Cannabidiolic acid and cannabidiolThirty-five publications investigated the effects of light on CBD or CBDA (Figure 2.15). Kotirantaet al. (2024) found that increasing the proportion of far-red decreased CBDA and CBD indried inflorescences. Kotiranta et al. (2025) also reported a decrease in CBDA concentrationbetween treatments with the highest and lowest far-red content. Light intensity has a variedimpact on CBD or CBDA, possibly influenced by different growing conditions. For example,one paper found higher light intensity to negatively impact CBD in plants cultivated outdoors inthree different areas in Iran (Abdollahi et al., 2021). Three papers found a positive correlation(Konvalina et al., 2024Í¾ Wei et al., 2021Í¾ Sae-Tang et al., 2024) while four found no effectat all (Vanhove et al., 2011Í¾ Hawley et al., 2018Í¾ Rodriguez-Morrison et al., 2021Í¾ Holweget al., 2024). Abedini et al. (2024) found a quadratic relationship between UV-A/UV-B and38CBD concentration, with a point of diminishing return. Blue light exposure decreased whilered light exposure increased CBDA levels in young Cannabis seedlings (Qian et al., 2023).Exogenous application of gibberellic acid decreased CBDA and CBD in mature plants, whileCBD increased when jasmonate was applied in the same experiment (Oultram et al., 2024).Neither exogenous cytokinins (BAP) nor auxins (NAA) influenced CBD or CBDA concentration(Burgel et al., 2020). Xu et al. (2024) found the highest levels of upregulated transcriptionfactors associated with jasmonate-mediated signaling as well as CBD biosynthesis inCannabisplants grown under the shortest photoperiod tested.Figure 2.15: Influence of various aspects of light (spectra, intensity, and photoperiod) oncannabidiolic acid (CBDA) and cannabidiol (CBD) concentration (as denoted in square bracketswithin the label), as reported in the literature.392.5.4.3 Tetrahydrocannabinolic acid and tetrahydrocannabinolForty-five papers reported THCA or THC responses to light. No attribute of light was found tohave a consistent effect on either THC or THCA (Figure 2.16) - aside from broad white lighthaving a negative impact on THC (Jenkins, 2021Í¾ Islam et al., 2021a) - highlighting the intricateand nuanced mechanisms regulating THCA and THC synthesis and/or activity. The influenceof light intensity on Cannabis growth and THCA or THC production has yielded inconsistentfindings across the literature, in contrast to the clear positive impact on yield observed in section2.5.2.2. Overall, the majority of papers have demonstrated no impact on THC or THCA (Hawleyet al., 2018Í¾ Vanhove et al., 2011Í¾ Rodriguez-Morrison et al., 2021Í¾ Llewellyn et al., 2022). Bluelight appears to have little effect on THC or THCA in mature plants (Kotiranta et al., 2024Í¾Codesido et al., 2023Í¾ Hawley et al., 2018Í¾ Pojmann-Ezeonyilo et al., 2024Í¾ McGarvey et al.,2020Í¾ Westmoreland et al., 2021), although an increase in THC in dried inflorescence (Morelloet al., 2022) and a decrease in THCA in seedlings (Qian et al., 2023) were reported. Fourpapers also reported no significant effects due to red light (Holweg et al., 2024Í¾ Hawley et al.,2018Í¾ McGarvey et al., 2020) with respect to THC, and (Kotiranta et al., 2024) with respectto THCA. UV and its supposed positive impact on cannabinoid content has been a persistentbelief amongst Cannabis growers (e.g. Frender (2020)), largely influenced by established UVeffects on plant growth and development (e.g. Caldwell (1971)). The rapid evolution of lightingtechnologies makes it difficult to draw conclusions from the literature. THCA and THC contentis increased and decreased in different chemovars and different systems without an obviouspattern. For example: (a) supplemental UV-A increased THC in two cultivars (Jenkins, 2021),(b) 4-8 hours of 10% UV-B/30% UV-A increased THC in one chemovar (Abedini et al., 2024),(c) 3.5 hours of UV-B every day decreased THCA in two chemovars (Rodriquez-Morrisonet al., 2021), (d) UV-A or the combination of UV-A and UV-B did not significantly change theTHC levels in one chemovar (Llewellyn et al., 2022), (e) UV-B/UV-A combined spectra had nosignificant effect on THC levels (Westmoreland et al., 2023), and (f) 3.5 hours daily of UV-Aor UV-A plus UV-B did not significantly affect THCA levels (Kotiranta et al., 2024). Limitedliterature exists tying plant hormones to cannabinoidsÍ¾ the exogenous application of gibberellicacid decreased THCA and THC while jasmonate increased THC in mature plants (Oultramet al., 2024). In vitro cultures grown in media containing low amounts of the synthetic cytokinin-and auxin-mimic thidiazuron had the highest levels of THCA and THC (Trajkovska et al., 2025).40Figure 2.16: Influence of various aspects of light (spectra, intensity, and photoperiod) ontetrahydrocannabinolic acid (THCA) or tetrahydrocannabinol (THC) concentration (as denotedin square brackets within the label), as reported in the Cannabis literature.412.5.5 Germination and rooting metricsNine papers reported seedling germination rate or other rooting metrics in response to light.Root quality or rooting rating was the subjective assessment of the appearance of anyemerged roots, typically reserved for papers using clonal cuttings. Rooting rate refers tothe number of successfully rooted clonal cuttings while germination was typically reserved forsuccessful seedling sprouting. Root emergence was the number of days between seedingor clonal propagation before the first root was visible. Light intensity was shown to positivelyaffect rooting rating (Murphy and Adelberg, 2021), root emergence (Zarei et al., 2021), andgermination (Zarei et al., 2022). Similarly, red light also increased rooting rate (Basile et al.,2023Í¾ Sae-Tang et al., 2024), root quality (Moher et al., 2023), and germination (Aasim et al.,2024). There were mixed findings regarding blue light, as rooting rate was reported to bothincrease (Basile et al., 2023) and remain unaffected (Sae-Tang et al., 2024) while also havinga negative effect on root emergence (Pepe et al., 2021) and germination (Aasim et al., 2024).Figure 2.17: Influence of various aspects of light (spectra, intensity, and photoperiod) ongermination or rooting metrics, as reported in the literature.2.6 Conclusions and open questionsCannabis photobiology research is an exciting emerging field. This review captures thecurrent state of knowledge in this rapidly expanding field and identifies major research themes,researcher clusters, and cultivation trends. The existing literature and diversity in reportedresults reflects the multiplicities of environmental conditions and variability between Cannabis42chemovars. As technology advances, including improvements in lighting systems, AgriTechsensors, -omics analytical tools, and so on, it will become possible to fully understand thecomplex plant signaling networks controllingCannabis growth and crop production. Substantialopen questions have been identified over the course of this review and present severalopportunities for future study: (a) quantify and interpret changes in endogenous levels ofvarious PGRs in response to various spectra, intensity, or duration treatments, (b) investigateof the shade response within Cannabis with a focus on analyzing auxin, cytokinin, andjasmonate, (c) examine the signaling pathways of gibberellin, ethylene, and jasmonate, andpotential interactions with cannabinoid biosynthesis and activity under varying light spectra,(d) test for interactions between light characteristics by implementing spectra treatmentsat different intensities and photoperiods, (e) explore physiological responses to potentialprotective role of PGRs in ROS scavenging, photosystem II protection, and so on, and (f)fill significant knowledge gaps pertaining to understudied PGRs in Cannabis: indoleamines,brassinosteroids, and karrakins. Applied research can deliver trusted information to helpstreamline Cannabis production in a rapidly evolving industry and research space.43Chapter 3: Spectra-mediated changes in Cannabis sativa L. metabolism are driven bygibberellins and aromatic amino acid derivatives3.1 SynopsisThe Cannabis industry predominantly uses LED to provide light using a variety of custompractices. Many commercial growers enhance crop growth with selected recipes of red and/orblue spectra. Scientific evidence of the impacts of these spectra on cannabis chemistry islimited. I hypothesised that blue and red spectra provided by LED induces plant growthregulation pathways and secondary metabolite accumulation in Cannabis. To investigate thishypothesis, plantlets were grown in growth chambers under a full spectra control, isolated red,or isolated blue LED spectra for 7 days. I collected meristematic tissues and measured: (a)cannabinoids, (b) endogenous plant growth regulators, and (c) significant changes in metabolicpathways. Blue light stimulated the production of flavonoids and gibberellins. Red lightincreased accumulation of carboxylic acid derivatives. The significantly affected metabolicpathways included tryptophan, tyrosine, phenylalanine, shikimate, and MEP/MVA metabolism.3.2 ObjectivesMy objectives for this study were:1. Characterise changes in cannabinoid concentration using a commercial methodÍ¾2. Quantify plant growth regulators using targeted metabolomicsÍ¾3. Explore important compounds and pathways through untargeted metabolomics.3.3 Materials and methods3.3.1 Stock plant material and growth conditionsIn vitro mass propagated shoots of Cannabis sativa L. (cv. âDreamcatcherâ, hereafterCannabisÍ¾ Klonetics Plant Science Inc, Kelowna, BC, Canada) under Health Canada License(LIC-01W2DL5JY-2-24) and held in quarantine in the locked controlled environment facilityat UBC Okanagan. Plants were transported in a proprietary culture media in sealed plastic44vessels and immediately subcultured onto new growth media using aseptic techniques in aClass II Biosafety Cabinet (Labconco). The Cannabis culture media contained DKW basalsalts and vitamins (D2470, PhytoTech Laboratories, USAÍ¾ 0.32 g/L), 3% sucrose, and 0.1%plant preservative mixture (PPM) (Plant Cell Technology, USA) with pH adjusted to 5.8 with 1N NaOH prior to addition of 0.6% agar (Fisher, Mississauga, ON)), autoclaving for 20 min at121°C. Once cooled to about 35°C, 0.25 mg Lâ) meta-topolin (PhytoTech Labs, St, LenexaKS) was added in sterile conditions and approximately 40 mL of media were poured into eachpreviously autoclaved Magenta boxes (GA-7Í¾ 6.5 cm x 6.5 cm x 10 cmÍ¾ PhytoTech Labs) andallowed to solidify. To enable growth of taller plants, Magenta boxes were doubled in heightwith use of a stacking lid adapter. Stock plants were grown at 25°C with a 16 hour photoperiodand full spectra LED light (EcoGrowTech, Kelowna, ON) with light intensity (200 µmol mâ2 sâ)Í¾LI-250A Light MeterÍ¾ LI-COR Biosciences, USA) and photoperiod of 18 hours.3.3.2 Experimental conditionsThree growth chambers with 3 shelves (each area being 73 cm x 70 cm x 30 cm) (PercivalScientific, USA) were programmed to maintain 24.5°C and 45% relative humidity with a setphotoperiod of 18 hours. Each growth chamber had full spectra programmable (Percival)with the capacity to select spectra. The spectra in each chamber was determined using ahigh sensitivity spectrometer (Maya 2000 ProÍ¾ Ocean Optics Ocean QE, USA). Spectra wereobtained from the centre point of each chamber and intensity measurements were collectedover a continuous 24 hour period with every 100 scans being averaged, at 0 Boxcar width, anddata integration time set to 100 msec (Figure 3.1).(a) (b) (c)Figure 3.1: Spectra profiles for each of the growth chambers used in this experiment, where(a) is the full spectra control treatment chamber, (b) is the red light treatment chamber, and (c)is the blue light treatment chamber. Optima wavelengths are 437.5 nm, 668.6 nm, and 455.0nm, respectively.45For this experiment, 24 explants were selected from the mother plant cultures for their overallhealthy appearance and presence of roots. Plants were acclimatised to chamber conditions(24°C, 40% RH) under full spectra light (Figure 3.1a) for seven days prior to initiation ofexperiments. Photosynthetic photon flux density (PPFD) within the Percival chambers wasprogrammed at initially 115.0 ± 10.2 µmol mâ2 sâ), matching the PPFD plantlets werepreviously exposed to prior to relocation. PPFD was increased steadily over the seven dayacclimation period to 181.5 ± 3.7 µmol mâ2 sâ), reaching the PPFD used for the durationof the experiment and set in all three growth chambers. The red light chamber 3.1b) wascreated by selection of the red spectra in the Percival chamber supplemented with Far RedLED (700-780 nm, 300 W). The blue light chamber 3.1c) was created by addition of blue LED(Intertek #INGL0001C, 120V, 19WÍ¾ ETL, USA).3.3.3 In vitro tissue samplingAfter 7 days of light spectra treatment, in vitro plants were sampled using aseptic techniques inthe Class II Biosafety cabinet. In brief, each plant was subsampled into 3 tissue segmentsfor determination of plant growth regulators, untargeted metabolomics and cannabinoidquantification. Each excised tissue segment contained a meristem and surrounding stem orleaf tissue (â¼100 mg). Tissue segments were placed into a prelabelled, preweighed 1.5 mLmicrocentrifuge tube and immediately, flash frozen with liquid nitrogen to preserve metaboliteprofiles. Samples were stored at -80°C until further analysis. All apical meristematic tissueswere assigned to the untargeted metabolomics analysis with axillary meristematic regionsassayed for plant growth regulators and cannabinoids. Remaining plants were returned tothe controlled environment culture room for recovery and regrowth.3.3.4 Quantification of cannabinoidsA commercial method was used for analysis of CBD, CBDA, THC, THCA, and CBN in theexcised tissues. In brief, samples (0.2 g) were weighed and 20 mL methanol was addedÍ¾samples weighing less than 0.2 g had an equivalent 1:10 amount of methanol added. 100µL of internal ibuprofen standard was added to each sample and vortexed for 10 sec prior toincubation in an ultrasonic bath for 10 min and vortexing for 10 sec. Samples were then movedinto -20°C freezer for 1 hr in order to cold stabilise and then centrifuged for 5 min at 4200 rpm.46An aliquot of 10 µL of the supernatant was transferred into an autosampler vial containing 990µL mobile phase diluent mix and vortexed for 10 sec to mix. An independent check standard, aroutine recovery sample (RRS), and an RRS duplicate sample were prepared with each batchof samples, to ensure consistency of analysis across multiple batches by the instrumentation.Cannabinoids are then separated via UPLC (VanquishÍ¾ Thermo Scientific, USA) using a C18 15cm x 2.1 mm x 2 µm column (Ascentis ExpressÍ¾ Sigma-Aldrich, USA) and detected by UV-Vis(Vanquish VWD-G). UPLC parameters are summarised in Table 3.1 and method parametersare summarised in Table 3.2. Using Spyder v5.0 running Python 3.12, non-normality of datawas confirmed using the Shapiro-Wilk test and therefore significance was determined usingKruskal-Wallis tests with α = 0.05.Table 3.1: Operational parameter for commercial UPLCmethod used to quantify cannabinoids.Operational parameter SettingColumn Temperature 30°CMobile Phase A 5 mM ammonium formate in water+ 0.1% v/v formic acidMobile Phase B 0.1% formic acid in acetonitrileFlow Rate 0.4 mL minâ)Gradient0.0 min: 70% B3.0 min: 90% B5.0 min: 90% B5.1 min: 98% B6.0 min: 98% B6.1 min: 70% B8.0 min: 70% BDetection UV 228 nmInjection 25 µLElution Order Ibuprofen, CBDA, CBD, CBN, THC, THCATable 3.2: Method performance characteristics of the commercial UPLC method used toquantify cannabinoids. RSD refers to relative standard deviation, with r referring to repeatabilityand ip referring to intermediate precisionÍ¾ MDL refers to method detection limitÍ¾ MRL refers tomethod reporting limit.Analyte Spike Recovery (%) RSDr RSDipMDL(wt %)MRL(wt %)Uncertainty(%)Low0.05 wt %Mid1 wt %High20 wt %CBDA 93.2 100.5 107.2 0.41 15.8 0.0144 0.05 15.8CBD 100.5 101.9 101.3 0.54 12.4 0.0216 0.05 12.4CBN 98.1 105.9 112.8 0.60 13.9 0.0161 0.05 13.9THCA 100.3 101.7 103.5 0.28 13.8 0.0193 0.05 13.8THC 102.9 102.9 103.0 1.32 14.1 0.0177 0.05 14.1473.3.5 Targeted quantification of plant growth regulatorsSample extracts were analyzed for abscisic acid (ABAÍ¾ Sigma 90769Í¾ >98.0% (HPLC)),benzylaminopurine (BAPÍ¾ Sigma 13151Í¾ Reagent Plus â¥99.0% (HPLC)), tryptophan (TRPÍ¾Sigma), zeatin (ZEAÍ¾ Sigma Z0164Í¾ â¥98.0% (HPLC)), N6-isopentenyladenine (2IPÍ¾ SigmaD7674Í¾ â¥98.5% (HPLC)), salicylic acid (SAÍ¾ Sigma 247588Í¾ ACS reagent grade, â¥99.0%),indole-3-acetic acid (IAAÍ¾ PhytoTech Labs I885Í¾ â¥99.0% (COA)), indole-3-butyric acid (IBAÍ¾Sigma 57310Í¾ â¥98.0% (T)), jasmonic acid (JAÍ¾ Sigma 14631), melatonin (MELÍ¾ SigmaPHR1767Í¾ Pharmaceutical Secondary StandardÍ¾ Certified Reference Material), and serotonin(5HTÍ¾ Sigma 14827Í¾ Analytical Standard Grade) using a fully validated method and comparisonto authentic standards and an in-house spectral library.3.3.5.1 Preparation of tissue extractsFrozen sample tissues were transported to the University of the Fraser Valley on dry ice forextraction and analysis of endogenous plant growth regulators. Frozen tissues were weighed,homogenised using a micro-pestle in 500 µL of 50% MeOH in 4% acetic acid, vortexed andsonicated for 15 min in ice water. The resulting extracts were centrifuged at 14 600 x g for 1min to precipitate cellular debris and the supernatant was transferred to centrifugal spin filters(Ultrafree MCÍ¾ Millipore) and centrifuged again at the same settings. Filtered samples weretransferred to glass autosampler vials for injection. Alongside, extraction blanks were preparedidentically to the samples containing, but contained no plant material. Solvent blanks were alsoused containing 50% solvent, with either 50% methanol or 50% methanol with 4% acetic acid.3.3.5.2 Chromatography and mass spectrometryPlant growth regulators in tissue extracts were separated and detected with an Elute PlusLiquid Chromatography System connected to an Impact II Ultra High Resolution qTOF MassSpectrometer fitted with a VIP-HESI source (Bruker). Extracts were injected at 5 µL volume.Chromatography was performed using a two solvent system of A: 0.1% formic acid in waterand B: 0.1% formic acid in acetonitrile (CH+CN, Optima LC-MS Grade, Fisher). The gradientwas 0-1 min 98%A:2%B, 6 min 90%A:10%B, 15 min 2%A:98%B, 18 min 2%A:98%B, 18.1min 98%A:2%B, 20 min 98%A:2%B, at a flow rate of 0.3 mL/min on a Bruker Intensity SoloC18 column (2 2.1 mm x 100 mm) held at 45°C. Mass spectrometry was performed in positive48ion mode using full MS scan and auto-MS/MS scans. The VIP-HESI source was set to thefollowing settings: nebulizer gas, 2.0 barÍ¾ dry gas flow, 8.0 L minâ)Í¾ probe gas flow, 4.0 Lminâ)Í¾ source temperature, 230°CÍ¾ sheath gas probe gas temperature, 400°CÍ¾ and capillaryvoltage, 4500 V, with a scan range of 20-700 m/z. AutoMS-MS was set to a collision voltageof 5 eV, with additional collisions at 100, 200, and 300% collision voltage, collision energy of450, and transfer time of 45 µs.3.3.5.3 Data export and feature alignmentData were quantified using Bruker TASQ 2024 software. Peaks were found using the peak fitalgorithm, ion ratios calculated using height, and signal to noise parameters calculated usingthe smart algorithm set to default settings recommended for the Impact II (Bruker). Signals wereadditionally smoothed and denoised during processing, and each raw data file was calibratedto an internal calibration signal using Na formate clusters. After peaks were processed, theywere compared to an in-house library for identification, using the principal ion to determine ionquality, using most mandatory ions and closest to retention times as a determination filter. Theretention time tolerance was set to 0.25 min (narrow) and 0.60 min (wide), and signals werequantified using peak area. Quantification was performed in batch, against a standard curveof hormone standards (ABA, BAP, TRP, ZEA, 2IP, SA, IAA, IBA, JA, MEL) at 100 ng/mL, 10ng/mL, 1 ng/mL, 100 pg/mL, 10 pg/mL, and 1 pg/mL.3.3.6 Untargeted phytochemical and pathway discovery3.3.6.1 Preparations of extractsSample preparation followed previously established method (Giebelhaus et al., 2024) withminor deviations. Briefly, all sample preparation was performed under red light with sampleson ice. Extraction buffer of 80:20 methanol:formic acid was added in a 2:1 volume (µL):mass(mg) ratio to each sample, then homogenised with a disposable tissue grinder (Kontes PelletPestleÍ¾ Fisher). All samples were centrifuged for 3 min at 14 600 x g at 4°C (Eppendorf,Canada) with the supernatant then filtered through 0.2 µm Ultrafree-MC centrifugal filter(Millipore-Sigma, Canada) and centrifuged once more using the same settings. 100µL fromeach sample was aliquoted into autosampler vials (12 x 32 mm screw-top vialÍ¾ Canadian LifeSciences, Canada) and stored at 4°C for ultra-high performance liquid chromatography-mass49spectrometry (UHPLC-MS) analysis. Three custom, in house standard mixes containing48 authentic metabolites used for training the algorithm were included with each analysis(Giebelhaus et al., 2024). In addition, 3 quality control (QC) samples using previously acquiredCannabis leaf tissue were included to account for instrument variability parameters. Blanks ofextraction buffer, water and MeOH were included with each analysis.3.3.6.2 Chromatography and mass spectrometryA Waters Acquity UPLC BEH C18 column (3 x 100 mm x 2.6 µm) at 30°C was used toseparate metabolites via reverse phase chromatography (Vanquish UHPLCÍ¾ Thermo Scientific,USA). Metabolites were detected by high resolution mass spectrometry (Q Extractive OrbitrapÍ¾ThermoFisher, Canada) with a heated electrospray ionization (HESI) probe (Waltham, USA).The injection volume was 10µL. Elution gradient was 0.25% formic acid and 100% acetonitrile(Optima GradeÍ¾ Fisher) as eluent A and B, respectivelyÍ¾ flow rate was constant at 0.25 mL/min.Needle wash solvent and purge solvent were both 10:90% water:acetonitrile (v/v) with a 5 secpre-inject wash followed by 10 sec post-injection wash. Data capture used a mass range of100 to 1500 m/z and a scan dwell time of 0.1 s over a 25 min run time using positive polarity.Further instrument settings are shown in Figures A1 and A2.3.3.6.3 Data export and feature alignmentPeak detection and alignment were performed using MZMine v4.3.0 (Schmid et al., 2023) usinga noise level of 1.8 x 106 applied to all samples for MS1 and MS2 scans in positive mode. Exactmass was used as the mass detector for all scan types. A retention time and its associatedm/z is referred to features, which were detected using ADAP Chromatogram Builder with aminimum group size of 5, group intensity threshold of 3 x 104, a minimum intensity of 3 x10+, and a scan to scan accuracy of 0.0020 m/z. Generated chromatograms were resolvedusing Local Minimum Feature Resolver with retention time as the dimension, 95.0% as thechromatographic threshold, 0.050 as the RT/mobility value, with a minimum height of 0.0%, arange of 0.00-8.00 for peak duration, and with a minimum of 5 scans. Join Aligner was used toalign features into one list for .csv export with a m/z tolerance of 0.0010 (5.0000 ppm) and anRT tolerance of 1%. The Gap Fill tool was used with an intensity level set to 20.0% with a m/ztolerance of 0.0010 (5.0000 ppm). Duplicate features were removed with a tolerance of 0.00150m/z and 0.2 minÍ¾ split peaks were identified and combined when m/z and RT were within 0.01and 0.1, respectively.3.3.6.4 Data processing and annotationOur custom in-house database, HormonomicsDB v1.5 (Giebelhaus et al., 2024), was usedto putatively identify plant growth regulators and related metabolites. Extracted data wasanalyzed with 0.02 Da tolerance and features were compared against the plant growth regulator(PGR) monoisotopic and PGR M+H/M-H datasets. The in-house standard mixtures were usedto train the algorithm to predict the chromatography pattern and retention time (%RT) beforedata analysis. Features were ordered by %RT match between the predicted and measuredvalues. Split peaks were identified and combined when m/z and RT for the same putativelyidentified compound were within 0.01 and 0.1, respectively. Using Spyder v5.0 runningPython 3.12, non-normality of data was confirmed using the Shapiro-Wilk test and thereforesignificance was determined using Kruskal-Wallis tests with α = 0.05 on the peak intensityof the features from the dataset prior to log transformation and mean centering. Dunnâs testwas done as a post-hoc test to determine which treatments were different using a Bonferroniadjustment for p values (p vvlueant = p vvlue â number of xompvrisons being mvde), using 3as the number of comparisons being made to reflect the number of treatments used, to controlfor family-wise error rate.3.3.6.5 Multivariate analysisAligned data was analyzed using MetaboAnalyst v6.0 (Pang et al., 2024). Non-parametric datawere log transformed and mean centered prior to statistical testing. Variance was filtered usingrelative standard deviation (RSD) at 20%. Principal component analysis (PCA) and partialleast-squares discriminant analysis (PLS-DA) were used to identify metabolites correspondingto light treatment.3.3.6.6 Pathway analysis and mappingPattern Hunter algorithms ranked significant features affected by light spectra by comparingsignificance of p-values, depending on their increase using a full spectra control â red âblue light or red â blue light treatment pattern. Data were formatted as three columns51(m/z, p value, and t score) and subjected to Functional Analyst, which used the Mummichogalgorithm (Li et al., 2013) with a mass accuracy of 5 ppm, in positive ion mode and a p-valuecutoff of 0.01. The Arabidopsis thaliana KEGG library pathway was used for comparison.Spyder v5.0 running Python 3.12 was used to generate bar graphs of peak intensity for eachtreatment for putatively identified compounds with error bars representing standard deviation.Putatively identified compounds were plotted based on detection through Pathway Analysis.For compounds which were identified as plant growth regulators, results from Pathway Analysisand HormonomicsDB were pooled together. The array of features were then filtered forfeatures present in at least two samples for each treatment and then searched manually bym/z on FooDB v1.0 (https://foodb.ca/) - using a tolerance of m/z ± 0.02, positive ion mode,and M+H adducts - for compound identification at Metabolomics Standard Initiative (MSI)level 2 (Fiehn et al., 2007). Using Spyder v5.0 running Python 3.12, non-normality of datawas confirmed using the Shapiro-Wilk test and therefore significance was determined usingKruskal-Wallis tests with α = 0.05 on the peak intensity of the features from the datasetprior to log transformation and mean centering. Dunnâs test was done as a post-hoc testto determine which treatments were different using a Bonferroni adjustment for p values(p vvlueant = p vvlue â number of xompvrisons being mvde), using 3 as the number ofcomparisons being made to reflect the number of treatments used, to control for family-wiseerror rate.52Figure 3.2: Data processing flowchart used on the untargeted (left) and targeted (right)metabolomic analyses. Results from the plant growth regulator (PGR) compound quantificationand the PGR compound and discovery pathway are summarised in sections 3.4.2.1 and3.4.2.2, respectively. Results from the compound and pathway discovery are shown in section3.4.3 with overlap from PGR compound and pathway discovery.3.4 Results3.4.1 Cannabinoid quantificationA commercial method of measuring cannabinoids CBN, CBDA, CBD, THCA, and THC wasused to determine whether the light spectra treatment changed the THC and/or CBD pathwaysin tissue culture Cannabis plants. As plants were not grown to full size nor induced to flower,cannabinoid content was expected to be in low amounts in the vegetative young tissue.Three cannabinoids - CBD, CBDA, and CBN - were not detected in any sample while THCconcentration was either below the method detection limit or not detected. Only THCA wasdetected in quantifiable levels across all samples (Figure 3.3). Difference between treatment53means was determined to be insignificant using the Kruskal-Wallis test (p = 0.75).Figure 3.3: Average concentration of THCA (wt %) quantified in samples per light treatment,where mean concentration Â± std. dev. of THCA was 1.13 ± 0.5 for full spectra controltreatment, 1.06 ± 0.4 for red treatment, and 1.20 ± 0.3 for blue treatment tissueÍ¾ differencesbetween treatment means were not significant.3.4.2 Plant growth regulators3.4.2.1 QuantificationAbscisic acid, tryptophan, zeatin were quantified across all treatments (Table 3.3). Treatmentdifferences were insignificant for all quantifiable hormones at the p < 0.05 level. Abscisic acidand zeatin were significantly affected by light spectra at the p < 0.10 level.Table 3.3: Summary of hormone quantification results (pg/mg fresh weight) Â± std. dev.where ND means not detected, NQ means below quantitation threshold. p values weredetermined using the Kruskal-Wallis test. Note: for non-numerical values, the mode responsewas reported. *: std. dev. is greater than the mean value as the range of values was extremelyleft skewed.Treatment Abscisic Acid Tryptophan Zeatin Benzylaminopurine Jasmonic AcidFull SpectraControl Light 8.30 ± 1.13 117.85 ± 86.35 9.15 ± 2.89 NQ NDRed Light 6.88 ± 3.93 247.96 ± 288.73* 5.32 ± 1.48 ND NDBlue Light 5.77 ± 4.24 334.59 ± 375.97* 9.52 ± 1.52 ND NQp value 0.078 0.108 0.080 N/A N/A3.4.2.2 Hormonomics compound and pathway discoveryHormonomicsDB putatively detected hormone biosynthesis or metabolism pathways. 