A 2010 Census Bureau report found that the recession not only grew the wealth gap between rich and poor; it also exacerbated the gap between different racial/ethnic groups. Between 2007 and 2009, the wealth gap between whites and Blacks nearly doubled, with whites having 22 times as much household wealth as Blacks and 15 times as much as Latinos/as. By 2010, the median household net worth for whites was $110,729 while for Blacks it was $4,995 and for Latinos/as it was of $7,424. Between 2005 and 2010, furthermore, median household net worth for Blacks, Latinos/as, and Asian Americans fell by roughly 60%, while the median net worth for white households fell by only 23%. Many people of color were pushed into bad mortgages by the nation’s biggest banks, while the loss of 600,000 public sector jobs during the recession also had a significant impact on communities of color, as Black and Latino/a workers are more likely to hold government jobs than their white counterparts. Although the current slow economic recovery is not unusual, the cumulative and sustained impacts of unemployment, income loss, and housing loss disproportionately experienced by low-income communities and communities of color signal the value of a safety net that protects such marginalized communities from sustained poverty and food insecurity. Two major parts of the recessionary safety net are the USDA’s Supplemental Nutrition Assistance Program and the Unemployment Insurance program of the US Department of Labor, which provides financial support to workers who become unemployed through no fault of their own. As with SNAP, horticulture solutions expenditures for UI generally expand during economic downturns and shrink during times of economic growth, primarily because economic downturns result in wider eligibility and participation. 

Significantly, households that participate jointly in both SNAP and UI can improve their ability to sustain food expenditures, nutrition, and overall standard of living during times of economic challenge and are an indicator of the strength of the recessionary safety net itself. Toward this end, a 2010 USDA study found that the recession not only increased the number of SNAP households but also increased the extent of joint SNAP or UI households: an estimated 14.4% of SNAP households also received UI at some point in 2009—nearly double that of 7.8% in 2005. Moreover, an estimated 13.4% of UI households also received SNAP at some point in 2009, an increase of about one-fifth over the estimate of 11.1% from 2005. Significantly, people of color, hardest hit during the economic downturn, benefitted the most from the safety net. In 2009, the estimated joint SNAP and UI use for Blacks and for Latinos/as exceeded joint use by whites by about 16.6 and 9.8%, respectively. Together, SNAP and UI help sustain aggregate household spending and national production in economic downturns, making the impact of such downturns less severe than they would be in the absence of the programs. Such benefits are particularly pronounced for communities of color who not only experience relatively greater degrees of poverty, but also are hardest hit during economic downturns. In April 2012, the Congressional Budget Office estimated that temporarily higher benefit amounts enacted in the American Recovery and Reinvestment Act of 2009 accounted In April 2012, the Congressional Budget Office estimated that temporarily higher benefit amounts enacted in the American Recovery and Reinvestment Act of 2009 accounted THE STRUCTURE OF US AGRICULTURE determines and reflects the challenges faced by US farmers and rural communities. This includes farm size, type, cropping patterns, and ownership. Moreover, the federal food and agricultural policies, including the Farm Bill, affect the structure of US farmland through multiple forces and drivers, including taxes, lending programs, environmental and safety regulation, rural development programs, research and development funding, and commodity programs. 

In this light, Part III examines how such programs have shaped the structure of US farmland and, in turn, how they have affected the socio-economic well-being of low-income farmers and communities, as well as farmers and communities of color. It does so, first, by providing a snapshot of the structure of US farmland, including the outcomes of structural racialization with regard to farmland ownership and government payments . It then outlines the historical significance of change in the structure of US agriculture over the 20th century, and examines three federal rural and agricultural support programs in particular: Farm Service Agency lending programs, Farm Bill commodity programs, and Farm Bill Rural Development programs. Ultimately, Part III argues that such programs have historically undergirded white farmland ownership at the expense of farmland ownership by people of color. Significantly, these programs also highlight how white agricultural land ownership was held up amidst, and by way of, increasing consolidation and specialization, with farmers of color on the losing side of such shifts in the structure of US farmland. In the push for the dismantlement of corporate control and structural racialization, such trends thus require greater attention with regard to their role in intensifying marginality that low-income communities and communities of color face in terms of wealth, access to program benefits, and land access. One of the most significant changes in the US economy since the beginning of the 20th century is the national abandonment of farming as a household livelihood strategy. This “agricultural transition” is marked by a number of characteristics: the move away from farming by most Americans and the challenging conditions that remaining farmers experience; the decline in the number of farms and farm population; the growth of larger farms vis-à-vis acreage, sales, and real estate capitalization; and the gradual replacement of family with hired labor. The post-World War II period ushered in perhaps the most rapid transformation, particularly by way of New Deal interventions, and their reformulation and erosion over the next few decades. Between 1940 and 1980, for example, the farm population declined ten-fold, the farm numbers declined by more than half, acreage more than doubled, and real average sales increased six-fold.

