Previous attempts to assess the drivers of cannabis land use or predict the current or future distribution of cannabis production have relied heavily on biophysical and bio-climatic models, using variables such as slope, forest land cover, distance to streams, aspect, canopy cover, and precipitation . These models have demonstrated that compared to other forms of farming, cannabis is generally less influenced or predicted by biophysical variables . This is unsurprising, however, given that social and cultural variables are likely to profoundly shape the spatial distribution of cannabis production. For example, depending on the production style, a cannabis farmer might forgo a less bio-physically ideal production area in order to stay concealed, or to grow near hospitable neighbors or close to other cannabis farmers with whom they can share labor or knowledge. Thus, social variables may be relatively more predictive of cannabis industry dynamics than biophysical variables. Ultimately, bridging social and ecological knowledge may be key to understanding the spatial dynamics of cannabis land use. Integrating a more complete social-ecological context into models of land use presents multiple challenges. First, it requires an in-depth understanding of the system to be modeled. In the case of cannabis agriculture, its illicit history is an impediment to research. Federal restrictions on research funding to study an illicit crop have meant that there are few studies to draw on for characterizing patterns or trends in cannabis production, particularly on private lands . Given the lack of formal research on the fledgling recreational cannabis industry, those who understand the industry best are likely those engaged in it directly. Thus, rolling grow trays interviews of cannabis farmers may be a particularly valuable approach for identifying and understanding potential drivers of cannabis land use.
Interviews come with weaknesses, however; small or biased interview pools may fail to uncover the most important drivers of cannabis land use, or farmers themselves may be unable or unwilling to articulate the drivers that are most relevant to their landscape-scale decision-making. The second major challenge to integrating social and ecological understandings into land use models is that some potential drivers may not readily lend themselves to quantitative analysis. The transformation of qualitative knowledge into quantitative data is an inherent challenge for many interdisciplinary studies that attempt to merge opposing ontologies. For example, translating attitudes or perceptions into numerical data is a longstanding dilemma in quantitative social science where doing so risks losing context and being misunderstood . Nonetheless, integrating environmental modeling with social, economic and political drivers will enhance our understanding of system dynamics . n order to both generate a list of potential land use drivers, and to interpret and contextualize model results, we conducted semi-structured, in-depth interviews with 14 cannabis farmers in Josephine County in 2019. Farmers had to be over the age of 21, but could be engaged in any type of cannabis production on private land, whether licensed or unlicensed. Interviews were conducted by the same researcher for consistency, while living in Josephine County over a two year period. We interviewed farmers about drivers of cannabis land use, farming practices, influences on production methods, and farmer connection with the land. Although some farmers were also producing cannabis under a hemp license, we focused our questions on the cannabis industry because the hemp industry in Josephine County largely emerged after 2018, which is after the mapped data were collected.
We initially used known contacts in formal and informal cannabis producer networks, invited voluntary participation, and thereafter used a snowball recruitment method. We continued interviews until we reached saturation , at which point we considered the number of farmers interviewed to be sufficient. Because of the difficulties in attaining a representative sample of all cannabis farmers in the region, these interviews were viewed as generative rather than representative of all producers in the area. Interviews were recorded with permission, alongside hand written notes. Most interviews took place on the cannabis farm, or another location selected by the farmer, and often included a tour of the farm. Interviews typically lasted 2 hours, but ranged between 1 – 8 hrs, depending on the time constraints and preferences of the interviewee. All interviews were conducted under UC Berkeley Human Subjects Protocol CPHS# 2018-11-11619. We summarized interviews, grouped main themes or concepts, and transcribed quotes that represented the key emerging themes. We did not conduct a formal coding process because our purpose was largely generative. We then used these summaries to identify potential quantitative variables for our land use models. One of the most common factors mentioned in farmer interviews was the importance of community, both in terms of their connection to other cannabis farmers as well as to their surrounding neighbors. For example, in the quote above, the farmer was describing how his relationship with his neighbors instilled a sense of both community and responsibility that translated into on-the-ground decisions he made on his farm, such as when or how to use grow lights. The interviewed farmers explained that having a good relationship with neighbors was critical for surviving in the industry, regardless of whether they were licensed or not. In addition, they described that best growing practices were often communicated through social networks, both online and in person, and so they often relied on other cannabis farmers for advice or assistance.