13distinct compound classes were putatively identified (abscisic acid, auxins, brassinosteroids,54catecholamines, cytokinins, gibberellins, indoleamines, jasmonates, melatonin conjugates,polyamines, strigolactones, synthetic compounds, and tryptophan metabolism). Filtering theresults by retention time matches â¥ 0.70 narrowed the compound classes to 10, eliminatingbrassinosteroids, synthetic compounds and polyamines. The final matched compound list isshown in Table 3.4. Compounds are plotted together by class in Figures 3.4-3.9.55Table 3.4: Matched compounds from HormonomicsDB which had a retention time (RT) match greater or equal to 70%, grouped bycompound class and ordered by highest to lowest RT match. ABA refers to abscisic acidÍ¾ IAA refers to indole-3-acetic acidÍ¾ Mel refersto melatonin.Compound Name Class Adduct m/z Matched m/z m/z Difference RT Predicted RT RT Match (%)9-hydroxy-ABA ABA 278.116 278.1021 -0.0139 5.4705 5.49 99.647-hydroxy-ABA ABA 278.116 278.1021 -0.0139 5.4705 5.58 98.00Neophaseic acid ABA 278.116 278.1021 -0.0139 5.4705 6.01 90.14Phaseic acid ABA 278.116 278.1021 -0.0139 5.4705 5.14 93.96Abscisic acid ABA 262.1211 262.1071 -0.014 5.98535 5.56 92.90IAA leucine Auxins 286.1323 286.1284 -0.0039 4.32245 4.22 97.64IAA valine Auxins 287.1038 287.0912 -0.0126 7.7696 6.15 79.15IAA aspartate Auxins 448.1124 448.1131 0.0007 6.13695 4.72 76.92IAA alanine Auxins 244.0854 244.1046 0.0192 7.198 5.18 71.96Tyrosine Catecholamines 179.0588 179.0702 0.0114 4.53085 3.34 73.73Dihydrozeatin riboside-O-glucoside Cytokinins 513.2077 513.2076 -0.0001 4.3735 4.05 92.60Benzylaminopurine Cytokinins 223.0864 223.0964 0.00995 4.7609 4.21 88.44Zeatin Cytokinins 217.097 217.1044 0.0074 3.818 3.18 83.29Kinetin Cytokinins 213.0657 213.0756 0.0099 4.2487 4.98 82.79N6-benzyladenine-7,9-diglucoside Cytokinins 549.2077 549.2173 0.0096 6.2683 5.15 82.16Gibberellin A34 Gibberellins 346.1422 346.1494 0.0072 5.8378 5.54 94.90Gibberellin A4 Gibberellins 330.1473 330.1332 -0.0141 5.8716 6.24 93.73Gibberellin A29 Gibberellins 346.1422 346.1494 0.0072 6.4674 7.26 87.74Gibberellin A51 Gibberellins 330.1473 330.1332 -0.0141 5.8716 6.64 86.91Gibberellin A15 Gibberellins 342.1837 342.1756 -0.0081 4.6439 5.92 72.52Tryptophan Indoleamines 202.0748 202.071 -0.0038 3.7016 3.36 90.7756Table 3.4: Continued: ABA refers to abscisic acidÍ¾ IAA refers to indole-3-acetic acidÍ¾ Mel refers to melatonin.Compound Name Class Adduct m/z Matched m/z m/z Difference RT Predicted RT RT Match (%)Cyclic (3)-hydroxymelatonin Indoleamines 246.101 246.1202 0.0192 6.4036 5.62 87.765-methoxytryptophol Indoleamines 189.0796 189.0738 -0.0058 4.4261 5.24 81.602-hydroxymelatonin Indoleamines 244.0854 244.1046 0.0192 7.198 5.07 70.444-hydroxymelatonin Indoleamines 246.101 246.1202 0.0192 6.4036 4.97 77.616-hydroxymelatonin Indoleamines 246.101 246.1202 0.0192 6.4036 5.02 78.397-hydroxymelatonin Indoleamines 246.101 246.1202 0.0192 6.4036 5.23 81.67JA-phenylalanine Jasmonates 355.179 355.1898 0.0108 9.7709 7.05 72.15N-feruloyl Melatonin Mel Conjugates 406.1535 406.164 0.0105 4.8639 5.01 97.00Melatonin-Thr Mel Conjugates 331.1538 331.1383 -0.0155 4.3316 4.95 85.72Melatonin-Gln Mel Conjugates 358.1647 358.1474 -0.0173 6.3217 5.00 79.09Strigyl acetate Strigolactones 386.1372 386.1355 -0.0017 8.1909 7.22 88.15Orobanchyl acetate Strigolactones 386.1372 386.1355 -0.0017 8.1909 7.19 87.783-methyl-dioxy indole Trp Metabolism 161.0483 161.0596 0.0113 4.7124 4.6 97.61Hydroxymelatonin Trp Metabolism 246.101 246.1202 0.0192 7.69895 7.59 97.512-formylamino-benzaldehyde Trp Metabolism 119.0377 119.0493 0.01155 5.0343 5.23 96.102,3-dihydroxyindole Trp Metabolism 147.0326 147.0439 0.011338 4.766463 4.98 94.633-Hydroxyanthranilic acid Trp Metabolism 151.0275 151.039 0.0115 4.458 4.19 93.994,8-dihydroxyquinoline Trp Metabolism 159.0326 159.044 0.0114 5.3927 4.61 85.494,6-dihydroxyquinoline Trp Metabolism 159.0326 159.044 0.0114 5.3927 4.51 83.633-methoxy-anthranilate Trp Metabolism 165.0432 165.0544 0.0112 3.0599 3.77 76.79Formylanthranilate Trp Metabolism 163.0275 163.0389 0.01135 4.98165 3.78 76.542-Aminomuconic semialdehyde Trp Metabolism 139.0275 139.0388 0.0113 5.0744 3.71 73.13573.4.2.3 Tryptophan metabolismSeveral tryptophan-related metabolites were putatively detected (Figure 3.4):3-methyl-dioxy indole, 2-aminomuconic semildehyde, 2,3-dihydroxyindole,formylanthranilate, 2-formylamino-benzaldehyde, hydroxymelatonin, 3-methyoxy-anthranilate,3-hydroxyanthranilic acid, 4,8-dihydroxyquinoline, and 4,6-dihydroxyquinoline. Several ofthese compounds are oxidised or hydroxylated intermediates, and originate from the indolicor kynurenine branches of tryptophan metabolism branches.58Figure 3.4: Compounds part of the tryptophan metabolism class detected using HormonomicsDBÍ¾ * denotes significant differences (p< 0.05) in peak intensities between treatments as determined using the Kruskal-Wallis test. Error bars represent standard deviation. nreflects the number of samples per treatment in which the compound was putatively identified.593.4.2.4 AuxinsFour auxin-amino acid metabolites were putatively identified: indole-3-acetic acid(IAA)-alanine, IAA-valine, IAA-aspartate, and IAA-leucine (Figure 3.5). IAA-aspartatewas significantly higher in blue light treatments than either full spectra control (adj. p value =0.028) or red light (adj. p value = 0.022) treatment samples. All auxin-amino acid metaboliteswere detected in blue light treatment samples.Figure 3.5: Compounds part of the auxin class detected using HormonomicsDBÍ¾ * denotessignificant differences (p < 0.05) in peak intensities between treatments as determined usingthe Kruskal-Wallis test. Error bars represent standard deviation. n reflects the number ofsamples per treatment in which the compound was putatively identified.3.4.2.5 IndoleaminesDetected compounds classed as indoleamines were 5-methoxytryptophol,tryptophan, 4-hydroxymelatonin, 6-hydroxymelatonin, 7-hydroxymelatonin, and cyclic(3)-hydroxymelatonin (Figure 3.6). All of the indoleamines were detected across blue lighttreatment samples, while 5-methoxytryptophol and tryptophan were present in the red lightand full spectra control treatment tissues.60Figure 3.6: Compounds part of the indoleamine class detected using HormonomicsDBÍ¾ *denotes significant differences (p < 0.05) in peak intensities between treatments as determinedusing the Kruskal-Wallis test. Error bars represent standard deviation. n reflects the numberof samples per treatment in which the compound was putatively identified.3.4.2.6 Melatonin conjugatesThere were 3 putatively identified melatonin conjugates detected across the sampletypes: N-feruloyl melatonin, melatonin-threonine, and melatonin-glutamate (Figure 3.7).Melatonin-glutamate was not detected in blue light treatment tissue. N-feruloyl melatonin andmelatonin-glutamate were putatively detected in tissues grown under full spectra but not intissues grown under isolated red or blue light.61Figure 3.7: Compounds part of the melatonin conjugates class detected usingHormonomicsDBÍ¾ * denotes significant differences (p < 0.05) in peak intensities betweentreatments as determined using the Kruskal-Wallis test. Error bars represent standarddeviation. n reflects the number of samples per treatment in which the compound wasputatively identified.3.4.2.7 CytokininsTwo cytokinins were detected in full spectra control, red light, and blue light treatment tissues:dihydrozeatin riboside-O-glucoside and BAP (Figure 3.8). A further three cytokinins wereidentified as responding differently to the treatments: zeatin in full spectra control tissues,kinetin in red light and blue light tissues, and N6-benzyladenine-7,9-diglucoside in blue lighttissue. Zeatin had been detected in all light treatments through targetedmetabolomicsmethods(Table 3.3), but putatively detected in the full spectra control tissue with the untargeted Orbitrapmethods. BAP was putatively detected in all treatments with the untargeted method, and belowthe limit of detection or quantification by the targeted method.62Figure 3.8: Compounds part of the cytokinin class detected using HormonomicsDBÍ¾ * denotessignificant differences (p < 0.05) in peak intensities between treatments as determined using theKruskal-Wallis test. Error bars represent standard deviation. n reflects the number of samplesper treatment in which the compound was putatively identified.3.4.2.8 Abscisic acidFive metabolites of abscisic acid were putatively identified: ABA, neophaseic acid, phaseicacid, 9-hydroxy-ABA, and 7-hydroxy-ABA (Figure 3.9). ABA was detected in full spectracontrol treatment samples, though differences in peak intensity between treatments were notsignificant (adj. p values 0.15-0.25). Otherwise, this class of compounds was detected in redlight or blue light treatment tissue, although all treatment tissue had quantifiable levels of ABAdetected through the targeted method (Table 3.3).63Figure 3.9: Compounds part of the ABA class detected using HormonomicsDBÍ¾ * denotessignificant differences (p < 0.05) in peak intensities between treatments as determined usingthe Kruskal-Wallis test. n reflects the number of samples per treatment in which the compoundwas putatively identified.3.4.2.9 GibberellinsIn total 5 gibberellins were putatively identified through hormonomics: gibberellin A34, A4, A29,A51, and A15. Bioactive gibberellin A4, gibberellin A51, and gibberellin A34 were detected inblue light treatment samples. Gibberellin A15 was detected in red light treatment samples.Compound and pathway discovery algorithms identified gibberellins and related diterpenoidbiosynthesis pathway metabolites, discussed further in section 3.4.3.5 (Figures 3.14, 3.15).3.4.3 Untargeted phytochemical and pathway discovery3.4.3.1 Untargeted metabolomics data quality measuresThe data were compiled into an array containing 1977 features, with 631 in common acrossall samples in all treatments. The average number of features for each treatment was 612.364± 129.0, 655.7 ± 159.4, and 699.0 ± 124.9 for the full spectra control, red light, and bluelight treatments, respectively. Blue light treatment had the highest number of unique featuresat 570, compared to 306 in the full spectra control treatment and 214 in red light treatment(Figure 3.10).Figure 3.10: Venn diagram representing features unique to, or in common between variouscombinations of, treatments for the untargeted metabolomics data, where: C represents fullspectra control lightÍ¾ R represents red lightÍ¾ and B represents blue light treatment values.3.4.3.2 Data visualization analysisPCA found significant overlap between treatment clusters when full spectra is included (Figure3.11a-c). Percent cumulative variance for principal component (PC) 1 (23.4%), 2 (21.5%), and3 (10.0%) was 54.9%. When comparing the red light to blue light treatment (Figure 3.11d-f),there were significant differences between PCs 1 and 2 (p = 0.033) and PCs 2 and 3 (p =0.015). Percent cumulative variance for PC 1 (28.5%), 2 (22.3%), and 3 (13.7%) was 64.5%.PLS-DA was also used to visualise differences between all treatments where data classification(treatment) were included in the analysis (Figure 3.12). The components 1 (21.7%), 2 (8.5%),and 3 (17.8%) collectively compose 48.0% of the variance. Similarly, when comparing red lightand blue light treatments, components 1 (22.1%), 2 (18.1%), and 3 (21.0%) represent 61.2%of the variance. When the data are supervised, there is clear delineation of feature intensity bytreatment.65(a) (b) (c)(d) (e) (f)Figure 3.11: Principal component analysis (PCA) of principal components (PCs) 1 and 2 in (a), 1 and 3 in (b), and 2 and 3 in (c) for fullspectra control, red light, and blue light treatment samples. (d) shows PCs 1 and 2, (e) shows PCs 1 and 3, and (f) shows PCs 2 and 3for only the red light and blue light treatment samples.66(a) (b) (c)(d) (e) (f)Figure 3.12: Partial least squares discriminant analysis (PLS-DA) of components 1 and 2 in (a), 1 and 3 in (b), and 2 and 3 in (c) for fullspectra control, red light, and blue light treatment samples. (d) shows PCs 1 and 2, (e) shows PCs 1 and 3, and (f) shows PCs 2 and 3for only the red light and blue light treatment samples.673.4.3.3 Significant features analysisFull spectra control light, red light, and blue light features were assessed using Kruskal-Wallistest and revealed no significant features (p â¥ 0.05). When restricted to red light and blue lighttreatment features, a Wilcoxon rank-sum test revealed 119 significant features with a raw pvalue < 0.05Í¾ a heat map of the intensities of the top 100 features (Figure 3.13) visualisesunique features to the red light and blue light treatments, showing 14 features which were lowin the blue light treatment (group A) and 86 were high in the blue light treatment (group B).All 119 features were searched in FooDB (± 0.02) resulting in 668 unique putative matchesfor 92 features. The majority of these were classified as prenol lipids, flavonoids, carboxylicacids and derivatives, fatty acyls, and organooxygen compounds. The most common group Afeature matches were carboxylic acids and derivatives whereas the most common for group Bwere prenol lipids and flavonoids.Figure 3.13: Heat map of log)( peak intensities of the top 100 significant features (pairs ofmass-to-charge and retention time) for the red light treatment (top) and the blue light treatment(bottom).Pattern Hunter in MetaboAnalyst was used to detect trends within the data according to fullspectra controlâ red lightâ blue light. 2168 features were identified in total, with 178 havinga p value < 0.05 and 54 having an R2 > ± 0.7. When analyzing red light â blue light, 1716features were identified, with 118 having a p value < 0.05 and of those, 96 having an R2 > ±0.7. 90 of these features were identified using SIRIUS, with 34 being chemical formula-only68matches. 25% matched compounds were amino acids, peptides, and derivatives, while 19%were lipid or lipid derivatives, 19% were aromatic compounds, and 13% were terpenes orterpenoids. Several compound classes were not identified in full spectra control treatmentsamples, including flavonoid C-glycosides, epothilones and analogues, azoles, and O-glycosylcompounds. N-substituted imidazoles, straight chain fatty acids, terpene glycosides, andunsaturated fatty acid compoundswere identified in red light treatment samples. Blue treatmentsamples typically had the highest intensity for putatively identified compounds. Searchingfeatures on FooDB resulted in 101 of putative compound matches, many being potentiallysesquiterpenoids, coumarins, diterpenoids, and isoflavonoid o-glycosides. 21 features werematched exclusively using FooDB.3.4.3.4 Functional analysisFunctional Analysis using all features from Pattern Hunter on full spectra control, red light, andblue light treatment samples identified 26 overrepresented pathways with Fisherâs p value < 0.5(Table 3.5). All treatment data were included as Functional Analysis requires using completedata to accurately estimate the null model (background). The top 6 identified pathwayswere: sesquiterpenoid and triterpenoid biosynthesis, folate biosynthesis, terpenoid backbonebiosynthesis, lysine degradation, zeatin biosynthesis, and carotenoid biosynthesis. 35 featureswere putatively identified alongside potential matched form (Table A1).3.4.3.5 Pathway discoveryPathway Analysis using the significant features from Pattern Hunter on red light and bluelight treatment samples identified 16 overrepresented pathways with raw p < 0.05 (Table3.6). 8 of these pathways are associated with the shikimate pathway, 3 were associatedwith the 2-C-methyl-D-erythritol-4-phosphate (MEP)/mevalonate (MVA) pathways, and 3 wereassociated with other amino acid pathways. Compounds putatively identified in pathways withHolm adjusted pâ¤ 0.05 were searched for in the original dataset and peak intensity was plottedfor these matched compounds for each compound. Kruskal-Wallis test was used to determinewhether peak intensity differences were significant when features were detected in more thanone treatment.69Table 3.5: Pathways identified with significantly different features from Functional Analysis.a total number of compounds in the Arabidopsis thaliana KEGG pathwayÍ¾ l total number ofcompound matches based on m/zÍ¾ c total number of matches that were significantÍ¾ n number ofhits that were anticipated to be presentÍ¾ e Fisherâs Exact Test p value for the pathwayÍ¾ p EASEcore is conservative adjustment to Fisher exact probabilityÍ¾ g p values derived from Gammadistribution based on permutation tests for the pathway.PathwaytotalaHitstotallHitssigc Expectedn P(Fisher)e P(EASE)p P(Gamma)gSesquiterpenoid andtriterpenoid biosynthesis 24 8 8 0.75 0.00 0.02 0.00Folate biosynthesis 32 5 5 1.13 0.01 0.08 0.00Terpenoid backbonebiosynthesis 26 3 2 0.75 0.02 0.15 0.00Lysine degradation 13 2 2 0.38 0.03 0.33 0.00Zeatin biosynthesis 19 2 2 0.38 0.03 0.33 0.00Carotenoid biosynthesis 42 8 2 2.82 0.04 0.12 0.00Nitrogen metabolism 11 2 2 0.56 0.09 0.46 0.00Purine metabolism 75 5 2 0.75 0.16 0.56 0.00Pyrimidine metabolism 41 4 3 0.75 0.16 0.56 0.00Valine, leucine andisoleucine degradation 32 4 2 0.75 0.16 0.56 0.00Glyoxylate and dicarboxylatemetabolism 28 3 2 0.75 0.16 0.56 0.00Butanoate metabolism 17 3 2 0.75 0.16 0.56 0.00Vitamin B6 metabolism 12 1 1 0.19 0.19 1.00 0.00Alanine, aspartate andglutamate metabolism 22 7 3 1.69 0.23 0.52 0.00alpha-Linolenic acidmetabolism 25 10 5 1.69 0.23 0.52 0.00Glutathione metabolism 18 3 2 0.94 0.24 0.64 0.00Arginine and prolinemetabolism 31 12 5 2.82 0.30 0.54 0.00D-Amino acid metabolism 7 3 1 0.38 0.34 1.00 0.01Flavone and flavonolbiosynthesis 10 9 2 2.07 0.34 0.63 0.01Pantothenate and CoAbiosynthesis 23 5 2 1.32 0.39 0.76 0.01Biosynthesis of variousplant secondary metabolites 29 7 3 1.32 0.39 0.76 0.01Lysine biosynthesis 9 3 1 0.56 0.47 1.00 0.01Histidine metabolism 16 3 1 0.56 0.47 1.00 0.01Amino sugar and nucleotidesugar metabolism 46 5 1 0.56 0.47 1.00 0.01Nicotinate and nicotinamidemetabolism 13 3 1 0.56 0.47 1.00 0.01Sulfur metabolism 12 2 1 0.56 0.47 1.00 0.01Diterpenoid biosynthesis The Pathway Analysis results indicated a significant numberof matched compounds part of the diterpenoid biosynthesis pathway. Putative compoundidentification was done by mining the data for these matched compounds from the70Table 3.6: Overrepresented pathways detected using MetaboAnalystâs Pathway Analysismodule. a represents all empirical compounds as part of the pathway with l reflecting thenumber of compounds that were anticipated to be present. c represents compounds thatwere matched from the uploaded dataÍ¾ n original p value from enrichment analysisÍ¾ e p valueadjusted by the Holm-Bonferroni method, which accounts for issues which arise with multiplecomparisons leading to a higher chance of type I errors.â are pathways associated with MEP/Mevalonate pathwayÍ¾ * are pathways associated with theShikamate pathwayÍ¾ § are pathways associated with other amino acid pathways.Overrepresented Pathway Totala Expectedl Hitsc Raw pn Holm adj peâ Diterpenoid biosynthesis 29 4.24 19 3.54E-10 3.26E-08* Flavone and flavonol biosynthesis 10 1.46 9 2.33E-07 2.12E-05* Phenylalanine metabolism 12 1.76 9 3.92E-06 0.0004* Phenylpropanoid biosynthesis 43 6.29 18 9.90E-06 0.001* Tyrosine metabolism 17 2.49 9 0.000 0.02* Flavonoid biosynthesis 47 6.88 16 0.001 0.05â Monoterpenoid biosynthesis 7 1.02 5 0.001 0.09§ Arginine and proline metabolism 32 4.68 12 0.001 0.09alpha-Linolenic acid metabolism 26 3.80 10 0.002 0.19Arachidonic acid metabolism 9 1.32 5 0.005 0.41* Isoquinoline alkaloid biosynthesis 6 0.88 4 0.005 0.43§ Arginine biosynthesis 18 2.63 7 0.010 0.80â Sesquiterpenoid and triterpenoidbiosynthesis 24 3.51 8 0.016 1.00* Tropane, piperidine and pyridinealkaloid biosynthesis 9 1.32 4 0.031 1.00* Phenylalanine, tyrosine andtryptophan biosynthesis 22 3.22 7 0.032 1.00§ Alanine, aspartate and glutamatemetabolism 22 3.22 7 0.032 1.00MetaboAnalyst in addition to HormonomicsDB results and the majority of these compoundswere gibberellins. The results are summarised in Table A2. The two separate branches ofthe diterpenoid biosynthesis pathway are shown in Figures 3.14 and 3.15. Within the firstbranch of diterpenoid biosynthesis (Figure 3.14), upstream compounds like gibberellin A53,gibberellin A44, and gibberellin A20 were putatively identified in the full spectra control or redlight treatment. In contrast, downstream compounds were putatively identified in the blue lighttreatment, including bioactive gibberellin A1 (He et al., 2020). Similarly, bioactive gibberellin A4was putatively detected in the blue light treatment. Downstream products were again observedin blue light treatment tissue in the second branch of diterpenoid biosynthesis (Figure 3.15),including gibberellin A29, gibberellin A8, and their respective catabolites. Gibberellin A19 wasnot detected in any of the treatments.71Figure 3.14: Diterpenoid biosynthesis pathway starting with ent-7Î±-hydroxykaur-16-en-19-oicacidÍ¾ * denotes significant differences (p < 0.05) in peak intensities between treatments asdetermined using the Kruskal-Wallis test. â¡ indicates this compound was detected throughhormonomics. n reflects the number of samples per treatment in which the compound wasputatively identified.72Figure 3.15: Diterpenoid biosynthesis pathway starting from gibberellin A53, continuing fromgibberellin A12Í¾ * denotes significant differences (p < 0.05) in peak intensities betweentreatments as determined using the Kruskal-Wallis test. â¡ indicates this compound wasdetected through hormonomics. n reflects the number of samples per treatment in which thecompound was putatively identified.73Tyrosine metabolism The Pathway Analysis results indicated a significant number ofmatched compounds part of the tyrosine metabolism pathway (Figure 3.16). Putativecompound identification was done by mining the data for these matched compounds from theMetaboAnalyst results and summarised in Table A7. Tyrosine was detected in all treatmentsbut did not differ significantly in peak intensity. The metabolites directly downstream of tyrosinewere putatively identified in full spectra control and blue light treatment tissue, with red lighttreatment samples having 4-hydroxyphenylacetaldehyde detected.Figure 3.16: Tyrosine metabolism, starting from L-tyrosineÍ¾ * denotes significant differences (p< 0.05) in peak intensities between treatments as determined using the Kruskal-Wallis test. nreflects the number of samples per treatment in which the compound was putatively identified.74Phenylalanine metabolism The Pathway Analysis results indicated a significant number ofmatched compounds part of the phenylalanine metabolism pathway (Figure 3.17). Putativecompound identification was done by mining the data for these matched compounds from theMetaboAnalyst results and summarised in Table A3. 7 of these compounds were detectedin red light treatment tissue, including phenylacetaldehyde, trans-cinnamic acid, phenylaceticacid, 2-phenylacetamide, 2-hydroxyphenylacetate, and two which were identified in redlight treatment tissue: phenylethylamine and 2-hydroxy-3-phenylpropenoate. Interestingly,phenylalanine itself was not detected in red light treatment tissue, but rather in full spectracontrol and blue light treatment samples.Figure 3.17: Phenylalanine metabolism pathway starting from phenylpyruvateÍ¾ * denotessignificant differences (p < 0.05) in peak intensities between treatments as determined usingthe Kruskal-Wallis test. n reflects the number of samples per treatment in which the compoundwas putatively identified.75Phenylpropanoid biosynthesis The Pathway Analysis results indicated a significantnumber of matched compounds part of the phenylpropanoid biosynthesis pathway (Figures3.18 and 3.19). Putative compound identification was done by mining the data for thesematched compounds from the Pathway Analysis results and summarised in Table A4. Of thedetected compounds part of phenylpropanoid biosynthesis pathway, there was one compoundnot detected in blue light treatment tissue (sinapic acid). Many of the acyl-CoAs withinthis pathway were not detected in any samples, such as cinnamoyl-CoA, 4-coumaroyl-CoA,caffeoyl-CoA, and 5-hydroxyferuloyl-CoA.Figure 3.18: Phenylpropanoid biosynthesis pathway starting from L-phenylalanineÍ¾ * denotessignificant differences (p < 0.05) in peak intensities between treatments as determined usingthe Kruskal-Wallis test. n reflects the number of samples per treatment in which the compoundwas putatively identified.76Figure 3.19: Phenylpropanoid biosynthesis pathway, continuing onward from caffeic acidÍ¾ *denotes significant differences (p < 0.05) in peak intensities between treatments as determinedusing the Kruskal-Wallis test. n reflects the number of samples per treatment in which thecompound was putatively identified.77Flavonoid biosynthesis The Pathway Analysis results indicated a significant number ofmatched compounds part of the flavonoid biosynthesis pathway (Figure 3.20). Putativecompound identification was done by mining the data for these matched compounds fromPathway Analysis results and summarised in Table A5. All 7 of the detected compounds apartof flavonoid biosynthesis were found in blue light treatment tissue.Figure 3.20: Flavonoid biosynthesis, starting from cis-3,4-leucopelargonidinÍ¾ * denotessignificant differences (p < 0.05) in peak intensities between treatments as determined usingthe Kruskal-Wallis test. n reflects the number of samples per treatment in which the compoundwas putatively identified.78Flavone and flavonol biosynthesis The Pathway Analysis results indicated a significantnumber of matched compounds part of the flavone and flavonol biosynthesis pathway (Figure3.21). Putative compound identification was done by mining the data for these matchedcompounds from the MetaboAnalyst results and summarised in Table A6. Flavones andflavonols are subgroups of flavonoids, with both having a C6-C3-C6 carbon skeleton (Liu et al.,2021). A metabolite of quercitrin, quercetin 3-O-rhamnoside 7-O-glucoside, was found to differsignificantly (p = 0.041) between treatments, with peak intensity in blue light treatment tissuebeing significantly higher than in red light treatment tissue (adj p = 0.04). Within the flavone andflavonol biosynthesis pathway, two compounds were detected in full spectra control treatmenttissue (quercetin 3-O-rhamnoside 7-O-glucoside and isoquercitrin).Figure 3.21: Flavone and flavonol biosynthesis pathway starting from kaempferolÍ¾ * denotessignificant differences (p < 0.05) in peak intensities between treatments as determined usingthe Kruskal-Wallis test. n reflects the number of samples per treatment in which the compoundwas putatively identified.793.5 DiscussionThe legalization of Cannabis sativa L. for medicinal, research, and recreational purposeshas created an unprecedented opportunity to establish this species as a potential modelorganism for plant photobiology research. The regulatory shift has coincided with remarkableadvances in analytical instrumentation and data processing capabilities, enabling researchersto probe Cannabis physiology with a depth and precision that extends far beyond traditionalcultivation knowledge accumulated over millennia (Johnson and Colby, 2023). GrowingCannabis within specifically indoor controlled environments allows for the overseeing of allrequired inputs, such as temperature, humidity, nutrients, and lighting, so every aspect hasto be precisely dialed to produce a uniform crop every grow cycle (Nemati et al., 2021).The rapid technological evolution in horticultural lighting, shifting from fluorescent systemsthrough high-intensity discharge (HID) lamps to now widespread adoption of light-emittingdiodes (LEDs), is reflected in Cannabis cultivation demands (Wheeler, 2008Í¾ Hansen, 2024).Most importantly, Cannabis exhibits remarkable phenotypic plasticity in response to lightquality, intensity, and photoperiod variations (Rodriguez-Morrison et al., 2021), making it anideal system for investigating photomorphogenic responses and light-regulated metabolicpathways. It also has high economic value in legalised markets (Statistics Canada, 2023).These convergent factors position Cannabis as a uniquely powerful model organism foradvancing our understanding of plant photobiology in both fundamental and applied contexts.Phytocannabinoids are a large group of secondary metabolites, for which Cannabis is oftencultivated. Phytocannabinoids are typically in low concentration in tissues not from maturedinflorescences (Jin et al., 2020). Functions for cannabinoids have been suggested, such asmitigating drought stress (Park et al., 2022), wounding (Park et al., 2022), high temperatures(Bazzaz et al., 1975Í¾ Park et al., 2022), and pest pressure (Kostanda and Khatib, 2022),though definitive evidence is still lacking (Payment and Cvetkovska, 2023). Protection againstUV radiation has also been hypothesised and tested with mixed findings (Rodriquez-Morrisonet al., 2021Í¾ Vacek et al., 2021Í¾ Timmons and Mattson, 2023Í¾ Lydon et al., 1987Í¾ Kotirantaet al., 2024Í¾ Abedini et al., 2024Í¾ Llewellyn et al., 2022). THCA could be a potential marker forUV-induced stress (Islam et al., 2021aÍ¾ Li et al., 2025). The results of my experiment suggestthat the specific light treatments tested did not substantially alter cannabinoid production.80Hormonomics analysis revealed 10 plant growth regulator classes affected by light spectrain Cannabis. Tryptophan metabolism, auxins, indoleamines, and melatonin conjugates areall products of shikimate biosynthesis. Many of the putatively identified compounds were ina conjugated or modified formÍ¾ for example, all detected auxins were with amino acid moiety(IAA-alanine, IAA-valine, IAA-aspartate, IAA-leucine) in addition to melatonin-glutamateand melatonin-threonine. Within cytokinins, both dihydrozeatin riboside-O-glucosideand N6-benzyladenine-7,9-diglucosides contain sugar moieties. There has also beendemonstrated crosstalk between the identified classes, with established auxin:cytokinin(Bielach et al., 2017) and gibberellin:ABA (Golldack et al., 2013), related to light perceptionand signaling. ABA, tryptophan, and zeatin were quantified by targeted methods. Previously,ABA has been upregulated in response to drought stress in Cannabis (Yang et al., 2024),however to my knowledge, no studies have quantified these plant growth regulators inCannabis exposed to light spectra.Visualization through PCA, PLS-DA, and a heat map of 100 significant features revealeddifferences as a result of red light and blue light treatment. Putative matches using FooDBrevealed carboxylic acids and derivatives were high in red light tissue while low in blue lighttissue, and conversely prenol lipids and flavonoids were high in blue light tissue while low in redlight tissue samples. Prenol lipids included various gibberellins - A4, A20, A40, A45, A51, A61,A70, and A84. Detected carboxylic acids were also amino acid or peptide analogues, such as(+)-valine, tyrosyl-cysteine, glutaminyltryptophan. Amino acid conjugation of plant compoundshas been shown to play a variety of roles such as inducing the formation of defensivesecondary metabolites (e.g. jasmonate-inducible proteins (JIPs) (Li et