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Each data point is then projected into the resulting low dimension linear space

Any days where less than 75% of the herd was successfully recorded in the parlor were also dropped. This left a total of 80 days of milk order observations ± 26 recorded while cows remained overnight in their pen, and 54 after the transition to overnight pasture. Finally, cows that were not present in at least 50% of the remaining milkings were excluded from further analysis. Of the 177 cows with sufficient records, 114 had no recorded health events.With this metric, the more consistently a smaller set of cows are observed in a given segment of the queue, the smaller the entropy values becomes to reflect less stochasticity in the system. In standard statistical models, the nominal value of estimators such as log likelihood and AIC scale with the size of the data set, and must be interpreted relative the value of equivalent terms assessed against a null model. Analogously, the nominal value of the entropy estimates scales with the number of discrete categories used. The maximum theoretical value occurs when no underlying deterministic structures are present and all categories are equally likely to occur, which algebraically simplifies to the log of the number of discrete categories used . Here the maximum theoretical entropy value would be log2 = 6.83. To visually contrast differences in stochasticity across the queue, the observed entropy values were plotted against the median entry quantile of the corresponding queue segment using the ggplot2 package, vertical grow racks with maximum theoretical entropy added as a horizontal reference line . Nonrandom patterns in queue formation could also be explored by tracking the entry position of individual cows over time. As entry quantile has a numerical value, we can now also use variance to quantify and contrast stochasticity between animals.

As with all analytical approaches reviewed in this paper, there are both strengths and shortcomings to either approach . In this system there are two potential drawbacks to this conventional summary statistic. The first is that variance estimates are quite sensitive to outliers, making it difficult to empirically distinguish between cows that occupy a wider range of queue positions and animals who typically occupy a narrower range but might have gotten jostled far from their normal position on one or several occasions. The second drawback is that, because variance quantifies dispersion about a central value, it cannot distinguish between cows that demonstrate little consistency in entry position and multimodal queuing patterns. For example, if a cow always entered the parlor either first or last, we would intuitively determine that this pattern is nonrandom, but the corresponding variance estimate would be the largest in the herd. Having recovered evidence of nonrandom patterns, the next step was to begin characterizing the behavioral mechanisms driving this heterogeneity. The most fundamental question that need be answered to inform further analysis was the degree to which queueing patterns were driven by individual or collective behaviors. Because cows jockey for position with one another in the crowd pen, where they are pushed up to enter the parlor, we know intuitively that entry quantile records cannot be considered truly independent observations. If cows move through this melee as independent agents, such that their position within the queue is determined by individual attributes ±preferences, dominance, etc ±then a linear model may still provide a reasonable approximation of the underlying system. Early observational work on milking order, however, has suggested that cows may form consistent associations when entering the milking parlor, particularly when heifers are reared together .

If cows move into the parlor in cohesive units, such that queue position is more determined by clique-level than individual attributes, then network analyses may be a more appropriate. Principal Component Analysis is commonly employed to visualize relationships between observational units in high dimensional datasets. In this approach, redundancy between variables, here each milking record, is captured using either covariance or correlation assessed across all data points, here all animals. An eigenvector decomposition is then used to linearly compress the information contained in the data via rotation of the orthogonal axes. New axes are added iteratively such that each new dimension is pointed in the direction of greatest remaining variability until only noise remains . PCA was here performed only on animals with no recorded health in order to prevent any anomalous queuing behaviors recorded from acutely or chronically ill animals from obscuring the queuing patterns of the broader herd. The correlation matrix was constructed using all pairwise complete observations, and a scree plot was used to determine the dimensionality of the resulting space . The plotly package was then used to visualize the final embedding. While PCA provides a computationally expedient means of visualizing high dimensional data, the underlying assumption of linearity is not always appropriate . In some data sets complex geometric constraints, such as those commonly found with images or raw accelerometer data, and other latent deterministic features may project data points onto high dimensional geometric surfaces collectively called manifolds .

When these topologies are nonlinear , the spatial relationships between data points cannot always be reliably maintained when projected directly into a linear space, which can lead to incorrect inferences . Imagine, for example, you had a round globe of the world and wanted instead a flat map. Applying PCA to this task would be analogous to smooshing the globe flat on a table. Some of the original geographic relationships would be discernable, but some locations would appear erroneously close, and some landscapes would be entirely obscured. Modern manifold learning algorithms strive to more reliably project the complex geometric relationships between observational units into a standard Some geographic features will still be lost, particularly over sparsely sampled regions like the oceans, but the spatial relationships between landmarks would collectively prove more representative of the original topography. To further explore the underlying structure of this data absent assumptions of linearity, and thereby potentially accommodate any complex geometric constraints imposed on milk order records by latent social structures within the herd, a diffusion map algorithm was implemented using functions provided in base R . This was done here by first calculating the Euclidean distance between temporally aligned vectors of parlor entry quantiles for each pairwise combination of cows, scaled to adjust for missing records, and then inverting these values to create a similarity matrix. From this similarity matrix a weighted network was created by progressively adding links for the k = 10 nearest neighbors surrounding each data point. A spectral value decomposition was then performed on the corresponding graph Laplacian matrix . The resulting eigenvalues were used to select the appropriate number of dimensions, and the corresponding eigenvectors visualized using the 3D scatter tools from the plotly package . Finally, as a means of comparing geometric structures identified in the observed dataset with those of a completely randomized queuing process, the permutated dataset generated in the previous section was also embedded and visualized using plotly graphics .Having determined from the previous visualizations that a linear model might be a reasonable representation of the underlying deterministic structures of this system, the next step was to explore the temporal dynamics of this dataset. In a standard repeated measures model, multiple observations from the same animal are assumed to be identically and independently sampled, mobile shelving system implying that sampling order should not affect the observed value. If the observation period is sufficiently long to allow the underlying process to shift or evolve over time, however, stationarity cannot be assumed. Failure to statistically accommodate a temporal trend can not only lead to spurious inferences due to incorrect estimation of error variance, but also risks overlooking dynamic features of the behaviors under consideration . In practice temporal trends are often assessed by first fitting a stationary model and analyzing the resulting residuals. This may suffice when the temporal trend is uniform across animals, but risks overlooking more complex nonhomogeneous temporal affects. This could occur if only a subset of the larger group displays a non-stationary pattern, a risk that is likely heightened in large socially heterogeneous groups.

In this physically constrained system, where we know that every cow moving forwards in the queue must force other cows backwards, compensatory trends could also be easily overlooked in collective assessment of residuals. We first assessed temporal trend using two conventional EDA techniques. First, the ggplot2 package was used to generate scatter plots of entry quantile values against the corresponding observation date for each individual cow, with pasture access annotated with a verticalline. Plots were visually inspected for non-stationary, and are provided in supplementary materials. Next, to further explore the impact of the shift from pen to overnight pasture access on morning queueing patterns, median queue positions from the two subperiods were plotted against using the ggplot2 package , and Pearson correlation and Kendall Tau were computed using the stats package . While these preliminary visualizations were easy to both generate and interpret, both treat cows as independent and somewhat isolated units. With such a large number of animals to consider, the capacity for human pattern detection is quickly overwhelmed, making it difficult to contextualize trends within the broader herd. Further, this approach fails to leverage non-independence between animals entering the parlor, and thus risks overlooking subtler collective responses. Data mechanics visualizations were implemented to simultaneously explore systematic heterogeneity in milk entry quantiles both between animals and across the temporal axis. This was done by first using entry quantile values to compute two Euclidean distance matrices: one quantifying the similarity between pairwise combinations of cows, the second quantifying similarity between pairwise combinations of daily milking sequences. These distance matrices were then used to generate two independent hierarchical clustering trees using the Ward D2 method . By cutting both trees at a fixed number of clusters, observation days and cows were both partitioned into empirically defined categories, and a contingency table was then formed with cow clusters as the row variable and day clusters as the column variable. The original distance matrices were then updated, using the clustering structure between cows to create a weighted distance matrix between days and vice versa, thereby allowing mutual information to be shared between the temporal and social axes of the dataset . After several iterations of this algorithm, clusterings converged towards a contingency table with minimal entropy, wherein the entry quantile values within each cell were as homogenous as possible. When the entry quantile values were subsequently visualized using a heat map, this highly generalizable entropy minimization technique served to visually enhance heterogeneity within the data driven by nonrandom patterns along either axis. Further, by facilitating the transfer of information between axes, interaction effects between the social and temporal dimensions of this system were magnified, which here provided a means to explore nonhomogeneous temporal non-stationary between subgroups within the herd . The data mechanics pipeline was used to analyze the temporal dynamics present in both the complete milking order dataset and the subset of animals with no recorded health events. Instead this algorithm was applied on a grid from 1 to 10 clusters for either axis. The resulting 100 heat maps scanned visually to determine the clustering granularity required to bring into resolution any interactions between social and temporal mechanisms. While this process may be computationally cumbersome, it is empirically analogous to systematically varying the focus of a light microscope to bring into resolution microbes of unknown size ±a tedious but effective means of identifying all relevant structures within a sample . Finally, the RColorBrewer package was used to add color annotations to the column margin, to clarify temporal patterns, and to the row margins, which served to visualize potential relationships between queue position, a selection of individual cow attribute variables, and the onset of recorded health complications.Having thoroughly characterized the stochastic structures present in this dataset, the insights gleaned from the preceding visualizations were incorporated into a linear model to evaluate the relationship between queue position and several cow attributes. The four days identified as outliers by the data mechanics visualizations were first removed and the dataset converted to long format to be analyzed as a repeated measures model using the nlme package . Cow was fit as a random intercept via maximum likelihood method. Guided by the results of entropy and data mechanics visualizations, VarIdent was used to estimate separate error variance terms for each cow, and the necessity of this data-hungry heterogeneous variance model confirmed via likelihood ratio test against the null model with homogenous variance .

All analytes are baseline separated in the chromatographic spectrumusing accurate mass single-ion-chromatography

The mass concentrations of different carbonyls/acids in air were calculated by the total mass concentration of the specific carbonyls/acids in the HPLC-HRMS analysis divided by the total volume of air that flowed through the DNPH cartridge during the vaping collection process.The method reported in this work offers unambiguous identification and a large quantification range for functionalized carbonyl compounds and organic acids. This is useful for studying e-cigarette thermal degradation chemistry, as well as other environmental chemistry topics . A total of nineteen DNPH hydrazones in the e-cigarette aerosol sample were observed : five simple carbonyls, six hydroxycarbonyls, four dicarbonyls, three acids, and one phenolic carbonyl. Hydroxycarbonyls comprised 3 of the top 6 most abundant compounds. Uchiyama et al., recently found that some compounds are emitted purely as gas-phase species , some as purely particulates , and some as both . Both the concentration and phase information is useful for estimation of exposure risk. Much of the chemical identification for DNPH hydrazones can be directly derived from the exact mass of the detected [M-H]- ions alone. As the formation of DNPH hydrazones replaces only one atom , it is straightforward to deduce the original molecular formula of the carbonyl or acid from the hydrazone formula. The chemical structures were confirmed as in 2.3.1. Figure 2.6a shows the total ion chromatography and SIC of select carbonyl-DNPH compounds, Figure 2.6b shows the corresponding integrated mass spectrum of TIC and each SIC. From the TIC, it is clear that ecigarette aerosol is a complex system which contains a large number of carbonyls/acids.

Co-elution is common in the TIC ; however, the SIC isolates the chromatographic peaks of the desired m/z, avoiding co-elution and misidentification. We also found that acetone-DNPH co-eluted with vanillin-DNPH in the chromatography. This will have led to an overestimation of the abundance of acetone using a chromatography method without HRMS, as vanillin-DNPH is not commercially available.Beyond molecular formulas, it is advantageous to confirm the exact bonding sites of carbonyls and other moieties to give insight to chemical mechanisms and aid in theoretical calculations of reaction energies, as these calculations are sensitive to structures. The chemical structure of DNPH adducts was identified by their neutral and radical losses in tandem multistage mass spectrometry using collision induced dissociation , 148,149 which often helps to elucidate the exact carbon location of the moiety-of-interest for small molecules. For example, alcohols adjacent to a beta carbon with an abstractable hydrogen can lose H2O by H-shift rearrangement, 150 while those bonded to aromatic or other non-abstractable sites do not show this loss in the negative ion mode. For nitroaromatics such as DNPH, the electron-withdrawing groups of NO2 exerts a strong stabilizing effect on anion radicals, and facilitates NO2-mediated rearrangements . For small ions like acetaldehyde-DNPH, there is no other reasonable carbonyl structure that exists for the molecular formula, and MSn confirms this structure with expected fragmentation of CH3NO and CH3CHO . However, cannabis grow equipment there are some ambiguous formulas such as C3H6O3, which may belong to structural isomers dihydroxyacetone and glyceraldehyde. Both of these hydroxycarbonyls are proposed to exist in e-cigarette aerosol after NMR analysis, but are impossible to distinguish with chromatography as they have the same UV-absorption and m/z.34 With MSn fragmentation, we found that dihydroxyacetone is the main product.