Interviewed farmers explained that cultural norms dictated practices, which in Josephine County are often influenced by legacy production styles and attitudes. Some farmers also mentioned the advantage of being able to help each other with labor when living close to other farmers. In translating this theme into quantitative variables for potential land use drivers, we focused on farmer reliance on other local cannabis producers. We quantified proximity to other cannabis farms by calculating the smallest non-zero distance from each parcel to the nearest cannabis farm both pre- and post-legalization, using the ‘st_nn’ function from the nngeo package for R . This package calculates the k-nearest neighbor distance between features. We calculated a large number of neighbor distances for each parcel, then selected the minimum distance excluding all zero values. We also attempted to estimate neighborhood tolerance for cannabis farming. To do so, we used the density of cannabis within a 1 km radius around each parcel both pre- and post-legalization as our spatial proxy. Cannabis production in Josephine County is clustered at multiple spatial scales and so any distance threshold that represents a localized area might be appropriate, but we chose 1 km because this generally encompasses a local neighborhood. Using the sf package in R, we generated buffers around parcel centroids, intersected them with centroids of cannabis sites, and then converted the count to density by dividing by buffer area. All farmers interviewed expressed personal values related to environmental stewardship. In the context of the quote above, the farmer was comparing his impact from cannabis farming to nearby clear cut logging, and explaining his deep conviction that his style of land use was environmentally sustainable compared to larger industrial and extractive land uses. In the opening quote from the introduction, “Money actually does grow on trees out here, and that’s a blessing,” a different farmer expressed similar sentiments, horticulture trays connecting his farming to both nature and livelihood/profit, while expressing gratitude that the place itself, Josephine County, enabled that relationship. Many of the interviewed farmers explained that their motivations for growing cannabis stemmed from a desire to connect with the land or nature, although only a few had been farmers before cultivating cannabis. Interviewees often mentioned that the ruralness of Josephine County was an attraction because of its biodiversity. Many farmers reported personal connections with and fondness for the wildlife on their production sites. Many also expressed concerns about ecological damage from the cannabis industry. For example, farmers highlighted concerns about pesticide or rodenticide use, trash/plastic waste, animals caught in netting, water pollution , excessive water withdrawals, waterway diversion, imported soils, clearcuts, and paving. Multiple farmers raised concerns that the state or county regulatory process did not support environmental stewardship, and some expressed concerns that following regulations made it more difficult to practice what they saw as sustainable or regenerative farming practices such as intercropping, or crop rotation. The interviewed farmers generally considered themselves as having less impactful growing practices than other cannabis producers in the region, while farmer descriptions and farm visits both demonstrated a wide variety of production practices across all farms. Farmers mentioned the need for more crop research, information-sharing, and stronger norms around acceptable environmental practices.
While this theme did not translate easily into quantifiable spatial proxies, we focused on farmers’ expressed desire to grow in remote areas because of the opportunity to work the land in proximity to wild flora and fauna. We quantified this ruralness using the Human Footprint layer, which combines data on the built environment, population density, night-time lights, crop and pasture lands, roads and railways, and navigable waterways to create an index of direct and indirect human pressures at a 1 km2 resolution. We extracted the mean human impact value for each parcel using the exactextractr package in R . There was a wide range of responses regarding the importance of regulation for farmer decision making. In the quote above, the farmer explained how some aspects of regulation were more impactful to his daily farm management decisions than others as he navigated the licensed industry. Most farmers did not perceive that enforcement influenced their land use decisions, although the farmers navigating the licensed recreational market said that regulations were often their first consideration. One unlicensed farmer compared law enforcement to wildfire risk, explaining both as factors that were constant background risks but ultimately outside of his control. There was widespread confusion and frustration with the regulations around recreational cannabis. Multiple farmers said that they started growing hemp, or had considered growing hemp, to avoid the legal hurdles of recreational cannabis. Others raised questions about what the new recreational market would mean for medical producers. Some interviewees mentioned that a rural location made things easier from an enforcement perspective, particularly in avoiding the Grants Pass area . Even those who were attempting to navigate the legal industry expressed that it was useful to be less closely monitored because of the difficulty in complying with all regulations, the time needed to demonstrate compliance, or fear that they may be breaking rules without knowing it. To translate the preference for distance from law enforcement into a spatial driver, we estimated this both with ruralness as well as the straight line distance from the Grants Pass Sheriff’s office to each parcel using the sf package in R . However, because these measurements were significantly correlated, we ultimately dropped distance to law enforcement as a variable in our models. There were also a number of regulatory designations that cannabis farmers discussed as important when considering where to grow. Water rights were considered critical for legal production but specifics of parcel-level rights were often hard to acquire or interpret. Water rights were not generally discussed by unlicensed farmers, but water access, storage, and application were all considered critical. Because of the mixed response to regulated water use, we assessed water access as part of Parcel Qualities below, rather than in Regulation. The shifting policies in Josephine County around zoning restrictions, particularly for Rural Residential zones, led farmers to identify exclusive farm zoned parcels as the safest and highest quality lands for cannabis production. One farmer also mentioned Farm Resource zoned properties. To translate this into a land use driver, we created a binary variable that assigned a ‘1’ to each parcel that was zoned for either EF or FR zones and a 0 for those that did not. Zoning information was provided by Josephine County . Farmers identified multiple biophysical properties of parcels that factored into decisions about where to produce cannabis. In the quote above, the farmer was expressing confusion as to why some cannabis producers selected parcels that required a large labor input to clear or terrace land to begin farming, when other, more open parcels seemed to him to be a more ideal choice. In addition to open/cleared areas with access to sunlight, some of the other factors mentioned included relatively flat slopes, and medium elevation zones as helpful qualities for production.