al., 2009)), redirectingcompounds for oxidative degradation (e.g. IAA-Glu (Liao et al., 2022)), ameliorating plantimmunity (e.g. Lys catabolism leading to accumulation of an pipecolic acid which acts as animmune signal (Zeier, 2013)), or inhibiting or promoting hormone activity (e.g. Jasmonate-Trpand IAA-Trp impeding root gravitropism in ArabidopsisÍ¾ JA-Ile catalyzing interactions betweenjasmonate receptor COI1 and transcriptional repressors (Staswick, 2009)).81Functional analysis showed functional activity of features with the (a) sesquiterpenoid andtriterpenoid biosynthesis, (b) terpenoid backbone biosynthesis, (c) carotenoid biosynthesis,(d) zeatin biosynthesis, (e) folate biosynthesis, and (f) lysine degradation pathways. a-dpathways connect at dimethylallyl diphosphate (DMAPP), which is a precursor to theseisoprenoid-derived metabolites through either the MVA pathway within the cytosol or throughthe MEP pathway and cytokinin biosynthesis pathways within the plastid (Bouvier et al., 2005).e-f may be connected as folate metabolites enable the biosynthesis of some amino acids, andvice versa (Liao et al., 2022Í¾ Xie et al., 2022).Similarly, pathway analysis on red light and blue light treated cultures showed 16 significantlyoverrepresented pathways, where 14 could be traced to the shikimate, MEP/MVA, or otheramino acid pathways. Diterpenoid biosynthesis had 19/29 compounds putatively identified,with many hits being gibberellins. Diterpenoids, like other plant terpenoids, assist in plantinteractions with animals in their environment, e.g. attracting pollinators, deterring herbivores(Shah et al., 2023). Gibberellins regulate several aspects of plant physiology throughtranscriptional or post-transcriptional modulation (Shah et al., 2023). Phytochrome A and Bhave been shown to increase the levels of gibberellin synthesis genes and gibberellin A4within seeds during germination (Oh et al., 2007). Activation of phytochrome A and B hasbeen shown to increase levels of gibberellin synthesis genes and GA4 within Arabidopsisseeds during germination (Oh et al., 2007)Í¾ as phytochrome A and B perceive red/far-redlight, putative detection in the blue light treatment was unexpected. Further, cryptochromeâscapacity for repressing gibberellin signaling was demonstrated in Arabidopsis and wheat(Yan et al., 2021a). Light can act as an inhibitor of GA4 accumulation and reduce gibberellinsignaling after germination during photomorphogensis (Sun, 2008). Independent mechanismsfor gibberellin regulation have been proposed for phytochromes and cryptochromes (Hiroseet al., 2012): phytochromes will repress gibberellin biosynthesis, whereas cryptochromes willcause inactivation. Some species of plants show involvement of gibberellin and phytochromein the flowering process, such as Pharbitis nil (Kulikowska-Gulewska and Kopcewicz, 2002)and tobacco (Gallego-Giraldo et al., 2007). In this experiment, downstream gibberellinmetabolites were consistently observed in the blue light treatment samples, a trend which iscongruent with cryptochrome inactivation.82Phenylalanine is the precursor for phenylpropanoids, which consist of many critical compoundsin plants like phenolic acids, flavonoids, and anthocyaninsÍ¾ plants will dedicate approximately30% of photosynthetically fixed carbon into the synthesis of phenylalanine, reflecting theimportance of this compound (Kumari et al., 2023). Phenylpropanoids are key compoundswithin plants as they confer abiotic and biotic stress resistance (Dong and Lin, 2021). Flavonesand flavonols make up a variety of plant pigments and confer protective advantages to plantsthrough their ability to scavenge reactive oxygen species (Liu et al., 2021). In this study,kaempferol and kaempferol 3-O-rhamnoside-7-O-glucoside were putatively identified in bluelight treatment tissue, a result similar to findings in soybeans (Lim et al., 2023) and in ginkgo(Zhang et al., 2021). Likewise, total isoflavone content was also higher in blue light treatmentsamples in soybeans (Azad et al., 2018). Cross-species comparisons reveal a pattern ofelevated flavonoid production when exposed to blue light: Cyclocarya paliurus (Liu et al.,2018), Gingko biloba L. (Zhang et al., 2021), Gossypium hirsutum L. (Shao et al., 2022), Piceaabies L. (OuYang et al., 2015), and Scutellaria sp. (Costine et al., 2022), reflecting a deepevolutionary diversity and conservation of this response - from gymnosperms and ancientangiosperms to more recent flowering plants.Tyrosine was identified through hormonomics and its metabolism was found to be significantlyupregulated through pathway analysis. As an aromatic amino acid, tyrosine is also involvedin flavonoid and isoflavonoid biosynthesis via p-courmaryl-CoA (Parthasarathy et al., 2018). Itdiffers from phenylalanine through its hydroxyl phenyl group, which can be phosphorylated(Parthasarathy et al., 2018)Í¾ this phosphorylation has emerged as an important regulatorymechanism for plant growth and development, often mediated by brassinosteroids (Song et al.,2022). High light intensities appear to increase tyrosine content, demonstrated in Triticumdurum Desf. cv. Ofanto (Carillo et al., 2011) and cigar tobacco (Ma et al., 2023). Someof the downstream products of tyrosine, tyramine-derived hydroxycinnamic acid amides, aresecondary plant metabolites involved in plant defense, growth, and development (Leonardet al., 2020).833.6 ConclusionThis study demonstrated that Cannabis acts as an excellent model organism for investigatinglight-mediated metabolic responses, revealing distinct biochemical signatures under fullspectra control, red, and blue light treatments. My research showed that blue light exposurepromoted more flavonoid and prenol lipid accumulation, including various gibberellins, whilered light enhanced carboxylic acid and amino acid derivative production. There is anunderlying connection to phenylpropanoid pathways with the identification of tryptophan,tyrosine, and phenylalanine metabolism. The pathway analysis revealed that 14 of 16significantly overrepresented pathways could be traced back to the shikimate pathway, theMEP/MVA pathways, and other amino acid pathways, suggesting their involvement in spectraperception in Cannabis. These findings aligned with cross-species patterns that showed bluelight consistently stimulating flavonoid biosynthesis across diverse plant taxa suggesting anconserved response. The cannabinoid levels in samples remained largely unchanged betweentreatments. These results underscore the importance of optimizing light spectra in controlledcultivation environments and encourages future use of Cannabis as a valuable plant systemfor advancing our understanding of plant photobiology.84Chapter 4: Integration of agri-tech and analytical methods to assess nutrientmanagement and utilization in strawberry (Fragaria x ananassa Duchesne ex RozierâAlbionâ)4.1 SynopsisStrawberry (Fragaria x ananassa Duchesne ex Rozier) is a rapidly growing successfulgreenhouse crop in Canada with cultivation nearly tripling from â¼2.6 million kg in 2020 toâ¼7.5 million kg in 2024. This rapid rise in controlled environment agricultural cultivation isdriving the need for new agri-technologies to improve yield, improve nutrient use efficiency, andreduce nutrient runoff that contaminates municipal water systems and adjacent ecosystems. Ihypothesised that nutrient uptake is determined by the availability of nutrients in fertigationsolutions. To investigate this hypothesis, I used a Youden square experimental designwith seven fertilizer treatments applied by a precision fertigation system and I monitorednutrient flow at flow points: application, growing substrate, in planta, and runoff. Withthese data, I established statistical methods to determine relationships between plant tissues,substrate, and leachate nutrient composition, as well as yield. Strawberry plants maintainedrelatively consistent tissue and leachate elemental profiles regardless of fertilizer treatmentÍ¾this suggests limited ability to target specific elemental compositions through fertilizer selectionalone.4.2 ObjectivesMy specific objectives were:1. Successfully set-up and integrate agri-tech sensors into Fragaria x ananassa cultivationÍ¾2. Evaluate the accuracy and reliability of new agri-tech systems, using conventionalanalytical methods for comparisonÍ¾3. Demonstrate the utility of my cultivation system by testing several fertilizer componentsand monitor their effect on nutrient uptake and utilization in Fragaria x ananassa leaves,fruits, and growing substrate.854.3 Materials and methodsAll experiments were conducted at the Plant Growth Facility, University of British ColumbiaOkanagan (49.941609, -119.401707).4.3.1 Experimental growth roomThree black plastic reservoirs (Low Tide Tray 0.91 m x 0.91 m x 8.9 cmÍ¾ Botanicare, USA)were placed on top of a 7.3 m x 1.5 m table to create three distinct zones, each containing20 strawberry plants across 5 stone wool slabs. A 2.5 cm hole was drilled into each blackplastic reservoir at one end to allow for drainageÍ¾ each reservoir was also angled to achievea 2° slope in the direction of this hole to prevent stagnation within the reservoir. White plasticreservoirs (75 gal 91.4 cm x 141.0 cm x 36.1 cmÍ¾ Xtrays, Canada) were placed underneatheach of the black plastic reservoirs below the table to capture run off from irrigation events andsubsequent drainage. Figure 4.1 shows the strawberries the day after transplant into the threedistinct zones.Figure 4.1: Photograph showing the three distinct zones set up with black plastic reservoirswith white plastic reservoirs underneath to catch run off.Environmental conditions within the growth room for the duration of this experiment aresummarized in Table 4.1. While supplemental lighting was on, repeated light intensity86measurements taken with a spectrophotometer (LI-250AÍ¾ LI-COR, USA) at the centre andevery corner for each zone showed minimal difference between minimum and maximum PPFD(41.6-66.8%).Table 4.1: Summary of environmental conditions (avg. Â± std. dev.) within the growth roomaveraged over the course of the experiment (2025.03.31-2025.05.16).Climate Variable Units March April MayTemperature °C 26.1 ± 1.1 26.2 ± 1.2 26.3 ± 1.5Roof Vent Opening % day 4.5 ± 7.0 12.5 ± 14.6 19.3 ± 22.4Photosynthetic Active Radiation (PAR) µmol 440.31 ± 157.39 489.86 ± 252.12 508.73 ± 285.69Relative Humidity % 47.8 ± 2.27 34.7 ± 1.68 40.3 ± 1.764.3.2 Plant growth conditionsThe plastic material of each corner of 16 stone wool slabs (Vital 90 x 20 x 7.5 cmÍ¾ Grodan,the Netherlands) was cut to expose the stone wool and ensure proper drainage. In addition,4 squares (15 cm x 15 cm), evenly spaced apart, were cut out of the top plastic materialfor 15 out of the 16 slabs. Slabs were saturated completely with water sourced from thegreenhouse irrigation lines (EC = 0.29, pH = 6.2). 75 bare root strawberry crowns (Fragariax ananassa Duchesne ex Rozier âAlbionâÍ¾ groHERE, Edmonton, AB) were then transplantedinto the saturated slabs. The best 60 plants were selected for the prepared 15 slabs, with theremaining 15 transplanted into the final slab. All plants were hand watered to run off for 11days during a quarantine period in an enclosed growth chamber before being transferred intothe north-east facing experimental growth room within the greenhouse.4.3.2.1 Fertigation deliveryEach zone was plumbed independently to a central batch tank and irrigation system (NourishLabs, Canada) located within the head house (Figure 4.2). The fertigation solution applicationwas dosed on-demand through up to twelve peristaltic pumps connected to their own uniquesolution using 1/4â (0.63 cm) plastic tubingÍ¾ dosage and scheduling of pulses were programmedthrough the use of a custom, online portal (https://m240201.machines.nourishlabs.support).Liquid fertilizers were connected directly into the system while water-soluble fertilizers wereprepared as stock solutions according to package directions. In-line pH and EC sensors(Sensorex, USA) would record their respective measurements for each fill of the batch tank.The system would adjust pH to 5.8 by automatically dosing the correct amount of pH up or pH87down products. Drip emitters (0.5 gph) were punched along the irrigation tubing for each zonesuch that one emitter was inserted adjacent to each strawberry plant, for a total of 4 emittersper slab. Two scheduled 100 mL pulses (actual delivery between 70-90 mL), separated by 3hours, were used per treatment per zone for a total of 140-180 mL applied each day to everyplant. Excess leachate was induced by using at least one 250 mL pulse, with additional pulsesbeing delivered approx. 30 mins later as necessary.Figure 4.2: Photograph showing the automated fertigation system used during the experiment.12 peristaltic pumps (top centre) can connect to twelve independent containers (bottom centre)and are used to dose reverse osmosis (RO) water (storage container, left) as it fills the centralbatch tank (right).4.3.3 Experimental designStrawberry plants were allowed to acclimate and reach a level of equilibrium prior to thecommencement of any treatments, which was determined by week-over-week consistent fruityield production across all three zones. Seven commercially available fertilizer products -Hyclean, Part A, Part B, Hygrozyme Enzymes, Hygroben Microbes, Hypochlorous Acid, and88Boost (Table B1 for full product information) - with different elemental compositions (Table 4.2)were used for this experiment. To assess the impact of each, a specialised incomplete blockexperimental design (DOE), a Youden Square (Youden, 1972), was used with fertilizer, zone,and week as the treatment (Ï ), position (β), and block (α) factors, respectively (Equation 4.1).nitr = µ+ αi + Ït + βr + ϵitr (4.1)where µ is the overall mean and ϵ is the normally and independently distributed (NID(0,Ï2))error term. Each fertilizer treatment was applied in isolation exactly once to each zone fora period of 5 days (Monday-Friday) over the course of seven weeks, satisfying the Youdensquare design (Table 4.3). The Youden Square DOE allowed for simplified control of twodifferent blocking factors (in this case, week and zone) consisting of different levels (Gupta,2005), while accounting for the systemâs limitation of running a maximum of three treatments ina given week, in contrast to a full factorial DOE or typical Latin Square, which would necessitateevery combination to be run at the same time. After each treatment cycle, each zone wouldthen receive two 250 mL pulses of commonly used commercial, 2-part control fertigationsolution (combination of Part A and Part B products as per their feed chart) for two days(Saturday-Sunday) before the next treatment would commence.Table 4.2: Elemental analysis (avg. ppm Â± std. dev.) of the seven fertilizer products usedin this experiment, collected at the emitter for each zone (n = 3 samples for each fertilizer).The highest values for each element are in bold. A represents Hyclean, B represents Part A,C represents Part B, D represents Hygrozyme Enzymes, E represents Hygroben Microbes,F represents Hypochlorous Acid, and G represents Boost. Control refers to the combinationof Part A and Part B delivered to the strawberry plants during the weekend period betweentreatment applications.Element A B C D E F G ControlB 0.11 ± 0.1 0.20 ± 0.2 0.04 ± 0.0 0.11 ± 0.2 7.41 ± 0.4 0.16 ± 0.2 0.00 ± 0.0 0.19 ± 0.0Ca 9.84 ± 2.3 11.97 ± 5.9 86.19 ± 20.3 11.64 ± 1.4 10.28 ± 4.0 23.22 ± 22 8.86 ± 3.7 86.95 ± 2.4Cu 0.00 ± 0.0 0.15 ± 0.1 0.03 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.22 ± 0.0Fe 0.02 ± 0.0 0.61 ± 0.0 0.03 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.21 ± 0.0K 5.66 ± 4.3 131.2 ± 20.9 85.51 ± 28.7 21.62 ± 4.6 18.77 ± 1.0 23.67 ± 26 247.8 ± 27.2 182.2 ± 3.3Li 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0Mg 3.29 ± 0.8 42.78 ± 8.7 31.25 ± 8.9 5.38 ± 0.2 3.85 ± 0.7 10.44 ± 10.4 3.43 ± 1.1 60.29 ± 1.1Mn 0.00 ± 0.0 0.30 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.16 ± 0.0Mo 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0Na 192.2 ± 3.6 11.42 ± 2 8.14 ± 2.6 10.78 ± 2.9 24.1 ± 2.0 7.61 ± 1.1 7.63 ± 6.2 7.74 ± 1.1P 1.68 ± 0.6 82.32 ± 1.7 79.22 ± 69.9 70.02 ± 30.3 1.26 ± 0.7 55.91 ± 19.3 65.25 ± 7.9 153.2 ± 136.4S 0.86 ± 0.7 68.14 ± 13.1 1.72 ± 0.5 5.64 ± 1.6 1.00 ± 1.0 1.81 ± 0.4 69.57 ± 8.6 52.04 ± 1.2Si 1.83 ± 0.4 1.82 ± 0.2 1.86 ± 0.3 2.17 ± 0.2 2.05 ± 0.3 1.97 ± 0.2 2.08 ± 0.3 1.89 ± 0.2Sr 0.05 ± 0.0 0.06 ± 0.1 0.44 ± 0.1 0.09 ± 0.0 0.06 ± 0.0 0.12 ± 0.1 0.04 ± 0.1 0.45 ± 0.0Zn 0.00 ± 0.0 0.34 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.00 ± 0.0 0.20 ± 0.089Table 4.3: Youden Square experimental design where fertilizer product was the treatmentfactor, zone was the position factor, and week was the block factor. A represents Hyclean,B represents Part A, C represents Part B, D represents Hygrozyme Enzymes, E representsHygroben Microbes, F represents Hypochlorous Acid, and G represents Boost.Week No. 1 2 3 4 5 6 7DateRange2025.03.31-2025.04.04 2025.04.07-11 2025.04.14-18 2025.04.21-252025.04.28-2025.05.02 2025.05.05-09 2025.05.12-16Zone 4 E F G B C D AZone 3 G A C D F E BZone 2 B G D F E A C4.3.3.1 System design: Biosensor and sample collection overviewThe system was designed with real-time monitoring at 4 stages (Figure 4.3). In essence, ann number of stock solutions are dosed into the batch tank to create a batch tank mixtureÍ¾ itspH and EC are measured at this time using the inline pH and EC sensors and its elementalcomposition (B, Ca, Cu, Fe, K, Li, Mg, Mn, Mo, Na, P, S, Si, Sr, Zn) are determined usinginductively coupled plasma optical emission spectroscopy analysis (ICP-OES) on samplescollected from one emitter within a zone. Electrophysiological sensors (Vivent Biosignals,Switzerland) continuously measure elemental status (Ca, N, K, P, Fe, Mn) relative to watercontent (%) within the strawberry plant. Strawberry fruit and leaf tissue is collected for bothICP-OES and targeted metabolomics analysis. Lastly, electrophysical substrate sensors(GroSens 2.0Í¾ Grodan, the Netherlands) inserted into the stone wool substrate continuouslymeasure volumetric moisture content (%), pH, and electrical conductivity (EC), while elementalcomposition of run off leachate from the substrate was determined by ICP-OES.Figure 4.3: Block diagram of the overall system, indicating key sampling points and sensormeasurement points. Note, the targeted metabolomics sample results were not received priorto the completion of this thesis.904.3.4 Plant monitoring4.3.4.1 Plant electrophysiology3 electrophysiological sensors were used across the three zones. Every electrophysiologicalsensor consisted of 8 channels, each containing one pair of active electrode and one groundelectrode made from 50 â¦ impedance coax cable with an inner silver coated copper wire (d =0.50 mm), which utilised cyclic voltammetry to assign detected signals to changes in specificelements (Ca, Fe, K, Mn, P, N). Thus, one pair of active-ground electrode was insertedinto the same plant to measure the electrical potential differenceÍ¾ the active electrode wasinserted between the petiole of the most recently fully expanded leaf (at time of insertion),and the ground electrode was inserted into the base of the strawberry (Figure 4.4). Thesesignals were measured in milivolts (mV) and sampled at a rate of 256 Hz. Measurementswere stored locally on the device and uploaded to a cloud service every day to be accessedand visible on an online portalÍ¾ signals were automatically processed from their raw mVvalues into a representative elemental status value (manufacturer-processed values) throughViventâs machine learning algorithm (Tran et al., 2019), smoothed to produce an averagedvalue on a frequency of every 6 hours. There was an acclimatization period of 7 days toallow for signal quality to stabilise prior to the commencement of any treatments. Two signalquality parameters were monitored on the online portal to ensure the probes and sensorswere operating correctly: 1) short term signal variability, which is used to monitor potentialcontamination of the signal from electromagnetic noise for a single or all channels, and 2)daily average signal value, which can indicate electrode insertion issues (e.g. electrode beingaccidentally removed, electrode becoming loose). Manufacturer-processed values whichcorresponded to measurements taken at night were not used for correlation results.Data from each sensor were downloaded as .csv from the online dashboard(grafana.vivent-biosignals.com) using the date range of the experiment, reporting signalvalues (mV) for each electrode channel on an hourly basis. Data were compiled into onespreadsheet and corresponding zone was assigned to each channel alongside the fertigationtreatment being applied at that time. Spyder v5.0 running Python 3.12 was used to then plotthese signal values over the course of the experiment for each electrode channel (e.g. FigureB1). There was a high degree of noise so first-order differencing (where the previous value is91Figure 4.4: The active electrode (A) and the ground electrode (B) from the electrophysicalsensor inserted into the petiole and the base of the same strawberry plant, respectively.Photograph taken by Dr. Susan Murch.subtracted from the current value) and windowing with step size of 40 (where every 40tr row isselected) were implemented, similar to Wen and Ma (2025), to smooth the data. Step size of40 was selected as each fertigation treatment had approx. 120 signal readings, so 40 dividedsignals into morning, midday, and evening readings. These signals were then averaged byzone and colourised to visualise fertigation treatments (Figure 4.5). Overall average for eachfertigation is shown in Figure B2. Data were normalised usingm =xâ x¯ÏP (4.2)where m is the normalised feature vector, x is the signal data, x¯ is the average, and Ïis the standard deviation, following Wen and Ma (2025). The following parameters werethen calculated on this data (Vivent electrical data) as described by Wen and Ma (2025):mean, minimum, maximum, median, lower and upper quartile, summation, variance, kurtosis,92skewness, log energy entropy, sample entropy, and sample entropy with tunable parameters(Table B13 for equations).Figure 4.5: Differenced electrophysical signals (mV) measured every hour by zone from theVivent Biosignal devices over the course of the experiment, after noise removal throughwindowing with a step size of 40 and first-order differencing. Note, numbers labeled bysegments correspond with line colours for label redundancy.4.3.4.2 Substrate assessmentFertigation was applied to each plant in high enough quantity to generate leachate from thebase of the slab. A minimum of 10 mL of this leachate was collected in plastic saucers (d= 10 cm) and transferred into plastic conical tubes. If a leachate sample had a significantquantity of particulate matter, then it was filtered using a 0.2 µm Ultrafree-MC centrifugal filter(Millipore-Sigma, Canada) after collection. These samples were then analyzed using ICP-OES.Comparisons were then made between samples collected at the emitter and samples collectedfrom the leachate using paired t-test in Excelâ¢ using α = 0.05 for each of the elements analyzed93(Table B14). Throughout the experiment EC, temperature, and volumetric moisture content (%)was measured continuously every 3 minutes within select stone wool slabs through the use ofone electrophysical sensor in zones 2 and 4 each (GroSens).4.3.4.3 Leaf and fruit tissue assessmentBefore the start of every cycle of treatment application, fruit that had begun to ripen washarvested and weighed by zone to mitigate cross correlation of fruit yield from week-to-week.Similarly, all produced fruit was picked after the 5 day treatment application and gradedas either acceptable if the fruit was a uniform colour, ripe, firm, and typically shaped (notabnormally shaped or extremely small)Í¾ otherwise, the fruit was graded as unacceptable. Eachgroup of fruit was weighed and summed for a total fruit yield metric in addition to separateacceptable and unacceptable yield. Thus, the yield recorded reflected the rate of growth onlywithin a 5 day treatment period.Both leaves and fruit tissuewere sampled weekly, but implemented after the start of experiment,missing these combinations: Boost x Zone 2Í¾ Hyclean x Zone 3Í¾ Hygroben Microbes x Zone2Í¾ Hypochlorous Acid x Zone 3 and 4Í¾ and Part B x Zone 4. At least three leaves or fruitwere collected from three different individuals from each zone. Selected leaves were fullyexpanded and with minimal discoloration or deformities (though this was not possible at times,depending on the treatment) while fruit was collected on the basis of ripeness and size. Forelemental analysis, collected tissue was then moved into an oven (Gravity Convection OvenÍ¾ThermoScientific, USA) set to approx. 50°C and dried continuously for a minimum of 24 hours.Then, each sample was ground to a fine powder using a coffee bean grinder in intervals of 30sec pulses (Coffee and Spice GrinderÍ¾ Black + Decker, USA). Samples were retained at 4°Cprior to ICP-OES analysis. For targeted metabolomics analysis, collected tissue was weighed,flash frozen in liquid nitrogen immediately after harvesting, and then retained at -80°C untilanalysis.4.3.5 Inductively coupled plasm a optical emission spectroscopy analysisICP-OES analysis was conducted at the Fipke Laboratory for Trace Element Research(FiLTER) at the University of British Columbia Okanagan. All equipment (e.g. spatulas,94weighing scale, workbench) was cleaned with isopropanol before and between samples, andapproximately 250 mg of each powdered sample was weighed into a 50 mL polypropylenedigestion vial (Digitube, Analytichem, Canada) using a five place analytical balance (Denverinstrument, USA) and the final weights recorded. For liquid samples, samples were first diluted10x with 1%HNO+. 3 samples were prepared in duplicate to asses precision and 3 aliquotsof the certified reference material (CRMÍ¾ NIST SRM 1567a wheat flourÍ¾ National Institute ofStandards and Technology, 1988) were digested to assess accuracy. Samples were digestedon a graphite hot block (DigiPREP MS, Analytichem, Canada) after the addition of 10 mLof concentrated HNO+ (PlasmaPURE, SCP Science, Canada) at 95°C for approximately 3hrs until the digestion was complete. Following digestion, samples were removed from thedigestion block, allowed to cool and then made up to a final volume of 50 mL with 1% HNO+.Analysis was performed using Thermo iCAP 6000 series ICP-OES equipped with an ASX520autosampler (CETAC, USA). The instrument was calibrated using a five point a multi elementcurve (0, 0.04, 0.4, 4 and 40 ppm) and drift/matrix corrected using an internal standard (Y 5ppm)that was mixed with the sample via a polypropylene âYâ piece before entering the nebulizer.The run included a range of blanks, quality control solutions as well as the certified referencematerial and duplicate samples.4.4 Results4.4.1 Autocorrelation of dataAs treatments were applied consecutively, there was a potential for autocorrelation to influencesubsequent measurements. Moving average analysis was applied and the mean accuracypercentage of error (MAPE), the mean absolute deviation (MAD), and the mean squareddeviation (MSD) were determined for each measurement, as well as seasonality throughdecomposition of each series. Spyder v5.0 running Python 3.12 was used to plot eachmeasureby zone for electrophysiological (Vivent BioSignals), electrophysical (GroSens), and strawberryyield data (Tables B3, B4, B5). Batch tank data (pH, EC, temperature) and ICP-OES elementaldata on samples collected at the emitter was assessed by fertigation rather than by zone (TablesB6-B12), to determine the stability of each of the stock solutions used, however the limited datapoints resulted in high MSD and the small concentrations detected (for elements such as Cu,Fe) resulted in MAPE trending to infinity. Batch tank pH and EC for each fertigation treatment,95as well as the control nutrient mixture, remained stable across the experiment (Table 4.4).Strawberry yield for each zone decreased over time, with high MAD, MAPE, and MSD values,and with R2 for each linear regression ranging between 0.27-0.55.Table 4.4: Averaged batch tank pH or EC Â± std. dev. for each fertigation treatment acrosszones. Control refers to the combination of Part A and Part B delivered to the strawberryplants during the weekend period between treatment applications.Fertilizer Treatment pH ECBoost 5.98 ± 0.10 2.68 ± 0.54Hyclean 5.78 ± 0.23 2.08 ± 0.35Hygroben Microbes 5.68 ± 0.19 0.62 ± 0.15Hygrozyme Enzymes 5.99 ± 0.26 0.54 ± 0.20Hypochlorous Acid 6.09 ± 0.17 0.35 ± 0.20Part A 5.76 ± 0.13 3.10 ± 0.25Part B 5.82 ± 0.16 3.95 ± 0.31Control 5.75 ± 0.16 5.48 ± 1.03The Week factor within the Youden DOE was significant for: concentration of Ca, B, Mg, Na,S, and Si in both emitter and leachate samples, concentration of K in only emitter samples, andconcentration of P in only leachate samples, as quantified through ICP-OESÍ¾ strawberry yieldÍ¾volumetric moisture content, as quantified by Grodan GroSens sensorsÍ¾ and Mn, as quantifiedby the Vivent sensors.4.4.2 Significant factors from Youden analysisYouden F( values are summarised in Table 4.5. Week was a significant factor for the majorityof ICP-OES data while Zone was significant for the manufacturer-processed Vivent sensordata. Strawberry Yield was influenced by both Fertigation and Week. Within GroSens data,volumetric moisture content was influenced by Week.96Table 4.5: F( values for each parameter assessed using the Youden DOEÍ¾ significant valuesare in bold. E refers to emitter values, L refers to leachate values.ParameterFertigation(Fcritical = 2.996)Week(Fcritical = 2.996)Zone(Fcritical = 3.885)F( F( F(ICP-OESBE 347.2 111.1 1.02BL 4.08 6.79 0.07CvE 33.21 14.60 1.86CaL 2.26 4.24 7.13KE 88.63 28.16 1.93KL 1.43 2.75 0.90MgE 62.41 11.84 0.96MgL 1.85 3.81 1.90NaE 1179.7 393.1 2.63NaL 0.53 3.43 5.77PE 2.60 2.79 1.75PL 1.60 4.84 1.16SE 52.66 16.02 5.14SL 1.38 3.41 1.21SiE 9.10 405.4 1.72SiL 2.51 4.20 6.21GroSensEC 0.46 1.77 1.11Temp 0.52 3.12 2.06VMC 0.48 18.08 4.81ViventCa 1.32 1.08 15.41Fe 0.65 0.34 6.05K 1.26 2.50 2459.7Mn 6.21 4.15 32.67N 3.05 0.91 610.1P 1.40 0.34 30.88PBI 2.00 1.64 6.70Strawberry Yield 17.90 10.22 0.0504.4.3 Accumulation of total dissolved solidsTotal dissolved solids (TDS) were determined for each collected sample and shown in Table 4.6and averaged by zone. TDS for fruit and leaf samples were consistently elevated compared toemitter samples. Stock solution TDSwere higher than what was reported at the batch tank level(i.e., diluted), aside from the Part B fertilizer. Emitter TDS levels were all lower than their batchtank counterparts. The percent difference between emitter and leachate TDS levels showed awide variability, ranging from 259-2177% higher TDS in leachate samples.97Table 4.6: Total dissolved solids (TDS) Â± std. dev. reported in parts per million (ppm) for each sample type and fertilizer product. Valueswere first electrical conductivity (EC) measured directly by sensors (*) and converted to TDS (k = 0.67), or calculated from summingppm of all detected elements through ICP-OES (â ). Values in brackets indicate percent difference between Emitter and present value.Note, std. dev. could not be estimated for Hypochlorous Acid fruit or leaf tissue samples as only one sample was collected of each.Fertilizer Product Boost Hyclean HygrobenMicrobesHygrozymeEnzymesHypochlorousAcid Part A Part BStock Solution â 79333.1 70545.5 5948.9 3292.0 2839.3 4881.8 486.3Batch Tank (n = 3) * 1339.86 ± 182.0 1041.55 ± 68.9 307.60± 25.4 267.78 ± 56.8 172.55 ± 17.7 1551.34 ± 288.8 1976.95 ± 561.4Emitter (n = 3) â 404.65 ± 50.1 218.40 ± 8.1 70.69 ± 5.0 121.09 ± 74.0 131.86 ± 106.1 332.05 ± 125.8 264.01 ± 152.6Substrate * (n = 2) 1534.30 ± 86.0(279.2%)1695.10 ± 157.8(676.2%)1788.90 ± 132.9(2431%)1621.40 ± 221.4(1239%)1728.60 ± 302.2(1211%)1279.70 ± 390.6(285.4%)1293.10 ± 31.6(389.8%)Fruit â (n = 3) 23177.27 ± 2095.6(5628%)29091.61 ± 2734.0(13221%)23430.10 ± 1240.5(33044%)21004.75 ± 5341.5(17247%)21064.25(15875%)22687.89 ± 467.2(6733%)23118.19 ± 3305.9(8657%)Leaf â (n = 3) 51156.37 ± 2628.3(12542%)94206 ± 3321.5(43035%)59821.74 ± 8837.9(84524%)49844.16 ± 6953.0(41063%)47511.46(35932%)42274.43 ± 2965.6(12631%)40153.06 ± 3936.0(15109%)Leachate â (n = 3) 1790.03 ± 822.4(342.4%)707.33 ± 959.3(223.9%)1609.66 ± 982.4(2177%)1798.44 ± 1853.4(1385%)2333.61 ± 1488.3(1670%)1190.62 ± 851.9(258.6%)1095.85 ± 519.3(315.1%)984.4.3.1 Differences between emitter and leachate elemental compositionThere were significant differences in emitter TDS by fertigation when compared usingKruskal-Wallis test (p value = 0.0023) (Table 4.7). Dunnâs test was done as a post-hoc testto determine which treatments were different using a Bonferroni adjustment for p values,where Hygroben Microbes was significantly different from Boost and Part A treatments, butnot different from other supplemental additives (Hygrozyme Enzymes), cleaning treatments(Hypochlorous Acid, Hyclean), or Part B. Individual elements in leachate samples wereelevated, with elemental ppm being significantly higher in leachate samples than in emittersamples (Table B14) when compared using paired t-test. The Hygrozyme Enzymes and PartA treatments did not have any significant differences between emitter and leachate samplesin any of the quantified elements. However, overall leachate TDS did not significant differbetween fertigation treatments using Kruskal-Wallis test (p = 0.89), despite the differences infertilizer composition.Table 4.7: Summary of p values as determined using Kruskal-Wallis test on elemental ppmfrom ICP-OES analysis by fertilizer treatment (Boost, Hyclean, HygrobenMicrobes, HygrozymeEnzymes, Hypochlorous Acid, Part A, Part BÍ¾ n = 3*). TDS refers to total dissolved solids,which is the sum of all detected elements. Significant p values are in bold for emphasis.