Even though several fragmentation pathways for these isomers are similar and 269.05→ 239.04 , the H2O loss and C2H4O2 loss that is expected for glyceraldehyde-DNPH were observed to be negligible in the mass spectrum . The preferred formation of dihydroxyacetone over glyceraldehyde supports the radical-mediated oxidation pathways suggested by Diaz et al., as radical abstraction of the H in VG should lead preferentially to a secondary alkyl radical compared to the primary radical . The initiating radicals are suggested to be reactive oxygen species such as hydroxyl radical, and as such, the degradation products can be described by processes that occur in atmospheric chemistry. Some of the products identified here can be expected from the thermal degradation of PG and VG , which is in agreement with the proposed mechanism, while others are likely to be flavoring additives . A shared product ion after fragmentation of the DNPH hydrazones is C6H3N4O3 – , which is the modified DNPH after the O-rearrangement loss of the original carbonyl/acid. Other similar loss pathways are those of the DNPH itself, including loss of HONO, NO2, and NO . There are also distinctive fragmentation pathways for each ion, which are summarized in Table 2.2.While the process of ionization in ESI is complex, it has been demonstrated that there are key factors influencing the ionization efficiency of different compounds. For example, for the same family of compounds, there is a relationship between negative ion electrosprayionization response and pKa of the dissociation equilibrium HA ⇆ A – + H+ , which is directly related to basicity. We calculate the basicity in terms of ΔGdeprotonation , because the deprotonated [M-H]- ion is usually detected in the ESI negative mode. Our calculations of the electrostatic potential maps of carbonyl-DNPH hydrazones show that they have a primary acidic proton ; thus, they are excellent candidates for which gas phase basicity can be used to parameterize ionization efficiency in the ESI negative mode.

We emphasize that the theoretical chemistry results in this work only provide a relative indication of sensitivity, not absolute calibration factors, and only for the same family of compounds that are protonated or deprotonated. The relative theoretical sensitivities are then anchored by absolute ESI calibrations for the carbonyl-DNPH compounds where standards are commercially available.The trend of ΔGd and ESI sensitivity arises from the intrinsic relationship between deprotonation efficiency and the ability of the aromatic product ion to stabilize the negative charge initially formed on the N atom . Acrolein is the most sensitive compound in ESI negative mode because it has conjugated double bonds, i.e., additional pi orbitals for the negative charge to be delocalized. Also, ketones have lower sensitivities than aldehydes because the electron donating group on both sides of the C=N bond slightly destabilizes the negative ions. A limitation of this model occurs for compounds that have similar ΔGd. In this situation, other factors like molecular volume and polarity may also play an important role for these compounds. Despite the limitations, this method is applicable to the compounds found in e-cigarette aerosol and enables the first estimation of concentrations for complex carbonyls that have not yet been quantified with acceptable uncertainty. Furthermore, this computational technique offers an advantage compared to the time expenditure, costs, and chemical usage of synthesizing standards.The calculated concentrations of e-cigarette constituents characterized in this work are shown in Table 2.2 as mass per volume or mass per ten puffs analyzed. The most abundant compounds in the blu e-cigarette aerosol for our study conditions are hydroxyacetone, formaldehyde, acetaldehyde, lactaldehyde, acrolein, and dihydroxyacetone. While, within uncertainty, the exact order of abundance is not definitive, it is clear that hydroxycarbonyls are just as important assimple carbonyls to the composition of the e-cigarette aerosol. Hydroxyacetone has been found to be a major, sometimes dominant, emission in other e-cigarette brands and e-liquids, as quantified by gas chromatography. The agreement of the high abundance of hydroxyacetone lends support to the theoretical approach in this work, which enables all carbonyls and acids to be quantified by the same method. The high abundance of hydroxyacetone may be due to its multiple formation pathways in Scheme 2.2 and its possible role as an impurity in e-liquid, e.g., Sleiman et al., found hydroxyacetone in concentrations of < 1% of the sum of PG and VG in the e-liquids they used. We were not able to test the e-liquid in this work due to cartridge design; thus, are unable to comment on the extent of hydroxyacetone impurity in the e-liquid, if present. Dihydroxyacetone and lactaldehyde, in contrast, have not been regarded as major e-cigarette emissions until their unambiguous identification in this work. Their formation pathways from PG and VG are highly feasible, so their higher abundance is not unexpected. It’s not clear why these compounds have not been reported earlier; we suspect analytical challenges may be a reason. As we discussed previously, lactaldehyde-DNPH co-eluted with formaldehyde-DNPH in the TIC . Thus, HPLC-UV, one of most frequently used instrument for studying carbonyl compounds in e-cigarette aerosol, indoor grow cannabis will not be able to identify and quantify lactaldehyde. However, the HPLC-HRMS method overcomes co-elution challenges by distinguishing compounds based on their exact mass from the SIC and mass fragmentation patterns. Dihydroxyacetone-DNPH appeared to be baseline-separated in HPLC-UV, with a retention time slightly shorter than DNPH itself; however, its unambiguous identification is not possible without HRMS and/or authentic standards. Furthermore, both of these compounds are quite polar, and thus, not conventionally compatible with gas-chromatography.

A comparison of the absolute emission concentrations of thermal degradation products between studies is not straightforward, even for the same brand of e-cigarettes, as the puffing regimens and apparatus of reported works are all different and individual puffing parameters have non-linear effects on the thermal degradation chemistry. Klager et al., also reported high variability of carbonyl concentrations for the same brand, puffing-regimen, and flavor, suggesting that the factors driving the thermal degradation chemistry are not yet fully understood. Our work should be primarily viewed as a demonstration of a new method to the chemical characterization of our specific e-cigarette model at the stated puffing conditions, with noted insights into the thermal degradation mechanism. Formaldehyde, acetaldehyde, and acrolein are known to produce pathological and physiological effects on the respiratory tract. They are known to cause sensory irritation, inflammation, and changes in pulmonary function; formaldehyde is also carcinogenic. The average daily dose of aldehydes can be calculated by the amount of aldehydes per puff multiplied by the average number of puffs a user inhales per day. For example, the median puffs per day for e-cigarette users can be assumed to be 250171, so the average daily exposure dose of formaldehyde is 37.5 µg/day for this e-cigarette device, e-liquid, and operating conditions. The California Office of Health Hazard Assessment Chronic Reference Exposure Levels for formaldehyde is 9 µg/m3 , which could be translated to an acceptable daily dose of 180 µg/day and is higher than the e-cigarette aerosol exposure for formaldehyde in this work. In addition, OEHHA has a No Significant Risk Level recommendation of 40 µg/day which is intended to protect against cancer; this NSRL level is close to the exposure dose of formaldehyde in this work. The average exposure dose of acrolein for blue-cigarettes is 15.2 µg/day according to Table 2.2, which is higher than the OEHHA chREL value . Logue et al. used a similar approach to estimate health impacts and found that both formaldehyde and acrolein can exceed maximum daily doses derived from occupational health guidelines. Differences in results are likely due to the different devices, e-liquids, and puffing regimens used.While the reported emissions in this work may not be generalized to all e-cigarettes and use scenarios, it is informative to compare the aldehyde emissions normalized by nicotine, since ecigarette users transitioning from traditional tobacco products will self-titrate nicotine intake when using e-cigarette products. In this work, the nicotine yield is 10.4 ± 1.9 μg/10 puffs. We did not observe evidence of nicotine oxidation174 under the puffing conditions of this work, which will impact the ratio. The formaldehyde/nicotine ratio is 144 ±32 μg/mg nicotine, which is 4 times higher than the formaldehyde/nicotine ratio in combustible cigarettes . The acrolein/nicotine ratio measured in this work in close to that of tobacco products , while the acetaldehyde/nicotine ratio and propionaldehyde/nicotine ratio are lower than that in combustible cigarettes. Logue et al. observed similar trends using different e-cigarette products; however, the results were not normalized for nicotine so a direct comparison is not possible. Thus, we find e-cigarettes do not necessarily emit lower carbonyl compounds than tobacco products, but the comparisons may change depending on the specific e-cigarettes or tobacco products, or different puffing/smoking regimens. Although hydroxycarbonyls are abundant in e-cigarette aerosol, a general lack of toxicological data precludes health risk assessment. Smith et al. found that exogenous exposure to dihydroxyacetone is cytotoxic and will cause cell death by apoptosis. Glycolaldehyde is also suspected to have biological toxicity. For hydroxyacetone and lactaldehyde, toxicology data are currently unavailable on many toxicology databases like Hazardous Substances Data Bank , European Chemicals Agency and Research Institute of Fragrance Materials .

An online survey was also the most cost-effective means of reaching a large number of cannabis growers

The current study did not examine variability in sleep patterns and sleep problems that may be particularly salient to MC users.Additional research in this area is needed to better inform treatment interventions.Meanwhile, treatments such as cognitive– behavioral therapy for insomnia should be routinely available to veterans who may derive greater benefit from this behavioral strategy than resorting to using cannabis with its known adverse effects on health, cognitive, and psychological functioning.Finally, VHA providers should expect an increase in the number of veterans seeking voluntary treatment for CUD, because more cannabis users now seek treatment since the legalization of MC use.Therefore, routine screening or assessment for cannabis use and CUD in the VHA is recommended, particularly in the context of assessing for sleep problems and trauma related symptoms.At a minimum, researchers and clinicians should not be combining cannabis use with other illicit drugs of abuse in terms of screening and treatment recommendations.Several study limitations warrant mention.As with many veteran samples, a small number of female veterans limited the generalizability of our findings to female veterans who are using the VHA for health care services.The caveat to our and other similar cross-sectional findings is that these data cannot establish precedence of cannabis versus other substances or whether MC use leads to subsequent reductions in alcohol or other illicit or prescribed substances, or whether sleep problems amount to increased MC use or vice versa.Planned longitudinal analyses of the larger parent study will indeed help clarify the putative relationship between these variables and MC use in this veteran sample.Next, characterizing MC and RC groups as mutually exclusive categories does not take into account the nuance and complexity of using cannabis for reasons that can be viewed as both medicinal and recreational.Future studies might need to utilize a continuous index of the proportion of use for medicinal and recreational purposes and account for differences across states and jurisdictions in their definitions of medical use of cannabis.

Next, it is possible that responding to the questionnaires specific to medicinal cannabis grow set up use could have influenced responses on the subsequent MPS assessing cannabis-related problems for the MC users.Finally, the study was explicitly focused on examining differences between MC and RC users in terms of the presence of PTSD and MDD diagnoses, the two psychiatric disorders that are most prevalent among the returning veterans.However, comorbidity with other anxiety disorders may be important to investigate in future comparisons between MC and RC users.In conclusion, our findings suggest research on MC use in veterans needs to continue.In addition, although the line between cannabis use for medicinal and recreational reasons may often be blurred , current findings help identify motivations underlying medicinal cannabis use among veterans.Future research can further resolve and address specific needs of veterans seeking medicinal cannabis, which could inform mental health treatment within the VHA.Legalization of cannabis production in 2017 has generated demands for state regulatory, research and extension agencies, including UC, to address the ecological, social and agricultural aspects of this crop, which has an estimated retail value of over $10 billion.Despite its enormous value and importance to California’s agricultural economy, remarkably little is known about how the crop is cultivated.While general information exists on cannabis cultivation, such as plant density, growing conditions, and nutrient, pest and disease management , only a few studies have attempted to measure or characterize some more specific aspects of cannabis production, such as yield per plant and regional changes in total production area.These data represent only a very small fraction of domestic or global activity and are likely skewed since they were largely derived not from field studies but indirectly from police seizure data or aerial imagery.In California, where approximately 66% of U.S.marijuana is grown , knowledge of the specific practices across the wide range of conditions under which it is produced is almost nonexistent.Currently, 30 U.S.states have legalized cannabis production, sales and/or use, but strict regulations remain in place at the federal level, where it is classified as a Schedule I controlled substance.As a land-grant institution, UC receives federal support; were UC to engage in work that directly supports or enhances marijuana production or profitability, it would be in violation of federal law and risk losing federal support.As a result, UC research on California cannabis production has been limited and focused on the geography of production and its environmental impacts.

