High-THC Cannabis sativa in a New York Greenhouse: Yield and Economic Factors

High-THC Cannabis sativa in a New York Greenhouse: Yield and Economic Factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article High-THC Cannabis sativa in a New York Greenhouse: Yield and Economic Factors Kayla Ruterbories, John Hanchar, Daniela Vergara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8021436/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background The legalization of adult-use Cannabis sativa in New York State has created a need for research-based information on expected yield, production costs, revenue, and profitability for greenhouse cultivation. Limited data currently exist to inform growers and investors. This study evaluates both agronomic and economic outcomes for two flowering strategies—autoflower (light-insensitive) and photoperiod (light-sensitive) cannabis—grown in a NYS greenhouse. Methods A comparative agronomic and economic analysis was conducted to assess yield performance, input requirements, costs, revenue, and returns for autoflower and photoperiod C. sativa crops. Growth traits were measured and correlated with final yield. Cost components, including labor, seeds and plants, nutrients, and other variable inputs, were analyzed to determine their contribution to total production expenses. Economic returns were calculated on a per–square foot basis. Results Both autoflower and photoperiod plants showed strong correlations between early growth traits and final yield. Autoflowers, with shorter life cycles and independence from light manipulation, produced smaller plants with lower total biomass and THC content compared to photoperiod plants. Using assumed baseline values, autoflower cultivation resulted in a negative annual return above total costs of negative $ 1.48 per ft², whereas photoperiod cultivation generated a positive return of $ 7.18 per ft². Labor represented the largest share of variable costs for both systems, accounting for 52% of total costs in autoflower production and 34% in photoperiod production. Conclusions Autoflowers may be advantageous in space, capital, or labor-constrained environments requiring rapid crop turnover, while photoperiod plants appear more profitable for larger or well-resourced operations focused on maximizing yield and returns. Additional research is needed to identify practices and economic strategies that improve profitability, consistency, and efficiency for both cultivation approaches. This study underscores the need for continued economic analyses to guide decision-making in the emerging adult-use C. sativa industry. Autoflower vs. photoperiod Greenhouse production economics Labor costs Marijuana Profitability Yield optimization Figures Figure 1 Figure 2 Figure 3 Background Cannabis sativa L. (marijuana, hemp) is currently the largest emerging crop worldwide. This angiosperm from the dicot family Cannabaceae [ 1 ] was among the first plants domesticated by several ancient cultures [ 2 – 4 ] due to its versatility. Marijuana-type C. sativa is known for its production of cannabinoids, unique secondary metabolites with medicinal [ 5 – 7 ] or psychoactive [ 8 , 9 ] properties. The main cannabinoid produced by these recreational or adult-use plants is THCA (Δ-9-tetrahydrocannabinolic acid), which converts to its neutral form, THC (Δ-9-tetrahydrocannabinol), when heated. THC is highly regulated by the government due to its psychoactive effects [ 8 , 9 ] but remains the primary driver of the cannabis market, with product prices heavily influenced by its concentration [ 10 , 11 ] Cannabis sativa is a photosensitive plant, meaning its growth is influenced by light exposure. Like many other crops, it progresses through vegetative and reproductive stages, with the flowering phase triggered by reduced light duration. In contrast, autoflowering varieties, which are light insensitive, have the unique advantage of flowering regardless of daylength [ 12 , 13 ].. These plants also tend to grow shorter, making them particularly suited for environments where growers have limited control over light cycles, such as in greenhouse settings during mid-winter and early spring The global value of C. sativa production, processing, and manufacturing is in the billions of dollars and is expected to increase dramatically in the coming years. The legal marijuana industries, including medical and adult-use, in North America are predicted to grow substantially. The US adult-use market’s revenue is forecasted to reach $ 42.98 billion in 2024, with an annual growth rate of 2.89%, expected to reach $ 49.56 billion by 2029. The global adult-use market is expected to reach $ 64.73 billion in 2024 and grow at a 3.01% rate, reaching a value of $ 75.09 billion by 2029 [ 14 – 16 ]. The estimates for 2023 total legal revenues associated with C. sativa sales in the U.S. reached $ 28.8 billion, up from $ 26.1 billion the previous year, with average retail sales hitting $ 2.4 billion per month, reflecting a 10.3% increase over 2022’s sales [ 17 , 18 ]. Processed and manufactured goods play a crucial role in the industry, accounting for between 45% and 50% of total retail revenues [ 17 ]. Yield is a crucial factor in evaluating the success of cultivation practices. The harvest index quantifies the yield of a crop species versus the total amount of biomass that has been produced [ 19 ]. In general, temperate small-grain cereal crops achieve the highest harvest indices, typically within the range of 0.5 to 0.65, regardless of the time of sowing, whether winter or spring [ 19 ]. In C. sativa , there is a strong linear relationship between wet and dry biomass, with basal stem diameter identified as the best predictor of final dry stripped floral biomass. Smaller, earlier-flowering plants exhibit a higher dry-to-wet biomass ratio; however, maximizing floral biomass yield per unit area does not necessarily favor these plants due to weak correlations between flowering time and yield per unit area [ 20 ]. Cannabis sativa has been shown to lose between 25% and 77% of its original weight during processing, with an average weight loss of 13.51 ± 2.26 g (range: 9.73–16.65 g). This corresponds to an average percentage loss ranging from a minimum of 47.44% to a maximum of 77.07% [ 21 ]. Post-harvest analysis showed variations in biomass and cannabinoid concentration across and within cultivars. Strong correlations were observed between inflorescence and stripped biomass samples, with bulk biomass concentration aligning more closely with lower canopy sections. Plant morphology, canopy area, and dry canopy density significantly influenced cannabinoid content by plant section[ 22 ]. Additionally, the proportion of stripped biomass varies across different sections of the plant and by variety [ 22 ]. For instance, cannabinoid concentrations in axillary inflorescences from the lower parts of the plant were up to 90% lower than those in apical inflorescences at the top. Although higher planting density reduced cannabinoid concentrations in these lower inflorescences, it did not impact cannabinoid yield per cultivation area [ 23 ] This highlights the critical role of genotype in determining biomass yield and cannabinoid accumulation (Sandhu et al. 2022). Structural and genetic factors also play a pivotal role in yield and cannabinoid content, underscoring the complexity of optimizing these traits [ 24 ]. Differences between varieties extend to disease resistance, which may also be influenced by cultivation practices. For example, wet-bucked inflorescences showed significantly higher fungal growth compared to those hang-dried before bucking [ 25 ]. These metrics, however, are currently missing for autoflowering plants, which, as aforementioned, flower independently of light, reach smaller sizes, and therefore require less labor per grow. All of these variables must be taken into account as flower quality [ 26 ], and in particular THC [ 10 , 11 ] amounts are the drivers of the cannabis market and the price per pound of flower and/or biomass depends on these factors. Previous studies have found that floral dry weight in C. sativa is positively correlated with plant height and stem diameter, though unrelated to days to maturation. A broad phenotypic diversity in physiological traits indicates a positive link between plant vigor, growth rate, and inflorescence productivity, suggesting that selecting for fast-growing plants can enhance floral bud yield [ 27 ]. The average dry yield of inflorescences from various varieties ranged from approximately 257.28 g m² to 442.00 g m², with the maximum yield of CBDA ranging from 1,929.60 mg m² to 6,011.20 mg m². The cannabinoid content is significantly influenced by both the genotype and the plant's growth stage [ 28 ]. Environmental factors significantly influence the morphology of inflorescences in C. sativa . Weight distribution analysis revealed that processing 75% of the largest inflorescences accounts for approximately 90% of the total weight [ 29 ]. Plant width is influenced by transplant date and spacing, with earlier transplant dates resulting in taller and wider plants. Larger spacing leads to wider plants, while individual biomass increases with earlier transplanting and larger spacing. On a per-hectare basis, biomass was highest with earlier transplant dates and smaller spacings. Additionally, lower planting densities yield more biomass per plant, while higher densities produce more biomass overall. To maximize profits, farmers should aim to transplant early and use 1.22 m spacing [ 30 ]. In addition to information on yield and other agronomic traits, growers seek evidence-based insights into the expected economic outcomes of cultivating adult-use C. sativa . This economic analysis of growing adult-use C. sativa in a New York State greenhouse seeks to address the need for research-based information on expected economic outcomes. Helpful reviews of previous economic analyses predominantly associated with hemp for fiber, and grain enterprises, including several from state land grant systems, exist [ 31 – 33 ]. However, due to the recent legalization of adult-use C. sativa in New York and other states, expected costs, revenue, and returns with sensitivity analyses most helpful to growers are limited. Such work has not been done exhaustively for adult-use C. sativa . Established and prospective C. sativa growers require research-based knowledge to evaluate enterprise options and make informed decisions about integrating cannabis cultivation into their farm businesses. For instance, decision-makers need information to determine whether adding an adult-use cannabis enterprise is viable, which production scenarios and practices—such as outdoor cultivation, land-based high tunnels, or greenhouses—would work best for their specific goals, and how to navigate production, market, human resource, legal, and financial risks while effectively managing them. Additionally, another key consideration is whether to plant autoflowers or photoperiods, as both flowering strategies have different requirements, labor costs, and yields. Plant density and the amount of time spent in the vegetative versus flowering stages are also important factors to consider, which differ between both flowering strategies, and can influence overall yield and labor requirements. This economic analysis addresses the need for research with a focus on emerging high-cannabinoid C. sativa cultivation. Previous studies identified labor as the single largest variable cost in cannabis production [ 34 ]. This current analysis builds on this previous work [ 34 ], utilizing newly collected, detailed information on labor requirements and input usage to provide a comprehensive evaluation based upon farm level data collected in a greenhouse in upstate NY. Methods Autoflowers Autoflower, or light-insensitive, plants from the varieties “Sour Apple” and “Carmel Cream Gelato” were cultivated between February and May 2023. Seeds were started in mid-February, and plants were transplanted to their final location on March 6, allowing a 60-day growth period in the greenhouse. A total of 100 plants, 50 from each variety, were assessed at four intervals throughout the season and at harvest, totaling five measurement points. For each timepoint, three growth metrics were recorded: plant height (cm), stem diameter (mm), and node count. At the May 4 harvest, additional measurements were taken, including the size and width of the largest inflorescence (cm and mm, respectively) and the wet weight of each plant (g) including the root mass weight. Twelve days post-harvest, on May 16, dry weight (g) was recorded for 49 of the 100 plants. This comprehensive measurement approach provided insights into growth patterns and final yield across both varieties. The study’s structured data collection at multiple stages allowed for analysis of developmental differences within the two autoflower strains under consistent growing conditions. Photoperiods Photoperiod-sensitive plants from six varieties—"Bop Gun," "Doc Holiday 4," "GMO," "Animal Face," "Kosher Kush," and "Donkey Butter"—were cultivated from April to June 2023. The first batch of seeds was started in late March, transplanted to the greenhouse on April 3, and grew for 85 days until harvest on June 26. A second batch of "Bop Gun," "Doc Holiday 4," and "Kosher Kush" was started two weeks later and transplanted on April 14, resulting in 74 days of field growth. In total, 90 plants, with 10 from each variety across both planting batches, were assessed at two timepoints during the growing season and at harvest. Each measurement period included the same three metrics: height (cm), stem diameter (mm), and node count. At harvest, additional data were collected, including the size and width of the largest inflorescence, as well as the wet weight of the entire plant (g) and the wet bucked weight (g) after the removal of leaves and stems, leaving the usable biomass Cannabinoid testing Cannabinoid testing for THC was conducted through a third-party testing facility chosen by the company where we collected the data. The company provided us with the THC test results for both photoperiod and autoflower C. sativa plants. For photoperiod plants, testing was conducted through one testing facility, while autoflower plants were tested through a different testing facility. Statistical analyses T-tests were conducted to evaluate differences in the shared traits measured between autoflower and photoperiod plants, including wet plant weight (g), diameter of the main inflorescence (mm), size of the main inflorescence (cm), number of nodes, stem diameter (mm), height (cm), and number of days on the ground. Additionally, for the photoperiod plants, a linear mixed-effects model analyzed the fixed effects of timepoint, age, and their interaction, with plant ID as a random effect for repeated measures. F-tests evaluated the fixed effects, and post-hoc age comparisons were performed using Tukey's method. This analytical approach provided a detailed view of growth variation across photoperiod-sensitive plants and allowed for in-depth comparison of yield potential between the two planting ages. Economic analysis Enterprise budgeting concepts provide the general framework for the economic analysis [ 34 , 35 ]. USDA’s Hemp Report provides price received and production information, with definitions of key items (USDA/NASS, 2024). Detailed activity analysis, with an emphasis on tracking labor and other inputs, was conducted in 2023 to generate input data for the analyses of greenhouse cultivation systems for autoflower and photoperiod cannabis plants. This analysis focused on a well-equipped 30,000 sq. ft. greenhouse consisting of ten 3,000 sq. ft. bays, with autoflower plants grown in one bay and photoperiod plants grown in the remaining nine bays. Labor hours were recorded by task and by day from initial planting through harvest and final on-farm processing (bucked, dried flower) for both cultivation types. Autoflower plants were grown from seeds over approximately 62 days, resulting in about 450 plants harvested from one bay. Photoperiod plants, grown from purchased clones over approximately 91 days, produced about 3,150 plants harvested across nine bays. For comparison, annual expected costs, revenues, and returns were calculated assuming full use of the 30,000 sq. ft. growing space, with