Farmers also experienced periodic crises during key moments within such long term structural change, such as those that took place during the 1980s and in the mid-1990s. Such shifts were linked to the polarization of production. For example, between 1939 and 1987, the market share of sales by the largest 5% of producers increased from 38.3% to 54.5%. Agricultural firms have expanded not just through vertical and horizontal consolidation, as outlined in Part I, they have also done so through production contracts, wherein a farmer raises or grows an agricultural product, including livestock, for such firms. While only about 8.9% of farms operated under production contract in 2012—up from 3% only a decade earlier—they produced 96% of all poultry, 43% of all hogs, and around 25% of all cattle. Furthermore, most farms cannot fully employ or sustain families. To survive in farming, families have taken off-farm jobs. As of 2013, for example, 87% of farmers’ median household income came from non-farm sources. The median farm income for operations that specialize in grains, rice, tobacco, cotton, or peanuts, 23% of income came from on-farm sources. Conversely, livestock operations, apart from dairy, have generally not had a positive income from farming. That is, without income garnered by way of off-farm sources, such operations would go negative. As outlined below, the complete lack of profitability of such operations, and the relatively great profitability of grain and other commodity crop operations, cannot be understood as separate from the racialized distribution of operation types, with white producers generally running more profitable grain and other commodity crop operations, and producers of color running less profitable livestock operations. Shifts in agricultural production were tied not only to the polarization of production but also to racial, gender, and economic polarization. For example, although Blacks were able to establish a foothold in southern agriculture post-Emancipation, grow benches rural Blacks were virtually uprooted from farming over the next several decades. In 1920, 14% of all US farmers were Black , and they owned over 16 million acres. By 1997, however, fewer than 20,000 were Black, and they owned only about 2 million acres. While white farmers were losing their farms during these decades as well, the rate that Black farmers lost their land has been estimated at two and a half to five times the rate of white-owned farm loss. Furthermore, although between 1920 and 2002, the number of US farms shrank—from 6.5 million to 2.1 million, or by 67%—the decline was especially steep among Black farmers. Specifically, between 1920 and 1997, the loss of US farms operated by Blacks dropped 98%, while the loss of US farms operated by whites dropped 65.8%. As outlined above, such shifts have been attributed to the general decline of small farms, land erosion, boll weevil infestations of cotton, New Deal farm programs geared toward white landowners, postwar cotton mechanization, repressive racial and ethnic relations, and the lure of jobs and relative safety in the North. Remaining Black farmers were not only older and poorer than others, they also continued to disproportionately face structural discrimination with regard to land ownership and access to federal support, whether because of ineffectiveness, discrimination in implementation, poor design, lack of funding, or unintended shortcomings. The following section focuses on three sets of Farm Bill programs in particular and elaborates upon the history of each as they relate to racial and economic inequity, particularly in terms of income and wealth, access to program benefits, land access, access to positions of power, and degree of democratic influence.Discrimination by the USDA and FSA Loan Distribution Program is among the most significant causes of limited access to, and loss of, farmland by people of color. Specifically, lending program discrimination has undermined the economic capacity of farmers of color to anticipate and respond to rapid consolidation and specialization, such as limited capacity to adopt scientific and technological innovations in agricultural production, and greater vulnerability to price volatility. 