These studies have documented the negative effects of production on waterways, natural habitats and wildlife.While such effects are not unique to cannabis agriculture per se, they do present a significant threat to environmental quality and sensitive species in the watersheds where cannabis is grown.Science-based best management practices to mitigate or avoid impacts have not been developed for cannabis.Because information on cannabis production practices is so limited, it is currently not possible to identify key points of intervention to address the potential negative impacts of production.As a first step toward understanding cannabis production practices, we developed a statewide survey on cultivation techniques, pest and disease management, water use, labor and regulatory compliance.The objective was to provide a starting point from which UC scientists could build research and extension programs that promote best management practices — which are allowable as long as their intended purpose is not to improve yields, quality or profitability.Survey results also establish a baseline for documenting changes in cultivation practices over time as legal cannabis production evolves in California.To characterize key aspects of cannabis production in California, we developed an anonymous online survey using Qualtrics survey software.A web-based survey that masked participants’ identity was determined to be the most suitable approach given that in-person interviews were limited by legal restrictions on UC researchers visiting cannabis farms, and mail or telephone surveys were constrained by the lack of any readily available mailing address or telephone contact information for most cannabis growers, who are understandably discrete with this information.Survey questions focused on operational features , pest and water management, labor, farm revenue and grower demographics.Two draft surveys were reviewed by a subset of cannabis growers to improve the relevance of the questions and terminology.A consistent critique was that the survey was too long and asked for too much detail, taking up to 2 hours to complete, and that such a large time commitment would significantly reduce the response.We therefore made the survey more concise by eliminating or rephrasing many detailed questions across various aspects of cannabis production.

The final survey included 37 questions: 12 opened and 25 structured.Structured questions presented either a list of answer choices or a text box to fill in with a number.Each list of answer choices included an “Other” option with a box for growers to enter text.Open-ended questions had a text entry box with no character limit.Condensing the survey to capture more respondents resulted in less detailed data, but the overall nature of the survey remained the same — a survey to broadly characterize multiple aspects of cannabis production in California.Data from the survey has supported and contextualized research by other scientists on specific aspects of cannabis production, such as water use , permitting , law enforcement , testing requirements , crop prices and perceptions of cannabis cultivation in the broader community.Recruitment of survey participants leveraged networks of California cannabis growers who had organized themselves for various economic and political purposes.These were a combination of county, regional and large statewide organizations, with many growers affiliating with multiple groups.We identified the organizations through online searches and social media and sent recruitment emails to their membership list-serves.The emails contained an explanation of the survey goals, a link to the survey website and a message from the grower organization that endorsed the survey and encouraged members to participate.The emails were sent in July 2018 to approximately 17,500 email addresses, although not all members of these organizations necessarily cultivated cannabis, and the organizations noted that their mailing lists somewhat overlapped the lists of other groups that we contacted.For these reasons, the survey population was certainly less than 17,500 individual cannabis growers, outdoor cannabis grow but because we were not able to view mailing lists nor contact growers directly, and because there are no comprehensive surveys of the number of cannabis farms in California, we could not calculate a response rate or evaluate the representativeness of the sample.Respondents were given until Aug.15, 2018, to complete the survey.All survey participants remained anonymous, and response data did not include any specific participant identifiers.Our survey, although of limited sample size, is the first known survey of California cannabis growers and provided insights into common forms of cultivation, pest and disease management, water use and labor practices.Since completing this survey, we have discussed and/or presented the survey results with representatives from multiple cannabis grower organizations, and they confirmed that the data were generally in line with production trends.Evident in the survey results, however, was the need for more data on grower cultivation practices before best management practices or natural resource stewardship goals can be developed.All growers monitored crop health, and many reported using a preventative management strategy, but we have no information on treatment thresholds used or the efficacy of particular sprays on cannabis crops.Likewise, the details of species-level pest and disease identification, natural enemy augmentation and sanitation efforts remain unclear.

Growers did not report using synthetic pesticides, which contrasts with findings from previous studies that documented a wide range of synthetic pesticide residues on cannabis.Product selection for cannabis is very limited due to a mixed regulatory environment that currently does not allow for the registration of any insecticide or fungicide for use specifically on cannabis , although growers are allowed to use products that are exempt from residue tolerance requirements, exempt from registration requirements or registered for a use that is broad enough to include cannabis.As such, it may be that in the absence of legally available chemical controls growers were choosing allowable, biologically derived products or alternative strategies such as natural enemy augmentation and sanitation.Our survey population was perhaps biased toward non-chemical pest management — the organizations we contacted for participant recruitment included some that were formed to share and promote sustainability practices.Or, it may be that respondents were reluctant to report using synthetic chemicals or products not licensed for cannabis plants.The only other published data on water application rates for cannabis cultivation in California we are aware of is from Bauer et al., who used estimates for Humboldt County of 6 gallons per day per plant for outdoor cultivation over the growing season.Grower reported estimates of cannabis water use in this survey were similar to this rate in the peak growing season , but was otherwise lower.Due to the small sample size, we cannot say that groundwater is the primary water source for most cannabis growers in California or that few use surface water diversions.However, Dillis et al.found similar results on groundwater being a major water source for cannabis growers, at least in northwest California.If the irrigation practices reported in our survey represent patterns in California cannabis cultivation, best management practices would be helpful in limiting impacts to freshwater organisms and ecosystems.For example, where groundwater pumping has timely and proximate impacts to surface waters, limiting dry season groundwater extraction by storing groundwater or surface water in the wet season may be beneficial , though this will likely require increases in storage capacity.The recently adopted Cannabis Cultivation Policy requires a mandatory dry season forbearance period for surface water diversions, though not for groundwater pumping.Our survey results indicate that the practical constraints on adding storage may be a significant barrier for compliance with mandatory forbearance periods for many growers.More in-depth research with growers and workers is needed to explore the characteristics of the cannabis labor force and the trajectory of the cannabis labor market, especially in light of legalization.Several growers commented on experiencing labor shortages, a notable finding given that recent market analyses of the cannabis industry suggest that labor compliance costs are the most significant of all of the direct regulatory costs for growers.Higher rates of licensing compliance among medium and large farms is not surprising given the likelihood that they are better able to pay permitting costs.Yet, that the majority of respondents indicated they had not applied for a license to grow cannabis, with over half noting some income from cannabis sales, indicates potentially significant effects if these growers remain excluded from the legalization process.

HIV and cannabis affected the expression of a number of kinases and genes involved in kinase regulation

Neurode generation and inflammation were functional annotations identified in BIOCARTA. Given the large degree of overlap between these networks , we applied a merge network function in Cytoscape, which is shown in Fig. 7. The visualization of this gene network indicates that both HIV and cannabis increase genes with functions in neurode generation and inflammation , but cannabis decreased key contributors to the inflammatory process such as IL1b, TLR2, MyD88 and PARK7, as well as RASGRP1 . HIV infection in the context of cannabis indicated patterns that were similar to cannabis alone, with decreased expression in the same genes. Moreover, cannabis in the context of HIV elevated TLR2, TLR4 and MyD88, but had no or mild effects, or decreased a number of genes in this network . Another gene network related to neurode generation and inflammation in Fig. 6 but low overlap and smaller but significant enrichment , was functionally annotated to chemokines and cytokines . This network was highly sensitive to all the group conditions, with higher expression of most genes in cells from HIVþ/CAN- compared with HIV-/CAN- , and an effect of cannabis that was characterized by decreased expression of several genes, regardless of HIV . Yet, the genes decreased by cannabis differed in a context-dependent manner. For instance, the effects of cannabis alone as well as of HIV in the context of cannabis showed a lower expression of TLR2, GNAI3, AKT3,PRKACA, ITGAM, and GNG2, and a mild decrease of A1BG, Ly96, PIK3CD, BRAF, NAT1, NAT2, PRKAR2A and AADAC. Decrease in PLCB2 and RAC1 was a characteristic of cannabis alone , while decrease in RAF1 and NFKBIA was characteristic of HIV in the context of cannabis . The effects of cannabis in the context of HIV was also characterized by lower expression of MAPK3, PLCB3, CXCR6 and NFKB1 . Functional annotations associated with leukocyte-vascular adhesion and transmigration capacity were also sorted from pathway interactions. These functions were affected by HIV and cannabis . A large number of genes in this network were differentially increased by HIV and by cannabis . Yet cannabis lowered the expression of a large number of genes with cytoskeleton and signaling properties, including RHOA, AKT3, RAC1, BRAF and BCL2 .

HIV in the context of cannabis had also lower MAPK1 and CTNNB1 compared to uninfected cannabis users . HIVþ cannabis users had a high number of genes that were lower or mildly changed compared to HIV non-cannabis users . Inflammation is highly regulated by a kinases. The effects were differential and context-dependent. All the conditions showed decrease in CAMK4,vertical farming in comparison to respective controls . HIV alone decreased mTOR, CSF1R, EPHA4, PDPK1 and DGKE . Cannabis alone, as well as HIV in the context of cannabis , decreased ATK3 and MAPKPK2. Cannabis alone decreased CALM1 . HIV in the context of cannabis decreased the expression of PGK1 and RAF1 . Cannabis in the context of HIV decreased several genes in this network that were either not modified or increased by the other conditions . These included MAP2K1, MAPK9, MAPK3, PRKCA and PDPK1 .Networks analyzed above have shown distinct effects of cannabis, which differed between cannabis alone and in the context of HIV. We used iRegulon to make predictions on transcription factors usage associated with these context-dependent patterns, in order to identify regulatory and co-regulatory elements. Fig. 11 shows the same gene network assembled based on pathway interactions in Fig. 3, but now reorganized based on the expression of transcription factor motifs in these genes’ promoters. The table legend in Fig. 11 shows the transcription factors mostly associated with the genes in the network. We have identified a significant number of binding motifs to BHLHE40, BACH1, SPl1, NFKB1 , JUND, CEBPE, SRF, PRDM14, ATF4 and USF2. Mapping of genes regulated by these factors have revealed sub-clusters characterized by co-regulation. Interestingly, the visualization of effects of cannabis alone and of cannabis in the context of HIV suggests that the genes most affected by cannabis in the HIVþ subjects are more likely to be coregulated by at least two transcription factors. A closer visualization of these genes is shown in Fig. 12, mapping the majority of the blue genes . Of these, four genes were predictors of uninfected cannabis users: a disintegrin and metallo proteinase domain-containing protein 10 , the Calcium/Calmodulin Dependent Protein Kinase II Delta , the cAMP responsive element binding protein 1 , and the CREB binding protein .

Four genes were predictors of HIV and were also affected by HIV and cannabis interactions: Beclin 1 , Plexin C1 , survival of motor neuron 1 , and the class II major histocompatibility complex molecule HLA-DRA . Four genes marked HIV- cannabis users and also significantly distinguished between HIV- and HIVþ cannabis users: the Protein Phosphatase 3 Catalytic Subunit Beta and subunit gamma , the K-Ras gene and Cullin 2 . Overall, there was a trend to regulation of gene transcription by cannabis in the context of HIV, but not in the uninfected group, further highlighting interaction effects.The screening of a large number of transcripts associated with neurological disorders has shown that the effects of cannabis differed drastically between HIV- and HIVþ groups, particularly in gene networks playing a role in inflammation, neurodegeneration, apoptosis and leukocyte adhesion and transmigration. The results indicate that cannabis in the context of HIV may have beneficial effects. However, in individual genes, we identified detrimental effects that were associated with poly substance use as a covariate, particularly methamphetamine. Effects of cannabis, one of the most widely used drugs, on HIV and particularly on biomarkers of inflammation and cognition, are largely unknown, diverse or anecdotal. By examining a large number of transcripts associated with neurological disorders and pathways of inflammation in peripheral leukocytes, we fill a gap on the understanding of how drugs of abuse impact cellular phenotypes, with the goals of identifying biomarkers of HIV neurocognitive disorders that are sensitive to interactions with substance use. In this study, we examined cells from 102 subjects evenly distributed as HIVþ/-and CANþ/. In order to increase the power, the cohort was homogeneous in sex, age and education. The use of other substances was limited but not excluded, due to characteristics of the population. The sample size was a limitation for the identification of strong predictors. However, systems biology strategies helped us identify genes that exhibited interactive properties based on their co-involvement in highly overlapping molecular pathways. Visualization strategies helped identify gene networks with a concerted behavior in different groups, highlighting important trends in effects of cannabis use dependence.