five autoflower and four photoperiod grows annually. Results reflect a year of activity, reported as $ per 30,000 sq. ft. and $ per sq. ft. Use of Artificial Intelligence Tools Portions of this manuscript were drafted and revised with assistance from OpenAI’s ChatGPT (GPT-5, September 2025). The authors reviewed and edited all AI-assisted text and take full responsibility for the final content. ChatGPT was used exclusively to improve clarity, conciseness, and flow of the writing and was not used to generate or analyze data, perform statistical analyses, or draw scientific conclusions. Results Autoflowers The traits measured (height (cm), stem diameter (mm), node count, size (cm) and width (mm) of the largest inflorescence, the wet weight (g) and the bucked weight (g)) are almost always correlated. Therefore, those plants that are tall also have numerous nodes, and a thick stem diameter (Fig. 1 ). Additionally, traits are correlated among times, therefore those plants that are tall when young are also tall when old (Figure S1). The mean and standard deviation for the traits measured during harvest are given in Table 1 . Using linear mixed-effects models for repeated measures analysis, significant changes over time were observed in autoflower plants for height (F = 1075.3; P < 0.0001), stem diameter (F = 1081.7; P < 0.0001), and number of nodes (F = 483.58; P < 0.0001; Figure S1). However, the length and width of the main inflorescence did not differ by strain at harvest for autoflowers. Photoperiods Like the autoflowers, most traits (height (cm), stem diameter (mm), node count, size (cm) and width (mm) of the largest inflorescence, wet plant weight (g), and wet bucked weight (g)) are almost always correlated (Fig. 2 ). Therefore, those plants that are tall also have numerous nodes, and a thick stem diameter. Additionally, traits are correlated among times. Therefore, those plants that are tall when young are also tall when old (Figure S2). The mean and standard deviation for the traits measured during harvest are given in Table 1 . Using different linear mixed-effects models that allow for repeated measures analysis, in photoperiods height (F = 477.36; P < 0.0001), stem diameter (F = 170.44; P < 0.0001), and number of nodes (F = 116.88; P < 0.0001) all exhibited significant changes over time (Figure S2). The only strain whose size of main inflorescence differed at harvest was “Animal Face” which was significantly smaller than “Bop Gun”. The inflorescence diameter at harvest from the strain “Animal Face” was marginally smaller from the strain Bop Gun”, all other strains didn’t differ in their width. The age difference between those 3 strains -the ones that were planted two weeks before and therefore had 11 days more on the ground- made no difference in the last point during harvest except for three traits (Figure S3). In other words, there were statistically significant differences between the measured traits that got smaller as the plants aged, and therefore the differences at timepoint one are larger than timepoint three. At harvest, the mean wet plant weight per photoperiod plant was approximately 659.22 ± 277.24g, resulting in a total of 58,670.6g (58.67kg) for the 89 plants sampled. The mean wet bucked weight, which excludes stems and other non-essential parts, was approximately 459.5 ± 170.0334g per plant. For the 89 plants measured, the total wet bucked weight was 40,436.1 g (40.44 kg). Table 1 Measurements during harvest for six traits shared among both autoflower and photoperiod plants showing mean ± standard deviation (columns 3–8); the dry plant weight collected only for autoflower plants (column 9); the wet bucked weight collected only for photoperiod plants (column 10); the Total THC provided through third-party testing (column 11), and estimates in italics of the effective weight (column 12) and the estimated THC weight and range (column 13). Mean ± Standard Deviation Estimates Variety Flowering Strategy Height (cm) Width (mm) Number of Nodes Biggest Inflorescence Size (cm) Biggest Inflorescence Width (mm) Wet Plant Weight (g) Dry Plant Weight (g) Wet Bucked Weight (g) Total THC (mg/g) Effective Weight (g) mean ± s.d. THC Per Plant (g) -range Caramel Cream Gelato A 62.82 ± 15.46 11.04 ± 2.09 8.02 ± 1.12 12.94 ± 2.57 28.17 ± 6.59 146.96 ± 60.60 19.83 ± 11.36 NA 98.448 19.76 ± 8.15 1.95 (1.14–2.75) Sour Apple A 66.26 ± 15.06 10.80 ± 2.09 8.92 ± 1.19 12.81 ± 3.09 25.78 ± 6.87 167.76 ± 61.89 28.62 ± 11.19 NA 99.826 22.56 ± 8.32 2.25 (1.42–3.08) Animal Face P 138.85 ± 10.46 20.68 ± 3.08 16.20 ± 1.39 10.00 ± 1.18 11.93 ± 3.39 844.30 ± 256.00 NA 554.87 ± 158.63 150.3 113.54 ± 34.47 19.8 (13.8–25.79) Bop Gun P 112.10 ± 21.06 20.17 ± 3.11 13.00 ± 2.20 12.98 ± 3.23 15.24 ± 7.04 561.60 ± 203.33 NA 397.25 ± 141.04 NA 75.52 ± 27.34 NA Doc Holiday 4 P 87.02 ± 13.09 15.35 ± 8.40 12.59 ± 1.27 11.95 ± 3.16 16.20 ± 4.08 416.50 ± 122.40 NA 345.00 ± 99.55 NA 56.01 ± 16.46 NA Donkey Butter P 129.00 ± 10.93 19.26 ± 1.72 13.78 ± 1.09 11.17 ± 1.58 19.14 ± 5.29 630.56 ± 236.89 NA 414.47 ± 153.11 168.7 84.8 ± 31.86 14.31 (8.93–19.68) GMO P 153.70 ± 12.75 21.52 ± 2.74 17.60 ± 2.37 10.65 ± 2.3 11.09 ± 4.64 967.76 ± 245.22 NA 634.92 ± 92.44 179.8 130.14 ± 32.98 23.4 (17.47–29.33) Kosher Kush P 158.58 ± 18.84 20.10 ± 2.58 15.85 ± 1.59 12.25 ± 2.32 18.12 ± 5.15 765.65 ± 253.04 NA 524.37 ± 185.82 149.7 102.96 ± 34.03 15.41 (10.32–20.51) Weight correlations A positive correlation was identified between the wet weight and dry weight of autoflower plants for both Sour Apple and Caramel Cream Gelato varieties (P < 0.0001, r = 0.979, Fig. 3 A). At harvest, the average wet weight per plant was 157.36 ± 61.83 g, and the average dry weight was 24.49 ± 12 g, with a total dry weight of 1,200 g for all plants. On average, 18.61% of the weight remained after drying, indicating an ~ 82% weight loss during the drying process. The weights per autoflower variety are given in Table 1 . There is a positive correlation between the photoperiods plant’s wet weight and plant’s wet bucked weight for both all photoperiod strains (P < 0.0001, r = 0.9469358, Fig. 3 B). On average, 72% of the weight remains (~ 28% is lost) after the plant is bucked. The weights per photoperiod variety are given in Table 1 . Autoflowers vs Photoperiods There is a significant difference in all measured traits between autoflowers and photoperiods, as well as in the number of days on the ground (Table 2 ). Table 2 Comparison of the various traits measured in both autoflower and photoperiod plants, including the p-value and mean for each flowering strategy. Wet plant weight (g) Diameter of main inflorescence (mm) Size of main inflorescence (cm) Number of Nodes Stem diameter (mm) Height (cm) Number of Days on Ground P value < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 Mean autoflowers 157.36 26.98 12.875 8.470 10.92 64.54 59.76 Mean Photoperiods 659.22 15.66 11.803 14.503 19.17 126.30 80.11 Estimates for Autoflowers and Photoperiods From the autoflower analysis, approximately 82% of the plant's weight is lost due to water loss, leaving 18% as the remaining weight. In the photoperiod analysis, about 28% of the weight is lost after accounting for the removal of stems and twigs, with 72% of the weight remaining. Using these percentages, we calculated the effective weight—the usable plant material (Table 1 , Column 12). On average, the effective weight is 21.16 ± 8.32 g for autoflowers and 88.65 ± 37.28 g for photoperiods. Additionally, we estimated the harvest index for both plant types (Figure S4). Considering the combined losses from water, stems, and twigs, approximately 13.45% of the plant's wet weight at harvest remains as usable material. For the 100 autoflowers grown, the mean weight was 157.36 g and their total weight was 1,5736g (1.57kg). Their estimated effective weight would be 2,116.146g (2.12kg). If 450 autoflower plants were grown, their total wet weight at harvest would be an estimated 70,812g (7.08 Kg) and a projected effective weight of around 9,5221.66g (9.52kg). The average THC concentration for the autoflower varieties was determined to be 99.13 mg/g, based on Sour Apple (98.45 mg/g) and Caramel Cream Gelato (99.83 mg/g Table 1 column 11). Using this average THC potency, an autoflower plant is estimated to produce approximately 2.1 g of THC (Table 1 , Column 13). For the projected effective weight of 9,521.66 g (9.52 kg) from 450 autoflower plants, the total THC yield is estimated to be 943.98 g (0.94 kg). For the 89 photoperiod plants analyzed, the mean wet weight is 659.22 g, with a total wet weight of 58,670.6 g (58.67 kg). This corresponds to an estimated effective weight of 7,889.91 g (7.89 kg) and an estimated THC production of 1,279.15 g (1.25 kg). Scaling this to a scenario with 3,150 plants, the estimated total wet weight would be 2,076,544 g (2,076.54 kg), with an effective weight of 279,249.5 g (279.25 kg). The total THC produced by these 3,150 plants is projected to be 45,273.32 g (45.27 kg).THC estimates for individual varieties are provided in Table 1 , column 13. Economics of Autoflowers . Based upon available data, and for the median expected price received, yield combination -- 260 ( $ per lb. harvested floral, dried), and 0.04 (lbs. harvested floral, dried per plant) -- estimated value of production, variable input cost, total cost, and return above total costs total $ 7.80, $ 5.58, $ 9.29, and negative $ 1.48 per sq. ft., respectively (Table 3 ). Total values, and calculations reflect results of rounding. Cost values represent the value of the input(s) used in production. Since total costs exceed value of production (revenue), subtracting $ 9.29 from $ 7.80 yields a negative return above total costs, or negative $ 1.48 per sq. ft. Expressed in annual $ for the 30,000 sq. ft. facility, the return is negative $ 44,510. Total annual costs of $ 278,510 for the facility exceed the value of production, revenue of $ 234,000. The result is a return, profit value that is less than zero. Sensitivity analysis suggests that 3 of 9 output price, yield combinations produced positive returns above total costs annually (Table 4 ). Economics of Photoperiods . Based upon available data, and for the median expected price received, yield combination -- 260 ( $ per lb. harvested floral, dried), and 0.18 (lbs. harvested floral, dried per plant) -- estimated value of production, variable input cost, total cost, and return above total costs total $ 21.84, $ 10.95, $ 14.68, and $ 7.18 per sq. ft., respectively (Table 3 ). Since the value of production exceeds the total cost of production, return is greater than zero. Sensitivity analysis suggests that 5 of 9 output price, yield combinations produce positive returns above total costs annually (Table 4 ). Table 3 Annual value of production (revenue), costs and returns for high cannabinoid C. sativa cultivation, greenhouse (under protection) setting, by planting scenario (flowering strategy). These analyses are based on the following assumptions: price received is taken from the median point of the expected range at $ 260 per lb., yields assumed are 0.04 and to be 0.18 lbs. per plant for autos and photos, respectively, and the cost of hired labor is set at $ 20 per hour, as outlined in the methods section. Planting Scenario Autoflower Photoperiod $ / 30,000 sq. ft $ / sq. ft $ / 30,000 sq. ft. $ / sq. ft Value of Production (Revenue) Value of harvested floral, dried 234,000 7.80 655,200 21.84 Costs of Production Variable inputs Fertilizer & lime 24,500 0.82 19,600 0.65 Seeds & plants 46,650 1.56 185,000 6.17 Sprays, bios, other variable crop inputs 8,370 0.28 7,360 0.25 Labor 86,490 2.88 110,860 3.70 Interest on operating capital 1,380 0.05 5,380 0.18 Total variable inputs 167,390 5.58 328,200 10.95 Fixed inputs Land charge 140 0.01 140 0.01 Buildings, improvements, and mechanicals 89,140 2.97 89,140 2.97 Value of operator & family management 14,850 0.50 14,850 0.50 Other fixed inputs 6,990 0.23 7,470 0.25 Total fixed inputs 111,120 3.71 111,600 3.73 Total costs 278,510 9.29 439,800 14.68 Returns Revenue minus costs of variable inputs 66,610 2.22 327,000 10.90 Revenue minus costs of variable & fixed inputs -44,510 -1.48 215400 7.18 Table 4 Revenue less total costs by price and yield for high-cannabinoid C. sativa cultivation in a greenhouse setting (30,000 sq. ft.). The first three columns represent autoflower planting scenarios with five two-month cycles annually, and the last three columns represent photoperiod planting scenarios with four three-month cycles annually. Values in ()’s are less than 0. lbs. floral, dried per plant Autoflowers Photoperiods $ per lb. floral, dried 0.02 0.04 0.06 0.11 0.18 0.25 120 (224,510) (170,510) (116,510) (255,000) (137,400) (19,800) 260 (161,510) (44,510) 72,490 (39,400) 215,400 470,200 400 (98,510) 81,490 261,490 176,200 568,200 960,200 Discussion Our results show that autoflower plants are smaller than photoperiod plants and produce less weight, including lower THC content per gram of flower in the two autoflower varieties studied (Tables 1 and 2 ). The weight loss from wet to dry in autoflower plants, approximately 82%, was higher than the 77% previously reported [ 21 ]. Additionally, the photoperiod analysis suggests that the weight from stems and twigs accounts for about 18% of the wet weight. The positive correlations between various traits in both autoflower (Fig. 1 ) and photoperiod (Fig. 2 ) plants indicate that early plant characteristics can predict final size and yield. Because autoflowers have shorter growth cycles and flower independently of light, they may present a cost-effective option for indoor or greenhouse cultivation where available capital and labor are limited. These plants require less time in the ground and demand minimal pruning or trellising [ 36 ], which can reduce labor and costs. However, in controlled environments where light cycles can be easily adjusted, photoperiod plants may be manipulated to flower at smaller sizes and for shorter periods, offering flexibility in production. Another drawback of autoflowering plants is their lack of consistency, often attributed to poor breeding practices. However, this inconsistency has not been thoroughly quantified or directly compared with photoperiod plants, leaving it largely speculative. Our results also indicate that, beyond the differences observed between autoflower and photoperiod plants, significant variation exists within varieties of each flowering strategy in terms of THC content, yield, and biomass production. Previous estimates on cannabinoid production, specifically CBD [ 37 ], suggest higher yields compared to the estimated THC production observed in the varieties measured here. However, the THC yields reported in this study fall within these previous estimates [ 37 ]. It is important to note that the previously reported values were derived from plants grown outdoors, many of which spent over 85 days in the ground [ 37 ], allowing them to grow larger. The measured cannabinoid in those studies was CBD, not THC, which introduces another important difference and limitation when comparing these results. Cultivation practices that can affect quality and yield In photoperiod plants it has been shown that topping did not significantly improve flower yield or cannabinoid concentration. While flower yield per plant decreased with higher plant density, total yield per hectare increased. CBD production per hectare rose with greater density, but cannabinoid concentration remained unaffected. However, increased density does not guarantee higher economic returns due to the high input costs for hemp plant material and labor [ 38 ]. Topping plants 3–4 weeks after transplanting increased labor costs without improving yield or cannabinoid content. While topping increased inflorescences and CBD content in two varieties [ 39 ], it also significantly influenced plant height, with un-topped plants being taller. Architectural modulation methods, including selective pruning and defoliation, improved cannabinoid profile consistency by reducing concentration variability across the plant's height. Yet, methods like primary branch removal reduced total yield, highlighting the challenge of balancing plant structure and cannabinoid optimization [ 24 , 40 ]. As previously mentioned, these metrics are currently lacking for autoflowering plants. Economics of autoflower cultivation