* Hypochlorous Acid was not included in analysis for berry or leaf as only one sample wascollected of each.Element p valueEmitter Berry* Leaf* LeachateB 0.06114 0.11136 0.46038 0.30244Ca 0.24322 0.11731 0.28812 0.82017Cu 0.12053 0.36645 0.10615 0.95552Fe 0.02131 0.61770 0.24124 0.71203K 0.00791 0.61404 0.14457 0.86335Li N/A 0.30622 0.59833 0.51009Mg 0.02332 0.46411 0.44562 0.95183Mn 0.00293 0.13506 0.16156 0.85741Mo N/A 0.36645 0.30622 N/ANa 0.02442 N/A N/A 0.98478P 0.03257 0.81946 0.42055 0.82443S 0.01085 0.63422 0.55491 0.92207Si 0.80065 0.59407 0.25531 0.90396Sr 0.19028 0.19493 0.39983 0.79404Zn 0.00293 0.30437 0.61261 0.50463Total TDS 0.0023 0.37098 0.68792 0.89177994.4.3.2 Elemental accumulation in leaves and berriesOverall TDS in berries or leaves did not significantly differ by fertigation treatment (Table 4.7),nor did individual elements in leaves (Table B15) or berries (Table B16). Individual elementalanalysis of leaves for all fertilizer treatments revealed potassium or calcium as the dominatingelement while strawberry berries were predominately potassium (e.g. Part B leaf compositionin Figure 4.6). Levels of B and P were elevated in leaf tissue collected from every fertilizertreatment, compared to recommended values (Campbell and Miner, 2000Í¾ Bottoms et al.,2013Í¾ Osvalde et al., 2023Í¾ Dixon et al., 2023), while S, Zn, K, and Ca quantified in leaveswere in range (Campbell and Miner, 2000Í¾ Bottoms et al., 2013Í¾ Osvalde et al., 2023Í¾ Dixonet al., 2023). Cu was deficient for all fertilizer treatments aside from Part B (10.6 ± 0.3 ppm).Sufficient ranges for berries could not be found for comparison.Figure 4.6: Pie chart of elemental composition of strawberry fruit (left) and leaves (right) fromplants, using Part B as fertilizer as determined using ICP-OES, as an example.Averaging by fertilizer treatment, element concentration at the emitter presented correlationsto subsequently detected elemental concentration in leaf (Figure 4.7a) and berry tissue (Figure4.7b). B (-0.65), K (-0.80), Si (0.51), and Sr (-0.51) were correlated between leaf tissue andemitter samples. However, in fruit tissue, Mn showed correlation (-0.60) when compared toemitter sample data.Correlations between the same element in leaf and fruit ICP-OES data were poor (< 0.5) for allelements besides K (Figure B3). Between elements, K in berries was positively correlated withleaf B (0.68) and Cu (0.75). Leaf Ca was positively correlated with Zn (0.76), Mg (0.58), and100(a)(b)Figure 4.7: Pearson correlation plots of B, Ca, Cu, Fe, K, Mg, Mn, P, S, Si, Sr, and Zn databetween emitter across fertilizer and zones, and either (Figure 4.7a) leaf or (Figure 4.7b) fruitICP-OES data (n = 7 for each element).Cu (0.52) in berries. Negative correlations were found between berry Ca and leaf B (-0.75),and berry Sr with leaf B (-0.73), Cu (-0.69), K (-0.74), Li (-0.5), and S (-0.55). Na was notdetected in either fruit or leaf tissue data, despite being present in emitter samples. In contrast,Zn accumulated to some degree in fruit and leaf tissues, but was not quantified in Part A emittersamples. Correlations between the manufacturer-processed Vivent data were present for Ca(R2 = 0.61) and Fe (R2 = 0.66) in leaves (Figure 4.8) but were poor for any of the elementalfruit data (Figure 4.9) (all R2 < 0.23).101Figure 4.8: Scatterplots of Ca, Fe, K, Mn, and P data between manufacturer-processed Viventdata (% elemental change) and leaf ICP-OES data, averaged across zones (n = 7 for eachelement).102Figure 4.9: Scatterplots of Ca, Fe, K, Mn, and P data between manufacturer-processed Viventdata (% elemental change) and fruit ICP-OES data, averaged across zones (n = 7 for eachelement).1034.4.3.3 Elemental accumulation in the substrateLeachate elemental composition was primarily K, Ca, and Mg (Figure 4.10) and only Part Bcontaining a higher proportion of Na over Mg. There were no significant differences in anyelement in leachate samples (Table 4.7) nor in TDS in substrate by fertigation (Table 4.6).Youden analysis suggested leachate B was influenced by Fertigation and byWeek. ElementalMn and Sr in leachate were correlated (0.59 and 0.53, respectively) with substrate EC asmeasured with the GroSens sensors (Figure B4b), with positive albeit weak correlation for allother elements in leachate. In contrast, all elements in emitter samples negatively correlatedwith substrate EC (Figure B4a) - beside B and Si - with Fe (-0.60), Mn (-0.52), Mg (-0.50),and Zn (-0.50) with Pearson correlation values < -0.5. Daily irrigation pulses occurred in lowfrequency (twice daily) and in low volume (250 mL each)Í¾ thus, lower nutrient fertigation couldhave limited plant uptake, resulting in salts to accumulate in the substrate (elevating EC asdetected by Grodan GroSens sensors) but then readily leaching out during leachate samplecollection due to the higher volume of fertigation delivered to generate the runoff. Conversely,if the nutrient content delivered at the emitter was simply higher, then this could have promotedplant uptake inspite of the low pulse volume, which would have prevented salt accumulation(reflected in the low EC detected by Grodan GroSens sensors)Í¾ the leachate would still beexpected to have higher elemental concentration because of the leachate collection method.Manufacturer-processed Vivent Mn was negatively correlated with substrate EC (Figure 4.11),the inverse to the correlation between substrate EC and leachate ICP-OES., reflecting a flowof Mn from the plant into the leachate.104Figure 4.10: Stacked bar graph representing elemental composition of leachate samplesas quantified using ICP-OES by fertilizer treatment, consolidating elements < 1% into Othercategory. Order of bars is from largest to smallest % within each fertilizer treatment.Figure 4.11: 1-layer correllogram between manufacturer-processed Vivent elemental change(%) for Ca, Fe, K, Mn, and P, and substrate electrical conductivity (EC) as measured by GrodanGroSens sensors.1054.4.4 Electrophysiological changes in electrical potentialA summary of signal parameters on normalised data is presented by fertigation treatment andzone in Table B17. No signal parameter was significant by fertigation treatmentÍ¾ however, everyparameter was significant considering zone, aside from kurtosis (p value = 0.056). EP fromzone 3 plants were all negative, whereas zone 2 and 4 signal were all positive (Figure B5). Themean for zone 3 signals was significantly lower than zone 2 (adj. p value = 0.0293) and zone 4(adj. p value = 0.0005). No signal parameter showed strong correlations with EC (Figure B6).Several of the signal parameters showed strong correlations with the manufacturer-processedVivent data (Figure B7). Normalised averaged signal for each fertigation treatment (Figure4.12) showed signals sharply declining throughout the middle of the day from approx. 8 amonward, until sharply increasing again at approx. 5 pm. This pattern is typical for a diurnalcycle, defined as a pattern that repeats every 24 hours in line with day to night.Figure 4.12: Differenced, normalised electrophysical signals (mV) presented over the course ofa single day (0-24 hrs) by fertigation treatment averaged by zone, as collected from the ViventBiosignal devices.4.4.5 Comparison of technologiesData collected from the different technologies were directly compared throughPearson correlations to assess whether the measurements were in agreement.Manufacturer-processed Vivent data correlated with Ca and Fe in leaf tissue, though no106correlations to fruit tissue were observed. Substrate EC measured by GroSens showedcontrasting relationships with nutrient flows through the system: nearly all emitter elements(except B and Si) were negatively correlated with substrate EC, while all leachate elementsshowed positive correlations. The best performance of all sensors was for Mn, which could betraced throughout the system: as substrate EC increased, Vivent-detected Mn within plantsdecreased as leachate Mn increased, indicating substrate accumulation coupled with reducedplant uptake and increased losses. While similar element-specific networks could not befully constructed for other nutrients in this experiment, this indicate a potential for integratingmultiple sensor technologies to track nutrient fate through the crop system. The live dashboardreporting from sensors offers substantial advantages over ICP-OES analysis: sensors reducelabor demands by eliminating manual assessment of irrigation needs and provide real-timedata for immediate intervention when problems emerge, whereas ICP-OES requires additionallabor for sample collection, preparation, and transportation, with delays in receiving resultsthat can impede timely adjustments to cultivation conditions. However, ICP-OES remainsinvaluable for providing direct quantification of elemental concentrations in various crop tissuesand validating sensor interpretations. Both Vivent BioSignals and Grodan GroSens sensorsmonitored VMC throughout the experiment, and instances of programming malfunctioning ofthe NourishLab fertigation system or irrigation system within the greenhouse was immediatelydetected because of these sensors. The strengths and limitations of real-world application foreach of the technologies or methods used in this proof-of-concept study are summarised inTable 4.8.107Table 4.8: Operational benefits and limitations for greenhouse management of the technologies and methods used in this experiment:substrate sensors (Grodan GroSens), plant electrophysiology monitoring (Vivent BioSignals), automated fertigation (NourishLabsIrrigation), and tissue analysis (ICP-OES).Characteristic Grodan GroSens Vivent BioSignals NourishLabs Irrigation ICP-OES AnalysisLabour savings Reporting of EC, VMC canreduce need for workers toassess irrigation need of plantsmanuallyReporting of VMC can reduceneed for workers to assessirrigation need of plantsmanuallyAutomated dosage and nutrientdelivery replaces the need forhand-watering of cropsAdditional labour demandin sample collection,preparation, and transportationif analyses are not completedin-house/on-siteInformation forenhanced decisionmakingAdditional indirect informationon plant health via substrateEC, VMCAdditional indirect informationon plant health via change inelemental statusAutomated readings of batchtank pH, ECAdditional direct informationon plant health basedon quantified elementalconcentration in various croptissuesProductionconsistencyHigh as irrigation can beadjusted readily in responseto live data reporting, to reachdesired setpointsHigh as irrigation can beadjusted readily in responseto live data reporting, to reachdesired setpointsHigh as fertigation schedulescan be implemented repeatablyand pH, EC of batch tank mixescan be monitored over timeMedium as delay in receivingsample results can impedeability to adjust cultivationconditions to meet desiredsetpointsPest or diseasemanagementLow likelihood to assist High likelihood to benefit aselectrophysiological monitoringcan be correlated to some pestinfestations (e.g. Kurenda et al.(2025))Low likelihood to assist Low likelihood to assistGeneral reductionin inputs: e.g.fertilizer, pesticidesPossible benefit due to gleanedplant health information and livereporting for current and futurecrop cyclesPossible benefit due to gleanedplant health information and livereporting for current and futurecrop cyclesPossible benefit to high asvolume of fertilizer and watercan be finetuned, howevercurrent programming sendsleftover batch tank mixes todrain with no ability to use foranother scheduled feedingMinimal benefit, as thereis gleaned plant healthinformation but delay in resultsinhibit effectivenessÍ¾ however,findings could be applied tonext crop cycle1084.5 DiscussionCanada operates at a negative trade balance for fruit, at -$6.5 billion in 2024 (StatisticsCanada, 2025b). Out of all fresh fruit imported in Canada in 2024, strawberries represented8.9% of total cost, with the United States of America being the top country of import (StatisticsCanada, 2025b). There has been a building interest in producing more fruit domestically,with strawberries being a high-in-demand crop. Strawberry cultivation in Canada within CEA- specifically greenhouses - has grown from 2.6 million kg in 2020 to over 7.5 million kgin 2024, with an approximate value of $75.2 million (Statistics Canada, 2025c). However,CEA cultivation of strawberries still faces challenges to overcome, such as lighting, pestmanagement, energy and waste management, and sourcing high quality labour (Packer,2022),(Ashton, 2024). Many Canadian farmers are turning to agri-tech-based solutions, aspart of a proposed fourth agricultural revolution (Newell et al., 2021). Agri-tech has beendeveloped that can monitor in real-time numerous critical components of agriculture, suchas ambient temperature, humidity, soil volumetric moisture content, or soil quality, amongothers (Rajak et al., 2023). Various sensor types are used - leveraging different modes ofmechanisms - which include acoustic-based, electromagnetic, optical, mechanical, ultrasonic,or electrochemical options (Rajak et al., 2023). CEA is intrinsically positioned to harnessagri-tech, given that all aspects of cultivation can be modified.This proof-of-concept experiment aimed to compare and contrast new agri-tech sensors(Vivent Biosignal, Grodan GroSens) against analytical methods (ICP-OES) for the purposesof managing nutrient utilization in strawberries grown in a greenhouse. Technology adoptionby growers is slow, with functional needs like increasing revenue, reliability, saving time, andlowering operating cost ranked as higher priority to environmental friendliness, tax benefitopportunities, and minimal training (Kos et al., 2024)Í¾ these benefits are mirrored in whatagri-tech promises to deliver (Huneke et al., 2024). All technologies and methods used in thisstudy provide more information for enhanced decision making for growers, to better understandplant health status. The automated dosage and nutrient delivery of the NourishLabs irrigationcan save time and lower oneâs operating costs. There were several occasions of programmingerrors leading to missed or misapplied fertigation pulses by the NourishLabs system. Furtherequipment failures (e.g. peristaltic pump tubing ruptures) also harmed the systemâs perceived109reliability and durability. Proper system setup is key - determining pulse volume, minimumpressure required, and so on - for all benefits to vest. Both the Grodan GroSens and ViventBioSignals sensors reported data live to a dashboard, which was readily interpretable andeasily accessible, in contrast to the delay in sample results if relying on only ICP-OES. Thisallows for immediate intervention once problems emerge, such as build up of elemental saltsin the substrate or the beginnings of a nutrient deficiency within the plant. Vivent BioSignalsoffer the unique possibility of pest management, as changes in electrophysiological signalscan adumbrate an infestation. However, ongoing human intervention through routine auditingis necessary to periodically assess and confirm the proper functioning of these technologies.This study utilised a Youden square experimental design, developed originally for agriculturalresearch (Youden, 1972) and continues to be used to test different parameters in plant science(e.g. Olson and Kitur (1993), Jiang et al. (2017)). Week was treated as the block (α) factorin the Youden square, whereas fertilizer and zone were the treatment and position factors,respectively. Fertilizer was significant for many of the elements in the emitter samples, asexpected given the differences in elemental composition between the various fertilizers.Similarly, fertilizer significantly affected Mn and N manufacturer-processed Vivent readingsas well as strawberry yield. Zone was significant for nearly all manufacturer-processedVivent readings (besides Ca), although electrode channels were interspersed across multiplezones, eliminating the possibility for specific devices to be the culprit behind this trend.Microenvironmental gradients in heat or humidity may explain Zone being a significant factor.As the same strawberry plants were being used for each combination of fertilizer and zone overseveral weeks, the Week factor could reflect autocorrelation between measurements. Weekwas ultimately significant for nearly all of the ICP-OES leachate samples, at first suggestingnutrient build up within the substrate, but EC as measured by GroSens was not affected byWeek. Nearly all emitter ICP-OES samples were also affected by Week, pointing to potentialchanges within the stock solutions themselves over time and therefore affecting the associatedleachate, as environmental conditions within the head house changed month-over-month.Fertilizer treatments varied in their elemental composition as well as their targeted function,with some being recommended for nutrient delivery (Part A, Part B, Boost), for additives110(Hygrozyme Enzymes, Hygroben Microbes), or for cleaning and sanitation purposes (Hyclean,Hypochlorous Acid). There was a dissociation between fertilizer elemental composition withsubsequent accumulation in leachate, fruit, or leaves. Plant uptake was not reliably predictedbased off nutrient inputÍ¾ rather, there appears to be a common elemental composition tofruit and leaves, with K, Ca, and P being the most prevalent in both tissue types. LeachateTDS was largely higher than emitter TDS, but there were no differences between fertigationtreatments in leachate, leaf, or berry TDS. B, Ca, Mn, and Zn in berry tissue and Mn in leaftissue were significantly different by fertilizer. Thus, by using different nutrient stock solutionsalone, a grower would have limited ability to target specific element composition in the leavesor berries of their crop, or limit certain element in crop runoff, such as phosphorus. Phosphorusin effluent due to over-application in CEA (Westmoreland and Bugbee, 2022) can enter anddamage riverine ecosystems, due to eutrophication and algal blooms which risk animal andhuman well-being (Mainstone and Parr, 2002Í¾ Altamira-Algarra et al., 2022Í¾ Bishop et al.,2018). Some type of further conditioning of effluent may be necessary in CEA (Cowan et al.,2022) if modifying fertilizer usage or composition cannot reliably reduce effluent compositiondirectly. A limitation of this study is the absence of elemental composition data on strawberryshoot or root tissues. It is possible fertilizer treatments modified these other tissue types, asliterature has demonstrated the impact of fertilizer on these tissues (Tang et al., 2024Í¾ Salmanand Rasool, 2023Í¾ Miller et al., 2020).Data collected from the different technologies were directly compared throughPearson correlations to assess whether the measurements were in agreement.Manufacturer-processed Vivent data could be correlated to Ca and Fe in leaf tissue,with no correlations to fruit tissue ICP-OES data. Interestingly, correlations between substrateEC and leachate elements were all positive, while nearly all emitter elements were negativelycorrelated (aside from B and Si) with substrate EC, including Mn. Manufacturer-processedVivent Mn readings was also negatively correlated with substrate EC while leachate Mnincreased alongside substrate EC. Thus, as Mn accumulated in the substrate, there was adetected decrease within the plant and increase within the leachate. Such a network for otherelements could not be constructed, but the Mn example suggests the possibility to do so. Inthat way, growers could choose the sensor technologies over the ICP-OES analysisÍ¾ the latter111takes more time, both in sample collection and the turnaround in receiving sample results,compared to the live dashboard readings for the sensors.Lastly, the electrophysiological EP data collected using the Vivent BioSignals sensors showeda strong diurnal pattern. Plants grow and transpire throughout the entire course of the day, butnight-time activity is dependent on day-time activity (excluding plants with non-crassulaceanacidmetabolism (CAM)) (Fricke, 2019), as stomata open during daytime for photosynthesis andclose at night, when respiration dominates and stored carbohydrates are consumed (Burgess,2023). In the presence of sunlight and the supplemental lighting during the day, photosynthesisand transpiration reach their peak (Fricke, 2019), which would correspond to the observedsteep decrease in EP in Figure 4.12 as water stress and nutrient uptake intensified. Thesignal recovered in the late afternoon likely due to a decrease in transpirational demand,though neither transpiration nor photosynthetic rate were measured during this experimentto confirm. Stainless steel electrodes inserted into the xylem of Populus hopeiensis via thetrunk recorded the same diurnal trend in EPs (lowest during the day), with the transpirationrate consistently inverse to EPs (Hao et al., 2020)Í¾ further, the water content of the poplarsignificantly impacted the magnitude of the EPs measured. A similar pattern was observedin EPs in Persea americana trees (Gil et al., 2008) and in both Broussonetia papyrifera andMorus alba (Xie et al., 2021), as well as in self-potential signals in Quercus ssp. and Pinushalepensis (Hu et al., 2025). These sensors present an opportunity to detect changes in plantphysiology, where deviations could be detected and trigger alerts or automated responses inCEA systems.4.6 ConclusionsThis proof-of-concept study demonstrated the potential for agri-tech sensors (ViventBioSignals, Grodan GroSens) to provide valuable real-time monitoring of plant health,complimenting the use of analytical methods like ICP-OES. All technologies and analyticalmethods showed an absence of a clear trend in elemental accumulation in strawberry tissue, orin leachate volume, despite clear differences in fertilizer treatment composition. It was possibleto correlate the different measurement systems at various stages in the process: leaf tissue,fruit tissue, or leachate, especially for Mn. EPs as measured by the Vivent BioSignals sensors112showed a clear diurnal pattern that demonstrated the technologyâs capacity for detecting andmeasuring real physiological signals. This demonstrates the potential for this system set-up formonitoring elemental status using agri-tech. A combination approach of sensors and analyticalmethod is ideal: it provides immediate insight and benefit to growers through sensors, withoutcompromising accuracy through the continued use of analytical methods. As well, there weresome issues which arose in these technologies, including programming errors and equipmentfailure. Validation and consistent auditing of system performance is necessary, and cannot beskipped. Future research incorporating root and shoot tissue would provide a more completepicture in changes in elements throughout the system.113Chapter 5: ConclusionsI hypothesised that crop production in CEA systems can be effectively optimised by usinginnovative analytical techniques and technologies to manage the abiotic factors of light andnutrition. I developed two complimentary studies with their own set of objectives.5.1 Experiment 1I hypothesised that different LED spectra would differentially influence Cannabis metabolismand secondary metabolite production, with clear metabolite profiles correlated to the spectraused. To investigate this hypothesis, I designed and executed an experiment using threedifferent growth chambers using differing LED spectra and grew Cannabis plantlets within.5.1.1 Objective 1The commercial method used to detect and quantify cannabinoids did not detect CBN, CBDA,or CBD, and only detected THC in few of the samples. THCA was detected and quantified inall samples, but did not show a response to any of the LED spectra. Future work should utiliseolder Cannabis which have been induced to flower, as the young, vegetative tissue used mayhave had THCA levels too small to detect significant differences.5.1.2 Objective 2Abscisic acid, tryptophan, and zeatin were quantified using a targeted metabolomic approach.Endogenous 6-benzylaminopurine, salicylic acid, indole-3-acetic acid, indole-3-butryic acid,jasmonic acid, melatonin, serotonin, and N6-isopentenyl- adenine were not detected in theCannabis cultures. There were no significant differences between the light treatments at the p< 0.05 level, but future work could focus on abscisic acid and zeatin given their significance atthe p < 0.10 level.5.1.3 Objective 3Untargeted metabolomic workflows revealed pathways associated with the shikimate,MEP/MVA, and other amino acid pathways as significantly affected by red and blue114spectra treatments. Gibberellins were differentially expressed between treatments,alongside phenylalanine and associated downstream biosynthesis/metabolism pathways forphenylpropanoids, flavones, flavonols, and flavonoids. Future work should target thesepathways for analysis through targeted metabolomics and a combination approach usinggenomes and proteomics, to confirm up or downregulation of these compounds.5.2 Experiment 2I hypothesised that plant nutrient uptake in Fragaria x ananassa Duchesne ex Rozier tissueis determined by the nutrient composition of the fertigation solution. To investigate thishypothesis, I developed a cultivation system integrating novel agri-tech sensors and integratedregular sampling with confirmation by ICP-OES.5.2.1 Objective 1My study demonstrated the successful set-up and integrated of agri-tech sensors to cultivatestrawberries in a greenhouse, as I was able to reliably produce weekly harvests of strawberrieswith minimal plant mortality and no major pathogen or pest infestation. The agri-tech sensorsused were connected to the wireless WiFi network within the greenhouse and did transmit theirdata to their associated cloud-based storage and dashboard. However, proper connection tothe WiFi network proved a major obstacle, so future research could consider circumventing theneed for the wireless connection and run the agri-tech offline only.5.2.2 Objective 2My study showed that agri-tech systems and conventional analytical methods can correlate forspecific elements within specific tissue types. However, it would be inappropriate to completelyrely on one or the other, given the diverging results of the agri-tech sensors and the delayedturnaround time for results generated by the analytical methods. Instead, future researchshould use a hybridised-approach, using agri-tech where possible and incorporate submittingroutine validation samples for conventional analysis. Further agri-tech sensors could be testedand validated for purposes using the same strawberry cultivation system from this study.1155.2.3 Objective 3Fertilizer treatments were successfully applied within the designed cultivation system and theirdifferences in elemental composition were confirmed through agri-tech sensors and ICP-OESanalytical methods. However, neither method showed consistent differences in nutrient uptakein leaves, fruits, substrate, or the leachate due to fertilizer