Our results show that cannabis has strong effects on the expression of a number of genes in peripheral leukocytes, which serve as reporters of biological processes that are relevant both to HIV infection as well as to neurological disorders. For instance, the pathway identified as viral host interactions included class II HLA-DRA, CCR5 and CCR2. While HIV in the context of cannabis developed to lower expression of HLA-DRA,cannabis did not lower the transcription of CCR5, suggesting a limited impact on viral entry . On the other hand, HIV in the context of cannabis and cannabis alone showed detectable decrease in the transcription of SIRT1, a histone deacetylase with epigenetic silencing properties . We have previously reported that the transcriptional decrease of SIRT1 may be one factor in the dysregulation of the inflammatory environment and others have suggested that SIRT1 regulates viral transcription . Whether the effects of cannabis in this pathway have real implications to the infection remains to be addressed. In this cohort, we did not find correlations between the activation of these pathways in leukocytes and plasma or CSF viral load . Yet, the attenuating effects of cannabis observed in the context of HIV links and expands to pathways in inflammation and neurodegeneration, as well as to apoptosis, due to overlap in genes and transcriptional co-regulators . The actions of cannabis on the expression of genes involved in vascular adhesion and leukocyte transmigration have indicated that in HIVþ cannabis users, peripheral leukocytes may be less likely to focally adhere to endothelial cells and migrate into tissues. This may be beneficial at preventing inflammation in end-organs including the brain, while potentially impairing surveillance, but also viral spread . The implications of this findings must be addressed using experimental models. Overall, the findings were consistent across pathways, suggesting that, like HIV alone, cannabis alone may increase the expression of a number of inflammation-associated genes, but this may differ in the context of HIV, where cannabis use was associated with attenuated or decreased expression of pathway components. In end organs, the actions of cannabis may differ due to effects via distinct receptors. Cannabinoid receptor 1 is largely expressed in CNS but also in several tissues with links to psychoactive and physiological effects, while CB2R is expressed mainly by immune cells with described anti-inflammatory and immuno suppressive properties . Given the differences in distribution and signaling between the receptors, effects of cannabis or cannabinoids may differ in the presence or absence of inflammatory cells,flood tray or in the context of infection, where pro-inflammatory signals are occurring. Other less studied cannabinoid receptors and endocannabinoids may also play a role. Our data supports this idea that cannabis effects on molecular markers and biological processes is context-dependent, potentially driven by infection and inflammation, and the resulting differences in numbers and activation status of CB2R-expressing innate and adaptive immune cells.

The examination of changes in expression patterns in kinase networks can inform mechanisms of action by cannabis in the context of HIV. Aberrant kinase activity is linked to a wide range of diseases including neoplastic diseases, central nervous system disorders, vascular disorders, and chronic inflammatory diseases. The analysis of a gene networks assigned to kinases indicated that cannabis in the context of HIV decreased transcription of components of the p38 MAPK pathway, which is involved in a diversity of biological functions . The blockage of p38 MAPK by cannabinoids has been previously reported in other models, with both suppressor and stimulating effects . Suppression of this pathway may be associated with blockage of oxidative stress . The anti-oxidant activity of cannabis and cannabinoid compounds has been previously acknowledged , although healthy cannabis users do not differ from non-users regarding oxidative stress markers . HIV infection promotes changes in the number of immune cells, quality and activation status of cell subsets. The infection and the broken homeostasis are likely critical in the determination of the effects of cannabis as a therapy or a complication. It has been suggested that the effects of cannabinoids on macrophages are critical to resulting T-cell mediated responses and may differ according to those cells activation status and to stimuli . Moreover, here we have shown by transcription factor usage predictions, that the effects of cannabis are associated with transcriptional co-regulation at the individual gene promoters, by multiple factors that may vary by context. Co-regulation by different transcription factors is a critical factor in determination of transcriptional levels and kinetics , and is highly influenced by covariates and comorbidities. Cannabis use may be considered as a confounder in biomarker investigations as it tended to mask the expression of molecules upregulated by HIV, particularly if cognitive function was not improved in parallel with markers, for instance when other drugs were present. Cannabis users had better neurocognitive performance, overall and in learning and memory subdomains, particularly if they did not have a history of lifetime METH dependence. Such effect was stronger in METH users, but also observed in markers sensitive to HIV/alcohol and HIV/cocaine. This suggests differential effects of cannabis in the context of poly substance use and how the potentially beneficial effect of cannabis on HIV biomarkers may be relative when other drugs are also used. Overall, our work has screened effects of cannabis on an extensive number of transcript biomarkers of inflammation and neurological outcomes, which were peripherally expressed by uninfected and HIV infected subjects. Systems biology strategies have aided the identification of gene networks assigned to processes relevant to neuroHIV, which exhibited orchestrated behaviors in response to HIV or cannabis alone, or their interactions. Cannabis effects were largely dependent on context, with infection as the most significant interacting factor followed by polysubstance use. Other factors not examined here may include cannabis use frequency and dose.

The economic influence of cannabis can be seen throughout the county

The clear majority of respondents did not think cannabis growers manage timberlands sustainably and a similar percentage felt the same about ranchlands. Eighty-five percent of respondents regarded cannabis growing as negatively affecting wildlife and 87% regarded it as negatively affecting stream flow . Eighty-four percent thought cannabis growing leads to soil erosion and 70% thought it increases fire hazard. Seventy-eight percent believed that cannabis production in ranchlands and timberlands leads to habitat fragmentation and the same percentage suggested that the economic value of cannabis incentivizes the subdivision of large parcels. Fifty percent of landowners felt that their property value had increased due to cannabis production while 40% were neutral on that question. Eighty-three percent of respondents thought that Humboldt County was a safer place before cannabis and 76% of respondents perceived new cannabis growers as less responsible than cannabis growers who have been in the county for years. About half of respondents believed that increased cannabis legalization will be good for Humboldt County. Fifty-seven percent of respondents were not yet willing to accept that cannabis is a leading industry and that people should support it. Fifty-four percent of respondents believed that Humboldt County would be better off in the future without cannabis. Most landowners included in the survey reported having observed changes in grower demographics in the last decade. Most felt that the number of small cannabis growers is decreasing. Sixty-one percent felt that the number connected to organized crime is increasing and perceived that there is an increasing number of green rush growers in their communities. Most respondents were concerned about organized crime, while only 48% were concerned with green rush growers and 18% with small growers. Overall, resident and absentee owners expressed similar views on most issues. Of the survey’s 59 statements on experiences and perceptions, pots for cannabis plants statistically significant differences between the two groups appeared for only eight statements.

Absentee owners were more likely to report that their surface water resources had been impacted by growers; that their fences or infrastructure had been destroyed by growers; that their safety had been threatened by growers and that they had been threatened by growers on public land. Absentee owners were also more likely to be concerned that growers were taking over public land. They were less likely to agree that growers manage timberland sustainably and that cannabis production decreases their property values. With this study, we aimed to better understand the experiences and perceptions of traditional agricultural producers — the families who, in most cases for several generations, have made a living off their land, all the while watching changes occur in the social, economic and environmental dynamics that surround cannabis. This survey’s documentation of social tensions may not come as a surprise to those who have lived in Humboldt County . Even after many decades of cannabis cultivation, traditional agricultural producers have not warmed to the people or practices involved in the cannabis industry. Indeed, changes in the social fabric of the cannabis industry have only perpetuated and intensified existing tensions. As this survey shows, concerns about “small growers” are minimal now — those growers have become part of the community, and one-third of respondents agreed that they know growers whose values align with their own. What was novel 40 years ago is now a cultural norm. Today’s concerns center instead on the challenges of current cannabis culture: environmental degradation and the threat of major social and economic change. Respondents mostly agreed that growers today are less reasonable than those who have been in the county for many years. As one respondent wrote, “Growers are a cancer on Humboldt County.” This distrust highlights the challenges that, in rural areas, can often hinder community-building and mutual assistance mechanisms, which are often needed in isolated communities.As the survey shows, approximately 40% of respondents have been impacted indirectly by the cannabis industry, and some respondents have directly profited through cannabis production themselves. Interestingly, just over half the respondents chose not to say whether they grow cannabis, hinting at the possibility that, even for traditional agricultural producers, cannabis has presented an opportunity to supplement income and cover the costs of landownership. However, the broader economic growth attributed to the cannabis industry is not always viewed favorably, and a majority of respondents agreed that Humboldt County would be better off in the future without cannabis.

Some respondents claimed that the industry has increased the cost of labor and that, in many cases, it can be difficult to find laborers at all because the work force has been absorbed by higher-paying cannabis operations. Likewise, many respondents agreed that land values have increased because of cannabis. But for landowners whose property has been passed down through generations, and who have little intention of selling, increased land values translate into increased taxes and difficulty in expanding operations, both of which can be limiting for families who are often land-rich but cash-poor. One respondent wrote, “Yes, the price of land has gone up… but this is a negative. It increases the inheritance tax burden, and it has become so expensive that my own adult children cannot afford to live here.” In Humboldt County’s unique economic climate, it’s difficult for most landowners to decide whether the opportunities the cannabis industry provides are worth the toll that they believe the industry takes on their culture and community — it’s not a simple story. As one respondent noted, “If I had taken this survey 40 years ago, my response would have been very different. With Humboldt County’s poor economy, everyone is relying on the cannabis industry in one way or another.” Our survey provides an important baseline from which such changing attitudes can be measured. Our results should be seen in the context of larger trends involving population and agricultural land in Humboldt County. At the time we were preparing our survey, property records indicated that slightly more than 200 landowners in the county owned at least 500 acres; these individuals made up our survey population. Past research, however, has documented that cannabis was likely grown on over 5,000 distinct parcels in Humboldt County in 2016 . Our survey respondents, because of their large holdings, may be unusually exposed to cannabis growers physically because their larger properties may have more contact with cannabis growers. At the same time, these respondents might be better able to survive economically in a Humboldt County without cannabis. It is unclear if the experiences and perspectives of many Humboldt County smaller landowners would be similar to those of these large landowners. For many in Humboldt County, the impacts of cannabis production on property and the environment are a central concern. Respondents mentioned problems involving shared roads and fences, illegal garbage dumping and contamination, deforestation, fire hazards, feral dogs and impacts on wildlife and domestic livestock. One respondent wrote that “Growers leave a mess, steal water, tear up roads, let guard dogs damage neighbors’ property, including livestock, poison wildlife, increase soil erosion and threaten people.” In many ways, it seems that land ethics are at the center of the concerns that traditional agricultural producers harbor about the new wave of cannabis growers. Though respondents remarked on cannabis growing’s direct impacts on the environment, they also largely agreed that the cannabis industry is causing fewer young people to enter traditional farming careers — and that growers are taking over working lands. It is unknown if the rates at which successive generations stay in the family business are lower in Humboldt County than in rural communities less influenced by cannabis.

For families who have managed and lived off these lands for decades — most of them for more than 50 years — these shifting stewardship ethics threaten their immediate environment as well as their very identity. Medical cannabis use was illegal throughout the US until 1996, and recreational use was illegal until 2012. As of August 2021, 18 US states, the District of Columbia, Guam, and the Northern Marianas Islands had passed laws permitting recreational and medical cannabis and 17 states permitted only medical cannabis . Supporters’ reasoning for legalization includes arguments about therapeutic benefits, redirecting law enforcement to violent crimes, personal freedom, tax revenues, product regulations, and harmlessness . Both recreational and medical legalization increase cannabis use . In Colorado, the first state to legalize adult-use cannabis in 2012, past 30-day cannabis use increased among those aged 18–25 from 26.8% in 2011 to 34.4% in 2018 . The regulated cannabis market in Colorado registered $10 billion in sales between 2014, when adult-use sales began, and 2020, when sales reached $2.19 billion . Cannabis smoking, overwhelmingly the most common form of cannabis consumption , exposes users to many of the same toxins contained in tobacco smoke, including particulate matter , polycyclic aromatic hydrocarbons, gasses, and volatile organic compounds . Cannabis use is associated with more frequent chronic bronchitis episodes, airway injury, myocardial infarction, and ischemic stroke . Secondhand cannabis flood table smoke also poses a risk to nonsmokers . Commercial determinants of health research, which studies the commercial drivers of poor health outcomes, has identified mechanisms of influence that the tobacco, food, and alcohol industries employ to promote products in ways that compromise public health . Tobacco, alcohol, and gambling companies, for example, hire lobbyists to influence policy, connect with front groups and allied industries to oppose regulation, and build relationships with policymakers through political donations . Tobacco, alcohol, and food interests orchestrate lobbying across industries and transnationally to promote policies favorable to consumption. The cannabis industry has a similar interest in maximizing profits by creating a favorable regulatory environment. Cannabis corporations share links with the alcohol and tobacco industries. Tobacco companies Altria , Imperial Brands , and British American Tobacco , have all made significant investments in cannabis, a long-anticipated development . Constellation Brands, maker of Corona beer, has also made investments in Canopy Growth, a Canadian cannabis corporation . Tobacco and alcohol interests have openly formalized a cannabis-focused political association as members of the Coalition for Cannabis Policy, Education, and Regulation, a lobbying group that lists Altria, Constellation Brands, and Molson Coors Beverage Company as members.