Estimated value of production given initial price received and yield assumptions total $ 7.80 per sq. ft. (Table 3 ). Value of production estimates are a function of the number of grows per year, plants per grow, yield per plant, price received. Price received and yield variability substantially impact profit (Table 4 ). Results suggest that evaluating alternative practices for production and economic efficiencies are important to identifying the optimal set of production practices – planting settings, autoflowers and/or photoperiods, number and lengths of growing cycles which will differ between these two flowering strategies, number of plants per grow, among other considerations. Variable costs ( $ per sq. ft.) total 5.58 and account for 60 percent of total costs. Labor, seeds & plants, and nutrients are the three largest $ per sq. ft. items. Labor costs are the single largest item, accounting for 52 percent of total variable input costs ( $ per sq. ft.). Seeds & plants expense, the second largest item, and nutrients the third largest, account for 28 and 15 percent of total variable input costs ( $ per sq. ft.), respectively. These three greatest variable cost items account for 95 percent of all total variable input costs. Total cost fixed inputs ( $ per sq. ft.) total 3.71, and account for 40 percent of total costs. Fixed costs for buildings, improvements, and mechanicals for the greenhouse account for the vast majority of total fixed costs. Estimated total cost for variable and fixed inputs equals $ 9.29 per sq. ft., while revenue minus costs of variable inputs, and revenue minus total costs equal $ 2.22 and negative $ 1.49 per sq. ft., respectively. Economics of photoperiod cultivation Estimated value of production given initial price received and yield assumptions total $ 21.84 per sq. ft. (Table 3 ). Value of production estimates are a function of number of grows per year, plants per grow, yield per plant, price received. Price and yield variability substantially impact profit (Table 4 ). Results suggest that evaluating alternative practices for production and economic efficiencies are important for identifying the optimal set of production practices – planting settings, auto and, or photo; number of grows; length of grows; number of plants per grow; and others. Variable costs ( $ per sq. ft.) total 10.95 and account for 75 percent of total costs. Seeds & plants, labor, and nutrients are the three largest $ per sq. ft. items. Seeds & plants expense is the single largest item, accounting for 56 percent of total variable input costs ( $ per sq. ft.). Recall that for the photoperiod planting scenario, analysis reflects that purchased clones began the cultivation activities, and price paid for clones was about $ 13.50 per clone. Given this factor’s effect on results, future work would benefit from more accurate information regarding price paid, and or analysis of alternative practices, for example, analysis that assumes meeting the needs for clones in house. This analysis should quantify the tradeoffs between seeds & plants expense, labor, and other costs. Labor is the second largest item, and nutrients the third largest, accounting for 34 and 6 percent of total variable input costs ( $ per sq. ft.), respectively. These three greatest variable cost items account for 96 percent of all total variable input costs. Total cost fixed inputs ( $ per sq. ft.) total 3.73, and account for 25 percent of total costs. Fixed costs for buildings, improvements, and mechanicals for the greenhouse account for most total fixed costs. Estimated total cost for variable and fixed inputs equals $ 15.26 per sq. ft., while revenue minus costs of variable inputs, and revenue minus total costs equal 10.90 and 6.58 $ per sq. ft., respectively. Autoflower and photoperiod economic comparison Profit estimates for photoperiod cultivation are more favorable compared to autoflower cultivation given expected price and yield assumptions. Returns above total costs reflecting a year’s of activity show that the photoperiod planting scenario yielded returns above total costs of $ 7.18 per sq. ft., while the autoflower planting scenario yielded negative $ 1.48 per sq. ft. (Table 3 ). Sensitivity analysis results for the autoflower planting show that annual returns above total costs for a 30,000 sq. ft. facility ranged from negative $ 224,510 for the least favorable price received, yield combination to positive $ 261,490 for the most favorable price, yield combination (Table 4 ). Comparison values for the photoperiod planting ranged from negative $ 255,000 to positive $ 960,200, respectively. From a different perspective, sensitivity results show that for the autoflower planting, returns were greater than zero for three of the nine price, yield combinations, while the photoperiod planting produced returns greater than zero for 5 of 9 combinations. Photoperiod cultivation benefits from greater expected yields per plant, while autoflower plantings benefit from more annual grows and higher plant counts per grow. However, the net result is that photoperiod plantings annual revenue exceeds autoflower expected revenues. These higher revenues, even when combined with greater annual variable costs, drive the photoperiod scenario’s superior economic performance relative to autoflower cultivation. Two key expense items stand out in the cost comparison: seeds and plants, and labor. For seeds, the autoflower scenario involves purchasing seeds at approximately $ 1.50 each for five annual grows. In contrast, the photoperiod scenario relies on clones costing nearly $ 13.50 each, with only four grows annually. Labor costs also differ significantly. Autoflowers, requiring five grows per year with each grow lasting just over 62 days, demand less labor due to minimal need for trellising and trimming. Photoperiod plants, grown four times annually over 92-day cycles, demand more intensive trellising and trimming, increasing labor expenses. Risk and uncertainty are prominent concerns in cultivating high-cannabinoid C. sativa in newly legalized markets like New York State. Fees such as licensing, applications, sampling, and testing for heavy metals, pesticides, and THC content can accumulate significantly over a growing season. Additionally, uncertainties around how these fees and taxes will be assessed add complexity and financial risk, as reflected in the variability (Tables 3 and 4 ). Labor costs are a substantial component of variable input expenses, accounting for 52% in the autoflower scenario and 34% in the photoperiod scenario. Managing labor-related risks is critical due to uncertainties in labor availability, pricing, and skill levels. This analysis was strengthened by detailed tracking of labor hours by task and day, offering valuable insights for predicting labor needs and costs more accurately. Financial risk management is also essential, given the variability in economic performance influenced by price received and yield (Tables 3 and 4 ). Sound financial planning, including budgeting and scenario analysis, can help mitigate risks. For example, adopting best management practices can reduce the risk of low yields. Sensitivity analysis allows farm owners to evaluate the viability of their operations and identify focus areas for risk reduction, ensuring sustainable business performance amidst economic uncertainties. US and New York State adult-use economy The adult-use C. sativa market in the United States is growing rapidly, driven by shifting consumer preferences, regulatory changes, and macroeconomic factors [ 41 ]. In New York State, legal cannabis sales totaled $ 264 million in 2023, including $ 150 million from adult-use and $ 114 million from medical sales. By September 2024, 205 retail stores had opened, pushing sales to $ 651 million and putting the market on track to approach $ 1 billion annually. New York accounts for 16% of the $ 5.95 billion total addressable market, a share expected to rise with increased tourism [ 42 ]. New York City, the world’s largest cannabis-consuming city, records 62.3 metric tons of annual consumption at an average price of $ 12.50 per gram [ 41 ]. The industry’s economic impact is reflected in the creation of over 22,000 new jobs in 2023, signaling improving market stability after years of turbulence [ 18 ]. These agronomic and economic analyses are critical for understanding production capacity and highlighting areas where additional workforce development is needed. The launch of New York’s legal market was delayed by a complex licensing process prioritizing social equity applicants and by litigation, yet retail dispensary licenses are now expanding quickly. Between April and June 2024, New York State collected $ 26 million in cannabis taxes, compared to $ 42 million during all of 2023. Sales in 2024 have already reached $ 493.2 million, more than tripling 2023’s total and emphasizing the importance of supporting farmers to benefit from this rapidly maturing market [ 43 ]. Cannabis consumption continues to rise nationally. Seventeen percent of Americans reported smoking marijuana in 2024, up from 11 to 13% between 2015 and 2021 and consistent with 16% in 2022 [ 44 ]. Half of U.S. adults report having tried marijuana, compared to just 4% when Gallup first measured use in 1969 [ 44 ]. In New York State, 12.8% of adults—approximately 1.6 million people—reported cannabis use in the past 30 days in 2021, with more than half using it fewer than 20 days per month and 6% using it daily or nearly daily [ 45 ]. Consumer preferences are also evolving. In New York State, smoking remains the most common mode of use (73.8%), followed by eating (12.1%) and vaporizing (9.3%) [ 45 ]. Cannabis flower and pre-rolls represent the majority of sales among Baby Boomers (55%), Generation X (54%), and Millennials (53%), but only 45% among Generation Z, highlighting shifting product preferences [ 17 ]. The share of consumers using cannabis for non-medical reasons alone rose from 44% in 2020 to 49% in 2021, while those using it solely for medical purposes declined from 19% to 13% [ 45 ].Processed and manufactured products now account for nearly half of total retail revenues, reflecting demand for edibles, concentrates, and topicals [ 17 , 46 ]. Both photoperiod and autoflower production strategies, or a combination of the two, may be needed to meet this growing and diversifying demand. Caveats Several limitations should be considered when interpreting these results. First, the cannabinoid analysis was conducted by a third-party laboratory, and there is evidence suggesting potential data tampering [ 47 , 48 ]. While this raises concerns about the data's reliability, we must rely on the provided analysis for this study. It is also important to note that cannabinoid content can vary across different flowers of the plant [ 23 ], and in different plants from the same variety [ 49 ], yet commercial facilities typically test cannabinoids and other compounds in bulk, largely due to the high costs of testing. Additionally, the chosen varieties for this research may not represent those commonly used in other facilities, as cannabinoid content, yield, and other metrics are highly dependent on the specific variety grown. Different varieties inherently produce varying levels of cannabinoids [ 49 ] and biomass [ 22 ], which influences overall performance. Cultivation practices also play a significant role in determining yield and quality. These practices vary widely between facilities and cultivators, further complicating the generalization of these findings. Our results are based on data collected from a single facility at a specific point in time. While they provide a useful reference for guiding future cultivators and facilities in estimating production potential and costs, they may not be directly applicable to other settings. For example, we assume that photoperiod plants in this analysis are grown from purchased clones. However, plants may be grown from seeds in other facilities, or at this same facility at a different time. Differences would alter the analyses of revenue, costs, returns, and the estimates. For example, in a scenario where plants are grown from seeds, input costs for seed and plants, labor, other inputs would be expected show differences. Additionally, autoflowering and photoperiod plants were treated differently in this study due to the distinct ways they were processed. Autoflowers were hang dried and their bucked weight wasn’t taken, while photoperiod plants were bucked while wet, and the dried weight was not measured. This discrepancy introduces assumptions that the same amount of water was lost during drying for both flowering strategies, and that the weight of stem twigs lost in both strategies is similar. As a result, the estimates provided in this study reflect the effective weight and THC content under these assumptions. Needs and opportunities Policies and programs at both federal, and New York State levels create opportunities for adult-use C. sativa production enterprises. These enterprises offer farm business owners new crop selection options. To make informed management decisions regarding new opportunities in the cannabis industry, farm business owners benefit when production, and economic research-based information are developed, available, and accessible. Industry analysts, and experts point out that farm-level agronomic, and cost and return analyses for different cultivation scenarios are still limited. Limitations make it difficult for farmers to make optimal decisions amidst the inherent risks, and uncertainties in the market. The economic analysis presented in this study seeks to provide farm-level agronomic, and economic insights into the operation of C. sativa enterprises. Approach, methods, data Despite its limitations, our study provides estimates based on recorded, farm level, agronomic and economic data collected from a functioning farm using actual farming practices, yielding tangible products that are sold in the market. A valuable feature of this work is the comprehensive collection, recording, and analysis of labor usage, and other crop management inputs. Efforts provide actionable data for growers. This analysis benefits from a comprehensive collection, and reporting of labor hours by task, providing more informed expectations regarding labor needs, costs, and strategies for managing human resources risks. Results, outcomes The approach here offers grounded insights based upon peer reviewed methods, data, results, etc. In contrast, consider other works where: 1) methods, data, and results are not accessible and, or difficult to access; and, or 2) methods, data, results etc. are omitted and, or unclear. The former approach supports improved decision making, allowing for the effective management of risks, for example, human resource risks, by way of implementing practices that mitigate risk and uncertainty. Future work While this study serves as a valuable contribution to the field, there is a continued need for more production, and economic research to support farm business owners. Future studies could focus on the effects of alternative planting scenarios, the number of grows per year, plant densities, and the interactions among these factors on the overall value of production, costs, and returns. Additionally, research-based economic insights are needed to refine the understanding of price received, and yield metrics. Improved understanding ultimately empowers farm business owners to make more informed decisions about the profitability, and sustainability of their operations. Future needs for research-based information may be addressed by way of cooperation among value chain participants, stakeholders. Perhaps a periodic survey, and reporting of key agronomic, and economic metrics via an industry supported effort could be studied for its potential to benefit the industry. Cannabis industry value chain participants might be willing to provide data — perhaps anonymously, or in a way that does not compromise their intellectual property or operations — to a representative group of industry stakeholders charged with developing and implementing reporting activities for common use. Such collaborations could help the industry gain a clearer understanding of agronomic factors, production practices, costs, returns, and economic dynamics. Value chain participants seek to achieve