Toward this end, allegations of unlawful discrimination against farmers of color in the management and local administration of USDA lending programs—and the USDA’s limited response to such allegations—have been long-standing and well-documented. For example, in 1965, the US Commission on Civil Rights found evidence of discrimination in the USDA’s treatment of employees of color and in its program delivery. Furthermore, in the early 1970s, the USDA was found intentionally forcing farmers of color off their land through its loan practices. In 1982, the US Civil Rights Commission again found evidence of continued discrimination actively contributing to the decline in minority farm ownership. Despite such findings, in 1983, only one year later, President Reagan pushed for budget cuts that ultimately eliminated the USDA Office of Civil Rights, the primary body for addressing such claims of discrimination. Even after the USDA Office of Civil Rights was restored in 1996 during the Clinton Administration, discrimination in the lending programs continued for years. Although the USDA officially prohibits discrimination, the structure for the election of FSA county, area, and local committees that decide who receives loans and under what terms facilitates continued racial discrimination.Toward this end, a 1997 USDA Office of Civil Rights report observed that FSA county committees operate as closed networks and are disproportionately comprised of white men, noting that, in 1994, 94% of the county farm loan committees included no women or people of color. As of 2007, such trends continue, with just 90 Black committee members among a total 7,882 committee members around the country, slightly over 1%. Decades of discrimination and lack of access to such crucial positions have sparked several class-action lawsuits by women farmers and by various groups of farmers of color. Only recently has the Farm Bill attempted to address a major cause of racially discriminatory FSA lending program outcomes by targeting the lack of people of color within FSA committees. Specifically, it was not until a provision, Section 10708, in the 2002 Farm Bill that the composition of FSA county, area, and local committees were required to be “representative of the agricultural producers within the area covered by the county, area, or local committee,” and to accept nominations from organizations representing the interests of socio-economically marginalized communities. Furthermore, a provision, Section 1615, of the 2008 Farm Bill required county or area committees that are themselves undergoing rapid consolidation to develop procedures to maintain representation of farmers of color on such committees. 

Such trends culminated in the 1996 Farm Bill—the “Freedom to Farm” bill. This Farm Bill eliminated the structural safety nets that had long protected producers during lean years. Corporate buyers and groups such as the National Grain and Feed Association, composed of firms in the grain and feed industry, pushed the 1996 Farm Bill to completely eliminate price floors, the requirement to keep some land idle, and the grain reserves that were meant to stabilize supplies and therefore stabilize prices, while simultaneously encouraging farmers to plant as much as possible. The 1996 Farm Bill thus marked the culmination of the shift from the federal government subsidizing production and consumption to diminishing price supports and the subsidization of agribusiness itself. The dismantling of such price controls drove prices down and allowed corporate buyers to profit off heavily subsidized commodities while securing their power over producers. Specifically, deregulation left farmers increasingly vulnerable to market fluctuations caused by speculation, price volatility, and the profit-motives of corporate buyers. The shifts under the 1996 Farm Bill were deemed a failure by both farmers and legislators, and by 1997, rapidly falling farm prices resulted in direct government emergency payments to farmers, despite the fact that the legislation was designed to completely phase out farm program payments. Between 1996 and 1998, expenditures for farm programs rose dramatically, from $7.3 billion to $12.4 billion. They then soared to $21.5 billion in 1999 to over $22 billion in 2001. From 1996 to 2001, US net farm income dropped by 16.5% despite these payments. Rather than address the underlying cause of the price drop—overproduction—Congress voted to make these “emergency” payments permanent in the 2002 Farm Bill.