treatment. Growers will need toreassess and validate their fertigation practices if they are relying on over-application to meettheir plantsâ nutrient needs.5.3 LimitationsBroadly, findings from both studies have limited ability to be generalised for field agricultureapplication, given the focus on, and execution within, CEA systems. Highly controlledconditions of growth chambers and greenhouses mitigates the impact of many of theenvironmental variables that crops grown in fields would experience, like temperature, soilcomposition, and pest pressure, and field crops are rarely fertigation in the same way thatthe strawberry plants of this research were. At-scale, economic consideration may limit theimplementation of technologies such as the agri-tech sensors or LED lighting. Additionally,both experiments investigated only single cultivars of two high-value specialty crops (Cannabisand strawberry), whereas other crop species may respond differently to the treatments utilisedin the studies.5.3.1 Experiment 1The first experiment faced several methodological constraints that limited the detection andinterpretation of spectral effects on Cannabis metabolism. The use of young, vegetativeplantlets rather than flowering plants restricted cannabinoid analysis, as THCA levels werepotentially too low to reveal significant spectral differencesÍ¾ further, the Dreamcatcher cultivarused in this study is primarily cultivated for THC rather than CBD, so potential impact ofspectra could not be explored on the latter. The use of in vitro plantlets necessitated a shorterexperimental duration, and the 7 day exposure to the spectra treatments was likely insufficientto promote acclimatization to the treatmentÍ¾ rather, findings may reflect more acute responsesin Cannabis. Additionally, this experiment investigated only spectral composition as a lightparameter, without examining its interactions with light intensity or duration.1165.3.2 Experiment 2The second experiment encountered significant challenges in both experimental designimplementation and technology integration that affected the reliability and interpretation ofresults. While the Youden Square DOE made it possible to test the high number of fertilizertreatments, the logistical implementation of treatments consecutively was compromised byautocorrelation, where the previous weekâs fertilizer treatment affected plant responses in thesubsequent week and that the strawberry plants matured over the course of the experiment.A standard nutrient mix was selected as the fertigation between treatments, whereas watercould have been selected to better flush the substrateÍ¾ however, the lack of nutrition beingprovided could have stalled plant growth. Beyond DOE limitations, the Vivent BioSignalsmanufactured-process data was only reported every 6 hours, providing a coarse temporalresolution that likely missed important short-term fluctuations in nutrient dynamics, plantphysiological responses, or environmental conditions. Similarly, collection of leaf or fruit tissuefor ICP-OES analysis was limited to once a week, so there was no opportunity to assessnutrient dynamics daily. The diverging results between agri-tech sensors and conventionalICP-OES analytical methods prevented definitive conclusions about actual plant nutrient status,especially without accompanied root or shoot tissue analysis.5.4 Future directionsMy research established a foundation for understanding how light spectra influence plantmetabolism and demonstrated both the potential and limitations of agri-tech sensors in nutrientmanagement. Building on these findings, there are several directions that future research couldfollow to continue the advancement of CEA optimization.5.4.1 Experiment 1My first study revealed significant potential for metabolic pathway manipulation through lightspectra, most notably in the shikimate, MEP/MVA, and amino acid biosynthetic pathwaysidentified through untargeted metabolomics. Future research should focus on validating thesediscoveries using a variety of -omics approaches, such as targeted metabolomics paired withtranscriptomics and proteomics to confirm whether the putative metabolite changes I detectedreflect up- or down-regulation of these biosynthetic pathways. Additionally, future research117could utilise flowering Cannabis plants, rather than young vegetative plantlets, to investigatecannabinoid responses using similar or modified spectral treatments to what was used in thisresearch. This would allow for the comparison of metabolic changes due to spectra beforeand after the transition into reproduction of the Cannabis plant. Integrating different aspects oflight, such as intensity or duration, could be studied by using dose treatments and investigatingdose-response relationships. This would allow for the discovery of optimal light intensitiesand photoperiods for each spectrum, as an all-in-one light recipe, to target specific or desiredmetabolic pathways.5.4.2 Experiment 2My second study demonstrated the complexity of translating analytical innovations into reliableoperational systems, revealing differences between agri-tech sensor readings and results fromconventional methods in tracking nutrient uptake patterns. Future research should focuson understanding the temporal dynamics of nutrient uptake by investigating when and whynutrient uptake variesÍ¾ this could be accomplished with more frequent sampling protocolscorrelated with specific plant growth stages and environmental conditions, as well as samplingadditional plant tissue (i.e. roots, stems). The sensor validation work should be expandedto test additional agri-tech sensors across different crop species, and focus on developingstandardised protocols for validating sensor accuracy against conventional analytical methodsunder various growing conditions. Given that nutrient uptake due to fertilizer treatments waslimited, future studies should investigate whether the issue lies in the timing, concentration, orchemical form of nutrient delivery beyond just nutrient composition. Future approaches coulddevelop custommachine learning tools that integrate data from these various analytical sourcesand provide predictions of optimal fertigation strategies for specific crop outcomes and growthenvironments.5.5 ConclusionBoth experiments demonstrated that innovative analytical techniques or technologies canprovide valuable insights for optimizing crop production in CEA. The untargeted metabolomicapproach proved particularly powerful, as the workflow successfully identified previouslyunknown metabolic responses to LED spectral treatments in CannabisÍ¾ significant differential118expression was identified in key biosynthetic pathways, including shikimate, MEP/MVA, andother amino acid metabolism. This discovery-based approach uncovered metabolic networksthat targeted methods were not able to detected nor anticipate, validating the hypothesis thatthese new analytical techniques have the capacity to guide CEA optimization beyond traditionalassumptions. However, the integration of advanced agri-tech sensors with conventionalanalytical methods revealed the complexity of translating analytical innovations into operationalsystems. While both sensor-based and conventional ICP-OESmethods could detect elementaldifferences in fertilizer treatments, neither consistently tracked nutrient uptake patterns, and theagri-tech sensors and irrigation system faced significant connectivity and reliability challenges.Rather than a whole replacement of established methods, this research does support athoughtful, hybrid approach for CEA optimization. Thus, future of CEA optimization lies instrategically-integrated analytical approaches that balance operational reliability.119BibliographyAasim, M., Yíldírím, B., Say, A., Ali, S. A., Aytaç, S., and Nadeem, M. A. (2024). Artificialintelligence models for validating and predicting the impact of chemical priming of hydrogenperoxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativaL.). Plant Molecular Biology, 114(33).Abdollahi, M., Sefidkon, F., Peirovi, A., Calagari, M., and Mousavi, A. (2021). Assessmentof the cannabinoid content from different varieties of Cannabis sativa L. during the growthstages in three regions. Chemistry & Biodiversity, 18:e2100247.Abedini, M., Iranbakhsh, A., Saadatmand, S., Ebadi, M., and Oraghi Ardebili, Z. (2024). LowUV radiation influenced DNA methylation, gene regulation, cell proliferation, viability, andbiochemical differentiation in the cell suspension cultures of Cannabis indica. Journal ofPhotochemistry and Photobiology B: Biology, 254:112902.Adal, A. M., Doshi, K., Holbrook, L., and Mahmoud, S. S. (2021). Comparative RNA-Seqanalysis reveals genes associated with masculinization in female Cannabis sativa. Planta,253(17):45674.Adu, M. O., Chatot, A., Wiesel, L., Bennett, M. J., Broadley, M. R., White, P. J., and Dupuy,L. X. (2014). A scanner system for high-resolution quantification of variation in root growthdynamics of Brassica rapa genotypes. Journal of Experimental Botany, 65:2039â2048.Afreen, F., Zobayed, S. M. A., and Kozai, T. (2006). Melatonin in Glycyrrhiza uralensis:Response of plant roots to spectral quality of light and UV-B radiation. Journal of PinealResearch, 41:108â115.Ahrens, A., Llewellyn, D., and Zheng, Y. (2023). Is twelve hours really the optimum photoperiodfor promoting flowering in indoor-grown cultivars of Cannabis sativa? Plants (Basel),12(14):2605.Al Murad, M., Razi, K., Jeong, B. R., Samy, P. M. A., and Muneer, S. (2021). Light emittingdiodes (LEDs) as agricultural lighting: Impact and its potential on improving physiology,flowering, and secondary metabolites of crops. Sustainability, 13(4):1985.120Ali, A., Zhao, T.-T., Feng, C.-L., Zhao, X.-T., Li, L., Wu, R.-J., Liu, H.-B., Huang, Q.-X., Lin,J.-S., and Wang, J.-G. (2025). Decoding the hormonal, genetic, and environmental signalsregulating leaf angle in plants. Plant Stress, 17(100918):1â17.Altamira-Algarra, B., Puigagut, J., Day, J. W., Mitsch, W. J., Vymazal, J., Hunter, R. G.,and García, J. (2022). A review of technologies for closing the P loop in agriculturerunoff: Contributing to the transition towards a circular economy. Ecological Engineering,177(106571).Alter, H., Sade, Y., Sood, A., Carmeli-Weissberg, M., Shaya, F., Kamenetsky-Goldstein, R.,Bernstein, N., and Spitzer-Rimon, B. (2024). Inflorescence development in female cannabisplants is mediated by photoperiod and gibberellin. Horticulture Research, 11:uhae245.Alves, F. R. R., Bianchetti, R. E., and Freschi, L. (2021). Hormones and Plant Response,chapter Light-Mediated Regulation of Plant Hormone Metabolism, pages 117â135. SpringerCham.Ameran, M. F. H., Abdullah, R., Ismail, N., Shafie, R., Omar, S., and Che Kar, S. A. (2024).Design and implementation of an IoT integrated dual sensors for hydroponic cultivation rootgrowth monitoring system. In 2024 IEEE International Conference on Automatic Control andIntelligent Systems (I2CACIS).Apicella, P. V., Sands, L. B., Ma, Y., and Berkowitz, G. A. (2022). Delineating genetic regulationof cannabinoid biosynthesis during female flower development in Cannabis sativa. PlantDirect, 6:e412.Armada-Moreira, A., Diacci, C., Dar, A. M., Berggren, M., Simon, D. T., and Stavrinidou,E. (2022). Benchmarking organic electrochemical transistors for plant electrophysiology.Frontiers in Plant Science, 13.Arora, A. S. and Yun, C. M. (2023). Dynamic spectrum lighting impact on plant morphology andcannabinoid profile of medical and recreational cannabis - A novel leapfrog strategy towardsshaping the future of horticultural lighting. Industrial Crops and Products, 199.Ashton, L. (2024). The greenhouse boom: How indoor farming can transform food productionand exports.121Atal, C. K. and Sethi, J. K. (1961). Increased fibre content in hemp (Cannabis sativa) and sunn(Crotalaria juncea) by application of gibberellins. Current Science, 30(5):177â179.Azad, M. O. K., Kim, W. W., Park, C. H., and Cho, D. H. (2018). Effect of artificial LED Lightand far infrared irradiation on phenolic compound, isoflavones and antioxidant capacity insoybean (Glycine max L.) sprout. Foods, 7(174):1â10.Bafort, F., Libault, A., Maron, E., Kohnen, S., Ancion, N., and Jijakli, M. H. (2024). Operationalcosts and analysis of agronomic characteristics on cannabidiol and cannabigerol hemp(Cannabis sativa L.) in hydroponic soilless greenhouse and field cultivation. Horticulturae,10:1271.Baiyin, B., Xiang, Y., Shao, Y., Son, J. E., Tagawa, K., Yamada, S., Yamada, M., and Yang,Q. (2025). How the nutrient flow environment promotes lettuce growth in hydroponics.Environmental and Experimental Botany, 233(106137):1â17.Basak, R. and Wahid, K. A. (2022). An in situ electrical impedance tomography sensor systemfor biomass estimation of tap roots. Plants, 11(13):1713.Basile, G. N., Tedone, L., Pulvento, C., De Mastro, G., and Ruta, C. (2023). Establishmentof an efficient in vitro propagation protocol for Cannabis sativa L. subsp. ruderalis Janish.Horticulturae, 9:1241.Bazzaz, F., Dusek, D., Seigler, D., and Haney, A. (1975). Photosynthesis and cannabinoidcontent of temperate and tropical populations of Cannabis sativa. Biochemical Systematicsand Ecology, 3:15â18.Behr, M., Lutts, S., Hausman, J.-F., andGuerriero, G. (2018). Jasmonic acid to boost secondarygrowth in hemp hypocotyl. Planta, 248:1029â1036.Beilby, M. J., Turi, C. E., Baker, T. C., Tymm, F. J. M., and Murch, S. J. (2015). Circadianchanges in endogenous concentrations of indole-3-acetic acid, melatonin, serotonin,abscisic acid and jasmonic acid in Characeae (Chara australis Brown). Plant Signaling andBehavior, 10(11):e1082697.Bell, R. (2023). Marschnerâs Mineral Nutrition of Plants (Fourth Edition), chapter Diagnosis andprediction of deficiency and toxicity of nutrients, pages 477â495. Academic Press.122Bhagat, P. K., Verma, D., Sharma, D., and Sinha, A. K. (2021). HY5 and ABI5 transcriptionfactors physically interact to fine tune light and ABA signaling in Arabidopsis. Plant MolecularBiology, 107:117â127.Bielach, A., Hrtyan, M., and Tognetti, V. B. (2017). Plants under stress: Involvement of auxinand cytokinin. International Journal of Molecular Sciences, 18(1427):1â29.Bilodeau, S. E., Wu, B.-S., Rufyikiri, A.-S., MacPherson, S., and Lefsrud, M. (2019). An updateon plant photobiology and implications for cannabis production. Frontiers in Plant Science,10.Bishop, S. L., Kerkovius, J. K., Menard, F., and Murch, S. J. (2018).N-Beta-Methylamino-L-Alanine and its naturally occurring isomers in cyanobacterialblooms in Lake Winnipeg. Neurotoxicity Research, 33:133â142.Boruah, M. P. (2025). Assessment of antioxidant and antibacterial properties of ethnomedicinalvegetables in North East India. Journal of Pharmaceutical and Medicinal Research,9:153â156.Bottoms, T. G., Bolda, M. P., Gaskell, M. L., and Hartz, T. K. (2013). Determination of strawberrynutrient optimum ranges through diagnosis and recommendation integrated system analysis.HortTechnology, 23:312â318.Bouvier, F., Rahier, A., and Camara, B. (2005). Biogenesis, molecular regulation and functionof plant isoprenoids. Progress in Lipid Research, 44:357â429.Boyd, A. P., Zankowski, P., Wheeler, R., Stokes-Draut, J. R., Chudnovsky, Y., Ingram, D.,Tijerina, M., Mickens, M., Steward, D., Armstrong, K., Boggs, A. S. P., Neely, B. A., Lunney,J. K., and Luo, Y. (2025). Controlled environment agriculture: An opportunity to strengtheninteragency research collaboration in the US government. PNAS Nexus, 4(6):pgaf155.Brewer, P. B., Koltai, H., and Beveridge, C. A. (2013). Diverse roles of strigolactones in plantdevelopment. Molecular Plant, 6:18â28.Brini, F., Mseddi, K., Brestic, M., and Landi, M. (2022). Hormone-mediated plant responses tolight quality and quantity. Environmental and Experimental Botany, 202(105026).123Brunetti, C., Fini, A., Sebastiani, F., Gori, A., and Tattini, M. (2018). Modulation ofphytohormone signaling: A primary function of flavonoids in plantâenvironment interactions.Frontiers in Plant Science, 9(1042):8.Burgel, L., Hartung, J., Schibano, D., and Graeff-Hönninger, S. (2020). Impact of differentphytohormones on morphology, yield and cannabinoid content of Cannabis sativa L. Plants,9(6):725.Burgess, A. J. (2023). Breath of green life: Reduction in plant day and night respiration underelevated CO2. Plant Physiology, 192(1):31â33.Caldwell, M. M. (1971). Chapter 4 - Solar UV irradiation and the growth and development ofhigher plants. In Giese, A. C., editor, Photophysiology, pages 131â177. Academic Press.Campbell, C. R. and Miner, G. S. (2000). Reference Sufficiency Ranges for Plant Analysis inthe Southern Region of the United States, chapter Strawberry - Annual Hill Culture, pages111â112. North Carolina Department of Agriculture and Consumer Services AgronomicDivision.Campbell, L. G., Naraine, S. G. U., and Dusfresne, J. (2019). Phenotypic plasticity influencesthe success of clonal propagation in industrial pharmaceutical Cannabis sativa. PLoS ONE,14(3):e0213434.Caplan, D., Stemeroff, J., Dixon, M., and Zheng, Y. (2018). Vegetative propagation of cannabisby stem cuttings: Effects of leaf number, cutting position, rooting hormone, and leaf tipremoval. Canadian Journal of Plant Science, 98(5):1126â1132.Carabelli, M., Possenti, M., Sessa, G., Ciolfi, A., Sassi, M., Morelli, G., and Ruberti, I. (2007).Canopy shade causes a rapid and transient arrest in leaf development through auxin-inducedcytokinin oxidase activity. Genes and Development, 21:1863â1868.Carillo, P., Parisi, D., Woodrow, P., Pontecorvo, G., Massaro, G., Annunziata, M. G., Fuggi,A., and Sulpice, R. (2011). Salt-induced accumulation of glycine betaine is inhibited by highlight in durum wheat. Functional Plant Biology, 38(2):139â150.Cerrudo, I., Keller, M. M., Cargnel, M. D., Demkura, P. V., de Wit, M., Patitucci, M. S., Pierik,R., Pieterse, C. M. J., and Ballaré, C. L. (2012). Low red/far-red ratios reduce Arabidopsis124resistance to Botrytis cinerea and jasmonate responses via a COI1-JAZ10-dependent,salicylic acid-independent mechanism. Plant Physiology, 158:2042â2052.Chandok, M. R. and Sopory, S. K. (1994). 5-Hydroxytryptamine affects turnover ofpolyphosphoinositides in maize and stimulates nitrate reductase in the absence of light.FEBS Letters, 356(14826):39â42.Chen, H.-J., Fu, T.-Y., Yang, S.-L., and Hsieh, H.-L. (2018). IN219/JAR1 and cryptochrome1antagonize each other to modulate photomorphogenesis under blue light in Arabidopsis.PLoS Genetics, 14(3):e1007248.Cheng, X., Wang, R., Liu, X., Zhou, L., Dong, M., Rehman, M., Fahad, S., Liu, L., and Deng, G.(2022). Effects of light spectra on morphology, gaseous exchange, and antioxidant capacityof industrial hemp. Frontiers in Plant Science, 13.Cheng, X., Zhao, K., He, X., Zheng, H., Wang, R., Liu, C., Zhou, L., Fahad, S., and Deng, G.(2023). Differential protein response to different light quality conditions of industrial hempcultivation based on DIA technology. Industrial Crops and Products, 197(116650).Clarke, R. C. and Merlin, M. D. (2017). Cannabis domestication, breeding history, present-daygenetic diversity, and future prospects. Critical Reviews in Plant Sciences, 35:293â327.Codesido, V., Agui, L., Sánchez-Ramírez, J., Valencia Castellano, J. L., and Sancho, A.(2023). Light supplementation on Cannabis sativa greenhouses: What is the reality? ActaHorticulturae, 1358:311â318.Cohen, A. R., Chen, G., Berger, E. M., Warrier, S., Lan, G., Grubert, E., Dellaert, F., and Chen,Y. (2022). Dynamically controlled environment agriculture: Integrating machine learningand mechanistic and physiological models for sustainable food cultivation. ACS ES&TEngineering, 2(1):3â19.Collado, C. E., Hwang, S. J., and Hernández, R. (2024). Supplemental greenhouse lightingincreased the water use efficiency, crop growth, and cutting production in Cannabis sativa.Frontiers in Plant Science, 15.Cortleven, A., Nitschke, S., Klaumünzer, M., AbdElgawad, H., Asard, H., Grimm, B., Riefler, M.,and Schmülling, T. (2014). A novel protective function for cytokinin in the light stress response125is mediated by the ARABIDOPSIS HISTIDINE KINASE2 and ARABIDOPSIS HISTIDINEKINASE3 receptors. Plant Physiology, 164:1470â1483.Coste, S., Baraloto, C., Leroy, C., Marcon, E., Renaud, A., Richardson, A. D., Roggy, J.-C.,Schimann, H., Uddling, J., and Hérault, B. (2010). Assessing foliar chlorophyll contentswith the SPAD-502 chlorophyll meter: A calibration test with thirteen tree species of tropicalrainforest in French Guiana. Annals of Forest Science, 67(607).Costine, B., Zhang, M., Pearson, B., and Nadakuduti, S. S. (2022). Impact of blue light on plantgrowth, flowering and accumulation of medicinal flavones in Scutellaria baicalensis and S.lateriflora. Horticulturae, 8(1141):1â10.Cowan, N., White, S., Olszewska, J., Dobel, A., Sim, G., Eades, L. J., and Skiba, U. (2022).Integration of algae treatment with hydroponic crop waste to reduce impact of nutrient wastestreams. Journal of Sustainable Agriculture and Environment, 1:203â215.DâAgostino, I. B., Deruère, J., and Kieber, J. J. (2000). Characterization of the response of theArabidopsis response regulator gene family to cytokinin. Plant Physiology, 124:1706â1717.Dai, Y., Li, M., Li, H., Xia, L., and Zhang, S. (2024). Phytohormone-regulatedUV-B photomorphogenesis and acclimation. Environmental and Experimental Botany,224(105830).Danziger, N. and Bernstein, N. (2021). Light matters: Effect of light spectra on cannabinoidprofile and plant development of medical cannabis (Cannabis sativa L.). Industrial Cropsand Products, 164.Davis, P. A. and Burns, C. (2016). Photobiology in protected horticulture. Food and EnergySecurity, 5(4):223â238.De Pauw, M. A., Vidmar, J. J., Collins, J., Bennett, R. A., andDeyholos, M. K. (2007). Microarrayanalysis of bast fibre producing tissues of Cannabis sativa identifies transcripts associatedwith conserved and specialised processes of secondary wall development. Functional PlantBiology, 34(8):737â749.Dimopoulos, N., Guo, Q., Liu, L., Nolan, M., Das, R., Garcia-de Heer, L., Mieog, J. C., Barkla,B. J., and Kretzschmar, T. (2025). An in vitro phytohormone survey reveals concertedregulation of the cannabis glandular trichome disc cell proteome. Plants, 14(5):694.126Dixon, E., Strik, B., Wasko DeVetter, L., Chernoh, E., Ocana Gallegos, C., andFernandez-Salvador, J. (2023). Strawberry nutrient management guide for Oregon andWashington.Dobisova, T., Hrdinova, V., Cuesta, C., Michlickova, S., Urbankova, I., Hejatkova, R.,Zadnikova, P., Pernisova, M., Benkova, E., and Hejatko, J. (2017). Light controls cytokininsignaling via transcriptional regulation of constitutively active sensor histidine kinase CKI1.Plant Physiology, 174:387â404.Dong, H., Liu, J., He, G., Liu, P., and Sun, J. (2020). Photoexcited phytochrome B interactswith brassinazole resistant 1 to repress brassinosteroid signaling in Arabidopsis. Journal ofIntegrative Plant Biology, 62:652â667.Dong, N.-Q. and Lin, H.-X. (2021). Contribution of phenylpropanoid metabolism to plantdevelopment and plantâenvironment interactions. Journal of Integrative Plant Biology,63:180â209.Dsouza, A., Newman, L., Graham, T., and Fraser, E. D. G. (2023). Exploring the landscapeof controlled environment agriculture research: A systematic scoping review of trends andtopics. Agricultural Systems, 209(103673):1â13.Du, G., Zhang, H., Yang, Y., Zhao, Y., Tang, K., and Liu, F. (2022). Effects of gibberellinpre-treatment on seed germination and seedling physiology characteristics in industrial hempunder drought stress condition. Life, 12(11):1907.Eaves, J., Eaves, S., Morphy, C., and Murray, C. (2020). The relationship between lightintensity, cannabis yields, and profitability. Agronomy Journal, 112(2):1466â1470.Elgrishi, N., Rountree, K. J., McCarthy, B. D., Rountree, E. S., Eisenhart, T. T., and Dempsey,J. L. (2017). A practical beginnerâs guide to cyclic voltammetry. Journal of ChemicalEducation, 2018(95):197â206.Epstein, E. (1965). Plant Biochemistry, chapter Mineral metabolism, pages 438â466.Academic Press.Erland, L. A. E., Giebelhaus, R. T., Victor, J. M. R., Murch, S. J., and Saxena, P. K. (2020a).The morphoregulatory role of thidiazuron: Metabolomics-guided hypothesis generation formechanisms of activity. Biomolecules, 10(9):1253.127Erland, L. A. E., Turi, C. E., Saxena, P. K., and Murch, S. J. (2020b). Metabolomics andhormonomics to crack the code of filbert growth. Metabolomics, 16(5):62.Espinosa-Calderon, A., Torres-Pacheco, I., Padilla-Medina, J. A., Osornio-Rios, R. A.,Romero-Troncoso, R. d. J., Villaseñor-Mora, C., Rico-Garcia, E., and Guevara-Gonzalez,R. G. (2011). Description of photosynthesis measurement methods in green plants involvingoptical techniques, advantages and limitations. African Journal of Agricultural Research,6(12):2638â2647.Farmer, E. E., Gao, Y.-Q., Lenzoni, G., Wolfender, J.-L., and Wu, Q. (2020). Wound-and mechanostimulated electrical signals control hormone responses. New Phytologist,227:1037â1050.Farrelly, K. N., Wardell, J. D., Marsden, E., Scarfe, M. L., Najdzionek, P., Turna, J., andMacKillop, J. (2023). The impact of recreational cannabis legalization on cannabis use andassociated outcomes: A systematic review. Substance Abuse: Research and Treatment,17.Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., and Willmitzer, L. (2000).Metabolite profiling for plant functional genomics. Nature Biotechnology, 18(11):1157â1161.Fiehn, O., Robertson, D., Griffin, J., van der Werf, M., Nikolau, B., Morrison, N., Sumner, L. W.,Goodacre, R., Hardy, N. W., Taylor, C., Fostel, J., Kristal, B., Kaddurah-Daouk, R., Mendes,P., van Ommen, B., Lindon, J. C., and Sansone, S.-A. (2007). The metabolomics standardsinitiative (MSI). Metabolomics, 3:175â178.Finkelstein, R. R., Gampala, S. S. L., and Rock, C. D. (2002). Abscisic acid signaling in seedsand seedlings. The Plant Cell, 14:S15â45.Finlayson, S. A., Krishnareddy, S. R., Kebrom, T. H., and Casal, J. J. (2010). Phytochromeregulation of branching in Arabidopsis. Plant Physiology, 152:1914â1927.Fiorani, F. and Schurr, U. (2013). Future scenarios for plant phenotyping. Annual Review ofPlant Biology, 64:267â291.Foo, E., Ross, J. J., Davies, N. W., Reid, J. B., andWeller, J. L. (2006). A role for ethylene in thephytochrome-mediated control of vegetative development. The Plant Journal, 46:911â921.128Frender, K. (2020). Lighting strategies for higher terpene and THC content in cannabis.Fricke, W. (2019). Night-time transpiration â Favouring growth? Plant Science, 24:311â317.Fromm, J. and Fei, H. (1998). Electrical signaling and gas exchange in maize plants of dryingsoil. Plant Science, 132:203â213.Fromm, J. and Lautner, S. (2006). Electrical signals and their physiological significance inplants. Plant, Cell & Environment, 30:249â257.Gallego-Giraldo, L., García-Martínez, J. L., Moritz, T., and López-Díaz, I. (2007). Flowering intobacco needs gibberellins but is not promoted by the levels of active GA) and GA4 in theapical shoot. Plant and Cell Physiology, 48:615â625.Galoch, E. (1978). The hormonal control of sex differentiation in dioecious plants of hemp(Cannabis sativd)). The influence of plant growth regulators on sex expression in male andfemale plants. Acta Societatis Botanicorum Poloniae, 47:153â162. )The published articleincludes this typo.Gao, C., Cheng, C., Zhao, L., Yu, Y., Tang, Q., Xin, P., Liu, T., Yan, Z., Guo, Y., and Zang,G. (2018). Genome-wide expression profiles of hemp (Cannabis sativa L.) in response todrought stress. International Journal of Genomics, 2018(3057272):45670.Garrido, J., Rico, S., Corral, C., Sánchez, C., Vidal, N., Martínez-Quesada, J. J., andFerreiro-Vera, C. (2022). Exogenous application of stress-related signaling molecules affectgrowth and cannabinoid accumulation in medical cannabis (Cannabis sativa L.). Frontiersin Plant Science, 13.Giebelhaus, R. T., Biggs, L., Murch, S. J., and Erland, L. A. E. (2023). Untargeted and targetedmetabolomics to understand plant growth regulation and evolution in Wollemi pine (Wollemianobilis). Botany, 101(9):377â390.Giebelhaus, R. T., Erland, L. A. E., and Murch, S. J. (2024). HormonomicsDB: A novel workflowfor the untargeted analysis of plant growth regulators and hormones [version 2Í¾ peer review:2 approved]. F1000Research, 11:1191.Gil, P. M., Gurovich, L., and Schaffer, B. (2008). The electrical response of fruit trees to soilwater availability and diurnal light-dark cycles. Plant Signaling & Behavior, 3(11):1026â1029.129Glaros, A., Marquis, S., Major, C., Quarshie, P., Ashton, L., Green, A. G., Kc, K. B., Newman,L., Newell, R., Yada, R. Y., and Fraser, E. D. G. (2022). Horizon scanning and review of theimpact of five food and food production models for the global food system in 2050. Trendsin Food Science & Technology, 119:550â564.Goeschl, J. D., Pratt, H. K., and Bonner, B. A. (1967). An effect of light on the production ofethylene and the growth of the plumular portion of etiolated pea seedlings. Plant Physiology,42:1077â1080.Goh, C.-H., Ko, S.-M., Koh, S., Kim, Y.-J., and Bae, H.-J. (2012). Photosynthesis andenvironments: Photoinhibition and repair mechanisms in plants. Journal of Plant Biology,55:93â101.Golldack, D., Li, C., Mohan, H., and Probst, N. (2013). Gibberellins and abscisic acidsignal crosstalk: Living and developing under unfavorable conditions. Plant Cell Reports,32:1007â1016.Griffiths, M. and York, L. M. (2020). Targeting root ion uptake kinetics to increase plantproductivity and nutrient use efficiency. Plant Physiology, 182:1854â1868.Gupta, S. (2005). Encyclopedia of Biostatistics, chapter Youden squares and row-columndesigns.Guzmán, P. and Ecker, J. R. (1990). Exploiting the triple response of Arabidopsis to identifyethylene-related mutants. The Plant Cell, 2(6):513â523.Hahm, S., Lee, Y., Lee, K., and Park, J. (2024). Optimization of cannabinoid production inhemp through methyl jasmonate application in a vertical farming system. Horticulturae,10(11):1165.Hall, J., Bhattarai, S. P., and Midmore, D. J. (2013). The effects of different sowing times onmaturity rates, biomass, and plant growth of industrial fiber hemp. Journal of Natural Fibers,10:40â50.Han, R., Lin, R., Zhou, Y., and Thomas, H. R. (2025). Here comes the sun: Integration of light,temperature, and auxin during herbaceous plant grafting. Planta, 261(124):1â20.Hansen, J. (2024). 