economic, environmental, and community objectives given available resources. Their efforts towards continuous improvement – What worked? What did not work, and why? – benefit from efforts to improve availability, and accessibility of research-based information. The issues related to data quantity, quality and availability; data tampering; the speculative nature of the industry; lingering illicit operations; unreliable players; and the potential for undisclosed methods, data, and assumptions of analyses concern industry value chain participants, and stakeholders. Companies that know their enterprises’ costs and production metrics hold valuable insights that could greatly benefit the cannabis industry as a whole, particularly in emerging markets like New York State. For example, research-based insight regarding value of production, costs, and returns associated with systems, and practices that did not achieve objectives, and goals might accelerate progress toward sustainable viability, and growth of a New York State cannabis industry. Periodic sharing of knowledge from, and among value chain participants, stakeholders could be transformative. Conclusions This study provides one of the first farm-level agronomic and economic evaluations of high-THC C. sativa cultivation in a New York State greenhouse. Autoflowering plants offered shorter cycles and lower labor needs but produced smaller yields and lower profitability compared to photoperiod plants under the conditions analyzed. Photoperiod cultivation generated higher annual returns per square foot despite greater labor and cloning costs, suggesting it is better suited for operations prioritizing yield and profit optimization. However, autoflowers may still be advantageous in settings with limited capital, labor, or space, or where rapid turnover is needed. Findings highlight the importance of early growth traits for predicting final yield, the substantial role of labor and plant material in production costs, and the need for improved breeding and production data, particularly for autoflowers. Continued research on cultivation strategies, economic risks, and pricing dynamics will be essential to support informed decision-making and long-term sustainability for C. sativa producers. Abbreviations CBDA: Cannabidiolic acid CBD: Cannabidiol THCA: Delta-9-Tetrahydrocannabinolic acid THC: Delta-9-Tetrahydrocannabinol Declarations Funding Declaration: not applicable. Consent to Publish: not applicable. Consent to Participate: not applicable. Ethics declaration: not applicable. Data Availability: Upon acceptance, the raw data supporting this study will be made publicly available through recognized online repositories, the Cornell University data repository, and the authors’ professional websites. Competing Interest: DV is a board member of the non-profit Agricultural Genomics Foundation and the sole owner of the company CGRI, LLC Author Contribution JH analyzed all economic estimates, KR collected all data, DV analyzed production estimates. JH and DV wrote the first draft of the manuscript, all authors contributed to the revision and editing Acknowledgements We would like to thank Wheatfield Gardens-TruCann for allowing us to collect data on their facility, Seth Brophy, Timothy McDowell, Bill Nichols, and George Stack for useful discussion, and Lynn M. Johnson from the Cornell Statistical Consulting Unit for helpful statistical direction. References Bell CD, Soltis DE, Soltis PS: The age and diversification of the angiosperms re-revisited . 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DV is a board member of the non-profit Agricultural Genomics Foundation and the sole owner of the company CGRI, LLC Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 28 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 10 Nov, 2025 Submission checks completed at journal 10 Nov, 2025 First submitted to journal 03 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":258074,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the 10 traits measured during harvest for autoflower plants. The distribution of each variable is displayed along the diagonal. The bivariate scatter plots with fitted lines are shown below the diagonal. Above the diagonal, the correlation values along with their corresponding significance levels represented by p-values (0, 0.001, 0.01, 0.05, 0.1, and 1) are indicated by symbols “***”, “**”, “*”, “.”, \" “, and \" \" respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8021436/v1/2b6cf70cce061e024052bde1.png"},{"id":96454279,"identity":"836cb44a-ffb5-45a5-93a5-66f2bd6c8f90","added_by":"auto","created_at":"2025-11-21 10:02:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":202813,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the 7 traits measured during harvest for photoperiod plants. The distribution of each variable is shown along the diagonal. Below the diagonal, bivariate scatter plots with fitted lines are presented. Above the diagonal, correlation values are displayed alongside their significance levels, which are represented by p-values (0, 0.001, 0.01, 0.05, 0.1, and 1) and symbolized as . “***”, “**”, “*”, “.”, \" “, and \" \".\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8021436/v1/eef4f2b84362bfcbccff7482.png"},{"id":96408790,"identity":"4b5c7599-c772-4815-8be2-a49a9b809d7a","added_by":"auto","created_at":"2025-11-20 17:59:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61555,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between plant weights for different \u003cem\u003eC. sativa\u003c/em\u003e varieties. \u003cstrong\u003e(A)\u003c/strong\u003e A positive correlation is observed between wet weight and dry weight for two autoflower varieties (P \u0026lt; 0.0001, r = 0.979; Y= 0.31 + 0.17X). \u003cstrong\u003e(B)\u003c/strong\u003eA positive correlation is also noted between wet weight and wet bucked weight for six photoperiod strains (P \u0026lt; 0.0001, r = 0.94690; Y = 78 + 0.58X).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8021436/v1/698aefa55385f2d83410816e.png"},{"id":96708099,"identity":"4fead167-8403-4502-8814-19a3668073d2","added_by":"auto","created_at":"2025-11-25 09:56:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3596600,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8021436/v1/5a1bea3e-16d8-481f-b825-553a5e20c104.pdf"},{"id":96408793,"identity":"483b4951-9dd9-46c5-9f98-d9318e92edc6","added_by":"auto","created_at":"2025-11-20 17:59:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":347302,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8021436/v1/79ed1fa7624076b75639c0ea.docx"}],"financialInterests":"Competing interest reported. DV is a board member of the non-profit Agricultural Genomics Foundation and the sole owner of the company CGRI, LLC","formattedTitle":"High-THC Cannabis sativa in a New York Greenhouse: Yield and Economic Factors","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cem\u003eCannabis sativa\u003c/em\u003e L. (marijuana, hemp) is currently the largest emerging crop worldwide. This angiosperm from the dicot family Cannabaceae [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] was among the first plants domesticated by several ancient cultures [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] due to its versatility. Marijuana-type \u003cem\u003eC. sativa\u003c/em\u003e is known for its production of cannabinoids, unique secondary metabolites with medicinal [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or psychoactive [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] properties. The main cannabinoid produced by these recreational or adult-use plants is THCA (Δ-9-tetrahydrocannabinolic acid), which converts to its neutral form, THC (Δ-9-tetrahydrocannabinol), when heated. THC is highly regulated by the government due to its psychoactive effects [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] but remains the primary driver of the cannabis market, with product prices heavily influenced by its concentration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cem\u003eCannabis sativa\u003c/em\u003e is a photosensitive plant, meaning its growth is influenced by light exposure. Like many other crops, it progresses through vegetative and reproductive stages, with the flowering phase triggered by reduced light duration. In contrast, autoflowering varieties, which are light insensitive, have the unique advantage of flowering regardless of daylength [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].. These plants also tend to grow shorter, making them particularly suited for environments where growers have limited control over light cycles, such as in greenhouse settings during mid-winter and early spring\u003c/p\u003e\u003cp\u003eThe global value of \u003cem\u003eC. sativa\u003c/em\u003e production, processing, and manufacturing is in the billions of dollars and is expected to increase dramatically in the coming years. The legal marijuana industries, including medical and adult-use, in North America are predicted to grow substantially. The US adult-use market\u0026rsquo;s revenue is forecasted to reach \u003cspan\u003e$\u003c/span\u003e42.98\u0026nbsp;billion in 2024, with an annual growth rate of 2.89%, expected to reach \u003cspan\u003e$\u003c/span\u003e49.56\u0026nbsp;billion by 2029. The global adult-use market is expected to reach \u003cspan\u003e$\u003c/span\u003e64.73\u0026nbsp;billion in 2024 and grow at a 3.01% rate, reaching a value of \u003cspan\u003e$\u003c/span\u003e75.09\u0026nbsp;billion by 2029 [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The estimates for 2023 total legal revenues associated with \u003cem\u003eC. sativa\u003c/em\u003e sales in the U.S. reached \u003cspan\u003e$\u003c/span\u003e28.8\u0026nbsp;billion, up from \u003cspan\u003e$\u003c/span\u003e26.1\u0026nbsp;billion the previous year, with average retail sales hitting \u003cspan\u003e$\u003c/span\u003e2.4\u0026nbsp;billion per month, reflecting a 10.3% increase over 2022\u0026rsquo;s sales [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Processed and manufactured goods play a crucial role in the industry, accounting for between 45% and 50% of total retail revenues [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eYield is a crucial factor in evaluating the success of cultivation practices. The harvest index quantifies the yield of a crop species versus the total amount of biomass that has been produced [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In general, temperate small-grain cereal crops achieve the highest harvest indices, typically within the range of 0.5 to 0.65, regardless of the time of sowing, whether winter or spring [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn \u003cem\u003eC. sativa\u003c/em\u003e, there is a strong linear relationship between wet and dry biomass, with basal stem diameter identified as the best predictor of final dry stripped floral biomass. Smaller, earlier-flowering plants exhibit a higher dry-to-wet biomass ratio; however, maximizing floral biomass yield per unit area does not necessarily favor these plants due to weak correlations between flowering time and yield per unit area [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. \u003cem\u003eCannabis sativa\u003c/em\u003e has been shown to lose between 25% and 77% of its original weight during processing, with an average weight loss of 13.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26 g (range: 9.73\u0026ndash;16.65 g). This corresponds to an average percentage loss ranging from a minimum of 47.44% to a maximum of 77.07% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Post-harvest analysis showed variations in biomass and cannabinoid concentration across and within cultivars. Strong correlations were observed between inflorescence and stripped biomass samples, with bulk biomass concentration aligning more closely with lower canopy sections. Plant morphology, canopy area, and dry canopy density significantly influenced cannabinoid content by plant section[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, the proportion of stripped biomass varies across different sections of the plant and by variety [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For instance, cannabinoid concentrations in axillary inflorescences from the lower parts of the plant were up to 90% lower than those in apical inflorescences at the top. Although higher planting density reduced cannabinoid concentrations in these lower inflorescences, it did not impact cannabinoid yield per cultivation area [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThis highlights the critical role of genotype in determining biomass yield and cannabinoid accumulation (Sandhu et al. 2022). Structural and genetic factors also play a pivotal role in yield and cannabinoid content, underscoring the complexity of optimizing these traits [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Differences between varieties extend to disease resistance, which may also be influenced by cultivation practices. For example, wet-bucked inflorescences showed significantly higher fungal growth compared to those hang-dried before bucking [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These metrics, however, are currently missing for autoflowering plants, which, as aforementioned, flower independently of light, reach smaller sizes, and therefore require less labor per grow. All of these variables must be taken into account as flower quality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and in particular THC [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] amounts are the drivers of the cannabis market and the price per pound of flower and/or biomass depends on these factors.\u003c/p\u003e\u003cp\u003ePrevious studies have found that floral dry weight in \u003cem\u003eC. sativa\u003c/em\u003e is positively correlated with plant height and stem diameter, though unrelated to days to maturation. A broad phenotypic diversity in physiological traits indicates a positive link between plant vigor, growth rate, and inflorescence productivity, suggesting that selecting for fast-growing plants can enhance floral bud yield [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The average dry yield of inflorescences from various varieties ranged from approximately 257.28 g m\u0026sup2; to 442.00 g m\u0026sup2;, with the maximum yield of CBDA ranging from 1,929.60 mg m\u0026sup2; to 6,011.20 mg m\u0026sup2;. The cannabinoid content is significantly influenced by both the genotype and the plant's growth stage [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEnvironmental factors significantly influence the morphology of inflorescences in \u003cem\u003eC. sativa\u003c/em\u003e. Weight distribution analysis revealed that processing 75% of the largest inflorescences accounts for approximately 90% of the total weight [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Plant width is influenced by transplant date and spacing, with earlier transplant dates resulting in taller and wider plants. Larger spacing leads to wider plants, while individual biomass increases with earlier transplanting and larger spacing. On a per-hectare basis, biomass was highest with earlier transplant dates and smaller spacings. Additionally, lower planting densities yield more biomass per plant, while higher densities produce more biomass overall. To maximize profits, farmers should aim to transplant early and use 1.22 m spacing [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition to information on yield and other agronomic traits, growers seek evidence-based insights into the expected economic outcomes of cultivating adult-use \u003cem\u003eC. sativa\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThis economic analysis of growing adult-use \u003cem\u003eC. sativa\u003c/em\u003e in a New York State greenhouse seeks to address the need for research-based information on expected economic outcomes. Helpful reviews of previous economic analyses predominantly associated with hemp for fiber, and grain enterprises, including several from state land grant systems, exist [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, due to the recent legalization of adult-use \u003cem\u003eC. sativa\u003c/em\u003e in New York and other states, expected costs, revenue, and returns with sensitivity analyses most helpful to growers are limited. Such work has not been done exhaustively for adult-use \u003cem\u003eC. sativa\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eEstablished and prospective \u003cem\u003eC. sativa\u003c/em\u003e growers require research-based knowledge to evaluate enterprise options and make informed decisions about integrating cannabis cultivation into their farm businesses. For instance, decision-makers need information to determine whether adding an adult-use cannabis enterprise is viable, which production scenarios and practices\u0026mdash;such as outdoor cultivation, land-based high tunnels, or greenhouses\u0026mdash;would work best for their specific goals, and how to navigate production, market, human resource, legal, and financial risks while effectively managing them. Additionally, another key consideration is whether to plant autoflowers or photoperiods, as both flowering strategies have different requirements, labor costs, and yields. Plant density and the amount of time spent in the vegetative versus flowering stages are also important factors to consider, which differ between both flowering strategies, and can influence overall yield and labor requirements. This economic analysis addresses the need for research with a focus on emerging high-cannabinoid \u003cem\u003eC. sativa\u003c/em\u003e cultivation. Previous studies identified labor as the single largest variable cost in cannabis production [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This current analysis builds on this previous work [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], utilizing newly collected, detailed information on labor requirements and input usage to provide a comprehensive evaluation based upon farm level data collected in a greenhouse in upstate NY.