Employing tactics used by the tobacco industry for decades , cannabis companies are also vested in major sports through sponsorship of athletes and leagues in the USS . Considering the health risks involved with cannabis use and the conflict between public health and the commercial interests of these industries, systematic analyses of cannabis industry influence on policy making are essential. There has been little study on the topic despite several calls for research . Although there have been popular media reports on cannabis industry lobbying expenditures, we identified no systematic analyses that assessed cannabis lobbying over time or identified connections between the cannabis industry and affiliates. Cannabis products are legal in multiple states, while remaining illegal at the federal level. Even though federal law technically supersedes state law, gaps in enforcement have been carved out by the federal government to allow for state legalization of adult-use and medical cannabis . As a result, it remains to be seen whether cannabis industry efforts to influence policy are comparable to other industries for which recreational consumption has historically been legal. In this study we sought to describe cannabis industry lobbying in the Colorado state legislature, which dictates product standards, licensing requirements, and other policies relevant to cannabis sales. We hypothesized that the cannabis industry would use strategies similar to those of other similar industries including relying on hired lobbyists , obscuring industry funding, working with related industries, and building national networks to support policies likely to increase consumption . We focused on Colorado because it was the first state to legalize recreational cannabis in 2012, making it possible to assess whether cannabis industry lobbying activities have become comparable to other industries in nature and scope over time. Because of the complexity of relationships between the cannabis industry, lobbyists, and government officials, we supplemented the quantitative analyses with a case study illustrating cannabis industry tactics to influence the Colorado legislature. Colorado requires lobbyists to file reports on their activities with the Secretary of State, even if they are a salaried employee of the business they represent.

Future studies that utilize the IGT in young adults during fMRI are needed to explore this question

Places whose citizens grant legitimacy to cannabis might not be ready to publicly display cannabis within their territories. Potential tax revenues and employment opportunities are not worth the moral trade-off for middle- and upper-class communities. For example, Santa Monica and Laguna Beach residents were among the strongest supporters of cannabis legalization—75% and 62% of cannabis support, respectively—but their city governments banned any cannabis-related economic activities. Both Santa Monica and Laguna Beach are predominantly non-Hispanic and wealthy.88 In contrast, economically and socially disadvantaged cities have to rely on potential tax revenues and jobs generated by legal cannabis businesses and, thus, permit cannabis companies even without public support. Take, for example, Calexico and Firebaugh, whose citizens did not support cannabis legalization , but city governments permitted cannabis companies. Both cities are predominantly Hispanic and poor . Irvine and Santa Ana—cases that are familiar to most UCI residents—are yet another example of the disparity between supply and demand. Irvine residents supported cannabis legalization at higher rates than Santa Ana residents . However, Santa Ana permitted all kinds of cannabis-related economic activities and has more than 20 cannabis dispensaries, and Irvine allowed only cannabis testing labs. Remarkably, Irvine has 9.7% of the Hispanic population, and Santa Ana—77.3%. In Outsiders, Howard Becker defines three types of social control of cannabis use: limiting supply and access to the drug; keeping nonusers from discovering that one is a user; defining the act as immoral. Since Becker published his book, cannabis has been depenalized,heavy duty propagation trays decriminalized, and finally legalized in California. Although the situation has significantly improved in terms of supply and access to cannabis, the stigmatization of cannabis use is still a pressing issue in the legal cannabis market.

The war on drugs generated various misconceptions about cannabis, which detrimentally affected public perceptions. First, despite scientific research showing that cannabis is no more harmful than nicotine or alcohol, some people still believe that cannabis is a gateway drug to heavier substances that induces criminal activity and violence.89 Second, although most people recognize the medicinal benefits of cannabis , they continue to disfavor the recreational use of cannabis, perceiving it as a non-conforming and risky behavior and its users as weak and non-productive members of society. Finally, people who tolerate the recreational use of cannabis in private spaces do not always accept its public display and consumption. Agreeing with legalization as a concept, Californians are not ready to embrace it entirely and allow dispensaries in their own neighborhoods—this occasionally leads to their obtaining court rulings against cannabis-growing operations.The drug problem cannot be adequately understood without examining the underpinning issues of poverty and disadvantage.There is nothing inherently criminogenic about drugs, nor drugs necessarily relate to poverty. Stigmatization of drug use is a product of a culture in which the consumption of pleasurable intoxicants is deemed intolerable and punishable. Drug use is a heavily moralized territory, and the lower social strata suffer worse outcomes than more affluent people for the same drug-related behavior . The literature on the history of drugs portrays drug regulation as a moral tale, in which the “blurry lines between us and them, privileged and repressed, strong and weak, keep getting rewritten as the boundaries between good and evil” . Existing at all social levels, drug use is recognized as a problem in specific social contexts—namely, it is clustered in the communities suffering already from multiple socio-economic difficulties . Even if the number of cannabis arrests is declining every year,Hispanics and African Americans continue to be disproportionately arrested.

In California, in 2019, Hispanics accounted for 41.7% of cannabis felony arrests, African Americans for 22.3%, and whites for 21.3%.Despite the fact that cannabis consumption rates are higher among whites, they are less likely to be arrested for cannabis-related offenses .The idea behind socio-spatial control is that deviance should be contained within designated territories, i.e., if objects, practices, and behaviors do not fit the existing social order, they are to be spatially excluded. In this chapter, I tested the reverse hypothesis, i.e., if things are geographically put out of place, it means that they are viewed as socially undesirable and inappropriate. The statistical analysis shows that city governments act as moral entrepreneurs when deciding whether they want to forbid or allow legal cannabis businesses. The prohibition erastereotypes continue to influence the development of the legal cannabis market: most jurisdictions decide to keep aloof from spoiled identities and tainted places associated with cannabis use, even at the cost of not reaping financial rewards. On the contrary, economically disadvantaged communities with a larger Hispanic population are more likely to permit cannabis dispensaries because: they have higher financial incentives, and they have lower reputational risks. Since these communities are already marginalized and associated with crime, disadvantage, and social exclusion, having legal cannabis dispensaries will not exacerbate their stigmatization.A secondary aim was to conduct an exploratory analysis examining group-by-sex interactions on risky decision-making in young adult college students. Since we were interested in examining decision making within active MJ users who were not yet undergoing cannabis withdrawal, we asked participants to remain abstinent from all substance use for 12 h prior to the study visit to attempt to avoid any withdrawal symptoms that may contribute to impairments in decision making. We hypothesized that frequent MJ users would have poorer performance than healthy controls, indicated by lower net IGT scores; frequent MJ users would show faster reaction times in card selection compared with healthy controls, which would reflect greater impulsive tendencies during decision-making; and younger age at first MJ use, greater cumulative MJ use and greater recent MJ use would be related to lower net IGT scores in MJ users.Sixty-five participants, 18–22 years old, completed the study. All participants were native English speakers currently enrolled in college or university. Of these participants, 32 were healthy controls and 33 were frequent MJ users.

Exclusionary criteria included uncorrected visual impairments, pregnancy, lack of fluency in English, self-reported lifetime history of a diagnosed psychiatric disorder or learning disability, self-reported current use of psychotropic medications, major neurological/medical illness or significant head trauma, prenatal exposure to drugs or alcohol, premature birth and reported history of psychotic disorders in immediate family of biological relatives. Additional exclusion criteria for healthy controls included: significant substance use history , any history of heavy episodic alcohol use: >5 drinks/occasion for males and >4 drinks/ occasion for females, >90 lifetime days of cigarette use, MJ use more than once/month in the past year and any other lifetime illicit drug use. Inclusionary criteria for frequent MJ users was ≥5 occasions of MJ use/week in the past year. Given the comorbidity of MJ and alcohol use , alcohol use was assessed but not exclusionary for the MJ+ group. MJ+ reporting >15 lifetime occasions of other illicit substance use combined across substances were excluded from study participation. While no participants reported lifetime history of a psychiatric disorder, scores from the Cannabis Use Disorders Identification Test-Revised indicated 23 MJ+ met criteria for a possible cannabis use disorder.Participants were recruited through flyers posted around the community and at MJ dispensaries as well as through social media advertising. Written consent was obtained from participants who contacted the laboratory to complete an interview to determine eligibility for the study. Following an eligibility interview, eligible participants were invited to take part in a study visit that included measures of substance use and psychosocial functioning as well as neurocognitive tasks of executive functioning. All participants were asked to abstain from substance use for at least 12 h prior to the study visit to limit effects of acute intoxication on neurocognitive measures. No participants appeared intoxicated at the time of the study visit. After providing consent for participating in the study visit, participants provided a urine sample for a 12-panel urine toxicology test and completed a breathalyzer test to confirm absence of alcohol intoxication. All MJ+ had a positive urine toxicology screen for THC, while all HC had a negative urine toxicology screen for THC. Further, all participants had a blood alcohol concentration of 0.00 at the time of the study visit. A nicotine metabolite test for cotinine was not conducted for this study; thus, recent nicotine use was assessed through self-report. At the end of the study visit,participants were compensated with an Amazon e-gift card. All study procedures were approved by the Oregon State University Institutional Review Board and were in accordance with ethical guidelines of research with human participants.Participants completed a brief demographics questionnaire, vertical cannabis which included questions on race, and socioeconomic status . As many college students in this age range lack personal income, we asked participants to select their perceived socioeconomic status . Additionally, participants were asked to estimate lifetime alcohol, MJ and cigarette use, and to report all substance use in the past 30 days using the Timeline Follow back procedure. Participants also reported age at first use for alcohol, MJ and cigarettes. All participants completed a 2-subtest version of the Wechsler Abbreviated Scale of Intelligence-II. Here, we report on the findings from the Iowa Gambling Task , one of the tasks from a larger neurocognitive assessment that was selected as a measure of risky decision-making.

Findings from other tasks included in the larger neurocognitive assessment have been previously reported .Data were analyzed using IBM Statistical Package for the Social Sciences . For parametric, normally distributed data, independent samples t-tests were used to examine group differences on demographic variables and reaction times in card selection on the IGT with a significance value set at p < 0.05. Mann-Whitney U-tests were used to examine group differences on substance use variables that violated normality , including past 30 day and lifetime substance use variables. Using a repeated measures ANCOVA with age and IQ as co-variates, we investigated group differences on net IGT scores across five bins, each consisting of 20 trials. Substance use variables not normally distributed were log-transformed to improve normality and were examined in relation to IGT performance using Pearson correlations. Finally, an exploratory analysis using a repeated measures ANCOVA examined the main effect of group, sex and their interaction on net IGT scores, controlling for age and IQ.This study examined the relationship between frequent MJ use and risky decision-making in young adult college students using the IGT. To our knowledge, only one other study has examined risky decision making using the IGT in a similar and narrow age range of young adults . In the current study, MJ+ were older and had significantly lower IQ scores relative to HC. As both age and IQ were related to IGT performance, they were included as co-variates in the analyses. There was a significant main effect of group on net IGT scores, suggesting that MJ+ had lower net IGT scores relative to HC . Although MJ+ made advantageous card selections as indicated by the positive net IGT scores, they made less advantageous choices compared to HC. This effect is consistent with prior research examining group differences between MJ users and healthy controls in young adults . Research suggests that MJ users are more likely to make risky judgments despite subsequent monetary punishment than healthy controls and exhibit increased impulsive decision-making by selecting more disadvantageous cards than healthy controls . Additionally, the current findings support prior research that found young adult MJ users made more selections from disadvantageous decks A and B compared to healthy controls . However, in the current study, MJ+ also made fewer card selections than HC from deck C, an advantageous deck, but one that is associated with frequent punishments relative to deck D . This could suggest MJ users may prefer decks that are associated with frequent rewards and infrequent losses, which could drive reward-driven behavior. This observed performance difference in reward-driven behavior may be attributed to differences in utilization of the prefrontal cortex during strategy and choice selection.Furthermore, we found that the effect of group on net IGT scores was significant when including sex as a factor in the model. Overall, MJ + had lower net IGT scores compared with HC .

Many college students began smoking cannabis to protest the war in Vietnam

Yet another limitation of the criminal justice perspective is its focus on the national trends, federal mass media, or the general public. It is important to remember that the early regulatory efforts happen at the local level. When scholars use the term “public” , they often refer to “national” in its scope, impact, or character . According to James Hunter, public debate should mean not national but local debate “among people who live and work in relative proximity to each other and who care about their common neighborhoods and communities, towns, cities, and regions; and within institutions that are prominent and integrated into the communities where these people live” . There is a vast territory of social life between national culture and individual meanings, which is often overlooked in the canonical socio-legal literature on drugs. The focus on local processes allow us to see that criminalization and legalization were happening at the same time; that the debate on the decriminalization of cannabis took place before the full-fledged war on drugs ; that African Americans supported cannabis legalization at the lower rates but eventually had higher rates of incarceration for cannabis arrests ; that the decriminalization efforts of the Kennedy, Johnson, and Ford administration were not temporary and incidental but had long lasting effects and eventually resulted in cannabis legalization; that many legislative proposals failed and did not become “events” but affected the future legislations; that 2/3 of California cities supported cannabis legalization, but only 1/3 of them allowed legal cannabis businesses . In this section, I focus on the history of cannabis legalization in California. In particular, I describe the role of social movements in legalizing medical and recreational cannabis, their failures and victories, tactical repertoires,vertical grow shelf political threats, and discursive opportunities.