2024 State of the Cannabis lighting market.130Hao, Z., Li, W., and Hao, X. (2020). Variations of electric potential in the xylem of tree trunksassociated with water content rhythms. Journal of Experimental Botany, 72:1321â1335.Harkey, A. F., Yoon, G. M., Seo, D. H., DeLong, A., and Muday, G. K. (2019). Light modulatesethylene synthesis, signaling, and downstream transcriptional networks to control plantdevelopment. Frontiers in Plant Science, 10.Hawley, D., Graham, T., Stasiak, M., and Dixon, M. (2018). Improving cannabis bud qualityand yield with subcanopy lighting. HortScience, 53(11):1593â1599.Hayashi, M. and Kinoshita, T. (2011). Crosstalk between blue-light- and ABA-signalingpathways in stomatal guard cells. Plant Signaling & Behavior, 6(11):1662â1664.He, G., Liu, J., Dong, H., and Sun, J. (2019). The blue-light receptor CRY1 interacts with BZR1and BIN2 to modulate the phosphorylation and nuclear function of BZR1 in repressing BRsignaling in Arabidopsis. Molecular Plant, 12:689â703.He, J., Xin, P., Ma, X., Chu, J., and Wang, G. (2020). Gibberellin metabolism in floweringplants: An update and perspectives. Frontiers in Plant Science, 11.Heinze, J. (1984). Cyclic voltammetry - âElectrochemical spectroscopyâ. Angewandte Chemie- International Edition in English, 23(11):831â918.Hesami, M., Pepe, M., de Ronne, M., Yoosefzadeh Najafabadi, M., Adamek, K., Torkamaneh,D., and Jones, A. M. P. (2024). Cannabis leaf arrangement: Transcriptome insights intoCannabis sativa phyllotactic regulation. Plant Physiology Reports, 29:488â498.Heslop-Harrison, J. (1956). Auxin and sexuality in Cannabis sativa. Physiologia Plantarum,9:588â597.Higuchi, Y. and Hisamatsu, T. (2016). LED lighting for urban agriculture, chapter Light Actsas a Signal for Regulation of Growth and Development, pages 57â73. Springer Singapore,Singapore.Hirose, F., Inagaki, N., Hanada, A., Yamaguchi, S., Kamiya, Y., Miyao, A., Hirochika, H., andTakano, M. (2012). Cryptochrome and phytochrome cooperatively but independently reduceactive gibberellin content in rice seedlings under light irradiation. Plant & Cell Physiology,53:1570â1582.131Holalu, S. V. and Finlayson, S. A. (2017). The ratio of red light to far red light alters Arabidopsisaxillary bud growth and abscisic acid signalling before stem auxin changes. Journal ofExperimental Botany, 68:943â952.Holalu, S. V., Reddy, S. K., and Finlayson, S. A. (2021). Low red light:far red light inhibitsbranching by promoting auxin signaling. Journal of Plant Growth Regulation, 40:2028â2036.Holweg, M. M. S. F., Kaiser, E., Kappers, I. F., Heuvelink, E., and Marcelis, L. F. M. (2024).The role of red and white light in optimizing growth and accumulation of plant specializedmetabolites at two light intensities in medical cannabis (Cannabis sativa L.). Frontiers inPlant Science, 15.Hou, C., Ning, K., Wei, X., Cheng, Y., Yu, H., Yu, H., Liu, X., and Dong, L. (2023).Identification and expression analysis of abscisic acid signal transduction genes in hempseeds. Phyton-International Journal of Experimental Botany, 92(7):2087â2103.Hu, K., Loiseau, B., Carrière, S. D., Lesparre, N., Champollion, C., Martin-StPaul, N. K.,Linde, N., and Jougnot, D. (2025). Self-potential signals related to tree transpiration in aMediterranean climate. Hydrology and Earth System Sciences, 29:2997â3018.Huang, W., Ratkowsky, D. A., Hui, C., Wang, P., Su, J., and Shi, P. (2019). Leaf freshweight versus dry weight: Which is better for describing the scaling relationship betweenleaf biomass and leaf area for broad-leaved plants? Forests, 10(3):256.Huang, X., Chen, W., Zhao, Y., Chen, J., Ouyang, Y., Li, M., Gu, Y., Wu, Q., Cai, S.,Guo, F., Zhu, P., Ao, D., You, S., Vasseur, L., and Liu, Y. (2024). Deep learning-basedquantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandulartrichome formation pathway in Cannabis sativa. The Plant Journal, 118:1155â1173.Huneke, M., Vinodrai, T., and Hall, H. M. (2024). Crunching the numbers: A snapshotof Canadaâs agricultural technology landscape. Technical report, University of Waterloo,Faculty of Environment.Hutchinson, G. K., Nguyen, L. X., Rubio Ames, Z., Nemali, K., and Ferrarezi, R. S.(2024). Sensor-controlled fertigation management for higher yield and quality in greenhousehydroponic strawberries. Frontiers in Plant Science, 15.132Hwang, O. J., Kang, K., and Back, K. (2020). Effects of light quality and phytochrome form onmelatonin biosynthesis in rice. Biomolecules, 10(4):523.Ioannidis, K., Dadiotis, E., Mitsis, V., Melliou, E., and Magiatis, P. (2020). Biotechnologicalapproaches on two high CBD and CBG Cannabis sativa L. (Cannabaceae) varieties: in vitroregeneration and phytochemical consistency evaluation of micropropagated plants usingquantitative 1H-NMR. Molecules, 25(24):5928.Ippel, J. H., Pouvreau, L., Kroef, T., Gruppen, H., Versteeg, G., van den Putten, P., Struik, P. C.,and van Mierlo, C. P. M. (2004). In vivo uniform )5N-isotope labelling of plants: Using thegreenhouse for structural proteomics. Proteomics, 4:226â234.Islam, M. J., Ryu, B. R., Azad, M. O. K., Rahman, M. H., Cheong, E. J., Lim, J.-D., and Lim,Y.-S. (2021a). Cannabinoids accumulation in hemp (Cannabis sativa L.) plants under LEDlight spectra and their discrete role as a stress marker. Biology, 10(8):710.Islam, M. J., Ryu, B. R., Azad, M. O. K., Rahman, M. H., Rana, M. S., Kang, C.-W., Lim,J.-D., and Lim, Y.-S. (2021b). Comparative growth, photosynthetic pigments, and osmolytesanalysis of hemp (Cannabis sativa L.) seedlings under an aeroponics system with differentLED light sources. Horticulturae, 7(8):239.Islam, M. J., Ryu, B. R., Rana, M. S., Cheong, E. J., Wang, M.-H., Lim, J.-D., Hossain,M. A., and Lim, Y.-S. (2022a). Cannabinoid accumulation in hemp depends on ROSgeneration and interlinked with morpho-physiological acclimation and plasticity under indoorLED environment. Frontiers in Plant Science, 13.Islam, M. M., Rengel, Z., Storer, P., Siddique, K. H. M., and Solaiman, Z. M. (2022b). Industrialhemp (Cannabis sativa L.) varieties and seed pre-treatments affect seed germination andearly growth of seedlings. Agronomy, 12(1):6.Jalali, S., Salami, S. A., Sharifi, M., and Sohrabi, S. (2019). Signaling compounds elicitexpression of key genes in cannabinoid pathway and related metabolites in cannabis.Industrial Crops and Products, 133:105â110.Janssen, B. J. and Snowden, K. C. (2012). Strigolactone and karrikin signal perception:Receptors, enzymes, or both? Frontiers in Plant Science, 3.133Jenkins, M. W. (2021). Cannabis sativa L. response to narrow bandwidth UV and thecombination of blue and red light during the final stages of flowering on leaf levelgas-exchange parameters, secondary metabolite production, and yield. AgriculturalSciences, 12:1414â1432.Jenkins, M.W. and Livesay, C. B. (2021). Photosynthetic performance and potency ofCannabissativa L. grown under LED and HPS illumination. Agricultural Sciences, 12:293â304.Jia, K.-P., Luo, Q., He, S.-B., Lu, X.-D., and Yang, H.-Q. (2014). Strigolactone-regulatedhypocotyl elongation is dependent on cryptochrome and phytochrome signaling pathwaysin Arabidopsis. Molecular Plant, 7:528â540.Jiang, S., Wang, F., Shen, L., Liao, G., and Wang, L. (2017). Extracting sensitive spectrumbands of rapeseed using multiscale multifractal detrended fluctuation analysis. Journal ofApplied Physics, 121(104702):1â13.Jiao, Y., Kilmartin, P. A., Fan, M., and Quek, S. Y. (2018). Assessment of phenolic contributorsto antioxidant activity of new kiwifruit cultivars using cyclic voltammetry combined with HPLC.Food Chemistry, 268:77â85.Jin, D., Dai, K., Xie, Z., and Chen, J. (2020). Secondary metabolites profiled in cannabisinflorescences, leaves, stem barks, and roots for medicinal purposes. Scientific Reports,10(3309):1â14.Johnson, J. K. and Colby, A. (2023). History of cannabis regulation and medicinal therapeutics:Itâs complicated. Clinical Therapeutics, 45:521â526.Kalcsits, L. A. and Guy, R. D. (2015). Variation in fluxes estimated from nitrogenisotope discrimination corresponds with independent measures of nitrogen flux in Populusbalsamifera L. Plant, Cell & Environment, 39:310â319.Karthick, P. V., Senthil, A., Djanaguiraman, M., Anitha, K., Kuttimani, R., Boominathan, P.,Karthikeyan, R., and Raveendran, M. (2024). Improving crop yield through increasing carbongain and reducing carbon loss. Plants, 13(10):1317.Khanna, R., Shen, Y., Marion, C. M., Tsuchisaka, A., Theologis, A., Schäfer, E., and Quail, P. H.(2007). The basic helix-loop-helix transcription factor PIF5 acts on ethylene biosynthesis andphytochrome signaling by distinct mechanisms. The Plant Cell, 19:3915â3929.134Kim, D.-W., Rakwal, R., Agrawal, G. K., Jung, Y.-H., Shibato, J., Jwa, N.-S., Iwahashi, Y.,Iwahashi, H., Kim, D. H., Shim, I.-S., and Usui, K. (2005). A hydroponic rice seedlingculture model system for investigating proteome of salt stress in rice leaf. Electrophoresis,26:4521â4539.Kim, J. Y., Park, Y.-J., Lee, J.-H., and Park, C.-M. (2022). SMAX1 integrates karrikin and lightsignals into GA-mediated hypocotyl growth during seedling establishment. Plant and CellPhysiology, 63:932â943.King, R. W., Moritz, T., Evans, L. T., Martin, J., Andersen, C. H., Blundell, C., Kardailsky, I., andChandler, P. M. (2006). Regulation of flowering in the long-day grass Lolium temulentum bygibberellins and the FLOWERING LOCUS T gene. Plant Physiology, 141:498â507.KoláÅ, J., Johnson, C. H., and MacháÄková, I. (2003). Exogenously applied melatonin(N-acetyl-5-methoxytryptamine) affects flowering of the short-day plant Chenopodiumrubrum. Physiologia Plantarum, 118:605â612.Kong, Y. and Zheng, Y. (2024). Magic blue light: A versatile mediator of plant elongation.Plants, 13:115.Konvalina, P., Neumann, J., Hoang, T. N., Bernas, J., Trojan, V., KuchaÅ, M., Lošák, T., andVarga, L. (2024). Effect of light intensity and two different nutrient solutions on the yield offlowers and cannabinoids in Cannabis sativa L. grown in controlled environment. Agronomy,14(12):2960.Kos, E., Potere, D., Child, H., and Conant, H. (2024). What 1,000 farmers told us about techadoption.Kostanda, E. and Khatib, S. (2022). Biotic stress caused by Tetranychus urticae mites elevatesthe quantity of secondary metabolites, cannabinoids and terpenes, in Cannabis sativa L.Industrial Crops and Products, 176(114331):1â9.Kotiranta, S., Pihlava, J.-M., Kotilainen, T., and Palonen, P. (2024). The morphology,inflorescence yield, and secondary metabolite accumulation in hemp type Cannabis sativacan be influenced by the R:FR ratio or the amount of short wavelength radiation in aspectrum. Industrial Crops and Products, 208(117772).135Kotiranta, S., Sarka, A., Kotilainen, T., and Palonen, P. (2025). Decreasing R:FR ratio in agrow light spectrum increases inflorescence yield but decreases plant specialized metaboliteconcentrations in Cannabis sativa. Environmental and Experimental Botany, 229:106059.Kula-Maximenko, M., Niewiadomska, E., Maksymowicz, A., Ostrowska, A., Oklestkova, J.,PÄnÄík, A., and Janeczko, A. (2021). Insight into details of the photosynthetic light reactionsand selected metabolic changes in tomato seedlings growing under various light spectra.International Journal of Molecular Sciences, 22(21):11517.Kulikowska-Gulewska, H. and Kopcewicz, J. (2002). The involvement of gibberellins inphytochrome-controlled flowering of Pharbitis nil. Acta Societatis Botanicorum Poloniae,71(1):23â27.Kumar, P., Mahato, D. K., Kamle, M., Borah, R., Sharma, B., Pandhi, S., Tripathi, V., Yadav,H. S., Devi, S., Patil, U., Xiao, J., and Mishra, A. K. (2021). Pharmacological properties,therapeutic potential, and legal status of Cannabis sativa L.: An overview. PhytotherapyResearch, 35(11):6010â6029.Kumari, A., Kumar, V., Ovadia, R., and Oren-Shamir, M. (2023). Phenylalanine in motion: Atale of an essential molecule with many faces. Biotechnology Advances, 68(108246).Kurenda, A., Jenni, D., Lecci, S., and Buchholz, A. (2025). Bringing light into the darkâplantelectrophysiological monitoring of root knot nematode infestation and real-time nematicideefficacy. Journal of Pest Science, 98:493â507.Kurepin, L. V., Walton, L. J., Reid, D. M., and Chinnappa, C. C. (2010). Light regulation ofendogenous salicylic acid levels in hypocotyls of Helianthus annuus seedlings. Botany,88(7):668â674.Lakshmanan, M., Lim, S.-H., Mohanty, B., Kim, J. K., Ha, S.-H., and Lee, D.-Y. (2015).Unraveling the light-specific metabolic and regulatory signatures of rice through combined insilico modeling and multiomics analysis. Plant Physiology, 169(4):3002â3020.Lam, V. P., Hernandez, R., Lee, J., Kim, S. J., and Park, J. (2022). Generation of adventitiousroots and characteristics of gas exchange according to leaf number of hemp (Cannabis sativaL.) cuttings. Korean Journal of Horticultural Science and Technology, 40(1):30â38.136Langa, S., Magwaza, L. S., Mditshwa, A., and Tesfay, S. Z. (2024). Seed dormancy andgermination responses of cannabis landraces to various pre-treatments. South AfricanJournal of Botany, 165:91â100.Langenfeld, N. J. and Bugbee, B. (2021). Evaluation of three electrochemical dissolved oxygenmeters. HortTechnology, 31(4):428â431.Langridge, P. (2022). Wheat Improvement, chapter Micronutrient toxicity and deficiency, pages433â449. Springer Cham.Lanoue, J., Hao, X., VaÅ¡takaitÄ-KairienÄ, V., and Marcelis, L. F. M. (2024). Editorial:Physiological growth responses to light in controlled environment agriculture. Frontiers inPlant Science, 15.Lata, H., Chandra, S., Techen, N., Khan, I. A., and ElSohly, M. A. (2016). In vitro masspropagation of Cannabis sativa L.: A protocol refinement using novel aromatic cytokininmeta-topolin and the assessment of eco-physiological, biochemical and genetic fidelity ofmicropropagated plants. Journal of Applied Research on Medicinal and Aromatic Plants,3:18â26.Lau, O. S. and Deng, X. W. (2010). Plant hormone signaling lightens up: Integrators of lightand hormones. Current Opinion in Plant Biology, 13:571â577.Lazar, D., Murch, S. J., Beilby, M. J., and Al Khazaaly, S. (2013). Exogenous melatonin affectsphotosynthesis in characeae Chara australis. Plant Signaling & Behavior, 8(e23279):1â5.Lee, I., Choi, S., Lee, S., and Soh, M.-S. (2019). KAI2-KL signaling intersects withlight-signaling for photomorphogenesis. Plant Signaling & Behavior, 14(4):e1588660.Leister, D. (2023). Photobiology: Introduction, overview and challenges. Frontiers inPhotobiology, 1.Leonard, W., Zhang, P., Ying, D., and Fang, Z. (2020). Tyramine-derived hydroxycinnamic acidamides in plant foods: Sources, synthesis, health effects and potential applications in foodindustry. Critical Reviews in Food Science and Nutrition, 62:1608â1625.Li, J.-H., Fan, L.-F., Zhao, D.-J., Zhou, Q., Yao, J.-P., Wang, Z.-Y., and Huang, L. (2021). Plantelectrical signals: A multidisciplinary challenge. Journal of Plant Physiology, 261(153418).137Li, K., Yu, R., Fan, L.-M., Wei, N., Chen, H., and Deng, X. W. (2016). DELLA-mediatedPIF degradation contributes to coordination of light and gibberellin signalling in Arabidopsis.Nature Communications, 7(11868).Li, M., Ouyang, Y., Cai, S., Zhang, Z., Wu, Q., Li, S., Huang, X., Huang, S., Ao, D., You, S.,Cai, J., Vasseur, L., Lin, C., and Liu, Y. (2025). A new UV-B protectant in plant: Insights intoTHCAS and THCAâs role in UV-B tolerance. Plant, Cell & Environment, pages 1â14.Li, Q. and Yan, J. (2020). Sustainable agriculture in the era of omics: Knowledge-driven cropbreeding. Genome Biology, 21(154).Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., Jones, D. P., andPulendran, B. (2013). Predicting network activity from high throughput metabolomics. PLoSComputational Biology, 9(7):e1003123.Li, S., Ran, S., Yuan, S., Chang, K., Han, M., and Zhong, F. (2024). Gibberellin-mediatedfar-red light-induced leaf expansion in cucumber seedlings. Protoplasma, 261:571â579.Li, Y., Zhou, B., Teng, Z., Zhang, M., Yu, L., Luo, Y., Chen, P., and Sun, J. (2023).Improved metabolomic approach for evaluation of phytochemicals in mustard, kale, andbroccoli microgreens under different controlled environment agriculture conditions. Journalof Agriculture and Food Research, 14(100719).Li, Z.-G., Chen, K.-X., Xie, H.-Y., and Gao, J.-R. (2009). Quantitative structure-propertyrelationship studies on amino acid conjugates of jasmonic acid as defense signalingmolecules. Journal of Integrative Plant Biology, 51:581â592.Lian, H., Xu, P., He, S., Wu, J., Pan, J., Wang, W., Xu, F., Wang, S., Pan, J., Huang, J.,and Yang, H.-Q. (2018). Photoexcited CRYPTOCHROME 1 interacts directly with G-proteinβ subunit AGB1 to regulate the DNA-binding activity of HY5 and photomorphogenesis inArabidopsis. Molecular Plant, 11:1248â1263.Liang, T., Mei, S., Shi, C., Yang, Y., Peng, Y., Ma, L., Wang, F., Li, X., Huang, X., Yin,Y., and Liu, H. (2018). UVR8 interacts with BES1 and BIM1 to regulate transcription andphotomorphogenesis in Arabidopsis. Developmental Cell, 44:512â523.e5.Liao, H.-S., Chung, Y.-H., and Hsieh, M.-H. (2022). Glutamate: A multifunctional amino acid inplants. Plant Science, 318(111238):1â15.138Lim, Y. J., Kwon, S.-J., and Eom, S. H. (2023). Red and blue light-specific metabolic changesin soybean seedlings. Frontiers in Plant Science, 14.Lin, F., Cao, J., Yuan, J., Liang, Y., and Li, J. (2021). Integration of light and brassinosteroidsignaling during seedling establishment. International Journal of Molecular Sciences,22(23):12971.Lisson, S. N. and Mendham, N. J. (2000). Cultivar, sowing date and plant density studies offibre hemp (Cannabis sativa L.) in Tasmania. Australian Journal of Experimental Agriculture,40(7):975â986.Liu, W., Feng, Y., Yu, S., Fan, Z., Li, X., Li, J., and Yin, H. (2021). The flavonoid biosynthesisnetwork in plants. International Journal of Molecular Science, 22(23):12824.Liu, Y., Fang, S., Yang, W., Shang, X., and Fu, X. (2018). Light quality affects flavonoidproduction and related gene expression in Cyclocarya paliurus. Journal of Photochemistryand Photobiology B: Biology, 179:66â73.Livadariu, O., Raiciu, D., Maximilian, C., and Capitanu, E. (2019). Studies regarding treatmentsof LED-s emitted light on sprouting hemp (Cannabis sativa L.). Romanian BiotechnologicalLetters, 24(3):485â490.Llewellyn, D., Golem, S., Foley, E., Dinka, S., Jones, A. M. P., and Zheng, Y. (2022). Indoorgrown cannabis yield increased proportionally with light intensity, but ultraviolet radiation didnot affect yield or cannabinoid content. Frontiers in Plant Science, 13.Lydon, J., Teramura, A. H., and Coffman, C. B. (1987). UV-B radiation effects onphotosynthesis, growth and cannabinoid production of two Cannabis sativa chemotypes.Photochemistry and Photobiology, 46(2):201â206.Ma, G., Zelman, A. K., Apicella, P. V., and Berkowitz, G. (2022). Genome-wide identification andexpression analysis of homeodomain leucine zipper subfamily IV (HD-ZIP IV) gene family inCannabis sativa L. Plants, 11(10):1307.Ma, X., Ren, X., Gao, H., Wu, X., Chen, K., and Khan, R. (2023). Proteomic, transcriptomic,biochemical, and physio-anatomical analyses provide insights into energy metabolism, lightusage, and photosynthesis in cigar tobacco under different light intensities. Industrial Cropsand Products, 198(116651):1â17.139Mahapatra, K., Dwivedi, S., Mukherjee, A., Pradhan, A. A., Rao, K. V., Singh, D., Bhagavatula,L., and Datta, S. (2025). Interplay of light and abscisic acid signaling to modulate plantdevelopment. Journal of Experimental Botany, 76:730â745.Mainstone, C. P. and Parr, W. (2002). Phosphorus in rivers â ecology and management.Science of the Total Environment, 282-283:25â47.Mansouri, H. and Asrar, Z. (2012). Effects of abscisic acid on content and biosynthesis ofterpenoids in Cannabis sativa at vegetative stage. Biologia Plantarum, 56(1):153â156.Mansouri, H., Asrar, Z., and Amarowicz, R. (2011). The response of terpenoids to exogenousgibberellic acid in Cannabis sativa L. at vegetative stage. Acta Physiologiae Plantarum,33:1085â1091.Mansouri, H., Asrar, Z., and Mehrabani, M. (2009a). Effects of gibberellic acid on primaryterpenoids and â9-tetrahydrocannabinol in Cannabis sativa at flowering stage. Journal ofIntegrative Plant Biology, 51:553â561.Mansouri, H., Asrar, Z., and Szopa, J. (2009b). Effects of ABA on primary terpenoids andâ9-tetrahydrocannabinol in Cannabis sativa L. at flowering stage. Plant Growth Regulation,58:269â277.Mansouri, H., Salari, F., Asrar, Z., and Nasibi, F. (2016). Effects of ethephon on terpenoids inCannabis sativa L. in vegetative stage. Journal of Essential Oil Bearing Plants, 19:94â102.Masoud, A. N., Doorenbos, N. J., and Quimby, M. W. (1973). Mississippi-grown Cannabissativa L. IV: Effects of gibberellic acid and indoleacetic acid. Journal of PharmaceuticalSciences, 62:316â318.Mateo, A., Funck, D., Mühlenbock, P., Kular, B., Mullineaux, P. M., and Karpinski, S.(2006). Controlled levels of salicylic acid are required for optimal photosynthesis and redoxhomeostasis. Journl of Experimental Botany, 57:1795â1807.Mawphlang, O. I. L. and Kharshiing, E. V. (2017). Photoreceptor mediated plant growthresponses: Implications for photoreceptor engineering toward improved performance incrops. Frontiers in Plant Science, 8.140McCauley, A., Jones, C., and Jacobsen, J. (2011). Nutrient management module no. 9: Plantnutrient functions and deficiency and toxicity symptoms. Technical report, Montana StateUniversity.McCree, K. J. (1971). The action spectrum, absorptance and quantum yield of photosynthesisin crop plants. Agricultural Meteorology, 9:191â216.McGarvey, P., Huang, J., McCoy, M., Orvis, J., Katsir, Y., Lotringer, N., Nesher, I., Kavarana,M., Sun, M., Peet, R., Meiri, D., and Madhavan, S. (2020). De novo assembly and annotationof transcriptomes from two cultivars of Cannabis sativa with different cannabinoid profiles.Gene, 762(145026).Meng, Y., Shuai, H., Luo, X., Chen, F., Zhou, W., Yang, W., and Shu, K. (2017). Karrikins:Regulators involved in phytohormone signaling networks during seed germination andseedling development. Frontiers in Plant Science, 7.Meselmani, M. A. A. (2023). Recent Research and Advances in Soilless Culture, chapterNutrient Solution for Hydroponics. IntechOpen.Mewis, I., Schreiner, M., Nguyen, C. N., Krumbein, A., Ulrichs, C., Lohse, M., and Zrenner,R. (2012). UV-B irradiation changes specifically the secondary metabolite profile in broccolisprouts: Induced signaling overlaps with defense response to biotic stressors. Plant andCell Physiology, 53:1546â1560.Mikula, K., Izydorczyk, G., Skrzypczak, D., Mironiuk, M., Moustakas, K., Witek-Krowiak, A., andChojnacka, K. (2020). Controlled release micronutrient fertilizers for precision agriculture âA review. Science of the Total Environment, 712(136365).Miller, A., Adhikari, R., and Nemali, K. (2020). Recycling nutrient solution can reduce growthdue to nutrient deficiencies in hydroponic production. Frontiers in Plant Science, 11.Min, H. J., Qin, J., Yadav, P. K., Frederick, Q., Burks, T., Dewdney, M., Baek, I., andKim, M. (2024). Classification of citrus leaf diseases using hyperspectral reflectance andfluorescence imaging and machine learning techniques. Horticulturae, 10(11):1124.Mirzamohammad, E., Alirezalu, A., Alirezalu, K., Norozi, A., and Ansari, A. (2021).Improvement of the antioxidant activity, phytochemicals, and cannabinoid compounds ofCannabis sativa by salicylic acid elicitor. Food Science & Nutrition, 9:6873â6881.141Mkhabela, S. S., Shimelis, H., Gerrano, A. S., and Mashilo, J. (2023). Drought toleranceassessment of okra (Abelmoschus esculentus [L.] Moench) accessions based on leaf gasexchange and chlorophyll fluorescence. Life, 13(3):682.Mohan Ram, H. Y. and Jaiswal, V. S. (1972). Induction of male flowers on female plants ofCannabis sativa by gibberellins and its inhibition by abscisic acid. Planta, 105:263â266.Moher, M., Llewellyn, D., Golem, S., Foley, E., Dinka, S., Jones, M., and Zheng, Y. (2023). Lightspectra have minimal effects on rooting and vegetative growth responses of clonal cannabiscuttings. HortScience, 58(2):215â221.Moher, M., Llewellyn, D., Jones, M., and Zheng, Y. (2022). Light intensity can be used tomodify the growth and morphological characteristics of cannabis during the vegetative stageof indoor production. Industrial Crops and Products, 183(114909).Monthony, A. S., de Ronne, M., and Torkamaneh, D. (2024). Exploring ethylene-related genesin Cannabis sativa: Implications for sexual plasticity. Plant Reproduction, 37:321â339.Morello, V., Brousseau, V. D., Wu, N., Wu, B.-S., MacPherson, S., and Lefsrud, M. (2022).Light quality impacts vertical growth rate, phytochemical yield and cannabinoid productionefficiency in Cannabis sativa. Plants, 11(21):2982.Murch, S. J. and Erland, L. A. E. (2021). A systematic review of melatonin in plants: An exampleof evolution of literature. Frontiers in Plant Science, 12.Murch, S. J., Rupasinghe, H. P. V., Goodenowe, D., and Saxena, P. K. (2004). A metabolomicanalysis of medicinal diversity in Huang-qin (Scutellaria baicalensis Georgi) genotypes:discovery of novel compounds. Plant Cell Reports, 23:419â425.Murphy, R. and Adelberg, J. (2021). Physical factors increased quantity and quality ofmicropropagated shoots of Cannabis sativa L. in a repeated harvest system with ex vitrorooting. In Vitro Cellular & Developmental Biology - Plant, 57:923â931.Nagy, K., Darkó, E., Szalai, G., Janda, T., Jókai, Z., Ladányi, M., Rady, M. R., and Dernovics,M. (2023). UPLC-ESI-QTOF-MS assisted targeted metabolomics to study the enrichment ofvinca alkaloids and relatedmetabolites inCatharanthus roseus plants grown under controlledLED environment. Journal of Pharmaceutical and Biomedical Analysis, 235(115611).142Namdar, D., Charuvi, D., Ajjampura, V., Mazuz, M., Ion, A., Kamara, I., and Koltai, H. (2019).LED lighting affects the composition and biological activity of Cannabis sativa secondarymetabolites. Industrial Crops and Products, 132:177â185.Neher, E. and Sakmann, B. (1976). Single-channel currents recorded from membrane ofdenervated frog muscle fibres. Nature, 260:799â802.Nelson, D. C., Flematti, G. R., Riseborough, J.-A., Ghisalberti, E. L., Dixon, K. W., andSmith, S. M. (2010). Karrikins enhance light responses during germination and seedlingdevelopment in Arabidopsis thaliana. Proceedings of the National Academy of SciencesU.S.A., 107(15):7095â7100.Nemati, R., Fortin, J.-P., Craig, J., and Donald, S. (2021). Growing mediums for medicalcannabis production in North America. Agronomy, 11(7):1366.Neo, D. C. J., Ong, M. M. X., Lee, Y. Y., Teo, E. J., Ong, Q., Tanoto, H., Xu, J., Ong, K. S.,and Suresh, V. (2022). Shaping and tuning lighting conditions in controlled environmentagriculture: A review. ACS Agricultural Science & Technology, 2(1):3â16.Newell, R., Newman, L., andMendly-Zambo, Z. (2021). The role of incubators and acceleratorsin the fourth agricultural revolution: A case study of Canada. Agriculture, 11(1066).Newman, L., Newell, R., Dring, C., Glaros, A., Fraser, E., Mendly-Zambo, Z., Green, A. G., andKc, K. B. (2023). Agriculture for the anthropocene: Novel applications of technology and thefuture of food. Food Security, 15:613â627.Nicholson, R. S. (1965). Theory and application of cyclic voltammetry for measurement ofelectrode reaction kinetics. Analytical Chemistry, 37:1351â1355.Nie, W.-F., Li, Y., Chen, Y., Zhou, Y., Yu, T., Zhou, Y., and Yang, Y. (2023). Spectral light qualityregulates the morphogenesis, architecture, and flowering in pepper (Capsicum annuum L.).Journal of Photochemistry and Photobiology B: Biology, 241(112673).Oh, E., Yamaguchi, S., Hu, J., Yusuke, J., Jung, B., Paik, I., Lee, H.-S., Sun, T.-P., Kamiya,Y., and Choi, G. (2007). PIL5, a phytochrome-interacting bHLH protein, regulates gibberellinresponsiveness by binding directly to theGAI andRGA promoters in Arabidopsis seeds. ThePlant Cell, 19:1192â1208.143Oivukkamäki, J., Aalto, J., Pfündel, E. E., Tian, M., Zhang, C., Grebe, S., Salmon, Y., Hölttä, T.,and Porcar-Castell, A. (2025). Field integration of shoot gas-exchange and leaf chlorophyllfluorescence measurements to study the long-term regulation of photosynthesis in situ. TreePhysiology, 45:tpae162.Oloumi, H., Zamani, A., Mozaffari, H., Arvin, S. M. J., and Salari, H. (2022). Alleviating effect ofmelatonin on zinc-copper stressed seedlings of hemp (Cannabis sativa L.). Acta BiologicaCracoviensia s. Botanica, 64(2):49â64.Olson, K. R. and Kitur, B. K. (1993). Evaluation of experimental plot design for a sloping anderoding site. Communications in Soil Science and Plant Analysis, 24:2069â2084.Osvalde, A., Karlsons, A., Cekstere, G., and ÄboliÅa, L. (2023). Leaf nutrient status ofcommercially grown strawberries in Latvia, 2014â2022: A possible yield-limiting factor.Plants, 12(4):945.Oultram, J. M. J., Pegler, J. L., Eamens, A. L., Gordon, R., Korbie, D. J., and Grof, C. P. L.(2024). Exogenously applied gibberellic acid alters cannabinoid profile in Cannabis sativa L.Agronomy, 14(10):2417.OuYang, F., Mao, J.-F., Wang, J., Zhang, S., and Li, Y. (2015). Transcriptome analysis revealsthat red and blue light regulate growth and phytohormone metabolism in Norway spruce[Picea abies (L.) Karst.]. PLoS One, 10:e0127896.Ouzounis, T., Rosenqvist, E., and Ottosen, C.-O. (2015). Spectral effects of artificial light onplant physiology and secondary metabolism: A review. HortScience, 50(8):1128â1135.Oyarce, P. and Gurovich, L. (2010). Electrical signals in avocado trees: Responses to light andwater availability conditions. Plant Signaling & Behavior, 51:34â41.Packer, K. (2022). Rising to the challenge of greenhouse strawberry production.Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw,J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S.,McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P.,and Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reportingsystematic reviews. BMJ, 372(71).144Pál, M., Tajti, J., Szalai, G., Peeva, V., Végh, B., and Janda, T. (2018). Interaction ofpolyamines, abscisic acid and proline under osmotic stress in the leaves of wheat plants.Scientific Report, 8(12839).Pang, Z., Lu, Y., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A. F., MacDonald, P. E.,Wishart, D. S., Li, S., and Xia, J. (2024). MetaboAnalyst 6.0: Towards a unified platformfor metabolomics data processing, analysis and interpretation. Nucleic Acids Research,52:W398âW406.Park, S.-H., Pauli, C. S., Gostin, E. L., Staples, S. K., Seifried, D., Kinney, C., andVanden Heuvel, B. D. (2022). Effects of short-term environmental stresses on the onsetof cannabinoid production in young immature flowers of industrial hemp (Cannabis sativaL.). Journal of Cannabis Research, 4(1):1â13.Parthasarathy, A., Cross, P. J., Dobson, R. C. J., Adams, L. E., Savka, M. A., and Hudson, A. O.