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAutoflowers\u003c/h2\u003e\u003cp\u003eAutoflower, or light-insensitive, plants from the varieties \u0026ldquo;Sour Apple\u0026rdquo; and \u0026ldquo;Carmel Cream Gelato\u0026rdquo; were cultivated between February and May 2023. Seeds were started in mid-February, and plants were transplanted to their final location on March 6, allowing a 60-day growth period in the greenhouse. A total of 100 plants, 50 from each variety, were assessed at four intervals throughout the season and at harvest, totaling five measurement points. For each timepoint, three growth metrics were recorded: plant height (cm), stem diameter (mm), and node count. At the May 4 harvest, additional measurements were taken, including the size and width of the largest inflorescence (cm and mm, respectively) and the wet weight of each plant (g) including the root mass weight. Twelve days post-harvest, on May 16, dry weight (g) was recorded for 49 of the 100 plants.\u003c/p\u003e\u003cp\u003eThis comprehensive measurement approach provided insights into growth patterns and final yield across both varieties. The study\u0026rsquo;s structured data collection at multiple stages allowed for analysis of developmental differences within the two autoflower strains under consistent growing conditions.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhotoperiods\u003c/h3\u003e\n\u003cp\u003ePhotoperiod-sensitive plants from six varieties\u0026mdash;\"Bop Gun,\" \"Doc Holiday 4,\" \"GMO,\" \"Animal Face,\" \"Kosher Kush,\" and \"Donkey Butter\"\u0026mdash;were cultivated from April to June 2023. The first batch of seeds was started in late March, transplanted to the greenhouse on April 3, and grew for 85 days until harvest on June 26. A second batch of \"Bop Gun,\" \"Doc Holiday 4,\" and \"Kosher Kush\" was started two weeks later and transplanted on April 14, resulting in 74 days of field growth. In total, 90 plants, with 10 from each variety across both planting batches, were assessed at two timepoints during the growing season and at harvest. Each measurement period included the same three metrics: height (cm), stem diameter (mm), and node count.\u003c/p\u003e\u003cp\u003eAt harvest, additional data were collected, including the size and width of the largest inflorescence, as well as the wet weight of the entire plant (g) and the wet bucked weight (g) after the removal of leaves and stems, leaving the usable biomass\u003c/p\u003e\n\u003ch3\u003eCannabinoid testing\u003c/h3\u003e\n\u003cp\u003eCannabinoid testing for THC was conducted through a third-party testing facility chosen by the company where we collected the data. The company provided us with the THC test results for both photoperiod and autoflower \u003cem\u003eC. sativa\u003c/em\u003e plants. For photoperiod plants, testing was conducted through one testing facility, while autoflower plants were tested through a different testing facility.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eT-tests were conducted to evaluate differences in the shared traits measured between autoflower and photoperiod plants, including wet plant weight (g), diameter of the main inflorescence (mm), size of the main inflorescence (cm), number of nodes, stem diameter (mm), height (cm), and number of days on the ground.\u003c/p\u003e\u003cp\u003eAdditionally, for the photoperiod plants, a linear mixed-effects model analyzed the fixed effects of timepoint, age, and their interaction, with plant ID as a random effect for repeated measures. F-tests evaluated the fixed effects, and post-hoc age comparisons were performed using Tukey's method. This analytical approach provided a detailed view of growth variation across photoperiod-sensitive plants and allowed for in-depth comparison of yield potential between the two planting ages.\u003c/p\u003e\n\u003ch3\u003eEconomic analysis\u003c/h3\u003e\n\u003cp\u003eEnterprise budgeting concepts provide the general framework for the economic analysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. USDA\u0026rsquo;s Hemp Report provides price received and production information, with definitions of key items (USDA/NASS, 2024). Detailed activity analysis, with an emphasis on tracking labor and other inputs, was conducted in 2023 to generate input data for the analyses of greenhouse cultivation systems for autoflower and photoperiod cannabis plants. This analysis focused on a well-equipped 30,000 sq. ft. greenhouse consisting of ten 3,000 sq. ft. bays, with autoflower plants grown in one bay and photoperiod plants grown in the remaining nine bays.\u003c/p\u003e\u003cp\u003eLabor hours were recorded by task and by day from initial planting through harvest and final on-farm processing (bucked, dried flower) for both cultivation types. Autoflower plants were grown from seeds over approximately 62 days, resulting in about 450 plants harvested from one bay. Photoperiod plants, grown from purchased clones over approximately 91 days, produced about 3,150 plants harvested across nine bays. For comparison, annual expected costs, revenues, and returns were calculated assuming full use of the 30,000 sq. ft. growing space, with five autoflower and four photoperiod grows annually. Results reflect a year of activity, reported as \u003cspan\u003e$\u003c/span\u003e per 30,000 sq. ft. and \u003cspan\u003e$\u003c/span\u003e per sq. ft.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eUse of Artificial Intelligence Tools\u003c/h2\u003e\u003cp\u003ePortions of this manuscript were drafted and revised with assistance from OpenAI\u0026rsquo;s ChatGPT (GPT-5, September 2025). The authors reviewed and edited all AI-assisted text and take full responsibility for the final content. ChatGPT was used exclusively to improve clarity, conciseness, and flow of the writing and was not used to generate or analyze data, perform statistical analyses, or draw scientific conclusions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eAutoflowers\u003c/h2\u003e\u003cp\u003eThe traits measured (height (cm), stem diameter (mm), node count, size (cm) and width (mm) of the largest inflorescence, the wet weight (g) and the bucked weight (g)) are almost always correlated. Therefore, those plants that are tall also have numerous nodes, and a thick stem diameter (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, traits are correlated among times, therefore those plants that are tall when young are also tall when old (Figure S1). The mean and standard deviation for the traits measured during harvest are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing linear mixed-effects models for repeated measures analysis, significant changes over time were observed in autoflower plants for height (F\u0026thinsp;=\u0026thinsp;1075.3; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), stem diameter (F\u0026thinsp;=\u0026thinsp;1081.7; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and number of nodes (F\u0026thinsp;=\u0026thinsp;483.58; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Figure S1). However, the length and width of the main inflorescence did not differ by strain at harvest for autoflowers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePhotoperiods\u003c/h2\u003e\u003cp\u003eLike the autoflowers, most traits (height (cm), stem diameter (mm), node count, size (cm) and width (mm) of the largest inflorescence, wet plant weight (g), and wet bucked weight (g)) are almost always correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, those plants that are tall also have numerous nodes, and a thick stem diameter. Additionally, traits are correlated among times. Therefore, those plants that are tall when young are also tall when old (Figure S2). The mean and standard deviation for the traits measured during harvest are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing different linear mixed-effects models that allow for repeated measures analysis, in photoperiods height (F\u0026thinsp;=\u0026thinsp;477.36; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), stem diameter (F\u0026thinsp;=\u0026thinsp;170.44; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and number of nodes (F\u0026thinsp;=\u0026thinsp;116.88; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) all exhibited significant changes over time (Figure S2).\u003c/p\u003e\u003cp\u003eThe only strain whose size of main inflorescence differed at harvest was \u0026ldquo;Animal Face\u0026rdquo; which was significantly smaller than \u0026ldquo;Bop Gun\u0026rdquo;. The inflorescence diameter at harvest from the strain \u0026ldquo;Animal Face\u0026rdquo; was marginally smaller from the strain Bop Gun\u0026rdquo;, all other strains didn\u0026rsquo;t differ in their width.\u003c/p\u003e\u003cp\u003eThe age difference between those 3 strains -the ones that were planted two weeks before and therefore had 11 days more on the ground- made no difference in the last point during harvest except for three traits (Figure S3). In other words, there were statistically significant differences between the measured traits that got smaller as the plants aged, and therefore the differences at timepoint one are larger than timepoint three.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt harvest, the mean wet plant weight per photoperiod plant was approximately 659.22\u0026thinsp;\u0026plusmn;\u0026thinsp;277.24g, resulting in a total of 58,670.6g (58.67kg) for the 89 plants sampled. The mean wet bucked weight, which excludes stems and other non-essential parts, was approximately 459.5\u0026thinsp;\u0026plusmn;\u0026thinsp;170.0334g per plant. For the 89 plants measured, the total wet bucked weight was 40,436.1 g (40.44 kg).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurements during harvest for six traits shared among both autoflower and photoperiod plants showing mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (columns 3\u0026ndash;8); the dry plant weight collected only for autoflower plants (column 9); the wet bucked weight collected only for photoperiod plants (column 10); the Total THC provided through third-party testing (column 11), and estimates in italics of the effective weight (column 12) and the estimated THC weight and range (column 13).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u003cp\u003eEstimates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariety\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlowering Strategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWidth (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNumber of Nodes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBiggest Inflorescence Size (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBiggest Inflorescence Width (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWet Plant Weight (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDry Plant Weight (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eWet Bucked Weight (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eTotal THC (mg/g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003eEffective Weight (g) mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.d.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003eTHC Per Plant (g) -range\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaramel Cream Gelato\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.82\u0026thinsp;\u0026plusmn;\u0026thinsp;15.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e146.96\u0026thinsp;\u0026plusmn;\u0026thinsp;60.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19.83\u0026thinsp;\u0026plusmn;\u0026thinsp;11.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e98.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e19.76\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003e1.95 (1.14\u0026ndash;2.75)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSour Apple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.26\u0026thinsp;\u0026plusmn;\u0026thinsp;15.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e167.76\u0026thinsp;\u0026plusmn;\u0026thinsp;61.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e28.62\u0026thinsp;\u0026plusmn;\u0026thinsp;11.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e99.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e22.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003e2.25 (1.42\u0026ndash;3.08)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnimal Face\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e844.30\u0026thinsp;\u0026plusmn;\u0026thinsp;256.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e554.87\u0026thinsp;\u0026plusmn;\u0026thinsp;158.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e150.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e113.54\u0026thinsp;\u0026plusmn;\u0026thinsp;34.47\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003e19.8 (13.8\u0026ndash;25.79)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBop Gun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112.10 \u0026plusmn; 21.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.98\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.24\u0026thinsp;\u0026plusmn;\u0026thinsp;7.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e561.60\u0026thinsp;\u0026plusmn;\u0026thinsp;203.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e397.25\u0026thinsp;\u0026plusmn;\u0026thinsp;141.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e75.52\u0026thinsp;\u0026plusmn;\u0026thinsp;27.34\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDoc Holiday 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.02\u0026thinsp;\u0026plusmn;\u0026thinsp;13.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e416.50\u0026thinsp;\u0026plusmn;\u0026thinsp;122.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e345.00\u0026thinsp;\u0026plusmn;\u0026thinsp;99.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e56.01\u0026thinsp;\u0026plusmn;\u0026thinsp;16.46\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDonkey Butter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e630.56\u0026thinsp;\u0026plusmn;\u0026thinsp;236.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e414.47\u0026thinsp;\u0026plusmn;\u0026thinsp;153.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e168.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e84.8\u0026thinsp;\u0026plusmn;\u0026thinsp;31.86\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003e14.31 (8.93\u0026ndash;19.68)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGMO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153.70\u0026thinsp;\u0026plusmn;\u0026thinsp;12.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e967.76\u0026thinsp;\u0026plusmn;\u0026thinsp;245.