As I discussed above, cannabis prohibition and cannabis legalization are not separate processes; these are two dialectically united phenomena that interact, contradict, negate, and reaffirm each other. Socio-legal scholars, who are interested in cannabis as a criminal justice issue, often overlook the fact that control does not exist without resistance and that criminalization and legalization are two sides of the same coin. It is impossible to understand lawmaking processes without considering the work of mobilized groups of citizens challenging the unfair laws and shifting agendas of resourceful players. As I show below, defining the “drug problem” is not a prerogative of mighty actors or institutions. Some less powerful actors can create and promote alternative narratives about drugs and drug users and ultimately succeed in changing the governing norms and public opinion. The ongoing cannabis legalization in California offers a clear illustration of how social movements can become a local source of power and foster ideational change. California has been a pioneer in both criminalizing and decriminalizing cannabis. It was the first state to prohibit recreational use of cannabis in 1913 and the first to allow its medical use in 1996. Pro-cannabis social movements emerged in response to the Controlled Substance Act that classified cannabis as a Schedule I drug . According to Andreas Glaeser , change becomes possible when the state fails to positively validate people’s understandings of the social world. Understandings contribute significantly to the stabilization of political institutions, but for this to happen, they need to be continuously validated. There are three modes of understanding based on: interpretations, emotions, and senses. In the case of cannabis, the de-fetishization of the prohibitionist policies started in the 1960s, when new scientific evidence, dissatisfaction with authorities, and people’s own experience challenged the domain of unquestioned background assumptions about cannabis and its users.

The first objection to the prohibitionist assumptions came from the scientific community, which provided a new interpretation of cannabis. Although Nixon disowned the Shafer Commission’s report , which called for the decriminalization of cannabis possession, its results were spreading in society, along with the La Guardia Report of 1944. From the 1970s, scientific evidence proving the medical benefits of cannabis and demystifying its deleterious effects was multiplying, but the government continued to ignore it. The scientific community was calling for thorough research of the chemical properties, pharmacological qualities, and therapeutic applications of cannabis . More and more studies had shown the possible benefits of cannabis use. However, the scientific evidence was constantly downplayed by the federal authorities and the National Institute of Drug Abuse, who continued to fixate on presumed adverse effects of cannabis . For instance, the Reagan administration ignored the National Academy of Science’s report published in 1982,40 which questioned the effectiveness of full prohibition and recommended removal of criminal sanctions. Under the democratic Clinton presidency, a study co-authored by Harvard Medical School and Yale University showed the efficacy of cannabis for a wide range of ailments.Yet, once again, scientific findings did not change the anti-cannabis course of the political establishment; on the contrary, the drug-war budget doubled, and the record number of Americans were arrested on cannabis charges in those years . The second challenge to the prohibitionist status quo was a growing dissatisfaction with the US drug policy and the defiance of state authority. Cannabis use became a form of protest, a central symbol of the counterculture, and a ritual that demonstrated the willingness of young Americans to run risks with their peers.The government responded with harsher enforcement of drug laws: cannabis-related arrests rose from 18,000 in 1965 to 220,000 in 1970. That inevitably amplified protest movements. According to Patrick Anderson, the legalization movement began on August 16, 1964, when a young man walked into a police station in San Francisco, lit a cannabis joint and asked to be arrested.

Later that year, his lawyer launched the Legalize Marijuana organization , which sponsored the first pro-cannabis demonstration in America. In sum, the legalization movement was developing in response to political threats, confronting the mythology of “reefer madness” and persuading Americans that the time had come to change political priorities and put an end to the incarceration of young people for using a mild intoxicant . The third challenge to the prohibitionist discourse arose at the level of individual senses. An increasing number of people who used cannabis realized that it was no more dangerous than alcohol. The fact that cannabis was classified as the most dangerous drug, causing more damage than cocaine, opium, or methadone, sounded preposterous to those who had experienced cannabis effects. Thus, less and less people believed in the myths propagated during the anti-cannabis campaign. In 1975, psychiatrist and social activist Tod Hiro Mikuriya wrote: “Marijuana use in America is reminiscent of the era of Prohibition, in that almost 30 million people have smoked pot and the police of the 180 million other Americans are trying to prevent them from doing so. Despite vigorous efforts of society to regulate by deterrent legal sanctions, they have obviously failed. The use continues to escalate. In fact, marijuana has become a permanent part of American society. Since those who try and continue to use pot find it enjoyable, and many more people are trying it all the time, marijuana use is clearly here to stay. The time has passed when prohibition against personal use and possession should have been repealed”.All three factors—scientific evidence, dissatisfaction with authorities, and personal experience—made Americans more susceptible to the proclaimed dangers of cannabis. And this, in turn, raised political dissidence. In the 1970s, many social activists felt that the decriminalization and legalization of cannabis were just “around the corner.” Not only scientists and activists but also some state actors favored the depenalization of cannabis. The National Organization for the Reform of Marijuana Laws became the main voice of the pro-cannabis movement. Founded in 1970 by Georgetown law graduate Keith Stroup, the organization brought together a group of young lawyers, scientists, civic leaders, and even politicians to fight for cannabis reform .43 From the very beginning, NORML was a public-interest lobby that represented the interests of cannabis users, focusing on individual rights and the social harm caused by incarceration for minor drug offenses. Although its ultimate goal was the complete legalization of cannabis, in the 1970s, NORML focused mainly on the depenalization of cannabis and its removal from the list of Schedule I controlled substances. A catalyst of the national pro-cannabis movement, NORML scored its first victory in 1973 when Oregon has ended criminal penalties for smoking cannabis. Over the decade, several more states—including California—have followed suit. In 1976, California approved the Moscone Act,44 which made possession of small amounts of cannabis a civil instead of a criminal offense. That was the beginning of cannabis decriminalization in California: felony arrests for cannabis decreased fourfold—from 99,587 in 1974 to 19,284 in 1976 .

In subsequent years, both the number of organizations working on pro-cannabis issues and the pressure imposed on the federal government increased. In 1977, President Carter asked Congress to decriminalize the possession of small amounts of cannabis grow indoor at the federal level . But his plan never came to life due to the scandal discrediting his drug advisor Peter Bourne and the emergence of the grassroots parents’ organizations, which were building strong opposition to cannabis decriminalization. In 1986, the Drug Enforcement Administration finally agreed to review a petition filed by NORML and the American Public Health Association that asked to recognize the medical value of cannabis and remove it from Schedule I classification . After careful investigation, the DEA’s chief administrative law judge Francis L. Young stated that cannabis “has been accepted as capable of relieving the distress of great numbers of very ill people […] and it would be unreasonable […] for DEA to continue to stand between those sufferers and the benefits of this substance.”The judge permitted the transfer of cannabis from Schedule I to Schedule II so that cannabis could be legally available for patients. However, the DEA director ignored such recommendations . Throughout the 1980s and 1990s, the federal government and the media launched the largest anti-drug and anti-cannabis campaign. In this cultural context, the pro-cannabis movements did not have any political or discursive opportunities to bring about legal change. To a great degree, the success of social movements depends on their ability to “offer frames that tap into a hegemonic discourse” . Cannabis activists had nothing to offer; their claims did not resonate with ideas widely accepted in the broader society. The situation has changed with the AIDS epidemic, which provided discursive opportunities for politically effective framing.Robert Randall, a young college professor from Washington, D.C., was the Rosa Parks of the medical cannabis movement . In 1976, he sued the federal government for the right to use cannabis to treat his glaucoma. His doctor testified that the use of cannabis significantly decreased eye pressure, one of the primary symptoms of glaucoma, which kept Randall from going blind . In 1978, D.C. Superior Court established an important legal precedent: Randall won his case and became the first legal cannabis smoker since cannabis prohibition in 1937. However, Randall’s victory did not solve the problem of obtaining cannabis legally. He was not allowed to grow cannabis for himself and filed a petition demanding that the government provides him enough cannabis from the federal experimental farm at the University of Mississippi . His victory forced the Food and Drug Administration to establish the Compassionate Investigational New Drug Program, which provided government-grown cannabis for seriously ill patients. However, the program was limited to a small number of patients since many people who had received medical approval were rejected by the program . By 1991, only 15 patients were enrolled in the program. Randall’s legal precedent was a landmark victory, and social movements continued to exploit the medical discourse in the following years. Pro-cannabis activists crafted the image of cannabis as a compassionate palliative for seriously ill people and the image of cannabis users as patients, not criminals . However, such a medical frame was not very successful until the AIDS epidemic in the 1980s, which made medical cannabis an urgent issue and provided first discursive and later political opportunities for the movement. Many AIDS patients experienced wasting syndrome, and cannabis helped them stimulate appetite, retain weight, and prolong lives. As Cyrus Dioun argues, “the death and devastation of the AIDS epidemic made it necessary to discuss previously unmentionable topics” . From 1980 to 1995, over 500,000 AIDS cases and over 300,000 AIDS deaths have been reported in the US. San Francisco was at the forefront of both the AIDS crisis and the medical cannabis movement.

The polysemy of the “drug problem” is itself a problem for researchers

The case of cannabis is very telling since it went through different stages of neutrality, hostility, and affirmation in the last hundred years. In the mid-19th century, cannabis was a legitimate medical substance , included in The Pharmacopeia of the United States and attributed to helping with rheumatism, tetanus, epidemic cholera, hysteria, depression, and other illnesses. In the course of the 1930’s anti-cannabis campaign, the plant was framed as an evil drug that leads to criminality and violence. The mass media and state officials popularized the term “marijuana,” a Spanish word used by farm workers, to transform the public perception of cannabis and tie it with “dangerous” Mexican migrants. In 1937, cannabis was prohibited at the federal level and, five years later, removed from The Pharmacopeia of the United States. The image of cannabis as a dangerous drug was promoted in the public discourse, which resulted in its classification as a Schedule I narcotic by the 1970’s Controlled Substances Act .11 In the 1980s, with the launch of the war on drugs, the prosecution of cannabis cultivators, distributors,hydroponic stands and consumers is escalated, which significantly contributed to the mass incarceration of minority groups. Meanwhile, cannabis supporters crafted an alternative image of cannabis as a safe and pleasurable alternative to alcohol. Social movements and their efforts to portray cannabis as an innocuous substance led to the decriminalization of cannabis in several states in the 1970s . However, neither the prohibitionists nor the proponents of cannabis viewed it as a medicine, but primarily as an intoxicant used for hedonistic pleasure.The medical conceptualization of cannabis came back with the AIDS epidemic in the 1980s.

Cannabis use helped patients to increase appetite, retain weight, and hence prolonged their lives. Pro-legalization activists created a new concept of cannabis as a compassionate palliative for dying people. Thence began the process of cannabis legalization in the US.This brief historical overview suggests that cannabis is more than a plant in modern America. As Alan Bock argues, “It is something of a cultural signifier, a totem laden with assumptions and attitudes about what constitutes a good life” . Nowadays, cannabis has three equally powerful meanings. In different situations, cannabis is described as a dangerous drug, a medical treatment, or soft tonic. This polysemy creates a significant challenge for developing consistent legal and cultural infrastructure related to cannabis consumption and distribution. Although both medical and recreational cannabis were legalized in California, the idea of “legal cannabis” is still vague. The very distinction between medical and recreational meanings exacerbates this ambiguity, leaving cannabis is in a limbo: it is a pain-relief medicine that is not available in the pharmacy, and a recreational intoxicant , that cannot be bought at the supermarket. Cannabis was removed from the criminal justice context but was refused a place in the context of existing medical or market institutions. At present, cannabis is going through a moment of transition and institutional change. In order to become a “thing,” legal cannabis should settle in a new institutional environment. When we say that something is institutionalized, we mean that it is cognitively, behaviorally, and organizationally established.13 First, institutionalization rests on meaning making. There should be a consensus about what cannabis is and what it is not, a cognitive convention upon which individuals can jointly rely when they make decisions. An idea is institutionalized when it is built into the language, logic, values, social relations, or—as Mary Douglas put it—when it finds “its rightness in reason and in nature” . Second, institutionalization manifests itself through practices, actions, preformed roles, and shaped identities.