(2018). A three-ring circus: Metabolism of the three proteogenic aromatic amino acids andtheir role in the health of plants and animals. Frontiers in Molecular Biosciences, 5(29):1â30.Pashkovskiy, P., Kreslavski, V. D., Ivanov, Y., Ivanova, A., Kartashov, A., Shmarev, A., Strokina,V., Kuznetsov, V. V., and Allakhverdiev, S. I. (2021). Influence of light of different spectralcompositions on the growth, photosynthesis, and expression of light-dependent genes ofScots pine seedlings. Cells, 10(12):3284.Payment, J. and Cvetkovska, M. (2023). The responses of Cannabis sativa to environmentalstress: A balancing act. Botany, 101(8):318â332.Pepe, M., Hesami, M., Small, F., and Jones, A. M. P. (2021). Comparative analysis of machinelearning and evolutionary optimization algorithms for precisionmicropropagation ofCannabissativa: Prediction and validation of in vitro shoot growth and development based on theoptimization of light and carbohydrate sources. Frontiers in Plant Science, 12.Peterswald, T. J., Mieog, J. C., Halimi, R. A., Magner, N. J., Treblico, A., Kretzschmar, T., andPurdy, S. J. (2023). Moving away from 12:12: The effect of different photoperiods on biomassyield and cannabinoids in medicinal cannabis. Plants (Basel), 12(5):1061.Pierik, R. and Ballaré, C. L. (2021). Control of plant growth and defense by photoreceptors:From mechanisms to opportunities in agriculture. Molecular Plant, 14:61â76.145Piunno, K. F., Golenia, G., Boudko, E. A., Downey, C., and Jones, A. M. P. (2019). Regenerationof shoots from immature and mature inflorescences of Cannabis sativa. Canadian Journalof Plant Science, 99(4):556â559.Pojmann-Ezeonyilo, E., Langenhoven, P., and Ingwell, L. L. (2024). Evaluating the impact oflight quality on plant-herbivore interactions using hemp as the model system. EnvironmentalEntomology, 53:40â49.Potter, D. J. and Duncombe, P. (2012). The effect of electrical lighting power and irradiance onindoor-grown cannabis potency and yield. Journal of Forensic Sciences, 57(3):618â622.Prashar, D. B., Sharma, M., Sharma, D., Mir, Z. A., Sood, H., and Sharma, M. (2025). Exploringthe complexity of plant stress response: Role of metabolomics and signalling pathways inmetabolic diversification. Journal of Plant Biochemistry and Biotechnology.Punja, Z. K. and Rodriguez, G. (2018). Fusarium and Pythium species infecting roots ofhydroponically grown marijuana (Cannabis sativa L.) plants. Canadian Journal of PlantPathology, 40:498â513.Puttharak, P., Wangnai, P., Puttharak, J., and Baisaeng, N. (2024). Optimizing medicinal hempproduction with synergistic light-enhanced technologies and organic biorefinery approaches.Journal of Photochemistry and Photobiology B: Biology, 254(112890).Qi, L., Liu, S., Li, C., Fu, J., Jing, Y., Cheng, J., Li, H., Zhang, D., Wang, X., Dong, X.,Han, R., Li, B., Zhang, Y., Li, Z., Terzaghi, W., Song, C.-P., Lin, R., Gong, Z., and Li, J.(2020). PHYTOCHROME-INTERACTING FACTORS interact with the ABA receptors PYL8and PYL9 to orchestrate ABA signaling in darkness. Molecular Plant, 13:414â430.Qian, G., Meng, X., Wang, S., Mi, Y., Qin, Z., Liu, T., Zhang, Y., Wan, H., Chen, W., Sun,W., Cao, X., and Li, L. (2023). Genome-wide identification of HSF gene family and theirexpression analysis in vegetative tissue of young seedlings of hemp under different lighttreatments. Industrial Crops and Products, 204(Part B).Ragaveena, S., Edward, A. S., and Surendran, U. (2021). Smart controlled environmentagriculture methods: A holistic review. Reviews in Environmental Science andBio/Technology, 20:887â913.146Rajak, P., Ganguly, A., Adhikary, S., and Bhattacharya, S. (2023). Internet of Things and smartsensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research,14(100776):1â13.Reichel, P., Munz, S., Hartung, J., and Graeff-Hönninger, S. (2024). Harvesting light: Theinterrelation of spectrum, plant density, secondary metabolites, and Cannabis sativa L. yield.Agronomy, 14:2565.Reichel, P., Munz, S., Hartung, J., Kotiranta, S., and Graeff-Hönninger, S. (2022). Impacts ofdifferent light spectra on CBD, CBDA and terpene concentrations in relation to the flowerpositions of different Cannabis sativa L. strains. Plants, 11(20):2695.Reichel, P., Munz, S., Hartung, J., Präger, A., Kotiranta, S., Burgel, L., Schober, T., andGraeff-Hönninger, S. (2021). Impact of three different light spectra on the yield, morphologyand growth trajectory of three different Cannabis sativa L. strains. Plants, 10(9):1866.Renzetti, M., Funck, D., and Trovato, M. (2025). Proline and ROS: A unified mechanism inplant development and stress response? Plants, 14(1):2.Riaz, M., Yasmeen, E., Saleem, B., Hameed, M. K., Almheiri, M. T. S., Al Mir, R. O. S., Alameri,G., Alghafri, J. S. K., and Gururani, M. A. (2025). Evolution of agricultural biotechnology isthe paradigm shift in crop resilience and development: A review. Frontiers in Plant Science,16.Richez-Dumanois, C., Braut-Boucher, F., Cosson, L., and Paris, M. (1986). Multiplicationvégétative in vitro du chanvre (Cannabis sativa L.). application Ã la conservation des clonessélectionnés. Agronomie, 6(5):487â495.Rodrigues, M. A., Bianchetti, R. E., and Freschi, L. (2014). Shedding light on ethylenemetabolism in higher plants. Frontiers in Plant Science, 5.Rodriguez-Morrison, V., Llewellyn, D., and Zheng, Y. (2021). Cannabis yield, potency, andleaf photosynthesis respond differently to increasing light levels in an indoor environment.Frontiers in Plant Science, 12.Rodriquez-Morrison, V., Llewellyn, D., and Zheng, Y. (2021). Cannabis inflorescence yield andcannabinoid concentration are not increased with exposure to short-wavelength ultraviolet-Bradiation. Frontiers in Plant Science, 12.147Ruan, J., Zhou, Y., Zhou, M., Yan, J., Khurshid, M., Weng, W., Cheng, J., and Zhang, K. (2019).Jasmonic acid signaling pathway in plants. International Journal of Molecular Sciences,20(10):2479.Ruzgas, R., TilvikienÄ, V., BarÄauskaitÄ, K., VirÅ¡ilÄ, A., and Žydelis, R. (2024). Ethephonâseffects on sexual expression and cannabinoid production in monoecious and dioeciousvarieties of hemp (Cannabis sativa L.): A field trial. Industrial Crops and Products,210(118064):45668.Sae-Tang, W., Heuvelink, E., Kohlen, W., Argyri, E., Nicole, C. C., and Marcelis, L. F. (2024).Effect of far-red and blue light on rooting in medicinal cannabis cuttings and related changesin endogenous auxin and carbohydrates. Scientia Horticulturae, 325:112614.Salman, A. D. and Rasool, I. J. A. (2023). Response of yield and quality of broccoli to type ofnutrient solution under hydroponic system with modified nft technology. The Iraqi Journal ofAgricultural Science, 54:1679â1688.Sambo, P., Nicoletto, C., Giro, A., Pii, Y., Valentinuzzi, F., Mimmo, T., Lugli, P., Orzes, G.,Mazzetto, F., Astolfi, S., Terzano, R., and Cesco, S. (2019). Hydroponic solutions for soillessproduction systems: Issues and opportunities in a smart agriculture perspective. Frontiersin Plant Science, 10.Sarkar, A., Islam, M. T., Zargar, S. M., Dogra, V., Kim, S. T., Gupta, R., Deswal, R., Bagler, G.,Sreenivasulu, Y., Waditee-Sirisattha, R., Sirisattha, S., Rohila, J. S., Raorane, M., Kohli, A.,Kim, D.-W., Cho, K., Saidajan, A. A., Agrawal, G. K., and Rakwal, R. (2014). Agroecology,Ecosystems, and Sustainability, chapter Proteomics Potential and its Contribution towardSustainable Agriculture, pages 151â179. CRC Press.Sassi, M. and Vernoux, T. (2013). Auxin and self-organization at the shoot apical meristem.Journal of Experimental Botany, 64:2579â2592.Sato, M., Nishiuchi, T., and Sakamoto, T. (2015). Responses to intermittent exposure to greenlight during the dark period in Arabidopsis thaliana and possible involvement of cryptochrome2. Plant Biotechnology, 32:263â266.Schmid, R., Heuckeroth, S., Korf, A., Smirnov, A., Myers, O., Dyrlund, T. S., Bushuiev, R.,Murray, K. J., Hoffmann, N., Lu, M., Sarvepalli, A., Zhang, Z., Fleischauer, M., Dührkop, K.,148Wesner, M., Hoogstra, S. J., Rudt, E., Mokshyna, O., Brungs, C., Ponomarov, K., Mutabdžija,L., Damiani, T., Pudney, C. J., Earll, M., Helmer, P. O., Fallon, T. R., Schulze, T., Rivas-Ubach,A., Bilbao, A., Richter, H., Nothias, L.-F., Wang, M., OreÅ¡iÄ, M., Weng, J.-K., Böcker, S.,Jeibmann, A., Hayen, H., Karst, U., Dorrestein, P. C., Petras, D., Du, X., and Pluskal, T.(2023). Integrative analysis of multimodal mass spectrometry data in MZmine 3. NatureBiotechnology, 41:447â449.Schwender, J. andOhlrogge, J. B. (2002). Probing in vivometabolism by stable isotope labelingof storage lipids and proteins in developing Brassica napus embryos. Plant Physiology,130:347â361.Sengloung, T., Kaveeta, L., and Nanakorn, W. (2009). Effect of sowing date on growthand development of Thai hemp (Cannabis sativa L.). Kasetsart Journal - Natural Science,43(3):423â431.Shah, S. H., Islam, S., Mohammad, F., and Siddiqui, M. H. (2023). Gibberellic acid: A versatileregulator of plant growth, development and stress responses. Journal of Plant GrowthRegulation, 42:7352â7373.Shamabadi, A. and Akhondzadeh, S. (2024). Considerations before the legalization ofrecreational and medical cannabis in Iran. Asian Journal of Psychiatry, 101(104237).Shamshiri, R. R., Kalantari, F., Ting, K. C., Thorp, K. R., Hameed, I. A., Weltzien, C., Ahmad, D.,and Shad, Z. M. (2018). Advances in greenhouse automation and controlled environmentagriculture: A transition to plant factories and urban agriculture . International Journal ofAgricultural and Biological Engineering, 11(1):1â22.Shao, D., Zhu, Q.-h., Liang, Q., Wang, X., Li, Y., Sun, Y., Zhang, X., Liu, F., Xue, F., and Sun,J. (2022). Transcriptome analysis reveals differences in anthocyanin accumulation in cotton(Gossypium hirsutum L.) induced by red and blue light. Frontiers in Plant Science, 13.Shu, K., Chen, Q., Wu, Y., Liu, R., Zhang, H., Wang, S., Tang, S., Yang, W., and Xie, Q.(2015). ABSCISIC ACID-INSENSITIVE 4 negatively regulates flowering through directlypromoting Arabidopsis FLOWERING LOCUS C transcription. Journal of ExperimentalBotany, 67:195â205.149Shu, K., Luo, X., Meng, Y., and Yang, W. (2018). Toward a molecular understanding of abscisicacid actions in floral transition. Plant and Cell Physiology, 59:215â221.Siracusa, L., Ruberto, G., and Cristino, L. (2019). Medical marijuana and marijuanalegalization. Annual Review of Clinical Psychology, 13:397â419.Song, W., Hu, L., Ma, Z., Yang, L., and Li, J. (2022). Importance of tyrosine phosphorylationin hormone-regulated plant growth and development. International Journal of MolecularSciences, 23(12):6603.Souza, G. M., Ferreira, A. S., Saraiva, G. F. R., and Toledo, G. R. A. (2017). Plant âelectromeâcan be pushed toward a self-organized critical state by external cues: Evidences from astudy with soybean seedlings subject to different environmental conditions. Plant Signaling& Behavior, 12:e1290040.Stant, M. Y. (1963). The effect of gibberellic acid on cell width and the cell-wall of some phloemfibres. Annals of Botany, 27:185â196.Staswick, P. (2009). Plant hormone conjugation. Plant Signaling & Behavior, 4:757â759.Statistics Canada (2023). Five years since legalization, what have we learned about cannabisin Canada?Statistics Canada (2025a). Overview of Canadaâs agriculture and agri-food sector.Statistics Canada (2025b). Statistical overview of the Canadian fruit industry, 2024.Statistics Canada (2025c). Strawberry fields for weather, or inside: Field and greenhouseproduction increase in 2024.Stephen, C., Zayas, V. A., Galic, A., and Bridgen, M. P. (2023). Micropropagation of hemp(Cannabis sativa L.). HortScience, 58:307â316.Stolarz, M. and Dziubinska, H. (2017). Osmotic and salt stresses modulate spontaneous andglutamate-induced action potentials and distinguish between growth and circumnutation inHelianthus annuus Seedlings. Frontiers in Plant Science, 8.Sun, T.-p. (2008). Gibberellin metabolism, perception and signaling pathways in Arabidopsis.The Arabidopsis Book, 2008.150Sun, X. (2023). Research progress on cannabinoids in cannabis (Cannabis sativa L.) in China.Molecules, 28(9):3806.Suzuki, A., Suriyagoda, L., Shigeyama, T., Tominaga, A., Sasaki, M., Hiratsuka, Y., Yoshinaga,A., Arima, S., Agarie, S., Sakai, T., Inada, S., Jikumaru, Y., Kamiya, Y., Uchiumi, T., Abe,M., Hashiguchi, M., Akashi, R., Sato, S., Kaneko, T., Tabata, S., and Hirsch, A. M. (2011).Lotus japonicus nodulation is photomorphogenetically controlled by sensing the red/far red(R/FR) ratio through jasmonic acid (JA) signaling. Proceedings of the National Academy ofSciences U.S.A., 108(40):16837â16842.Sweere, U., Eichenberg, K., Lohrmann, J., Mira-Rodado, V., Bäurle, I., Kudla, J., Nagy,F., Schäfer, E., and Harter, K. (2001). Interaction of the response regulator ARR4 withphytochrome B in modulating red light signaling. Science, 294:1108â1111.Takahashi, H. (2013). Auxin biology in roots. Plant Root, 7:49â64.Takahashi, S. and Badger, M. R. (2011). Photoprotection in plants: A new light on photosystemII damage. Trends in Plant Science, 16:P53â60.Tan, D.-X., Manchester, L. C., Di Mascio, P., Martinez, G. R., Prado, F. M., and Reiter,R. J. (2007). Novel rhythms of N1-acetyl-N2-formyl-5-methoxykynuramine and its precursormelatonin in water hyacinth: Importance for phytoremediation. The FASEB Journal,21(8):1724â1729.Tan, T., Li, S., Fan, Y., Wang, Z., Raza, M. A., Shafiq, I., Wang, B., Wu, X., Yong, T., Wang, X.,Wu, Y., Yang, F., and Yang, W. (2022). Far-red light: A regulator of plant morphology andphotosynthetic capacity. The Crop Journal, 10:300â309.Tang, S., Zhang, L., Tong, Z., Wu, Z., Wang, H., Zhan, P., Shao, L., Qing, Y., Wu, Y., and Liu, J.(2024). Encapsulated lignin-based slow-release manganese fertilizer with reduced cadmiumaccumulation in rice (Oryza sativa L.). International Journal of Biological Macromolecules,262, Part 1(130019).Thwe, A. A., Kim, Y. B., Li, X., Seo, J. M., Kim, S.-J., Suzuki, T., Chung, S.-O., and Park,S. U. (2014). Effects of light-emitting diodes on expression of phenylpropanoid biosyntheticgenes and accumulation of phenylpropanoids in Fagopyrum tataricum sprouts. Journal ofAgricultural and Food Chemistry, 62(21):4389â4845.151Timmons, B. and Mattson, N. (2023). Effect of end of cycle supplemental UV-B radiation onyield and cannabinoid quality of two hemp cultivars2. HortScience, 58:S247. 2Conferencepresentation abstract.Ting, K. C., Lin, T., and Davidson, P. C. (2016). LED lighting for urban agriculture, chapterIntegrated urban controlled environment agriculture systems, pages 19â36. Springer,Singapore.Toth, J. A., Stack, G. M., Carlson, C. H., and Smart, L. B. (2022). Identification and mappingof major-effect flowering time loci Autoflower1 and Early1 in Cannabis sativa L. Frontiers inPlant Science, 13(991680):1â14.Trajkovska, A., Tusevski, O., Stoilkovska Gjorgievska, V., Cvetkovikj Karanfilova, I., Davkova,I., Karapandzova, M., Stefkov, G., Kulevanova, S., and Gadzovska Simic, S. (2025).Cannabinoid production in various Cannabis sativa L. in vitro cultures. Plant Cell, Tissueand Organ Culture, 160(10).Tran, D., Dutoit, F., Najdenovska, E., Wallbridge, N., Plummer, C., Mazza, M., Raileanu, L. E.,and Camps, C. (2019). Electrophysiological assessment of plant status outside a Faradaycage using supervised machine learning. Scientific Reports, 9(17073):1â9.Trusiak, M., ZieliÅska, D., LuÅnia, P., MikoÅajczyk, T., Michalak, M., and Plitta-Michalak, B. P.(2025). Cyclic voltammetry as a method for determining the viability of seeds: A case studyon silver maple (Acer saccharinum L.). BMC Plant Biology, 25(1074):1â15.Tsuchiya, Y., Vidaurre, D., Toh, S., Hanada, A., Nambara, E., Kamiya, Y., Yamaguchi, S., andMcCourt, P. (2010). A small-molecule screen identifies new functions for the plant hormonestrigolactone. Nature Chemical Biology, 6:741â749.Turi, C. E., Finley, J., Shipley, P. R., Murch, S. J., and Brown, P. N. (2015). Metabolomics forphytochemical discovery: Development of statistical approaches using a cranberry modelsystem. Journal of Natural Products, 78:953â966.Vacek, J., Vostalova, J., Papouskova, B., Skarupova, D., Kos, M., Kabelac, M., and Storch,J. (2021). Antioxidant function of phytocannabinoids: Molecular basis of their stabilityand cytoprotective properties under UV-irradiation. Free Radical Biology and Medicine,164:258â270.152Van Ginkel, S. W., Igou, T., and Chen, Y. (2017). Energy, water and nutrient impacts ofCalifornia-grown vegetables compared to controlled environmental agriculture systems inAtlanta, GA. Resources, Conservation and Recycling, 122:319â325.Vandenbussche, F., Habricot, Y., Condiff, A. S., Maldiney, R., Van Der Straeten, D., and Ahmad,M. (2007). HY5 is a point of convergence between cryptochrome and cytokinin signallingpathways in Arabidopsis thaliana. The Plant Journal, 49:428â441.Vanhaelewyn, L., Prinsen, E., Van Der Straeten, D., and Vandenbussche, F. (2016).Hormone-controlled UV-B responses in plants. Journal of Experimental Botany,67:4469â4482.Vanhove, W., Van Damme, P., and Meert, N. (2011). Factors determining yield and qualityof illicit indoor cannabis (Cannabis spp.) production. Forensic Science International,212:158â163.Varga, I., KristiÄ, M., Lisjak, M., Tkalec KojiÄ, M., IljkiÄ, D., JoviÄ, J., Kristek, S.,Markulj KulundÅ¾iÄ, A., and AntunoviÄ, M. (2024). Antioxidative response and phenoliccontent of young industrial hemp leaves at different light and mycorrhiza. Plants, 13(6):840.Vogelmann, A. F., Turner, J. C., and Mahlberg, P. G. (1987). Cannabinoid occurrence inseedlings of Cannabis sativa L.: Quantitation in seedlings of known age and primary leaflength. Botanical Gazette, 148(4):468â474.Volkov, A. G. and Ranatunga, D. R. A. (2006). Plants as environmental biosensors. PlantSignaling & Behavior, 1:105â115.Wahba, R. H., El-Sonbati, A. Z., Diab, M. A., Gomaa, E. A., and AbouElleef, E. M. (2024).Electrochemical sensing of strontium ions cyclic voltammetry and investigation of rosemaryextractâs effects on their behavior: Antibacterial properties of rosemary extract and moleculardocking analysis for potential COVID-19 therapeutic benefits. Microchemical Journal,200(110398).Wang, Y., Wang, D., Li, S., Zhai, Y., Zhao, Y., Zhong, F., Wang, Z., Zhao, M., and Li, S. (2024).Methyl jasmonate and salicylic acid enhance the total flavonoid, phenolics, and cannabidiolcontents of Cannabis sativa L. adventitious roots. Plant Cell, Tissue and Organ Culture(PCTOC), 159(49):45671.153Wei, X., Zhao, X., Long, S., Xiao, Q., Guo, Y., Qiu, C., Qiu, H., and Wang, Y. (2021).Wavelengths of LED light affect the growth and cannabidiol content in Cannabis sativa L.Industrial Crops and Products, 165.Welling, M. T., Deseo, M. A., Bacic, A., and Doblin, M. S. (2021). Untargeted metabolomicanalyses reveal chemical complexity of dioecious cannabis flowers. Australian Journal ofChemistry, 74(6):463â479.Welling, M. T., Deseo, M. A., OâBrien, M., Clifton, J., Bacic, A., and Doblin, M. S. (2023).Metabolomic analysis of methyl jasmonate treatment on phytocannabinoid production inCannabis sativa. Frontiers in Plant Science, 14.Wen, G. and Ma, B.-L. (2025). Decoding canola and oat crop health and productivityunder drought and heat stress using bioelectrical signals and machine learning. ArtificialIntelligence in Agriculture, 15:696â706.Went, F. W. (1935). Auxin, the plant growth-hormone. Botanical Review, 1(5):162â182.Werbrouck, S. P. O., Strnad, M., Van Onckelen, H. A., and Debergh, P. C. (1996). Meta-topolin,an alternative to benzyladenine in tissue culture? Physiologia Plantarum, 98:291â297.Westmoreland, F. M. and Bugbee, B. (2022). Sustainable Cannabis nutrition: Elevatedroot-zone phosphorus significantly increases leachate P and does not improve yield orquality. Frontiers in Plant Science, 13.Westmoreland, F. M., Kusuma, P., and Bugbee, B. (2023). Elevated UV photon fluxesminimallyaffected cannabinoid concentration in a high-CBD cultivar. Frontiers in Plant Science, 14.Westmoreland, F. M., Kusuma, P., and Bugbee, P. (2021). Cannabis lighting: Decreasing bluephoton fraction increases yield but efficacy is more important for cost effective production ofcannabinoids. PLoS One, 16(3).Wheeler, R. M. (2008). A historical background of plant lighting: An introduction to theworkshop. HortScience, 43:1942â1943.Wilkinson, A., Gerlach, C., Karlsson, M., and Penn, H. (2021). Controlled environmentagriculture and containerized food production in northern North America. Journal ofAgriculture, Food Systems, and Community Development, 10(4):127â142.154Xie, C., Wang, P., Chang, J., Wang, Q., Han, Y., and Yang, R. (2022). Effect of amino acidson folates accumulation in wheat seedlings during germination under red light radiation.Molecules, 27(6868):1â11.Xie, J., Wu, Y., Xing, D., Li, Z., Chen, T., Duan, R., and Zhu, X. (2021). A comparative studyon the circadian rhythm of the electrical signals of Broussonetia papyrifera and Morus alba.Plant Signaling & Behavior, 16:1950899.Xu, K., Zhang, X., Chen, S., Hou, K., and Che, Y. (2015). Design and calibration of anew compound sensor for water content and electrical conductivity in soilless substrates.Transactions of the Institute of Measurement and Control, 39(1):96â106.Xu, Y., Zhang, J., Tang, Q., Dai, Z., Deng, C., Chen, Y., Cheng, C., Yang, Z., Zhang, X., Chen,J., Luan, M., and Su, J. (2024). Integrated metabolomic and transcriptomic analysis revealedthe regulation of yields, cannabinoid, and terpene biosynthesis in Cannabis sativa L. underdifferent photoperiods. South African Journal of Botany, 174:735â746.Yadukrishnan, P., Rahul, P. V., Ravindran, N., Bursch, K., Johansson, H., and Datta, S.(2020). CONSTITUTIVELY PHOTOMORPHOGENIC1 promotes ABA-mediated inhibitionof post-germination seedling establishment. The Plant Journal, 103:481â496.Yalpani, N., Enyedi, A. J., León, J., and Raskin, I. (1994). Ultraviolet light and ozone stimulateaccumulation of salicylic acid, pathogenesis-related proteins and virus resistance in tobacco.Planta, 193:372â376.Yan, B., Yang, Z., He, G., Jing, Y., Dong, H., Ju, L., Zhang, Y., Zhu, Y., Zhou, Y., and Sun, J.(2021a). The blue light receptor CRY1 interacts with GID1 and DELLA proteins to repressgibberellin signaling and plant growth. Plant Communications, 2(100245):1â14.Yan, L., Chang, L., Qinghua, S., Fengjuan, Y., and Min, W. (2021b). Mixed red and bluelight promotes ripening and improves quality of tomato fruit by influencing melatonin content.Environmental and Experimental Botany, 185(104407).Yang, Y., Liu, S., Du, G., Liu, F., Tang, K., Deng, G., Li, J., and Jiang, W. (2024). Exogenousapplication of phytohormones alleviates salt injury to growth, physiological, and biochemicalattributes of hemp seedlings. Journal of Plant Growth Regulation, 43:2439â2453.155Yang, Y., Zhang, L., Chen, P., Liang, T., Li, X., and Liu, H. (2020). UV-B photoreceptor UVR8interacts with MYB73/MYB77 to regulate auxin responses and lateral root development. TheEMBO Journal, 39(2):e101928.Yang, Y.-X., Wang, M.-M., Yin, Y.-L., Onac, E., Zhou, G.-F., Peng, S., Xia, X.-J., Shi, K., Yu,J.-Q., and Zhou, Y.-H. (2015). RNA-seq analysis reveals the role of red light in resistanceagainst Pseudomonas syringae pv. tomato DC3000 in tomato plants. BMC Genomics,16(120).Ye, J., Erland, L. A. E., Gill, S. K., Bishop, S. L., Verdugo-Meza, A., Murch, S. J., and Gibson,D. L. (2021). Metabolomics-guided hypothesis generation for mechanisms of intestinalprotection by live biotherapeutic products. Biomolecules, 11(5):738.Yi, G., Shin, H., Min, K., and Lee, E. J. (2021). Expanded transcriptomic view of strawberryfruit ripening through meta-analysis. PLoS One, 16(6):e0252685.Yin, S., Ibrahim, H., Schnable, P. S., Castellano, M. J., and Dong, L. (2021). A field-deployable,wearable leaf sensor for continuousmonitoring of vapor-pressure deficit. AdvancedMaterialsTechnologies, 6:2001246.Youden, W. J. (1972). Use of incomplete block replications in estimating Tobacco-mosaic virus.Journal of Quality Technology, 4:50â57. Originally published in 1937 inContributions - BoyceThompson Institute for Plant Research.Yu, M., Chen, Y., Zhang, J., Wang, X., Jin, Z., Li, S., and Liu, L. (2024). Transcriptomic analysisof the cytokinin response in industrial hemp (Cannabis sativa L.) leaves. Journal of PlantBiochemistry and Biotechnology, 33:533â546.Zarei, A., Behdarvandi, B., Tavakouli Dinani, E., and Maccarone, J. (2021). Cannabis sativa L.photoautotrophic micropropagation: A powerful tool for industrial scale in vitro propagation.In Vitro Cellular & Developmental Biology - Plant, 57:932â941.Zarei, A., Feyissa, B. A. A., Davis, B., and Tavakouli Dinani, E. (2022). Cannabis syntheticseeds: An alternative approach for commercial scale of clonal propagation and germplasmconservation. Plants (Basel), 11(23):3186.Zeier, J. (2013). New insights into the regulation of plant immunity by amino acid metabolicpathways. Plant, Cell & Environment, 36:2085â2103.156Zhang, H., Wang, L., Shi, K., Shan, D., Zhu, Y., Wang, C., Bai, Y., Yan, T., Zheng, X., and Kong,J. (2019a). Apple tree flowering is mediated by low level of melatonin under the regulationof seasonal light signal. Journal of Pineal Research, 66(e12551):1â12.Zhang, L., Guan, R., Li, G., Su, K., Duan, L., Sun, W., Meng, X., Wan, H., Wang, S., Chen, S.,Mi, Y., and Ji, A. (2022). Genomic identification of ARF transcription factors and expressionanalysis in Cannabis sativa L. Industrial Crops and Products, 186(115118):45669.Zhang, L., Wang, G., Wang, G., and Cao, F. (2021). Ginkgo biloba L. responds to red and bluelight: via phenylpropanoid and flavonoid biosynthesis pathway. Forests, 12(1079):1â16.Zhang, T., Xu, P., Wang, W., Wang, S., Caruana, J. C., Yang, H.-Q., and Lian, H. (2018).Arabidopsis G-protein β subunit AGB1 interacts with BES1 to regulate brassinosteroidsignaling and cell elongation. Frontiers in Plant Science, 8.Zhang, X., Bian, Z., Li, S., Chen, X., and Lu, C. (2019b). Comparative analysis of phenoliccompound profiles, antioxidant capacities, and expressions of phenolic biosynthesis-relatedgenes in soybean microgreens grown under different light spectra. Journal of Agriculturaland Food Chemistry, 67:13577â13588.Zhang, Y., Yu, Q., Jiang, N., Yan, X., Wang, C., Wang, Q., Liu, J., Zhu, M., Bednarek, S. Y., Xu,J., and Pan, J. (2016). Clathrin regulates blue light-triggered lateral auxin distribution andhypocotyl phototropism in Arabidopsis. Plant, Cell & Environment, 40:165â176.Zhao, J., Yang, G., Jiang, L., Zhang, S., Miao, L., Xu, P., Chen, H., Chen, L., Mao, Z.,Guo, T., Kou, S., Yang, H.