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e634.92\u0026thinsp;\u0026plusmn;\u0026thinsp;92.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e179.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e130.14\u0026thinsp;\u0026plusmn;\u0026thinsp;32.98\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003e23.4 (17.47\u0026ndash;29.33)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKosher Kush\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158.58\u0026thinsp;\u0026plusmn;\u0026thinsp;18.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e765.65\u0026thinsp;\u0026plusmn;\u0026thinsp;253.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e524.37\u0026thinsp;\u0026plusmn;\u0026thinsp;185.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e149.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003e102.96\u0026thinsp;\u0026plusmn;\u0026thinsp;34.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003e\u003cem\u003e15.41 (10.32\u0026ndash;20.51)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eWeight correlations\u003c/h2\u003e\u003cp\u003eA positive correlation was identified between the wet weight and dry weight of autoflower plants for both Sour Apple and Caramel Cream Gelato varieties (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, r\u0026thinsp;=\u0026thinsp;0.979, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). At harvest, the average wet weight per plant was 157.36\u0026thinsp;\u0026plusmn;\u0026thinsp;61.83 g, and the average dry weight was 24.49\u0026thinsp;\u0026plusmn;\u0026thinsp;12 g, with a total dry weight of 1,200 g for all plants. On average, 18.61% of the weight remained after drying, indicating an ~\u0026thinsp;82% weight loss during the drying process. The weights per autoflower variety are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThere is a positive correlation between the photoperiods plant\u0026rsquo;s wet weight and plant\u0026rsquo;s wet bucked weight for both all photoperiod strains (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, r\u0026thinsp;=\u0026thinsp;0.9469358, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). On average, 72% of the weight remains (~\u0026thinsp;28% is lost) after the plant is bucked. The weights per photoperiod variety are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAutoflowers vs Photoperiods\u003c/h2\u003e\u003cp\u003eThere is a significant difference in all measured traits between autoflowers and photoperiods, as well as in the number of days on the ground (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of the various traits measured in both autoflower and photoperiod plants, including the p-value and mean for each flowering strategy.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWet plant weight (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiameter of main inflorescence (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSize of main inflorescence (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNumber of Nodes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStem diameter (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNumber of Days on Ground\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean autoflowers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e59.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Photoperiods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e659.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e126.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEstimates for Autoflowers and Photoperiods\u003c/h2\u003e\u003cp\u003eFrom the autoflower analysis, approximately 82% of the plant's weight is lost due to water loss, leaving 18% as the remaining weight. In the photoperiod analysis, about 28% of the weight is lost after accounting for the removal of stems and twigs, with 72% of the weight remaining. Using these percentages, we calculated the effective weight\u0026mdash;the usable plant material (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Column 12). On average, the effective weight is 21.16\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32 g for autoflowers and 88.65\u0026thinsp;\u0026plusmn;\u0026thinsp;37.28 g for photoperiods. Additionally, we estimated the harvest index for both plant types (Figure S4). Considering the combined losses from water, stems, and twigs, approximately 13.45% of the plant's wet weight at harvest remains as usable material.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the 100 autoflowers grown, the mean weight was 157.36 g and their total weight was 1,5736g (1.57kg). Their estimated effective weight would be 2,116.146g (2.12kg). If 450 autoflower plants were grown, their total wet weight at harvest would be an estimated 70,812g (7.08 Kg) and a projected effective weight of around 9,5221.66g (9.52kg).\u003c/p\u003e\u003cp\u003eThe average THC concentration for the autoflower varieties was determined to be 99.13 mg/g, based on Sour Apple (98.45 mg/g) and Caramel Cream Gelato (99.83 mg/g Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e column 11). Using this average THC potency, an autoflower plant is estimated to produce approximately 2.1 g of THC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Column 13). For the projected effective weight of 9,521.66 g (9.52 kg) from 450 autoflower plants, the total THC yield is estimated to be 943.98 g (0.94 kg).\u003c/p\u003e\u003cp\u003eFor the 89 photoperiod plants analyzed, the mean wet weight is 659.22 g, with a total wet weight of 58,670.6 g (58.67 kg). This corresponds to an estimated effective weight of 7,889.91 g (7.89 kg) and an estimated THC production of 1,279.15 g (1.25 kg). Scaling this to a scenario with 3,150 plants, the estimated total wet weight would be 2,076,544 g (2,076.54 kg), with an effective weight of 279,249.5 g (279.25 kg). The total THC produced by these 3,150 plants is projected to be 45,273.32 g (45.27 kg).THC estimates for individual varieties are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, column 13.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEconomics of Autoflowers\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBased upon available data, and for the median expected price received, yield combination -- 260 (\u003cspan\u003e$\u003c/span\u003e per lb. harvested floral, dried), and 0.04 (lbs. harvested floral, dried per plant) -- estimated value of production, variable input cost, total cost, and return above total costs total \u003cspan\u003e$\u003c/span\u003e7.80, \u003cspan\u003e$\u003c/span\u003e5.58, \u003cspan\u003e$\u003c/span\u003e9.29, and negative \u003cspan\u003e$\u003c/span\u003e1.48 per sq. ft., respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Total values, and calculations reflect results of rounding. Cost values represent the value of the input(s) used in production. Since total costs exceed value of production (revenue), subtracting \u003cspan\u003e$\u003c/span\u003e9.29 from \u003cspan\u003e$\u003c/span\u003e7.80 yields a negative return above total costs, or negative \u003cspan\u003e$\u003c/span\u003e1.48 per sq. ft. Expressed in annual \u003cspan\u003e$\u003c/span\u003e for the 30,000 sq. ft. facility, the return is negative \u003cspan\u003e$\u003c/span\u003e44,510. Total annual costs of \u003cspan\u003e$\u003c/span\u003e278,510 for the facility exceed the value of production, revenue of \u003cspan\u003e$\u003c/span\u003e234,000. The result is a return, profit value that is less than zero. Sensitivity analysis suggests that 3 of 9 output price, yield combinations produced positive returns above total costs annually (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEconomics of Photoperiods\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBased upon available data, and for the median expected price received, yield combination -- 260 (\u003cspan\u003e$\u003c/span\u003e per lb. harvested floral, dried), and 0.18 (lbs. harvested floral, dried per plant) -- estimated value of production, variable input cost, total cost, and return above total costs total \u003cspan\u003e$\u003c/span\u003e21.84, \u003cspan\u003e$\u003c/span\u003e10.95, \u003cspan\u003e$\u003c/span\u003e14.68, and \u003cspan\u003e$\u003c/span\u003e7.18 per sq. ft., respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Since the value of production exceeds the total cost of production, return is greater than zero. Sensitivity analysis suggests that 5 of 9 output price, yield combinations produce positive returns above total costs annually (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual value of production (revenue), costs and returns for high cannabinoid \u003cem\u003eC. sativa\u003c/em\u003e cultivation, greenhouse (under protection) setting, by planting scenario (flowering strategy). These analyses are based on the following assumptions: price received is taken from the median point of the expected range at \u003cspan\u003e$\u003c/span\u003e260 per lb., yields assumed are 0.04 and to be 0.18 lbs. per plant for autos and photos, respectively, and the cost of hired labor is set at \u003cspan\u003e$\u003c/span\u003e20 per hour, as outlined in the methods section.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePlanting Scenario\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAutoflower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ePhotoperiod\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e / 30,000 sq. ft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e / sq. ft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e / 30,000 sq. ft.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e / sq. ft\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eValue of Production (Revenue)\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue of harvested floral, dried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e234,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e655,200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCosts of Production\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVariable inputs\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFertilizer \u0026amp; lime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeeds \u0026amp; plants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46,650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSprays, bios, other variable crop inputs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86,490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110,860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterest on operating capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTotal variable inputs\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167,390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e328,200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFixed inputs\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand charge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuildings, improvements, and mechanicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89,140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89,140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue of operator \u0026amp; family management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14,850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther fixed inputs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTotal fixed inputs\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111,120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTotal costs\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e278,510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e439,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eReturns\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRevenue minus costs of variable inputs\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66,610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e327,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRevenue minus costs of variable \u0026amp; fixed inputs\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-44,510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e215400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRevenue less total costs by price and yield for high-cannabinoid \u003cem\u003eC. sativa\u003c/em\u003e cultivation in a greenhouse setting (30,000 sq. ft.). The first three columns represent autoflower planting scenarios with five two-month cycles annually, and the last three columns represent photoperiod planting scenarios with four three-month cycles annually. Values in ()\u0026rsquo;s are less than 0.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003elbs. floral, dried per plant\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAutoflowers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePhotoperiods\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e$ per lb. floral, dried\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(224,510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(170,510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(116,510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(255,000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(137,400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(19,800)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(161,510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(44,510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72,490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(39,400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e215,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e470,200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(98,510)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81,490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e261,490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e176,200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e568,200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e960,200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show that autoflower plants are smaller than photoperiod plants and produce less weight, including lower THC content per gram of flower in the two autoflower varieties studied (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The weight loss from wet to dry in autoflower plants, approximately 82%, was higher than the 77% previously reported [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, the photoperiod analysis suggests that the weight from stems and twigs accounts for about 18% of the wet weight. The positive correlations between various traits in both autoflower (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and photoperiod (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) plants indicate that early plant characteristics can predict final size and yield. Because autoflowers have shorter growth cycles and flower independently of light, they may present a cost-effective option for indoor or greenhouse cultivation where available capital and labor are limited. These plants require less time in the ground and demand minimal pruning or trellising [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which can reduce labor and costs. However, in controlled environments where light cycles can be easily adjusted, photoperiod plants may be manipulated to flower at smaller sizes and for shorter periods, offering flexibility in production. Another drawback of autoflowering plants is their lack of consistency, often attributed to poor breeding practices. However, this inconsistency has not been thoroughly quantified or directly compared with photoperiod plants, leaving it largely speculative.\u003c/p\u003e\u003cp\u003eOur results also indicate that, beyond the differences observed between autoflower and photoperiod plants, significant variation exists within varieties of each flowering strategy in terms of THC content, yield, and biomass production.\u003c/p\u003e\u003cp\u003ePrevious estimates on cannabinoid production, specifically CBD [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], suggest higher yields compared to the estimated THC production observed in the varieties measured here. However, the THC yields reported in this study fall within these previous estimates [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It is important to note that the previously reported values were derived from plants grown outdoors, many of which spent over 85 days in the ground [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], allowing them to grow larger. The measured cannabinoid in those studies was CBD, not THC, which introduces another important difference and limitation when comparing these results.