To understand the real meaning of cannabis, we need to look at what people do in their everyday lives—that is, how cannabis companies apply for licenses, how licensing agencies decide who gets a license, how landlords decide who gets a space, how consumers choose where to buy cannabis, how the police oversee the activity of illegal businesses, and so forth. Finally, the institutionalized phenomenon is represented through material reality, such as cannabis dispensaries, testing laboratories, greenhouses, licensing agencies, legal documents, licenses, permits, etc. Social phenomena can be institutionalized to a different degree . Complete institutionalization means that individuals experience an institution as an objective reality and take it for granted . In California, cannabis is not understood as a dangerous drug anymore. It is something else, but what exactly? Why distinguish between medical and recreational cannabis, given that it is the same herb, grown in the same conditions, and distributed by the same people? Does the persistence of the black market affect the institutionalization of legal cannabis? To understand the real status of cannabis nowadays, one should answer such and other questions. The idea of cannabis is not crystallized yet, and its vocabulary is still in a formative stage. For example, recently, activists and state officials began using the term “adult-use cannabis” instead of “recreational cannabis.” Such wording supposedly sounds more neutral and legitimate, deemphasizes the pleasure component, and denies the possibility of adolescent use. My research contributes to an understanding of institutional change. The legalization of cannabis is unfolding before our eyes at this very moment. It is a great opportunity to observe the process of institutional change in action, rather than post factum. Instead of examining what caused an institutional change in the past, I focus on what enables it right now, namely, what kind of background understandings, practices, and organizations make the legalization of cannabis possible . According to Rao et al. , institutional change is characterized by the transformations in institutional logics and governance structures . In the case of cannabis legalization, social movements were the most important motors of institutional and ideational change; their actions eventually led to the dissolution of old beliefs systems and governance structures and the necessity to create new ones. Since 2015, California has passed seven statutes and propositions regulating different aspects of cannabis-related activities and elaborating on the idea of cannabis. To be naturalized and reproduced in the future, these new understandings of cannabis come to be positively validated in the environment .

The real meaning of cannabis has to be defined by continuous interaction between regulators, local authorities, market actors, and society in general. When I say that cannabis legalization is the project under construction, I mean that institutional elements are not yet equilibria : power relations, roles, identities, potential benefits are still being validated and clarified. My research lies at the intersection of cultural criminology and lawmaking perspective. The primary focus of cultural criminology is the meaning, representation, and power in the contested construction of crime . Cultural criminology incorporates, on the one hand, traditional sociological perspectives and, on the other hand, postmodern theories . The concept of crime embodies a dynamic notion: it is defined as a project under construction, which is shaped by interaction, encoded with collective meaning, and attached to a particular social context. This view is essential for understanding several problems in my research, such as the criminalization of cannabis and stigmatization of its users through the 20th century, the role the mass media and power structures in the social control of illicit substances, the reasons and implications of the war of drugs, etc. Similarly, this approach helps to investigate the nature of the legalization process—the reverse mode of criminalization—and understand the construction of “legal cannabis”, i.e., how it is being depenalized, decriminalized, destigmatized, and deracialized. As for the lawmaking perspective, the following ideas are informative for the current study: Gusfield’s distinction between the instrumental and symbolic functions of law; and the ‘gap studies’ exploring the discrepancy between claims held out for law and its actual effects . According to Gusfield , lawmaking is not only a means of social control but also a symbol of cultural ideals and norms. Symbolic aspects of law are concerned with public morality and defining the line between right and wrong, appropriate and inappropriate, normal and pathological. In analyzing a legislative act as symbolic, we are oriented towards the meaning people attach to it rather than its instrumental functions. Legal rules are not automatically created and enforced; they result from a moral enterprise undertaken by individuals engaged in defense of their status position and the enforcement of their ethical standards . The temperance movement, for instance,grow table was the response of the old middle class to a changing status system and a perceived loss of moral authority . The government acted as a prestige-granting agency glorifying the values of one group and demeaning those of another. Similar to other culture wars, cannabis regulation in the 20th century reflects a general clash over cultural values between the progressive and conservative camps . In this project, I analyze cannabis legalization through the lens of symbolic politics, cultural dominance, and moral authority. The gap studies allow us to move beyond national-level explanations and empirically investigate the local factors—social, cultural, political, or economic—that affect policy implementation. As Mona Lynch has argued, law as practiced is significantly shaped by local norms and culture . Although the adoption of federal and state regulations predicts homogeneous outcomes across the jurisdiction, there are significant variations at the county and city levels.

The notion that legal change happens through ground-up—rather than top-down—processes has gained popularity in socio-legal scholarship recently . The case of cannabis legalization offers another illustration of how social and political culture affects local decision making. This study focuses on the law-before and the law-in-between processes exploring the adoption and enforcement of morality policies at the city level. Specifically, it explains the gap between public input on cannabis legalization and actual political decisions. This project covers several gaps in the existing literature. First, most studies focus on the legalization of cannabis for medical use. The legalization of cannabis for recreational purposes has a very different rationale behind it, but since it is a relatively new phenomenon, it has not been fully explored yet. Second, cannabis legalization is a subject that attracts the attention of economists, policy analysts, psychologists, biologists, but rarely socio-legal scholars. Criminologists are exclusively interested in how the legalization of cannabis affects crime rates—increases, decreases, or does not change them . Sociologists focus on public attitudes to cannabis, deviance and stigma, identities, or the market formation . However, there is no comprehensive socio-legal analysis of how cannabis shifts from an illicit drug to a legal intoxicant, how the idea of legal cannabis is constructed and institutionalized, or, in short, how cultural, social, and legal change happens. Third, the traditional gap studies focus on the discrepancy between the law-in-the-books and the law-in-action. In other words, scholars are interested in how the initial idea of legislators is implemented in practice. However, there is no single “gap” but multiple types of gaps at different levels of the decision-making process . The present study investigates a gap between people’s expectations and the adopted policies . This perspective is especially important when we analyze morality policies, such as the legalization of abortions, same-sex marriages, gambling, prostitution, or recreational drugs. Fourth, a large body of literature focuses on the symbolic qualities of law: the symbolic role of drug legislation ; the symbolic meaning of “crime control” in political campaigns ; the symbolic character of capital punishment ; the symbolic goals of anti-abortion campaign , and so forth. However, all these studies center on prohibitionist legislation while the permissive morality policies, like cannabis legalization, were not on the radar of the symbolic politics studies. The present study covers this gap in the literature.There is no single definition of the “drug problem.” The term may simultaneously refer to the mere use of illegal drugs, drug use by teenagers, the abuse of drugs, drug-induced behavior that harms others, or domestic and international drug trafficking .The two main traditions in the literature on drugs are the constructionist and the objectivist. The latter examines drugs as objective phenomena that can be measured, counted, and classified . This approach is popular among medical scholars, medical practitioners, psychologists, policy advocates, and legislators. The objectivists typically speak about “drug problems” in the plural and employ it as an umbrella term for drug use, drug abuse, drug addiction, drug trafficking, drug selling, etc. The constructionist approach is common among sociologists, socio-legal scholars, political scientists, journalists, and policymakers who see the drug problem as a product of political campaigns and social concerns.

The slippage from civil noncompliance to criminality was mirrored in enforcement practices

Residence in states with medically legal cannabis was associated with higher odds of cannabis use during the preconception period but not associated with use at any other time. The difference in odds of cannabis use between medically and recreationally legal states could be explained by several factors. Provider responses to women may vary based on legalization status and could impact a pregnant woman’s choice to discontinue use early in pregnancy. A recent study in Pennsylvania found healthcare providers were much more likely to focus on legal implications of use rather than health implications when women disclosed use in pregnancy . In medically legal states, cannabis use is often only allowed for a limited set of medical conditions . Therefore, if providers focus on the legality of use in states with more restrictions, pregnant women might be more convinced to quit using cannabis; whereas, in recreational states no “illegal use” exists and perhaps there is less pressure from providers for women to quit cannabis use. Similarly, another study found if providers did not discuss cannabis use during a visit most pregnant women assumed this meant cannabis use during pregnancy posed no health risk . Duration of legalization may also play a role in the differences observed between recreational and medical cannabis states. Medical cannabis legalization first took place in 1996 and in the subsequent two decades resulted in the development of cannabis prevention programs specific to pregnancy, whereas, context of more recent recreational legalization are in their infancy. Further research is warranted to examine how prevention practices differ between states with recreational and medical cannabis legalization and the resultant outcomes.As seen in other studies, the association with inadequate prenatal care and cannabis use in this study may be a result of selection bias insofar as women who use substances may not access prenatal care due to their substance use behaviors or fear of being reported. Alternatively, women using substances during pregnancy tend to be younger and with lower education attainment and may not access prenatal care due to some other external barriers irrespective of substance use and therefore continue use because they do not receive education about cessation of substances during pregnancy .

Inadequate prenatal care is associated with cannabis use across all time periods in this study suggesting a need for public health or clinical interventions prior to pregnancy. One possibility would be to consider delivering cannabis prevention education outside prenatal care through public service announcements and warning labels on legally sold cannabis products consistent with prevention strategies used for prenatal alcohol use . Furthermore, since the study found that parity was a protective factor against cannabis growing system use in all three time periods, offering prevention education for women of reproductive age at any medical appointment may be an effective strategy to reach women before future pregnancies and promote abstinence from any substance use prior to conception. Based on the review of the literature, this study is possibly the first to include e-cigarettes in the assessment of tobacco co-use with cannabis. E-cigarettes present an emerging public health crisis and are considered especially harmful during pregnancy given the increase in nicotine exposure to the pregnant woman and fetus . The odds of tobacco use in association with cannabis use were slightly higher than in other studies looking at traditional tobacco use alone . Possibly, as e-cigarette use increases during pregnancy, there is a concomitant increase in use of cannabis especially given new technology making it easy to “vape” nicotine and cannabis together .Whittington and et al., provided evidence that e-cigarette use is on the rise in pregnancy as is concurrently used with combustible tobacco which could account for the magnitude of the association found in this study. Notably, the indicator for tobacco use in this study was one or more cigarettes and did not differentiate between intensity of smoking possibly leading to an overestimation of use in our sample resulting in the higher reported odds. Interpretation of the study findings is subject to several limitations including the cross-sectional design which precludes causal inference. In addition, the stigma associated with substance use in pregnancy may have resulted in under reporting of use and underestimation of prevalence rates, although the PRAMS computer-assisted interviews could decrease this bias to some degree . The PRAMS also relies on women to recall their substance use from the past year, during the postpartum period, potentially leading to over- or under-reporting of past year use of cannabis.

Limitations due to the use of secondary data include the inability to measure cannabis use throughout the pregnancy and only at designated times as specified in the survey questions. Finally, due to the difficulty of analyzing policies in motion given that recreational cannabis legalization is a new policy, a possibility exists that not enough time has passed to estimate the full impact of the changing policy on use rates . Also, cannabis use rates may be higher in recreational or medical states prior to the passage of cannabis laws and therefore the higher rates of use were not associated with the policy change. Future studies should take advantage of additional years of post recreational legalization data as they become available and analyze the direct impact on policies on prenatal use.With the passage of Proposition 64 , state voters elected to integrate cannabis into civil regulation. The California Department of Food and Agriculture oversees state-licensed cannabis cultivation and defined it as agriculture.Prior to the possibility of state licensure for cultivators, however, counties can decide on other designations and implement strict limitations. In effect, local governments have become gatekeepers to whether and how cultivation of personal, medical or recreational cannabis can occur and the repercussions of noncompliance. When cannabis is denied a consistent status as agriculture, despite being a legal agricultural commodity according to the state, localities can determine who counts as a farmer and who is considered compliant, non-compliant and even criminal. In Siskiyou County’s unincorporated areas, the Sheriff’s Office now arbitrates between the effectively criminal and agricultural. Paradoxically for this libertarian county, the furor around cannabis has seen calls for government intervention, and has led to officials passing highly stringent cannabis cultivation regulations that have been enforced largely by law enforcement, muddying the line between noncompliance and criminality. These strict regulations produced a situation where “not one person” has been able to come into compliance, according to a knowledgeable government official. Nonetheless, at the sheriff’s urging, Siskiyou declared a “state of emergency” due to “nearly universal non-compliance” , branding cannabis cultivation an “out-of-control problem.” Such a strong reaction against cannabis can be understood in terms of cannabis’s potential to reorganize Siskiyou’s agricultural and economic landscape.

According to some estimates, there are now approximately twice as many cannabis cultivators as non-cannabis farmers and ranchers in Siskiyou , a significant change from just a few years ago. Although cannabis has been cultivated in this mostly white county for decades, since 2015 it has become associated with an in-migration of Hmong-American cultivators. Made highly visible through enforcement practices, policy forums and media discourses, Hmong-Americans have become symbolically representative of the “problem.” This high visibility, however, obscures a deeper issue, what Doremus et al. see as a nostalgic, static conception of rural culture that requires defensive action as a bulwark against change. Such locally-defined conceptions need to be understood , especially in how they are defined and defended and what effects they have on parity among farmers growing different types of crops. Our goals in this study were to consider the consequences of an enforcement-first regulatory approach — a common regulatory strategy across California — and its differential effects across local populations. Using Siskiyou County as a case study, we paid attention to the public agencies, actors and discourses that guided the formation and enforcement of restrictive cannabis cultivation regulations as well as attempts to ameliorate perceptions of racialized enforcement. This study attends to novel post legalization apparatuses, their grounding in traditional definitions of culture and the ways these dynamics reactivate prohibition. We used qualitative ethnographic methods of research, including participant observation and interviews. In situations of criminalization, which we define not only as the leveling of criminal sanctions but being discursively labeled or responded to as criminal-like , quantitative data can be unreliable and opaque, which necessitates the use of qualitative ethnographic methods . In 2018–2019, we talked to a wide range of people — including cannabis growers from a diversity of ethnic backgrounds, government officials, business people, subdivision residents, farm service providers, medical cannabis advocates, realtors, lawyers, farmers and ranchers, and,hydroponics rack system with the assistance of a Hmong-American interpreter, members of the Hmong-American community. We also analyzed public records and county ordinances, Board of Supervisors meeting minutes and audio , Sheriff’s Office press releases and documents, related media articles and videos, and websites of owners’ associations in the subdivisions where cannabis law enforcement efforts have focused. Some cannabis cultivators regarded us suspiciously and were hesitant to speak openly, an unsurprising phenomenon when researching hidden, illegal and stigmatized activities, like “drug” commerce . This circumspection was most intense among Hmong-American growers on subdivisions, who had been particularly highlighted through enforcement efforts and local, regional and national media accounts linking their relatively recent presence in Siskiyou to cannabis growing. Human subjects in this research are protected under the Committee for Protection of Human Subjects, protocol number 2018-04-1136 , of the Office for Protection of Human Subjects at UC Berkeley.Siskiyou is a large rural county located in the mid-Klamath River basin in Northern California . Since the mid-19th century, inmigrants have historically engaged in agriculture, predominantly livestock grazing and hay production, and natural resource extraction, primarily timber and mining.