As outlined below, neoliberal corporate influence remains particularly salient within two domains: the first is food production, processing, distribution, and service, rolling grow tables and the second is education, research, and development.One major way corporations continue to profit and exert their influence on food production, distribution, and consumption is through commodity support programs. Once the safety nets of the New Deal farm programs were cut back during the 1980s and 1990s, and completely eliminated in the 1996 Farm Bill, farmers began to produce much more corn, soybeans, wheat, and other commodity crops. Specifically, the 1996 Farm Bill eliminated the requirement to keep some land idle, which encouraged farmers to plant far more than they had before. As a result, the higher supplies of these crops brought down their prices, which drastically hurt farmer incomes and greatly increased the profits corporate purchasers reaped from purchasing even cheaper commodities. These low prices undermined the economic viability of most crop farms in the late 1990s, and subsequently, Congress provided a series of emergency payments to farmers. Furthermore, because continued oversupply kept prices from recovering, Congress eventually made such payments permanent in the 2002 Farm Bill. The dismantling of direct payment support for farmers thus ushered in another form of federally subsidized cheap commodities for corporate buyers that still leaves farmers themselves relatively vulnerable: disaster assistance programs and other emergency aid. The 2014 Farm Bill in particular cut funding allocated to direct payments by about $19 billion over 10 years—the most drastic policy change in this Farm Bill—with much of this money going into other types of farm aid, including disaster assistance for livestock producers, subsidized loans for farmers, and, most significantly, the crop insurance program. As fundamental as direct payments and emergency payments have been for subsidizing agribusiness profits, under neoliberal political and economic restructuring, crop insurance has surpassed them as the most egregious and expensive subsidy for agribusiness.

For decades, farmers have been able to buy federally subsidized crop insurance in order to protect against crop failure or a decline in commodity prices. However, private insurance corporations and banks that administer the program, such as Wells Fargo, benefit the most from crop insurance subsidies. In 2011, these corporations received $1.3 billion for administrative expenses with $10 billion in profits over the past decade. In order to help cushion the blow from the reduction of direct payments, under the 2014 Farm Bill, $90 billion over 10 years will go toward crop insurance, which is $7 billion more than the previous farm bill. However, much of this money will go to private insurance corporations and banks instead of farmers. On the production side, the increase in government support will be directed toward the deductibles that farmers have to pay before insurance benefits begin. In other words, unlike non-farm insurance policies , crop insurance insures not only the crops, but also the expected revenue from selling those crops. Thus, Agricultural Risk Coverage and Price Loss Coverage only pays out when prices drop below a certain threshold. As of early 2015, corn crops have already reached this threshold. There exists a risk that this insurance program could cost far more than expected depending on how crop prices continue to shift: therefore, this is one of the more contentious aspects of the 2014 Farm Bill. Another contentious part is the uneven distribution of benefits. A 2014 report by the Environmental Working Group estimates that 10,000 policyholders receive over $100,000 a year in subsidies, with some receiving over $1 million, while the bottom 80% of farmers collect only $5,000 annually. In short, under the guise of cutting subsidies by repealing unpopular direct payments to farmers, the 2014 Farm Bill instead increases more costly crop insurance subsidies.The pressure for corporate profit and the history of corporate consolidation with regard to the food system, both vertical and horizontal, has driven corporations to continue to lower wages for millions of food system workers and accumulate more wealth. A 2011 national survey of over 630 food system workers conducted by the Food Chain Workers Alliance found that the median hourly wage was $9.65 per hour. More than 86% of food system workers were paid poverty wages while 23% of food system workers were paid less than the minimum wage.