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCultivation practices that can affect quality and yield\u003c/h2\u003e\u003cp\u003eIn photoperiod plants it has been shown that topping did not significantly improve flower yield or cannabinoid concentration. While flower yield per plant decreased with higher plant density, total yield per hectare increased. CBD production per hectare rose with greater density, but cannabinoid concentration remained unaffected. However, increased density does not guarantee higher economic returns due to the high input costs for hemp plant material and labor [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Topping plants 3\u0026ndash;4 weeks after transplanting increased labor costs without improving yield or cannabinoid content. While topping increased inflorescences and CBD content in two varieties [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], it also significantly influenced plant height, with un-topped plants being taller. Architectural modulation methods, including selective pruning and defoliation, improved cannabinoid profile consistency by reducing concentration variability across the plant's height. Yet, methods like primary branch removal reduced total yield, highlighting the challenge of balancing plant structure and cannabinoid optimization [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As previously mentioned, these metrics are currently lacking for autoflowering plants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEconomics of autoflower cultivation\u003c/h2\u003e\u003cp\u003eEstimated value of production given initial price received and yield assumptions total \u003cspan\u003e$\u003c/span\u003e7.80 per sq. ft. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Value of production estimates are a function of the number of grows per year, plants per grow, yield per plant, price received. Price received and yield variability substantially impact profit (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Results suggest that evaluating alternative practices for production and economic efficiencies are important to identifying the optimal set of production practices \u0026ndash; planting settings, autoflowers and/or photoperiods, number and lengths of growing cycles which will differ between these two flowering strategies, number of plants per grow, among other considerations.\u003c/p\u003e\u003cp\u003eVariable costs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.) total 5.58 and account for 60 percent of total costs. Labor, seeds \u0026amp; plants, and nutrients are the three largest \u003cspan\u003e$\u003c/span\u003e per sq. ft. items. Labor costs are the single largest item, accounting for 52 percent of total variable input costs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.). Seeds \u0026amp; plants expense, the second largest item, and nutrients the third largest, account for 28 and 15 percent of total variable input costs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.), respectively. These three greatest variable cost items account for 95 percent of all total variable input costs.\u003c/p\u003e\u003cp\u003eTotal cost fixed inputs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.) total 3.71, and account for 40 percent of total costs. Fixed costs for buildings, improvements, and mechanicals for the greenhouse account for the vast majority of total fixed costs. Estimated total cost for variable and fixed inputs equals \u003cspan\u003e$\u003c/span\u003e9.29 per sq. ft., while revenue minus costs of variable inputs, and revenue minus total costs equal \u003cspan\u003e$\u003c/span\u003e2.22 and negative \u003cspan\u003e$\u003c/span\u003e1.49 per sq. ft., respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eEconomics of photoperiod cultivation\u003c/h2\u003e\u003cp\u003eEstimated value of production given initial price received and yield assumptions total \u003cspan\u003e$\u003c/span\u003e21.84 per sq. ft. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Value of production estimates are a function of number of grows per year, plants per grow, yield per plant, price received. Price and yield variability substantially impact profit (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Results suggest that evaluating alternative practices for production and economic efficiencies are important for identifying the optimal set of production practices \u0026ndash; planting settings, auto and, or photo; number of grows; length of grows; number of plants per grow; and others.\u003c/p\u003e\u003cp\u003eVariable costs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.) total 10.95 and account for 75 percent of total costs. Seeds \u0026amp; plants, labor, and nutrients are the three largest \u003cspan\u003e$\u003c/span\u003e per sq. ft. items. Seeds \u0026amp; plants expense is the single largest item, accounting for 56 percent of total variable input costs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.). Recall that for the photoperiod planting scenario, analysis reflects that purchased clones began the cultivation activities, and price paid for clones was about \u003cspan\u003e$\u003c/span\u003e13.50 per clone. Given this factor\u0026rsquo;s effect on results, future work would benefit from more accurate information regarding price paid, and or analysis of alternative practices, for example, analysis that assumes meeting the needs for clones in house. This analysis should quantify the tradeoffs between seeds \u0026amp; plants expense, labor, and other costs. Labor is the second largest item, and nutrients the third largest, accounting for 34 and 6 percent of total variable input costs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.), respectively. These three greatest variable cost items account for 96 percent of all total variable input costs.\u003c/p\u003e\u003cp\u003eTotal cost fixed inputs (\u003cspan\u003e$\u003c/span\u003e per sq. ft.) total 3.73, and account for 25 percent of total costs. Fixed costs for buildings, improvements, and mechanicals for the greenhouse account for most total fixed costs. Estimated total cost for variable and fixed inputs equals \u003cspan\u003e$\u003c/span\u003e15.26 per sq. ft., while revenue minus costs of variable inputs, and revenue minus total costs equal 10.90 and 6.58 \u003cspan\u003e$\u003c/span\u003e per sq. ft., respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eAutoflower and photoperiod economic comparison\u003c/h2\u003e\u003cp\u003eProfit estimates for photoperiod cultivation are more favorable compared to autoflower cultivation given expected price and yield assumptions. Returns above total costs reflecting a year\u0026rsquo;s of activity show that the photoperiod planting scenario yielded returns above total costs of \u003cspan\u003e$\u003c/span\u003e7.18 per sq. ft., while the autoflower planting scenario yielded negative \u003cspan\u003e$\u003c/span\u003e1.48 per sq. ft. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sensitivity analysis results for the autoflower planting show that annual returns above total costs for a 30,000 sq. ft. facility ranged from negative \u003cspan\u003e$\u003c/span\u003e224,510 for the least favorable price received, yield combination to positive \u003cspan\u003e$\u003c/span\u003e261,490 for the most favorable price, yield combination (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Comparison values for the photoperiod planting ranged from negative \u003cspan\u003e$\u003c/span\u003e255,000 to positive \u003cspan\u003e$\u003c/span\u003e960,200, respectively. From a different perspective, sensitivity results show that for the autoflower planting, returns were greater than zero for three of the nine price, yield combinations, while the photoperiod planting produced returns greater than zero for 5 of 9 combinations. Photoperiod cultivation benefits from greater expected yields per plant, while autoflower plantings benefit from more annual grows and higher plant counts per grow. However, the net result is that photoperiod plantings annual revenue exceeds autoflower expected revenues. These higher revenues, even when combined with greater annual variable costs, drive the photoperiod scenario\u0026rsquo;s superior economic performance relative to autoflower cultivation.\u003c/p\u003e\u003cp\u003eTwo key expense items stand out in the cost comparison: seeds and plants, and labor. For seeds, the autoflower scenario involves purchasing seeds at approximately \u003cspan\u003e$\u003c/span\u003e1.50 each for five annual grows. In contrast, the photoperiod scenario relies on clones costing nearly \u003cspan\u003e$\u003c/span\u003e13.50 each, with only four grows annually. Labor costs also differ significantly. Autoflowers, requiring five grows per year with each grow lasting just over 62 days, demand less labor due to minimal need for trellising and trimming. Photoperiod plants, grown four times annually over 92-day cycles, demand more intensive trellising and trimming, increasing labor expenses.\u003c/p\u003e\u003cp\u003eRisk and uncertainty are prominent concerns in cultivating high-cannabinoid \u003cem\u003eC. sativa\u003c/em\u003e in newly legalized markets like New York State. Fees such as licensing, applications, sampling, and testing for heavy metals, pesticides, and THC content can accumulate significantly over a growing season. Additionally, uncertainties around how these fees and taxes will be assessed add complexity and financial risk, as reflected in the variability (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLabor costs are a substantial component of variable input expenses, accounting for 52% in the autoflower scenario and 34% in the photoperiod scenario. Managing labor-related risks is critical due to uncertainties in labor availability, pricing, and skill levels. This analysis was strengthened by detailed tracking of labor hours by task and day, offering valuable insights for predicting labor needs and costs more accurately.\u003c/p\u003e\u003cp\u003eFinancial risk management is also essential, given the variability in economic performance influenced by price received and yield (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Sound financial planning, including budgeting and scenario analysis, can help mitigate risks. For example, adopting best management practices can reduce the risk of low yields. Sensitivity analysis allows farm owners to evaluate the viability of their operations and identify focus areas for risk reduction, ensuring sustainable business performance amidst economic uncertainties.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eUS and New York State adult-use economy\u003c/h2\u003e\u003cp\u003eThe adult-use \u003cem\u003eC. sativa\u003c/em\u003e market in the United States is growing rapidly, driven by shifting consumer preferences, regulatory changes, and macroeconomic factors [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In New York State, legal cannabis sales totaled \u003cspan\u003e$\u003c/span\u003e264\u0026nbsp;million in 2023, including \u003cspan\u003e$\u003c/span\u003e150\u0026nbsp;million from adult-use and \u003cspan\u003e$\u003c/span\u003e114\u0026nbsp;million from medical sales. By September 2024, 205 retail stores had opened, pushing sales to \u003cspan\u003e$\u003c/span\u003e651\u0026nbsp;million and putting the market on track to approach \u003cspan\u003e$\u003c/span\u003e1\u0026nbsp;billion annually. New York accounts for 16% of the \u003cspan\u003e$\u003c/span\u003e5.95\u0026nbsp;billion total addressable market, a share expected to rise with increased tourism [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. New York City, the world\u0026rsquo;s largest cannabis-consuming city, records 62.3 metric tons of annual consumption at an average price of \u003cspan\u003e$\u003c/span\u003e12.50 per gram [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The industry\u0026rsquo;s economic impact is reflected in the creation of over 22,000 new jobs in 2023, signaling improving market stability after years of turbulence [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These agronomic and economic analyses are critical for understanding production capacity and highlighting areas where additional workforce development is needed.\u003c/p\u003e\u003cp\u003eThe launch of New York\u0026rsquo;s legal market was delayed by a complex licensing process prioritizing social equity applicants and by litigation, yet retail dispensary licenses are now expanding quickly. Between April and June 2024, New York State collected \u003cspan\u003e$\u003c/span\u003e26\u0026nbsp;million in cannabis taxes, compared to \u003cspan\u003e$\u003c/span\u003e42\u0026nbsp;million during all of 2023. Sales in 2024 have already reached \u003cspan\u003e$\u003c/span\u003e493.2\u0026nbsp;million, more than tripling 2023\u0026rsquo;s total and emphasizing the importance of supporting farmers to benefit from this rapidly maturing market [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCannabis consumption continues to rise nationally. Seventeen percent of Americans reported smoking marijuana in 2024, up from 11 to 13% between 2015 and 2021 and consistent with 16% in 2022 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Half of U.S. adults report having tried marijuana, compared to just 4% when Gallup first measured use in 1969 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In New York State, 12.8% of adults\u0026mdash;approximately 1.6\u0026nbsp;million people\u0026mdash;reported cannabis use in the past 30 days in 2021, with more than half using it fewer than 20 days per month and 6% using it daily or nearly daily [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConsumer preferences are also evolving. In New York State, smoking remains the most common mode of use (73.8%), followed by eating (12.1%) and vaporizing (9.3%) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Cannabis flower and pre-rolls represent the majority of sales among Baby Boomers (55%), Generation X (54%), and Millennials (53%), but only 45% among Generation Z, highlighting shifting product preferences [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The share of consumers using cannabis for non-medical reasons alone rose from 44% in 2020 to 49% in 2021, while those using it solely for medical purposes declined from 19% to 13% [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].Processed and manufactured products now account for nearly half of total retail revenues, reflecting demand for edibles, concentrates, and topicals [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Both photoperiod and autoflower production strategies, or a combination of the two, may be needed to meet this growing and diversifying demand.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCaveats\u003c/h2\u003e\u003cp\u003eSeveral limitations should be considered when interpreting these results. First, the cannabinoid analysis was conducted by a third-party laboratory, and there is evidence suggesting potential data tampering [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. While this raises concerns about the data's reliability, we must rely on the provided analysis for this study. It is also important to note that cannabinoid content can vary across different flowers of the plant [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and in different plants from the same variety [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], yet commercial facilities typically test cannabinoids and other compounds in bulk, largely due to the high costs of testing. Additionally, the chosen varieties for this research may not represent those commonly used in other facilities, as cannabinoid content, yield, and other metrics are highly dependent on the specific variety grown. Different varieties inherently produce varying levels of cannabinoids [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and biomass [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which influences overall performance.