Public records demonstrate that although the value of the county’s agricultural output and natural resource extraction is declining, these cultural livelihoods still shape the area’s dominant rural values of self-reliance, hard work and property rights . For instance, one county document stated that Siskiyou’s cultural-economic stability depends on nonintervention from “outside groups and governments” and residents should be “subject only to the rule of nature and free markets” . Another document, a “Primer for living in Siskiyou County” from the county administrator, outlined “the Code of the West” for “newcomers,” asserting that locals are “rugged individuals” who live “outside city limits,” and that the “right to be rural” protects and prioritizes working agricultural land for “economic purpose[s]” . We heard a common refrain that localities will eventually succumb to the allure of a taxable, profitable cannabis industry. Indeed, interviewees in Siskiyou universally reported economic contributions from cannabis cultivation, especially apparent in rising property values and tax rolls and booming business at horticultural, farm supply, soil, generator, food and hardware stores . However, a belief in an inevitable free market economic rationality may underestimate the deep cultural logics that have historically superseded economic gains in regional resource conflicts . As one local store owner told us, “I’d give up this new profit in a heartbeat for the benefit of our society.” Many long-time farming and ranching families remain committed to agricultural livelihoods for cultural reasons , even as the economic viability of family farms is threatened by increasing farmland financialization , corporate consolidation and biophysical decline . Many interviewees felt that the recent rapid expansion of county cannabis cultivation and corresponding demographic changes were a visible marker of broader tensions of cultural continuity and endangerment. As the sheriff expressed, cannabis cultivation would “jeopardize our way of life … [and] the future of our children” . This sense of cultural jeopardy , echoed by numerous interviewees, materialized in a range of negative quality-of-life comments about cannabis cultivation: noisy generators, increased traffic, litter and blighted properties, and unsafe conditions for residents. Non-cannabis farmers also reported farm equipment and water theft, livestock killed by abandoned dogs, wildfire danger, illicit chemical use and poisoned wildlife. Some non-cannabis farmers expressed a sense of regulatory unfairness — that their farms were subject to onerous water and chemical use regulations while cannabis growers “don’t need to follow the government’s regulations.” Enabling cannabis cultivators to pursue state licensure would facilitate just such civil regulation, but some feared that regulating this crop as agriculture would threaten “the loss of prime agriculturally productive lands for traditional pursuits” .

Surface water and springs were the next–most common sources

The environmental impacts of stream diversions are likely to be greatest during the dry summer months,which coincide with the peak of the growing season for cannabis. Further, because cannabis cultivation operations often exhibit spatial clustering , some areas with higher densities of cultivation sites may contain multiple, small diversions that collectively exert significant effects on streams . An important assumption underlying these concerns, however, is that cultivators rely primarily on surface water diversions for irrigation during the growing season. Assessments of water use impacts on the environment may be inaccurate if cultivators in fact use water from other sources. For instance, withdrawals from wells may affect surface flows immediately, after a lag or not at all, depending on the well’s location and its degree of hydrologic connectivity with surface water sources . Documenting the degree to which cannabis cultivators extract their water from above ground and below ground sources is therefore a high priority. In 2015, the North Coast Regional Water Quality Control Board , one of nine regional boards of the State Water Resources Control Board, developed a Cannabis Waste Discharge Regulatory Program to address cannabis cultivation’s impacts on water, including stream flow depletion and water quality degradation. A key feature of the cannabis program is an annual reporting system that requires enrollees to report the water source they use and the amount of water they use each month of the year. Enrollees are further required to document their compliance status with several standard conditions of operation established by the cannabis program. These include a Water Storage and Use Condition, which requires cultivators to develop off-stream storage facilities to minimize surface water diversions during low flow periods, among other water conservation measures. Reports that demonstrate noncompliance with the Water Storage and Use Standard Condition indicate that enrollees have not yet implemented operational changes necessary for achieving regulatory compliance. In this research, we analyzed data gathered from annual reports covering 2017 to gain a greater understanding of how water is extracted from the environment for cannabis cultivation. The data used in this study was collected from cannabis sites enrolled for regulatory coverage under the cannabis program.

The program was adopted in August 2015, with the majority of enrollees entering the program in late 2016 and early 2017. The data presented in this article was collected from annual reports submitted in 2018 ,flood and drain tray which reflected site conditions during the 2017 cultivation year. The data therefore represents, for the majority of enrollees in the cannabis program, the first full season of cultivation regulated by the water quality control board. Because the data was self-reported, we screened reports for quality and restricted the dataset to reports prepared by professional consultants. Most such reports were prepared by approved third-party programs that partnered with the board to provide efficient administration of, and verification of conformity with, the cannabis program. Additional criteria for excluding reports included claims of applying water from storage without any corresponding input to storage, substantial water input from rain during dry summer months and failure to list a proper water source. Reports containing outliers of monthly water extraction amounts were also identified and excluded due to the likelihood of erroneous reporting or the difficulty of estimating water use at very large operations. Extreme outliers were defined as those values outside 1.5 times the bounds of the interquartile range . Farms were not required to use water meters, and those without meters often estimated usage based on how frequently they filled and emptied small, temporary storage tanks otherwise used for gravity feed systems or nutrient mixing. The final dataset included 901 reports. Parcels of land where cannabis was cultivated — including multiple contiguous parcels under single ownership — constituted a site, and this is the scale on which reporting was conducted. The spatial extent of the cannabis program included all of California’s North Coast region ; however, only a subset of the counties in this region allow cannabis cultivation and therefore reports were only received from the following counties: Humboldt , Trinity , Mendocino and Sonoma . Because Sonoma County contributed relatively little data, we combined Sonoma County’s enrollments with those from Mendocino County when making county-level comparisons. The data used for this analysis included the source and amount of water that cultivators added to storage each month as well as the source and amount of water applied to plants each month. We did not analyze absolute water extraction rates. Rather, we used the amount of water extracted each month — whether water was added to storage or applied to plants directly from the source — to analyze seasonal variation in each water source’s share of total water extraction. Water sources included: surface , spring , rain , well , delivery and municipal.

The two external sources — delivery and municipal — were consolidated into a single category.Because staff from the water quality control board were not able to corroborate the accuracy of reported data, enrollees may have classified water sources erroneously. A well placed in proximity to a stream, for example, might properly qualify as a diversion of surface water; so might rainwater catchment ponds or spring diversions that are hydrologically connected to a watercourse. We attempted to minimize these potential errors by restricting the dataset to reports prepared by professional consultants. As mentioned, enrollees were required to assess several standard conditions in their site reports, including water storage and use requirements. To encourage cultivators to join the regulated industry, and because many cultivation sites existed prior to adoption of the cannabis program, existing sites were not required to comply with standard conditions as a prerequisite for enrollment. Rather, cultivators unable to comply with the standards when they enrolled were required to indicate their lack of compliance and develop a plan for achieving compliance. Such sites were not held in violation of regulations, thus removing a potential motivation to falsely report site conditions. More than one-quarter of enrollees in the dataset reported noncompliance with the Water Storage and Use Standard Condition. To address question 1 — from which sources cannabis cultivators most frequently extract water across the North Coast region, and if extraction patterns differ across the region — we calculated the percentage of sites that reported use of each water source . We also calculated, for sites using each source, the percentage of sites that also used at least one other source category. Directly applying water to plants and also placing water in storage did not constitute use of multiple extraction sources if the water was drawn from the same source category. Additionally, sites that used multiple inputs from the same category — for example, multiple wells — were not considered users of multiple sources, as this classification was reserved for extraction from multiple categories of sources. We performed all elements of our analysis for the entire dataset and for each county individually. To address question 2 — how reliance on each water source differed from one month to another — we divided each site’s monthly water extraction total by its annual extraction total to calculate the relative percentage of water extracted in each month, and performed similar calculations for each source category. The median amount of water extracted and interquartile range were calculated for each month — both for overall extractions and for each source category individually. To address question 3 — whether sites reporting compliance with the Water Storage and Use Standard Condition relied on different water sources than those reporting noncompliance — we compared water source extraction patterns for sites of both types. Specifically, we calculated for each compliance status the percentage of sites that extracted water from each source category and made comparisons accordingly; and did likewise for monthly extraction patterns, following procedures similar to those described in regard to question 2.

The purpose of this comparison was strictly qualitative, and no inferential statistics were performed to determine statistically significant differences. Instead, this element of our analysis was performed for exploratory purposes, with the intention of identifying broad trends that warrant future attention.The most commonly reported water source was wells . Over half the sites reported at least some reliance on wells for their irrigation water.Rainwater catchment and off-site water were the least commonly used water sources . Sites using wells and off-site sources were the least likely to use additional sources . In contrast, sites using rain catchment systems most frequently reported using an additional source category,hydroponic tables canada followed by sites reporting use of spring diversions and surface diversions . To determine if the observed high frequency of well use was due to bias associated with examining only reports prepared by consultants, we reincorporated sites without consultants and reran the analysis on this dataset . Reported well use was slightly more common among sites not using consultants than among sites using consultants . Counties displayed notable variation in the frequency with which cannabis cultivators used particular water sources . Compared to all sites in the dataset, sites in Humboldt County relied more on surface water and spring diversions , with fewer relying on wells . The pattern was reversed in Trinity County, with a high percentage of sites there reporting well use and relatively few using surface and spring diversions. A large number of sites in Trinity County were located in a single watershed known for a high concentration of similar cultivation practices, so we recalculated the percentages with these sites excluded. The resulting totals for Trinity County were closer to the overall results: wells , surface , spring , rain and off-site . Mendocino and Sonoma counties reported a similar pattern of extraction sources per site: wells , surface , spring , rain and off-site . Patterns of using multiple sources varied among counties. Sites in Humboldt County using well water extraction much more commonly used additional sources of water than did similar sites in Trinity and Mendocino/Sonoma counties. Use of additional sources was also more common among Humboldt County sites extracting surface water and spring water than among sites using surface and spring water in Trinity County and Mendocino/Sonoma counties . Wells were a prominent water source for cannabis cultivators during the summer months . Extraction from wells generally peaked in August and declined in off-season months. The pattern was reversed for rainwater use, with most extraction occurring in off-season months. Spring water use was generally even across the year, with slightly higher use during the growing season. Surface diversions occurred throughout the year, but declined late in the growing season, likely reflecting declining availability of surface water. The pattern exhibited in off-site water use closely matched that of well water; the former, however, was a less substantial source of water in general. There appeared to be differences in the extraction sources reported by compliant and non-compliant sites .

Although nearly one-third of non-compliant sites used well extraction, this source was more than twice as frequently reported among compliant sites . In contrast, non-compliant sites reported surface diversion and spring diversion more commonly than did compliant sites . Rain and off-site sources were the least commonly used for both compliant sites and non-compliant sites . Use of additional alternative sources was lower for compliant sites with wells than for non-compliant sites with wells . The seasonal extraction patterns of compliant and non-compliant sites were generally similar , following the overall pattern discussed above.We found that well water is the most commonly reported source of extracted water for cannabis cultivation in the North Coast region of California. Furthermore, among the source categories, wells are least frequently supplemented with alternative sources. Spring and surface water diversions together are also important water sources, with seasonal patterns of use that are distinct from well water extraction. Reported timing of well water extraction closely tracks the water demand patterns of plants, indicating that cultivators are applying well water directly to plants, rather than storing it. In contrast, the timing of extractions of spring water and surface water remains relatively consistent throughout the year, suggesting that water from these sources may be diverted to storage in the winter, reducing the need for extraction in the summer months. These seasonal extraction patterns and the relative predominance of each source may inform assessments of cannabis cultivation’s impacts on water availability.

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