-Q., and Wang, W. (2022). Phytochromes A and B mediatelight stabilization of BIN2 to regulate brassinosteroid signaling and photomorphogenesis inArabidopsis. Frontiers in Plant Science, 13.Zhen, S., van Iersel, M., and Bugbee, B. (2021). Why far-red photons should be included inthe definition of photosynthetic photons and the measurement of horticultural fixture efficacy.Frontiers in Plant Science, 12.Zheng, Y., editor (2022). Handbook of Cannabis production in controlled environments. CRCPress.157Zimmermann, M. R. and Mithöfer, A. (2013). Long-distance systemic signaling andcommunication in plants, volume 19 of Signaling and Communication in Plants, chapterElectrical long-distance signaling in plants, pages 291â308. Springer, Berlin, Heidelberg.ZlatiÄ, G., ArapoviÄ, A., MartinoviÄ, I., MartinoviÄ Bevanda, A., BoÅ¡koviÄ, P., PrkiÄ, A., Paut,A., and VukuÅ¡iÄ, T. (2022). Antioxidant capacity of Herzegovinian wildflowers evaluated byUVâVIS and cyclic voltammetry analysis. Molecules, 27(17):5466.158AppendicesAppendix A: Associated appendix for chapter 3Figure A1: First set of ThermoFisher Q Extractive Orbitrap MS parameters.159Figure A2: Second set of ThermoFisher Q Extractive Orbitrap MS parameters.160Table A1: Putatively identified compounds alongside their KEGG ID, m/z, RT, and potential form as identified in Functional Analysisusing full spectrum control light (âcontrolâ), red light, and blue light treatment samples.Compound Name and KEGG ID Matched m/z and RT Matched Form Control Red BlueS-Adenosyl-L-homocysteine C00021365.10494/2.3541 M-H2O-H[-] 341000 0 1903000365.10502/2.1949 M-H2O-H[-] 0 428500 0Uracil C00106 111.02043/18.7837 M-H[-] 246233.33 0 06-Phospho-D-gluconate C00345275.01606/2.9725 M-H[-] 1112000 1977250 203600275.01609/2.8418 M-H[-] 0 545500 318400Geranylgeranyldiphosphate C00353 431.1739/6.6839 M-H2O-H[-] 113600 49775 0Saccharopine C00449 137.05936/8.1831 M-2H[2-] 0 44450 1575203-Hydroxyanthranilate C00632152.03593/1.8462 M-H[-] 152233.33 263500 90740152.03597/1.9228 M-H[-] 0 0 163000Coniferin C00761 341.12275/4.5134 M-H[-] 0 0 28620Vitamin K2 C00828425.28649/12.5796 M-H2O-H[-] 472333.33 0 0425.28661/12.4263 M-H2O-H[-] 93733.33 71350 133500Pantetheine C00831 259.11102/11.0384 M-H2O-H[-] 269166.67 0 0Chitobiose C01674 405.14979/5.521 M-H2O-H[-] 0 0 50700Quercitrin C01750447.09164/6.0791 M-H[-] 3963000 3059250 4651400448.09493/6.0786 M(C13)-H[-] 893600 710575 1077660463.08641/5.7936 M-H+O[-] 0 0 873000463.08665/5.6961 M-H+O[-] 3022000 2229500 3183800Secologanin C01852369.11769/5.1402 M-H2O-H[-] 0 0 59600369.11771/5.5025 M-H2O-H[-] 0 0 48900387.12819/5.5042 M-H[-] 0 160550 277480161Table A1: Continued: Putatively identified compounds alongside their KEGG ID, m/z, RT, and potential form as identified in FunctionalAnalysis using full spectrum control light (âcontrolâ), red light, and blue light treatment samples.Compound Name and KEGG ID Matched m/z and RT Matched Form Control Red BlueSecologanin C01852 387.12828/5.1524 M-H[-] 252966.67 269275 459480Leukotriene C4 C02166606.28318/13.7132 M-H2O-H[-] 329000 753625 206800606.28321/14.2076 M-H2O-H[-] 128500 0 0606.28322/13.9651 M-H2O-H[-] 343333.33 156425 0alpha-Tocopherol/Vitamin E C02477429.3718/14.0927 M-H[-] 0 154125 0429.3722/13.0006 M-H[-] 1614333.33 138175 0430.37703/13.0115 M(C13)-H[-] 701666.67 0 04-Coumaroylshikimate C02947301.07027/9.6035 M-H2O-H[-] 0 0 440080301.07041/7.658 M-H2O-H[-] 0 0 721800(9S,13S,15Z)-12-Oxo-10,11-dihydrophyto-15-enoateC04780275.20031/9.1712 M-H2O-H[-] 0 130775 0(+)-Pinoresinol C05366339.12224/9.4724 M-H2O-H[-] 0 279175 2977800339.12229/9.5559 M-H2O-H[-] 1617200 1797250 2242600Phytyl diphosphate C05427437.22073/6.4176 M-H2O-H[-] 201466.67 453000 333500437.22081/6.5303 M-H2O-H[-] 0 276800 0Isoquercitrin C05623 464.09006/5.6961 M(C13)-H[-] 502466.67 237900 893440cis-beta-D-Glucosyl-2-hydroxycinnamate C05839307.08301/2.9186 M-H2O-H[-] 0 338500 0307.08313/2.8032 M-H2O-H[-] 0 866000 938800325.09141/4.7349 M-H[-] 211300 189150 174500325.09154/4.58 M-H[-] 221633.33 502500 4005604-Hydroxycinnamylalcohol 4-D-glucoside C05855 311.11211/6.5208 M-H[-] 0 0 36200162Table A1: Continued: Putatively identified compounds alongside their KEGG ID, m/z, RT, and potential form as identified in FunctionalAnalysis using full spectrum control light (âcontrolâ), red light, and blue light treatment samples.Compound Name and KEGG ID Matched m/z and RT Matched Form Control Red BlueDehydrodolichol diphosphate C05859 258.12016/10.6449 M-2H[2-] 0 51075 201680Antheraxanthin C08579565.4031/10.7591 M-H2O-H[-] 0 0 61500583.41358/9.5495 M-H[-] 440000 146175 229600584.41677/9.5551 M(C13)-H[-] 168600 0 85580Violaxanthin C08614599.40845/12.425 M-H[-] 0 0 184740600.41496/12.5617 M(C13)-H[-] 256966.67 109250 0600.41548/12.4684 M(C13)-H[-] 397000 162600 0600.41571/12.3779 M(C13)-H[-] 0 382350 0Lariciresinol C10646341.13786/9.2679 M-H2O-H[-] 0 56075 0341.13801/4.9512 M-H2O-H[-] 0 73750 239280341.13807/5.0277 M-H2O-H[-] 0 0 37100Gibberellin A12 C11857313.17942/9.3703 M-H2O-H[-] 0 156325 759120313.17946/9.7674 M-H2O-H[-] 0 633950 347800313.17953/10.0469 M-H2O-H[-] 0 0 96380331.18987/11.9564 M-H[-] 1589333.33 1371000 0377.19533/9.8487 M+HCOO[-] 421333.33 365250 1027840391.21079/10.356 M+CH3COO[-] 0 0 409660391.21095/10.4766 M+CH3COO[-] 205400 278750 192800Gibberellin A9 C11863315.15872/9.773 M-H[-] 0 68200 0375.17945/9.7279 M+CH3COO[-] 0 0 69700Abscisic aldehyde C13455 247.13271/6.4935 M-H[-] 74200 0 192720163Table A1: Continued: Putatively identified compounds alongside their KEGG ID, m/z, RT, and potential form as identified in FunctionalAnalysis using full spectrum control light (âcontrolâ), red light, and blue light treatment samples.Compound Name and KEGG ID Matched m/z and RT Matched Form Control Red BluePhytosphingosine-4-D-Hydroxysphingamine C12144316.2842/8.0578 M-H[-] 65300 104300 51660316.28426/8.1777 M-H[-] 131600 0 08â-Hydroxyabscisate C15514139.0581/2.5 M-2H[2-] 0 0 3773400139.05815/2.3583 M-2H[2-] 6124000 5802000 5598000Abscisic acid glucose ester C15970407.16954/6.369 M-H2O-H[-] 79233.33 303050 808700425.18005/6.3462 M-H[-] 817666.67 1012675 1076680426.18332/6.3403 M(C13)-H[-] 0 125225 85180S-(4-Methylthiobutyl-thiohydroximoyl)-L-cysteineC17242233.04193/2.1922 M-H2O-H[-] 2742000 4929250 5009000233.04194/2.3168 M-H2O-H[-] 1249666.67 543750 1599200Red chlorophyll catabolite C18022312.12856/3.9053 M-2H[2-] 1672000 0 1047960312.12864/3.7968 M-2H[2-] 0 328000 0607.25418/12.0177 M-H2O-H[-] 360066.67 478800 496400607.25429/12.1461 M-H2O-H[-] 226433.33 0 0Quercetin 3-O-rhamnoside 7-O-glucoside C19796609.14432/5.6616 M-H[-] 0 0 96040609.14455/5.5878 M-H[-] 134233.33 289500 411540Kaempferol 3-O-rhamnoside-7-O-glucoside C21854593.14954/6.6014 M-H[-] 742033.33 703000 864060594.15287/6.6025 M(C13)-H[-] 76266.67 0 94480164Table A2: Matched compounds in the diterpenoid biosynthesis pathway through either MetaboAnalyst (M.A.) or HormonomicsDB (HBD)results.KEGG ID Compound Name Formula Adduct Mass Matched Mass m/z Difference Adduct Sig. KW (p value) M.A. HDBC06094 Gibberellin A53 C20H28O5 313.1797765 313.17932 0.00046 M-H4O2+H[1+] 0.135343 xC02035 Gibberellin A20 C19H24O5 315.1590765 315.15872 0.00036 M-H2O+H[1+] N/A xC11863 Gibberellin A9 C19H24O4 317.1747765 317.17439 0.00039 M-H[-] N/A xC06093 Gibberellin A12 aldehyde C20H28O3 317.2110765 317.21062 0.00046 M-H4O2+H[1+] 0.09072 xC11864 Gibberellin A4 C19H24O5 330.1473 330.1332 0.01410 M+H[1+] N/A xC11865 Gibberellin A51 C19H24O5 330.1473 330.1332 0.01410 M+H[1+] N/A xC11854 Gibberellin A51-catabolite C19H22O5 331.1539765 331.1532 0.00078 M-H2O+H[1+] N/A xC11875 ent-7α-Hydroxykaur-16-en-19-oic acid C20H30O3 341.2121035 341.21044 0.00166 M+Na[1+] 0.01886 xC14162 Gibberellin A15 C20H26O4 342.1837 342.1756 0.00810 M+H[1+] 0.10540 xC11861 Gibberellin A24 C20H26O5 344.163 344.1564 0.00660 M+H[1+] N/A xC12308 Gibberellin A44 C20H26O5 344.163 344.1564 0.00660 M+H[1+] N/A xC00859 Gibberellin A1 C19H24O6 346.1422 346.1494 0.00720 M+H[1+] N/A xC06096 Gibberellin A29 C19H24O6 346.1422 346.1494 0.00720 M+H[1+] 0.13534 xC11868 Gibberellin A34 C19H24O6 346.1422 346.1494 0.00720 M+H[1+] N/A xC11857 Gibberellin A12 C20H28O4 355.1913635 355.18972 0.00164 M-H2O-H[-] 0.13534 xC11876 6β, 7β-Di-hydroxykaurenoic acid C20H30O4 357.2069235 357.2053 0.00162 M+Na[1+] N/A xC11855 Gibberellin A29-catabolite C19H22O6 369.1343035 369.13259 0.00171 M+Na[1+] 0.08208 xC11869 Gibberellin A34-catabolite C19H22O6 369.1343035 369.13259 0.00171 M+Na[1+] 0.08208 xC11870 Gibberellin A8-catabolite C19H22O7 385.1288765 385.12755 0.00133 M+H[1+] 0.05784 xC03579 Gibberellin A8 C19H24O7 387.1449835 387.14323 0.00175 M+Na[1+] 0.04505 x165Table A3: Matched compounds in the phenylalanine metabolism pathway.KEGG ID Name Formula Adduct Mass Matched Mass m/z Difference Adduct Sig. KW (p value)C02505 2-Phenylacetamide C8H9NO 118.0653035 118.06519 0.00011 M+H[1+] 0.13534C00601 Phenylacetylaldehyde C8H8O 121.0649635 121.06487 0.00009 M-CO2+H[1+] 0.10540C05332 Phenylethylamine C8H11N 122.0966235 122.09655 0.00007 M-CO2+H[1+] N/AC07086 Phenylacetic acid C8H8O2 137.0596765 137.05936 0.00032 M+H[1+] 0.09072C00423 trans-Cinnamic acid C9H8O2 149.0597765 149.05967 0.00011 M-NH3+H[1+] 0.06721C05852 2-Hydroxyphenylacetate C8H8O3 153.0545765 153.05442 0.00016 M+H[1+] 0.13534C02763 2-Hydroxy-3-phenylpropenoate C9H8O3 165.0546765 165.05438 0.00030 M-NH3+H[1+] N/AC00166 Phenylpyruvate C9H8O3 165.0546765 165.05442 0.00026 M-NH3+H[1+] N/AC00079 L-Phenylalanine C9H11NO2 166.0862765 166.08617 0.00011 M+H[1+] 0.13534Table A4: Matched compounds in the phenylpropanoid biosynthesis pathway.KEGG ID Name Formula Adduct Mass Matched Mass m/z Difference Adduct Sig. KW (p value)C01494 Ferulic acid C10H10O4 123.0440765 123.04407 0.00000647 M-C3H4O2+H[1+] N/AC00423 trans-Cinnamic acid C9H8O2 149.0596765 149.05967 0.00000647 M+H[1+] 0.06721C05608 p-Coumaraldehyde C9H8O2 149.0596765 149.05967 0.00000647 M+H[1+] 0.06721C12206 Caffeyl alcohol C9H10O3 149.0596765 149.05967 0.00000647 M-H2O+H[1+] 0.06721C01197 Caffeic acid C9H8O4 163.0389765 163.03889 0.0000865 M-H2O+H[1+] 0.09072C00590 Coniferol C10H12O3 163.0752765 163.07517 0.000106467 M-H2O+H[1+] N/AC00811 4-Hydroxycinnamic acid C9H8O3 165.0545765 165.05442 0.000156467 M+H[1+] N/AC10945 3,4-Dihydroxycinnamaldehyde C9H8O3 165.0545765 165.05442 0.000156467 M+H[1+] N/AC00079 L-Phenylalanine C9H11NO2 166.0862765 166.08617 0.000106467 M+H[1+] 0.13534C12204 5-Hydroxyconiferaldehyde C10H10O4 177.0545765 177.05454 0.0000365 M-H2O+H[1+] 0.04594C02666 Coniferaldehyde C10H10O3 179.0702765 179.07019 0.0000865 M+H[1+] 0.20190C02325 Sinapoyl alcohol C11H14O4 193.0858765 193.08587 0.00000647 M-H2O+H[1+] 0.22313C12205 5-Hydroxyconiferyl alcohol C10H12O4 197.0808765 197.08066 0.000216467 M+H[1+] N/AC00482 Sinapic acid C11H12O5 207.0651835 207.06518 0.00000353 M-H2O+H[1+] N/AC05610 Sinapaldehyde C11H12O4 209.0808765 209.08076 0.000116467 M+H[1+] 0.08208C05619 5-Hydroxyferulic acid C10H10O5 233.0420765 233.04194 0.000136467 M+Na[1+] 0.08208C12208 p-Coumaroylquinic acid C16H18O8 339.1074765 339.10735 0.000126467 M+H[1+] N/AC01175 1-O-Sinapoyl beta-D-glucoside C17H22O10 387.1285765 387.12828 0.000296467 M+H[1+] 0.13059166Table A5: Matched compounds in the flavonoid biosynthesis pathway.KEGG ID Name Formula Adduct Mass Matched Mass m/z Difference Adduct p valueC00974 Dihydrokaempferol C15H12O6 271.0600765 271.0597 0.000376467 M-H2O+H[1+] N/AC01378 Fustin C15H12O6 271.0600765 271.0597 0.000376467 M-H2O+H[1+] N/AC01477 Apigenin C15H10O5 271.0600765 271.0597 0.000376467 M+H[1+] N/AC05631 Eriodictyol C15H12O6 271.0600765 271.0597 0.000376467 M-H2O+H[1+] N/AC10044 3,5,7-Trihydroxyflavone C15H10O5 271.0600765 271.0597 0.000376467 M+H[1+] N/AC15525 Eriodictyol chalcone C15H12O6 271.0600765 271.0597 0.000376467 M-H2O+H[1+] N/AC16405 Homoeriodictyol chalcone C16H14O6 285.0756765 285.07557 0.000106467 M-H2O+H[1+] 0.22313C01514 Luteolin C15H10O6 287.0549765 287.05422 0.000756467 M+H[1+] N/AC01617 Taxifolin C15H12O7 287.0549765 287.05422 0.000756467 M-H2O+H[1+] N/AC05903 Kaempferol C15H10O6 287.0549765 287.05422 0.000756467 M+H[1+] N/AC05911 Pentahydroxyflavanone C15H12O7 287.0549765 287.05422 0.000756467 M-H2O+H[1+] N/AC03648 cis-3,4-Leucopelargonidin C15H14O6 291.0862765 291.08596 0.000316467 M+H[1+] 0.16049C05907 Luteoforol C15H14O6 291.0862765 291.08596 0.000316467 M+H[1+] 0.16049C09727 (-)-Epicatechin C15H14O6 291.0862765 291.08596 0.000316467 M+H[1+] 0.16049C09736 Fisetinidol-4β-ol C15H14O6 291.0862765 291.08596 0.000316467 M+H[1+] 0.16049C12208 p-Coumaroylquinic acid C16H18O8 339.1074765 339.10735 0.000126467 M+H[1+] N/ATable A6: Matched compounds in the flavone and flavonol biosynthesis pathway.KEGG ID Name Formula Adduct Mass Matched Mass m/z Difference Adduct p valueC16911 Afzelin C21H20O10 433.1128765 433.11261 0.00027 M+H[1+] N/AC05903 Kaempferol C15H10O6 433.1128765 433.11261 0.00027 M+H[1+] N/AC12249 Kaempferol 3-O-glucoside C21H20O11 449.1078765 449.10747 0.00041 M+H[1+] N/AC01750 Quercitrin C21H20O11 449.1078765 449.10747 0.00041 M+H[1+] N/AC05623 Isoquercitrin C21H20O12 465.1027765 465.1027 0.00008 M+H[1+] 0.09072C21854 Kaempferol 3-O-rhamnoside-7-O-glucoside C27H30O15 595.1657765 595.16528 N/A M+H[1+] N/AC19796 Quercetin 3-O-rhamnoside 7-O-glucoside C27H30O16 611.1606765 611.15998 0.00070 M+H[1+] 0.00916167Table A7: Matched compounds in the tyrosine metabolism pathway.KEGG ID Name Formula Adduct Mass Matched Mass m/z Difference Adduct Sig. KW (p value)C03765 4-Hydroxyphenylacetaldehyde C8H8O2 137.0596765 137.05936 0.000316467 M+H[1+] 0.09072C00483 Tyramine C8H11NO 138.0913765 138.09123 0.000146467 M+H[1+] 0.13534C04043 3,4-Dihydroxyphenylacetaldehyde C8H8O3 153.0545765 153.05442 0.000156467 M+H[1+] 0.13534C05576 3,4-Dihydroxyphenylethyleneglycol C8H10O4 153.0545765 153.05442 0.000156467 M-H2O+H[1+] 0.13534C01179 4-Hydroxyphenylpyruvate C9H8O4 163.0389765 163.03889 0.0000865 M-H2O+H[1+] 0.09072C05350 4-Hydroxyenolphenylpyruvate C9H8O4 163.0389765 163.03889 0.0000865 M-H2O+H[1+] 0.09072C00544 2,5-Dihydroxyphenylacetate C8H8O4 169.0495765 169.04948 0.0000965 M+H[1+] 0.13534C05577 3,4-Dihydroxymandelaldehyde C8H8O4 169.0495765 169.04948 0.0000965 M+H[1+] 0.13534C00082 L-Tyrosine C9H11NO3 182.0811765 182.0811 0.0000765 M+H[1+] 0.22313168Appendix B: Associated appendix for chapter 4Table B1: Overview of the fertilizer products used in this experiment with details on theirmanufacturer and product purpose.Product Name Treatment Name Manufacturer PurposeHyclean Hyclean HygrozymeLine cleaner to removeand prevent the build upof salts and minerals inirrigation lines and growingmediaRemoElementsPart APart A Remo NutrientsMicro- and macronutrientsfor usage throughoutentire growth cycle,7-12-15RemoElementsPart BPart B Remo NutrientsCalcium magnesiumsupplement with additionalnitrogen, 14-0-9Hygrozyme Hygrozyme Enzymes HygrozymeBlend of cellulase,xylanase, hemicellulase,and beta-glucanaseenzymes to encouragebreakdown of old rootmatterHygroben Hygroben Microbes HygrozymeMicrobial root inoculantcontaining Bacilluslicheniformis, B.subtilis, B. pumilus, B.amyloliquefaciens, and B.megateriumSafegrowActivator Hypochlorous AcidThe Safe DisinfectantCompany Inc. (BeeLeaf Productions Inc.)System descaler andcleanerRemoElementsSuperchargedBoostBoost Remo NutrientsPhosphorus andpotassium supplement,0-23-44169Table B2: Yield metrics of strawberry fruit tissue by zone and fertilizer. Control refers to thecombination of Part A and Part B delivered to the strawberry plants during the weekend periodbetween treatment applications.Fertilizer Total Weight (g) AcceptableStrawberry Weight (g)UnacceptableStrawberry Weight (g)Zone 2Boost 346.4 266.8 79.6Hyclean 191.6 151.4 40.2Hygroben Microbes 125.6 110.8 14.7Hygrozyme Enzymes 442.9 360.1 82.9Hypochlorous Acid missing 144.8 missingPart A 640.1 437.2 202.9Part B 93.3 82.7 10.7Control 223.6 161.6 62.0Zone 3Boost 304.3 224.4 79.9Hyclean 387.4 260.5 126.9Hygroben Microbes 240.4 186.8 53.6Hygrozyme Enzymes missing 202.7 missingHypochlorous Acid 234.6 185.0 49.6Part A 172.2 109.9 62.3Part B 350.4 269.7 80.8Control 235.3 158.4 76.8Zone 4Boost 322.7 233.0 89.7Hyclean 123.5 107.0 16.4Hygroben Microbes 379.7 267.5 112.2Hygrozyme Enzymes 211.4 191.1 20.3Hypochlorous Acid 441.0 290.5 150.5Part A missing 227.2 missingPart B 283.8 220.6 63.2Control 231.5 158.9 72.6Figure B1: Electrophysical signals (mV) measured every hour, as for electrode #5 from theVivent Biosignal device 76331e over the course of the experiment, prior to any noise signalremoval.170Table B3: Time series analysis parameters for sensor data and strawberry yield for datacollected from zone 2, across all fertilizer treatments. MAD is mean absolute deviation, MAPEis mean accuracy percentage of error, and MSD is mean squared deviation. Values includedthose collected from the Control treatment, which refers to the combination of Part A and Part Bdelivered to the strawberry plants during the weekend period between treatment applications.Source Measure Zone 2MAD MAPE(%) MSDLinear Trend AnalysisR2ElectrophysiologicalSensor(Vivent Biosignals)(n = 79)Ca (%) 0.031 5.46 0.002 0.10 (+)N (%) 0.022 5.37 0.001 0.11 (-)K (%) 0.010 18.13 0.000 0.14 (+)P (%) 0.044 14.93 0.003 0.07 (-)Fe (%) 0.019 35.78 0.001 0.02 (+)Mn (%) 0.048 10.45 0.004 0.14 (+)VMC (%) 0.033 5.67 0.002 0.02 (-)Electrophysical Sensor(GroSens)(n = 71)EC 0.159 6.73 0.067 0.45 (+)Temperature 0.304 1.19 0.156 0.56 (+)VMC (%) 2.217 13.28 8.729 0.01 (+)Strawberry Yield (n = 12) Yield (g) 107.432 56.83 19842.600 0.55 (-)Table B4: Time series analysis parameters for sensor data and strawberry yield for datacollected from zone 3, across all fertilizer treatments. Values included those collected fromthe Control treatment, which refers to the combination of Part A and Part B delivered to thestrawberry plants during the weekend period between treatment applications. MAD is meanabsolute deviation, MAPE is mean accuracy percentage of error, and MSD is mean squareddeviation.Source Measure Zone 3MAD MAPE(%) MSDLinear Trend AnalysisR2ElectrophysiologicalSensor(Vivent Biosignals)(n = 79)Ca (%) 0.028 4.55 0.001 0.06 (-)N (%) 0.021 4.63 0.001 0.00 (0)K (%) 0.021 8.49 0.001 0.00 (+)P (%) 0.042 10.44 0.003 0.16 (-)Fe (%) 0.037 37.61 0.003 0.14 (-)Mn (%) 0.043 7.41 0.004 0.38 (+)VMC (%) 0.035 6.16 0.002 0.06 (+)Electrophysical Sensor(GroSens)(n = 0)EC N/A N/A N/A N/ATemperature N/A N/A N/A N/AVMC (%) N/A N/A N/A N/AStrawberry Yield (n = 12) Yield (g) 68.257 41.18 6779.968 0.27 (-)171Table B5: Time series analysis parameters for sensor data and strawberry yield for datacollected from zone 4, across all fertilizer treatments. Values included those collected fromthe Control treatment, which refers to the combination of Part A and Part B delivered to thestrawberry plants during the weekend period between treatment applications. MAD is meanabsolute deviation, MAPE is mean accuracy percentage of error, and MSD is mean squareddeviation.Source Measure Zone 4MAD MAPE(%) MSDLinear Trend AnalysisR2ElectrophysiologicalSensor(Vivent Biosignals)(n = 79)Ca (%) 0.035 6.20 0.002 0.32 (-)N (%) 0.020 2.95 0.001 0.00 (0)K (%) 0.022 3.25 0.001 0.11 (-)P (%) 0.029 7.29 0.001 0.01 (-)Fe (%) 0.030 27.45 0.002 0.18 (+)Mn (%) 0.042 8.09 0.003 0.12 (+)VMC (%) 0.037 6.84 0.002 0.01 (+)Electrophysical Sensor(GroSens)(n = 71)EC 0.133 5.84 0.044 0.01 (-)Temperature 0.324 1.29 0.160 0.31 (+)VMC (%) 2.865 18.48 12.751 0.20 (-)Strawberry Yield (n = 12) Yield (g) 101.005 73.70 13663.684 0.45 (-)Table B6: Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Boost fertilizer treatment. MAD is mean absolute deviation, MAPE ismean accuracy percentage of error, and MSD is mean squared deviation.Measure Fertigation Solution: BoostMAD MAPE(%) MSDLinear Trend AnalysisR2B N/A N/A N/A N/ACa 4.468 41.22 20.925 1 (+)Cu N/A N/A N/A N/AFe N/A N/A N/A N/AK 28.95 10.84 1670.425 0.75 (+)Li N/A N/A N/A N/AMg 1.243 28.79 2.072 0.90 (+)Mn N/A N/A N/A N/AMo N/A N/A N/A N/ANa 13.19 271.32 174.091 0.00 (-)P 9.42 13.58 102.132 0.95 (+)S 10.84 14.16 190.437 0.65 (+)Si 0.348 15.52 0.121 1 (+)Sr 0.062 100 0.008 0.75 (+)Zn N/A N/A N/A N/ApH 0.04 0.67 0.003 0.35 (+)EC 0.294 10.39 0.282 0.28 (+)172Table B7: Time series analysis parameters for elemental data from emitter ICP-OES data (n =3) collected from the Hyclean fertilizer treatment. MAD is mean absolute deviation, MAPE ismean accuracy percentage of error, and MSD is mean squared deviation.Measure Fertigation Solution: HycleanMAD MAPE(%) MSDLinear Trend AnalysisR2B 0.299 infinity 0.179 0.19 (+)Ca 5.792 47.02 58.533 0.99 (+)Cu N/A N/A N/A N/AFe N/A N/A N/A N/AK 1.693 57.71 4.923 0.02 (-)Li N/A N/A N/A N/AMg 1.73 32.01 6.165 0.86 (+)Mn N/A N/A N/A N/AMo N/A N/A N/A N/ANa 14.375 7.85 226.838 0.88 (-)P 1.182 infinity 1.931 0.01 (+)S 1.229 infinity 2.077 0.08 (+)Si 0.581 27.14 0.494 0.98 (+)Sr 0.048 53.89 0.008 0.81 (+)Zn N/A N/A N/A N/ApH 0.14 2.35 0.051 0.27 (+)EC 0.258 13.09 0.133 0.00 (-)173Table B8: Time series analysis parameters for elemental data from emitter ICP-OES data (n =3) collected from the Hygroben Microbes fertilizer treatment. MAD is mean absolute deviation,MAPE is mean accuracy percentage of error, and MSD is mean squared deviation.Measure Fertigation Solution: Hygroben MicrobesMAD MAPE(%) MSDLinear Trend AnalysisR2B 1.496 24.63 3.303 0.59 (-)Ca 4.821 32.81 64.669 0.98 (+)Cu N/A N/A N/A N/AFe N/A N/A N/A N/AK 8.472 104.74 97.441 0.94 (-)Li N/A N/A N/A N/AMg 1.481 31.49 5.014 0.99 (+)Mn N/A N/A N/A N/AMo N/A N/A N/A N/ANa 4.535 18.06 23.825 0.37 (+)P 1.254 infinity 2.036 0.07 (-)S 1.133 189.6 2.426 0.01 (-)Si 0.544 22.32 0.473 0.99 (+)Sr 0.049 57.22 0.007 0.95 (+)Zn N/A N/A N/A N/ApH 0.118 2.05 0.033 0.55 (+)EC 0.057 9.16 0.006 0.78 (+)174Table B9: Time series analysis parameters for elemental data from emitter ICP-OES data(n = 3) collected from the Hygrozyme Enzymes fertilizer treatment. MAD is mean absolutedeviation, MAPE is mean accuracy percentage of error, and MSD is mean squared deviation.Measure Fertigation Solution: Hygrozyme EnzymesMAD MAPE(%) MSDLinear Trend AnalysisR2B 0.251 infinity 0.084 0.01 (-)Ca 4.453 39.23 26.051 0.97 (+)Cu N/A N/A N/A N/AFe N/A N/A N/A N/AK 6.922 32.73 57.541 0.01 (+)Li N/A N/A N/A N/AMg 1.574 23.61 4.061 0.85 (+)Mn N/A N/A N/A N/AMo N/A N/A N/A N/ANa 4.746 39.99 22.798 0.59 (+)P 72.166 288.61 7076.285 0.42 (+)S 6.158 116.9 55.1 0.09 (-)Si 0.533 24.74 0.332 0.97 (+)Sr 0.044 49.01 0.003 0.95 (+)Zn N/A N/A N/A N/ApH 0.088 1.44 0.025 0.64 (+)EC 0.083 16.75 0.011 0.85 (+)175Table B10: Time series analysis parameters for elemental data from emitter ICP-OES data (n =3) collected from the Hypochlorous Acid fertilizer treatment. MAD is mean absolute deviation,MAPE is mean accuracy percentage of error, and MSD is mean squared deviation.Measure Fertigation Solution: Hypochlorous AcidMAD MAPE(%) MSDLinear Trend AnalysisR2B 0.283 infinity 0.183 0.24 (+)Ca 25.667 60.52 1959.749 0.49 (+)Cu N/A N/A N/A N/AFe N/A N/A N/A N/AK 29.716 50.49 3204.462 0.41 (+)Li N/A N/A N/A N/AMg 11.875 52.18 443.449 0.48 (+)Mn N/A N/A N/A N/AMo N/A N/A N/A N/ANa 2.532 28.45 8.107 0.93 (+)P 67.393 222.02 6147.144 0.22 (+)S 1.098 346.85 1.581 0.59 (+)Si 0.609 28.96 0.41 0.99 (+)Sr 0.149 88.44 0.057 0.56 (+)Zn N/A N/A N/A N/ApH 0.073 1.14 0.036 0.20 (+)EC 0.076 24.18 0.013 0.85 (+)176Table B11: Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Part A fertilizer treatment. MAD is mean absolute deviation, MAPE ismean accuracy percentage of error, and MSD is mean squared deviation.Measure Fertigation Solution: Part AMAD MAPE(%) MSDLinear Trend AnalysisR2B 0.18 infinity 0.065 0.39 (-)Ca 5.907 42.52 51.091 0.97 (+)Cu 0.168 infinity 0.033 0.31 (+)Fe 0.158 22.07 0.039 0.03 (-)K 22.55 18.01 706.73 0.68 (-)Li N/A N/A N/A N/AMg 4.08 8.76 18.004 0.08 (-)Mn 0.046 10.83 0.005 0.59 (+)Mo N/A N/A N/A N/ANa 5.935 56.86 45.727 0.58 (+)P 24.163 26.91 1543.329 0.52 (-)S 4.562 5.95 27.993 0.67 (-)Si 0.624 30.13 0.423 0.98 (+)Sr 0.056 68.75 0.007 0.87 (+)Zn 0.045 9.59 0.005 0.76 (+)pH 0.107 1.85 0.023 0.40 (+)EC 0.153 5.04 0.041 0.49 (-)177Table B12: Time series analysis parameters for elemental data from emitter ICP-OES data (n= 3) collected from the Part B fertilizer treatment. MAD is mean absolute deviation, MAPE ismean accuracy percentage of error, and MSD is mean squared deviation.Measure Fertigation Solution: Part BMAD MAPE(%) MSDLinear Trend AnalysisR2B 0.043 infinity 0.005 0.77 (-)Ca 56.605 132.9 5791.231 0.00 (+)Cu 0.061 100 0.015 0.42 (+)Fe 0.074 infinity 0.022 0.49 (-)K 79.228 504.72 7245.026 0.44 (-)Li N/A N/A N/A N/AMg 21.391 146.98 758.584 0.01 (-)Mn N/A N/A N/A N/AMo N/A N/A N/A N/ANa 4.455 46.75 34.746 0.22 (+)P 89.441 1917.71 11888.434 0.16 (+)S 1.325 46.49 3.545 0.54 (+)Si 0.643 31.15 0.491 0.99 (+)Sr 0.286 89.24 0.133 0.02 (+)Zn N/A N/A N/A N/ApH 0.199 3.45 0.062 0.00 (-)EC 0.271 6.87 0.125 0.19 (+)178Figure B2: Differenced electrophysical signals (mV) measured every hour across zones (n =72) from the Vivent Biosignal devices over the course of the experiment, after noise removalthrough windowing with a step size of 40 and first-order differencing, for each fertigation type.Strawberry Nutrients refers to the combination of Part A and Part B delivered to the strawberryplants during the weekend period between treatment applications.179Table B13: Summary of parameters calculated on electrophysical signal alongside the formulaand Python script used for each calculation using from scipy.stats import kurtosis, skewand import math packages.Parameter Equation Python EquationMean E[mi] np.mean(signal)Median bn+12np.median(signal)Minimum â np.min(signal)Maximum â np.max(signal)Lower Quartile x+)4 np.percentile(signal, 25)Upper Quartile +x(x+))4np.percentile(signal, 75)Summationâxi5) xi np.sum(signal)Variance E[mi â µ]2 np.var(signal, ddof=0)Standard DeviationâV vr np.std(signal, ddof=0)Kurtosis E[xiâµÏ]4 kurtosis(signal, fisher=False)Skewness E[xiâµÏ]+ skew(signal)Log energy entropy ââxi5) ei log(ei)Sample entropy â log C(+E(.2Ï)C(2E(.2Ï)Sample entropy withtunable parameters â ln(AL)180Table B14: Differences in concentration (ppm) between emitter (n = 3) and leachate (n = 3)samples collected at the end of the treatment application with the corresponding p value, asdetermined using paired t-test (α = 0.05) Significant values are highlighted. Control refers tothe combination of Part A and Part B delivered to the strawberry plants during the weekendperiod between treatment applicationsÍ¾ its samples were collected after each weekend period,before the next fertilizer treatment had been implemented.ElementFertilizer Product Value B Ca Cu Fe K Li Mg Mn Mo Na P S Si Sr ZnBoost â ppm 4.2 432.1 0.7 1.8 248.0 0.0 251.8 0.7 0.0 179.3 104.6 108.2 52.3 1.4 0.3p value 0.002 0.049 0.069 0.070 0.119 N/A 0.051 0.020 N/A 0.083 0.125 0.100 0.029 0.054 0.107Hyclean â ppm 4.2 297.4 0.8 2.1 489.3 0.0 203.1 0.7 0.0 -44.8 174.7 193.1 40.0 1.0 0.3p value 0.113 0.052 0.088 0.086 0.066 N/A 0.078 0.030 N/A 0.327 0.085 0.089 0.022 0.047 0.051Hygroben Microbes â ppm -5.8 338.5 0.6 0.9 462.7 0.0 213.9 0.8 0.0 109.6 177.6 182.7 44.4 1.2 0.2p value 0.013 0.033 0.146 0.009 0.094 N/A 0.079 0.002 N/A 0.174 0.118 0.136 0.027 0.030 0.115Hygrozyme Enzymes â ppm 1.8 550.5 1.5 3.7 1024.6 0.0 370.8 1.3 0.0 180.9 280.1 346.4 71.1 2.0 0.5p value 0.062 0.071 0.128 0.107 0.094 N/A 0.081 0.084 N/A 0.070 0.118 0.080 0.067 0.083 0.147Hypochlorous Acid â ppm 2.6 457.2 1.5 1.7 773.2 0.0 321.8 1.2 0.0 120.4 200.7 264.5 60.8 1.7 1.5p value 0.110 0.058 0.144 0.107 0.102 N/A 0.083 0.103 N/A 0.043 0.158 0.090 0.078 0.061 0.141Part A â ppm 2.2 298.3 0.5 0.2 310.8 0.0 137.8 0.2 0.0 114.7 67.7 86.4 41.7 0.9 -0.2p value 0.050 0.077 0.149 0.294 0.184 N/A 0.112 0.284 N/A 0.073 0.271 0.200 0.081 0.084 0.149Part B â ppm 0.6 167.4 0.5 0.8 279.6 0.0 123.5 0.5 0.0 163.7 75.2 137.0 34.0 0.4 0.2p value 0.031 0.064 0.018 0.120 0.031 N/A 0.026 0.104 N/A 0.093 0.215 0.007 0.056 0.143 0.078Control â ppm 2.2 256.2 0.5 2.1 234.6 0.0 124.5 0.4 0.0 138.7 2.5 108.8 39.0 0.7 0.0p value 0.064 0.034 0.119 0.114 0.073 N/A 0.064 0.018 N/A 0.100 0.493 0.099 0.016 0.042 0.464181Table B15: Individual elemental concentration (mean Â± std. dev.) in ppm by fertilizer treatment of leaf ICP-OES data (n = 3)Í¾ note, std.dev. could not be estimated for Hypochlorous Acid leaf tissue samples as only one sample was collected.Element Fertilizer ProductBoost Hyclean Hygroben Microbes Hygrozyme Enzymes Hypochlorous Acid Part A Part BB 381.4 ± 52.5 221.7 ± 38.1 121.3 ± 39.3 169.6 ± 148.5 262.5 322.5 ± 184.4 276 ± 275.9Ca 19206.3 ± 4308.8 9744.4 ± 1386.5 17052.2 ± 4816.5 12945.3 ± 7691.6 13403.5 14449.7 ± 2857.4 10802.2 ± 1203.5Cu 0 ± 0 2.1 ± 3 0 ± 0 0 ± 0 0 1.8 ± 3.2 10.6 ± 0.4Fe 327 ± 30.9 120.6 ± 17.4 167.2 ± 72.5 76.1 ± 112.2 183.7 271.6 ± 140.4 90.8 ± 89.7K 15652.3 ± 3020.5 24921.2 ± 5970.8 18793.5 ± 3872.1 20722.5 ± 1094.2 21679.8 17496.8 ± 1312.8 20897.4 ± 1292.5Li 0 ± 0 0 ± 0 0 ± 0 0.1 ± 0.2 0.3 0 ± 0 0 ± 0Mg 5638.2 ± 552.6 3803.4 ± 612.9 4964.3 ± 1362.2 4041.6 ± 1169.3 3659.2 4388.2 ± 591.8 4395.1 ± 365.6Mn 453.2 ± 75.3 228 ± 106 429.1 ± 167 274.8 ± 86.3 259 256.7 ± 64.1 216.3 ± 33.4Mo 0 ± 0 0.5 ± 0.7 0 ± 0 0 ± 0 0 0 ± 0 0 ± 0Na 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 0 ± 0 0 ± 0P 7487.4 ± 2013.4 6156.3 ± 800.6 7197.1 ± 1897.5 6259.2 ± 105.2 6254.3 5065.6 ± 594.1 5485.1 ± 2019.9S 1688 ± 338.9 1722.5 ± 12.8 1930.8 ± 306.6 1541.5 ± 74.5 1609.2 1499.7 ± 328.6 1732.6 ± 220.7Si 216.8 ± 81.8 127.9 ± 19.8 241.3 ± 13.4 135.9 ± 78.1 135.1 130.3 ± 43.9 123 ± 66.5Sr 75.2 ± 18.2 33.9 ± 7.5 63.1 ± 19.7 48.2 ± 41.6 47.2 50.7 ± 13.2 35.3 ± 9.1Zn 30.6 ± 4.7 20.5 ± 6.2 24.1 ± 10.3 20.2 ± 10.5 17.7 22.7 ± 4.3 24.7 ± 11Table B16: Individual elemental concentration (mean Â± std. dev.) in ppm by fertilizer treatment of fruit ICP-OES data (n = 3)Í¾ note, std.dev. could not be estimated for Hypochlorous Acid leaf tissue samples as only one sample was collected.Element Fertilizer ProductBoost Hyclean Hygroben Microbes Hygrozyme Enzymes Hypochlorous Acid Part A Part BB 1.4 ± 2 57.5 ± 69.5 0 ± 0 0 ± 0 0 0 ± 0 47 ± 66.5Ca 2624.8 ± 175.1 1936 ± 259.5 1837.2 ± 265.1 2127.2 ± 351.4 1443.6 1741.7 ± 186.2 2643.2 ± 412.3Cu 0 ± 0 4.5 ± 6.4 0 ± 0 0 ± 0 0 0 ± 0 2.1 ± 2.9Fe 15.9 ± 22.4 32.4 ± 45.9 4.7 ± 6.6 0 ± 0 0 2 ± 3.4 0 ± 0K 14628.2 ± 2230.6 19992.6 ± 3392.5 14335.1 ± 1415.3 16090 ± 6044.7 14332.6 14780.3 ± 656.8 17430.5 ± 4508.3Li 0 ± 0 0.8 ± 1.1 0 ± 0 0 ± 0 0 0 ± 0 0 ± 0Mg 1646.8 ± 118.1 1702 ± 215.1 1370.1 ± 105.3 1505.6 ± 244.3 1382.9 1421.4 ± 175.1 1692 ± 310.3Mn 63.8 ± 17.4 78.5 ± 4.1 53 ± 8 55.5 ± 11.6 49.5 41.6 ± 5.4 67.2 ± 14.5Mo 0 ± 0 16.5 ± 23.4 0 ± 0 0 ± 0 0 0 ± 0 1.4 ± 2Na 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0P 3286.1 ± 224.1 4062 ± 941.6 3614.3 ± 409.4 3991.6 ± 688.1 3077.1 3369 ± 301.9 3444.6 ± 57.1S 866.1 ± 152.9 1147.9 ± 239 929.2 ± 89.7 923.8 ± 217 738 990.5 ± 62.4 1050.8 ± 114.1Si 27.8 ± 14.8 39.9 ± 21.9 32.8 ± 14.9 30.3 ± 28.7 35.6 13.9 ± 11.5 30.5 ± 7.9Sr 4.3 ± 5.1 7.1 ± 7.2 1.9 ± 2.7 0.9 ± 0.9 0 0.1 ± 0.2 4.8 ± 2.5Zn 12.1 ± 1 13.8 ± 4.8 11.3 ± 2.1 10.7 ± 4 5 7.5 ± 2.2 9.9 ± 1.4182Figure B3: Correlogram between elemental ICP-OES measurements (ppm) in leaf (n = 15)and fruit (n = 15) tissue samples across all fertilizer treatments. Mo and Na were omitted dueto low frequency of detection across samples.183(a)(b)Figure B4: Pearson correlation plots of B, Ca, Cu, Fe, K, Mg, Mn, P, S, Si, Sr, and Zn databetween substrate EC as measured by GroSens sensors and either (a) emitter or (b) leachatesample ICP-OES data (n = 8).184Table B17: Summary of parameters calculated for the electrophysiogical signals extracted, denoised, and normalized from the ViventBiosignal sensors (n = 24).Fertigation Zone Mean Min Max Median Q1 Q3 Sum Variance StdDev Kurtosis Skewness Log EnergyEntropySampleEntropySampleEntropy SETBoost 2 12.13 3.75 27.88 9.62 6.67 16.61 291.11 45.69 6.76 2.42 0.78 2.69 0.19 0.23Boost 3 -34.86 -74.57 -21.17 -29.17 -39.36 -26.38 -836.56 197.55 14.06 4.01 -1.41 2.87 0.11 0.16Boost 4 34.98 -8.96 54.47 44.18 19.10 50.72 839.62 327.02 18.08 2.37 -0.74 2.88 0.16 0.20Hyclean 2 50.69 45.07 59.14 49.29 48.39 51.72 1216.50 17.56 4.19 2.43 0.74 3.16 0.27 0.32Hyclean 3 -22.09 -48.68 -11.25 -21.62 -22.98 -16.50 -530.20 63.99 8.00 5.88 -1.57 2.92 0.21 0.26Hyclean 4 35.93 -21.06 61.41 52.81 12.39 55.03 862.26 721.92 26.87 2.41 -0.94 2.87 0.15 0.19Hygroben Microbes 2 45.96 39.65 54.96 45.51 43.64 47.36 1102.96 12.03 3.47 3.60 0.73 3.17 0.42 0.46Hygroben Microbes 3 -15.70 -21.71 -9.61 -15.54 -16.50 -14.32 -376.82 6.17 2.48 3.90 -0.31 3.13 0.32 0.36Hygroben Microbes 4 42.27 8.16 56.96 50.66 34.41 52.24 1014.37 207.16 14.39 2.68 -0.99 3.01 0.11 0.15Hygrozyme Enzymes 2 20.71 12.36 30.48 20.50 15.75 24.43 497.00 30.42 5.52 2.05 0.17 3.05 0.65 0.69Hygrozyme Enzymes 3 -27.86 -64.85 -7.62 -21.08 -35.73 -15.48 -668.72 273.56 16.54 2.66 -0.96 2.63 0.27 0.31Hygrozyme Enzymes 4 46.31 17.91 60.09 51.54 38.13 56.08 1111.50 165.79 12.88 2.59 -0.94 3.05 0.14 0.19Hypochlorous Acid 2 26.67 15.66 38.96 27.13 23.09 30.26 640.09 43.29 6.58 2.30 0.05 3.07 0.47 0.51Hypochlorous Acid 3 -15.83 -54.16 -3.74 -10.12 -18.56 -6.57 -379.97 193.64 13.92 3.97 -1.45 2.20 0.12 0.16Hypochlorous Acid 4 40.74 -2.47 60.35 50.87 23.24 56.18 977.67 432.48 20.80 2.30 -0.94 2.89 0.18 0.22Part A 2 6.87 1.53 20.21 5.14 3.18 8.62 164.87 28.50 5.34 3.74 1.30 2.36 0.18 0.22Part A 3 0.34 -19.95 10.26 5.06 -3.56 6.90 8.14 90.59 9.52 2.65 -1.08 2.58 0.16 0.21Part A 4 47.83 14.52 61.38 55.19 42.50 56.62 1147.92 196.11 14.00 3.25 -1.24 3.05 0.11 0.16Part B 2 26.28 16.08 37.22 25.43 23.47 28.91 630.77 28.72 5.36 2.46 0.09 3.10 0.45 0.49Part B 3 -15.57 -29.69 6.72 -17.91 -23.43 -11.70 -373.65 102.26 10.11 2.44 0.64 2.82 0.29 0.34Part B 4 47.01 16.94 61.06 51.82 39.59 54.48 1128.31 134.39 11.59 3.38 -1.11 3.08 0.20 0.25p value (Fertigation) 0.9579 0.92207 0.96778 0.97714 0.92364 0.98409 0.9579 0.76031 0.76031 0.66965 0.9917 0.21732 0.5544 0.53896p value (Zone) 0.00064 0.00113 0.00023 0.00019 0.00116 0.00016 0.00064 0.00193 0.00193 0.05596 0.00261 0.61048 0.00807 0.00918185Figure B5: Differenced, normalized electrophysical signals (mV) presented over the course ofa single day by zone averaged by fertigation treatment (n = 7), as collected from the ViventBiosignal devices.Figure B6: 1-layer correllogram between parameters of differenced, normalized electrophysicalsignals (mV) (n = 24) and substrate electrical conductivity (EC) as measured by GrodanGroSens sensors (n = 24).186Figure B7: Pearson correlation plot between parameters of differenced, normalizedelectrophysical signals (mV) (n = 24) and the manufacturer-processed Vivent data for N, Ca,Fe, K, Mn, and P (n = 24).187
Thesis/Dissertation
eng
Biochemistry and Molecular Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Murch, S. J. (Susan J.)
Using controlled environment agriculture to evaluate light and nutrient management strategies for Cannabis sativa L. and Fragaria × ananassa (Duchesne ex Rozier)
Text