\u003c/p\u003e\u003cp\u003eCultivation practices also play a significant role in determining yield and quality. These practices vary widely between facilities and cultivators, further complicating the generalization of these findings. Our results are based on data collected from a single facility at a specific point in time. While they provide a useful reference for guiding future cultivators and facilities in estimating production potential and costs, they may not be directly applicable to other settings. For example, we assume that photoperiod plants in this analysis are grown from purchased clones. However, plants may be grown from seeds in other facilities, or at this same facility at a different time. Differences would alter the analyses of revenue, costs, returns, and the estimates. For example, in a scenario where plants are grown from seeds, input costs for seed and plants, labor, other inputs would be expected show differences.\u003c/p\u003e\u003cp\u003eAdditionally, autoflowering and photoperiod plants were treated differently in this study due to the distinct ways they were processed. Autoflowers were hang dried and their bucked weight wasn\u0026rsquo;t taken, while photoperiod plants were bucked while wet, and the dried weight was not measured. This discrepancy introduces assumptions that the same amount of water was lost during drying for both flowering strategies, and that the weight of stem twigs lost in both strategies is similar. As a result, the estimates provided in this study reflect the effective weight and THC content under these assumptions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eNeeds and opportunities\u003c/h2\u003e\u003cp\u003ePolicies and programs at both federal, and New York State levels create opportunities for adult-use \u003cem\u003eC. sativa\u003c/em\u003e production enterprises. These enterprises offer farm business owners new crop selection options. To make informed management decisions regarding new opportunities in the cannabis industry, farm business owners benefit when production, and economic research-based information are developed, available, and accessible. Industry analysts, and experts point out that farm-level agronomic, and cost and return analyses for different cultivation scenarios are still limited. Limitations make it difficult for farmers to make optimal decisions amidst the inherent risks, and uncertainties in the market. The economic analysis presented in this study seeks to provide farm-level agronomic, and economic insights into the operation of \u003cem\u003eC. sativa\u003c/em\u003e enterprises.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eApproach, methods, data\u003c/h2\u003e\u003cp\u003eDespite its limitations, our study provides estimates based on recorded, farm level, agronomic and economic data collected from a functioning farm using actual farming practices, yielding tangible products that are sold in the market. A valuable feature of this work is the comprehensive collection, recording, and analysis of labor usage, and other crop management inputs. Efforts provide actionable data for growers. This analysis benefits from a comprehensive collection, and reporting of labor hours by task, providing more informed expectations regarding labor needs, costs, and strategies for managing human resources risks.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eResults, outcomes\u003c/h2\u003e\u003cp\u003eThe approach here offers grounded insights based upon peer reviewed methods, data, results, etc. In contrast, consider other works where: 1) methods, data, and results are not accessible and, or difficult to access; and, or 2) methods, data, results etc. are omitted and, or unclear. The former approach supports improved decision making, allowing for the effective management of risks, for example, human resource risks, by way of implementing practices that mitigate risk and uncertainty.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eFuture work\u003c/h2\u003e\u003cp\u003eWhile this study serves as a valuable contribution to the field, there is a continued need for more production, and economic research to support farm business owners. Future studies could focus on the effects of alternative planting scenarios, the number of grows per year, plant densities, and the interactions among these factors on the overall value of production, costs, and returns. Additionally, research-based economic insights are needed to refine the understanding of price received, and yield metrics. Improved understanding ultimately empowers farm business owners to make more informed decisions about the profitability, and sustainability of their operations.\u003c/p\u003e\u003cp\u003eFuture needs for research-based information may be addressed by way of cooperation among value chain participants, stakeholders. Perhaps a periodic survey, and reporting of key agronomic, and economic metrics via an industry supported effort could be studied for its potential to benefit the industry. Cannabis industry value chain participants might be willing to provide data \u0026mdash; perhaps anonymously, or in a way that does not compromise their intellectual property or operations \u0026mdash; to a representative group of industry stakeholders charged with developing and implementing reporting activities for common use. Such collaborations could help the industry gain a clearer understanding of agronomic factors, production practices, costs, returns, and economic dynamics.\u003c/p\u003e\u003cp\u003eValue chain participants seek to achieve economic, environmental, and community objectives given available resources. Their efforts towards continuous improvement \u0026ndash; What worked? What did not work, and why? \u0026ndash; benefit from efforts to improve availability, and accessibility of research-based information. The issues related to data quantity, quality and availability; data tampering; the speculative nature of the industry; lingering illicit operations; unreliable players; and the potential for undisclosed methods, data, and assumptions of analyses concern industry value chain participants, and stakeholders. Companies that know their enterprises\u0026rsquo; costs and production metrics hold valuable insights that could greatly benefit the cannabis industry as a whole, particularly in emerging markets like New York State. For example, research-based insight regarding value of production, costs, and returns associated with systems, and practices that did not achieve objectives, and goals might accelerate progress toward sustainable viability, and growth of a New York State cannabis industry. Periodic sharing of knowledge from, and among value chain participants, stakeholders could be transformative.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides one of the first farm-level agronomic and economic evaluations of high-THC \u003cem\u003eC. sativa\u003c/em\u003e cultivation in a New York State greenhouse. Autoflowering plants offered shorter cycles and lower labor needs but produced smaller yields and lower profitability compared to photoperiod plants under the conditions analyzed. Photoperiod cultivation generated higher annual returns per square foot despite greater labor and cloning costs, suggesting it is better suited for operations prioritizing yield and profit optimization. However, autoflowers may still be advantageous in settings with limited capital, labor, or space, or where rapid turnover is needed. Findings highlight the importance of early growth traits for predicting final yield, the substantial role of labor and plant material in production costs, and the need for improved breeding and production data, particularly for autoflowers. Continued research on cultivation strategies, economic risks, and pricing dynamics will be essential to support informed decision-making and long-term sustainability for \u003cem\u003eC. sativa\u003c/em\u003e producers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCBDA: Cannabidiolic acid\u003c/p\u003e\n\u003cp\u003eCBD: Cannabidiol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTHCA: Delta-9-Tetrahydrocannabinolic acid\u003c/p\u003e\n\u003cp\u003eTHC: Delta-9-Tetrahydrocannabinol\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eFunding Declaration:\u003c/u\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent to Publish:\u003c/u\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent to Participate:\u003c/u\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthics declaration:\u003c/u\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData Availability:\u003c/u\u003e Upon acceptance, the raw data supporting this study will be made publicly available through recognized online repositories, the Cornell University data repository, and the authors\u0026rsquo; professional websites.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting Interest:\u003c/u\u003e DV is a board member of the non-profit Agricultural Genomics Foundation and the sole owner of the company CGRI, LLC\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJH analyzed all economic estimates, KR collected all data, DV analyzed production estimates. JH and DV wrote the first draft of the manuscript, all authors contributed to the revision and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Wheatfield Gardens-TruCann for allowing us to collect data on their facility, Seth Brophy, Timothy McDowell, Bill Nichols, and George Stack for useful discussion, and Lynn M. Johnson from the Cornell Statistical Consulting Unit for helpful statistical direction.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBell CD, Soltis DE, Soltis PS: \u003cstrong\u003eThe age and diversification of the angiosperms re-revisited\u003c/strong\u003e. \u003cem\u003eAmerican Journal of Botany \u003c/em\u003e2010, \u003cstrong\u003e97\u003c/strong\u003e(8):1296-1303.\u003c/li\u003e\n\u003cli\u003eLi HL: \u003cstrong\u003eAn archaeological and historical account of cannabis in China\u003c/strong\u003e. \u003cem\u003eEconomic Botany \u003c/em\u003e1973, \u003cstrong\u003e28\u003c/strong\u003e(4):437-448.\u003c/li\u003e\n\u003cli\u003eLi HL: \u003cstrong\u003eOrigin and use of Cannabis in Eastern Asia; Linguistic-cultural implications\u003c/strong\u003e. \u003cem\u003eEconomic Botany \u003c/em\u003e1974, \u003cstrong\u003e28\u003c/strong\u003e(3):293-301.\u003c/li\u003e\n\u003cli\u003eRusso EB: \u003cstrong\u003eHistory of cannabis and its preparations in saga, science, and sobriquet\u003c/strong\u003e. \u003cem\u003eChemistry \u0026amp; 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Production Manual\u003c/strong\u003e. 2023.\u003c/li\u003e\n\u003cli\u003eStack GM, Toth JA, Carlson CH, Cala AR, Marrero‐Gonz\u0026aacute;lez MI, Wilk RL, Gentner DR, Crawford JL, Philippe G, Rose JK: \u003cstrong\u003eSeason\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003elong characterization of high\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003ecannabinoid hemp (Cannabis sativa L.) reveals variation in cannabinoid accumulation, flowering time, and disease resistance\u003c/strong\u003e. \u003cem\u003eGCB Bioenergy \u003c/em\u003e2021, \u003cstrong\u003e13\u003c/strong\u003e(4):546-561.\u003c/li\u003e\n\u003cli\u003eda Silva Benevenute S, Freeman JH, Yang R: \u003cstrong\u003eHow do pinching and plant density affect industrial hemp produced for cannabinoids in open field conditions?\u003c/strong\u003e \u003cem\u003eAgronomy Journal \u003c/em\u003e2022, \u003cstrong\u003e114\u003c/strong\u003e(1):618-626.\u003c/li\u003e\n\u003cli\u003eFolina A, Kakabouki I, Tourkochoriti E, Roussis I, Pateroulakis H, Bilalis D: \u003cstrong\u003eEvaluation of the effect of topping on cannabidiol (CBD) content in two industrial hemp (Cannabis sativa L.) cultivars\u003c/strong\u003e. \u003cem\u003eBull UASVM Hortic \u003c/em\u003e2020, \u003cstrong\u003e77\u003c/strong\u003e:46-52.\u003c/li\u003e\n\u003cli\u003eDanziger N, Bernstein N: \u003cstrong\u003ePlant architecture manipulation increases cannabinoid standardization in \u0026lsquo;drug-type\u0026rsquo;medical cannabis\u003c/strong\u003e. \u003cem\u003eIndustrial Crops and Products \u003c/em\u003e2021, \u003cstrong\u003e167\u003c/strong\u003e:113528.\u003c/li\u003e\n\u003cli\u003eCFAH:\u003cstrong\u003e Cannabis Global Price Index. 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Limited data currently exist to inform growers and investors. This study evaluates both agronomic and economic outcomes for two flowering strategies\u0026mdash;autoflower (light-insensitive) and photoperiod (light-sensitive) cannabis\u0026mdash;grown in a NYS greenhouse.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA comparative agronomic and economic analysis was conducted to assess yield performance, input requirements, costs, revenue, and returns for autoflower and photoperiod \u003cem\u003eC. sativa\u003c/em\u003e crops. Growth traits were measured and correlated with final yield. Cost components, including labor, seeds and plants, nutrients, and other variable inputs, were analyzed to determine their contribution to total production expenses. Economic returns were calculated on a per\u0026ndash;square foot basis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBoth autoflower and photoperiod plants showed strong correlations between early growth traits and final yield. Autoflowers, with shorter life cycles and independence from light manipulation, produced smaller plants with lower total biomass and THC content compared to photoperiod plants. Using assumed baseline values, autoflower cultivation resulted in a negative annual return above total costs of negative \u003cspan\u003e$\u003c/span\u003e1.48 per ft\u0026sup2;, whereas photoperiod cultivation generated a positive return of \u003cspan\u003e$\u003c/span\u003e7.18 per ft\u0026sup2;. Labor represented the largest share of variable costs for both systems, accounting for 52% of total costs in autoflower production and 34% in photoperiod production.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAutoflowers may be advantageous in space, capital, or labor-constrained environments requiring rapid crop turnover, while photoperiod plants appear more profitable for larger or well-resourced operations focused on maximizing yield and returns. Additional research is needed to identify practices and economic strategies that improve profitability, consistency, and efficiency for both cultivation approaches. This study underscores the need for continued economic analyses to guide decision-making in the emerging adult-use \u003cem\u003eC. sativa\u003c/em\u003e industry.\u003c/p\u003e","manuscriptTitle":"High-THC Cannabis sativa in a New York Greenhouse: Yield and Economic Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 17:59:27","doi":"10.21203/rs.3.rs-8021436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T09:51:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T14:49:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T14:29:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287142442716064635634061346784423538467","date":"2025-11-14T07:01:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176714175406589643246523179650013760606","date":"2025-11-12T10:00:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289192436416066647435035588365223593805","date":"2025-11-11T22:48:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126257507037140228373741159977391675544","date":"2025-11-11T17:35:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T11:17:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T01:32:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T01:31:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cannabis Research","date":"2025-11-03T16:42:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cannabis-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcan","sideBox":"Learn more about [Journal of Cannabis Research](https://jcannabisresearch.biomedcentral.com/)","snPcode":"42238","submissionUrl":"https://submission.springernature.com/new-submission/42238/3","title":"Journal of Cannabis Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe85ff4d-49fd-491c-a15a-a70f524a74c4","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T16:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 17:59:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8021436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8021436","identity":"rs-8021436","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}