Despite their significant role in every part of the food system—from production to processing to distribution and service—food system workers experience a greater degree of food insecurity than the rest of the US workforce. For example, according to the Food Chain Workers Alliance report, food system workers use SNAP at more than one and a half times the rate of the remainder of the US workforce. Additionally, as of 2014, twice as many restaurant workers were food insecure compared to the overall US population; as of 2011, in Fresno County, the country’s most productive agricultural county, 45% of farmworkers are food insecure. The situation is even worse in other parts of the country: in 2011, 63% of migrant farmworkers in Georgia were food insecure. Women and people of color disproportionately feel the economic pressure experienced by food system workers as a result of corporate consolidation. A comprehensive 2011 study of food workers and economic disparity found that people of color typically make less than whites working in the food chain. It found that half of white food workers earn $25,024 a year while workers of color earn $19,349. The study found that women of color in particular suffer the most, earning almost half of what white male workers earn. Furthermore, workers of color experience wage theft more frequently than white workers. More than 20% of all workers of color reported experiencing wage theft, while only 13.2% of all white workers reported having their wages misappropriated. Significantly, the study found that such discrepancies exist in all four sectors of the food system: production, processing, distribution, and service. Furthermore, such trends hold across the overall workforce. As of 2012, 11.8% of executive and senior level officials and managers, and 21% of all first- and mid-level officials and managers were people of color, growing rack despite people of color comprising over 25% of the US population. Agricultural workers in particular experience ongoing and widespread violations of the limited protections afforded to them by federal law. This is oftentimes the result of competing producers aiming to drive down their costs by not complying with employment laws. Between 2010 and 2013, for example, among agricultural employers, the Department of Labor found 1,901 violations of the Fair Labor Standards Act , which sets the federal minimum wage, overtime pay, child labor rules, and payroll record keeping requirements. A 2009 survey of approximately 200 farmworkers paid by “piece-rate” in Marion County, Oregon, found that workers experienced extensive violations of the state’s minimum wage law. Almost 90% of workers surveyed reported that their “piece-rate” earnings frequently amounted to less than minimum wage, averaging less than $5.30 per hour—37% below hourly minimum wage Furthermore, a 2013 survey of farmworkers in New Mexico found extremely low wages and high levels of wage theft: 67% of field workers surveyed were victim to wage theft within the year prior to the survey; 43% stated that they never received the minimum wage, and 95% said they were have never been paid for the time spent waiting each day in the field to begin working. 

The combination of employers’ exploitation of the immigration system, and workers’ low income, limited formal education, limited command of the English language, and undocumented status, greatly hinders farmworkers from seeking any retribution or recognition of their rights. For example, as of 2009, the National Agricultural Workers Survey found that 78% of all farmworkers were foreign born, with 75% born in Mexico; 42% of farmworkers surveyed were migrants, with 35% of migrants having traveled between the United States and another country, primarily Mexico. Furthermore, 44% said they couldn’t speak English “at all” and 26% said they could speak English only “a little”; and the median level of completed education was sixth grade, with a large group of farmworkers completing fourth to seventh grades. With limited legal aid, many agricultural workers fear that challenging the illegal and unfair practices of their employers will result in further abuses, jobs losses, and, ultimately, deportation. Worse yet, few attorneys are available to help poor agricultural workers, and federal legal aid programs are prohibited from representing undocumented immigrants. The exploitation of migrant agricultural workers begins long before they reach the United States, and this migration has largely been driven by US trade and foreign policy in Central and Latin America. Specifically, most agricultural workers are in the United States as part of the H-2A Temporary Agricultural Workers program, which allows US employers to bring foreign nationals to the United States to fill temporary or seasonal agricultural jobs. However, nearly all such employers rely on private recruiters to find available workers in their home countries and arrange their visas and transportation to the fields. US agricultural employers thrive and rely upon an immigration system and recruitment network that provides “cheap” labor , and, as such, this recruitment network outside US borders remains unregulated and highly exploitative. Among the most grievous of such practices, for example, is the collection of fees from workers as a prerequisite to being hired. Many growers are willfully ignorant of recruiters’ activities, despite recently revised regulations that require growers to promise that they have not received any such fees. With many potential workers striving to escape poor conditions in their respective homelands, there is much incentive for recruiters to charge “recruiting fees” for personal profit, leaving H-2A workers with a great deal of debt upon their arrival to the United States. While some have paid upwards of $11,000 for such opportunities to work, others have given the deed to their house or their car to recruiters as collateral so as to ensure “compliance” with the terms of their contract. Many farmworkers been deceived about their wages and working conditions , and, to make matters worse, many workers are tied to one employer and therefore have no choice but to work regardless of the low pay and abysmal working conditions of their employers. Ultimately, the H-2A program and US labor market creates conditions ripe for debt-peonage. Furthermore, although H-2A program regulations require employers to give job preference to qualified US workers, in practice the H-2A program ultimately puts US workers out of work given the seeming cost benefits of employing H-2A workers.

Table 5.2 shows the share of commercial dairies with at least one female operator by state and year. This has very interesting results with all commercial dairies reporting at least one female operator in 2017. The actual share of female operators compared to the share of operators gives us a better representation of demographic changes. The share of female core operators increased from 2002 to 2017 in every state but New Mexico, for which the share of female core operators decreased in 2007 and 2012 but was the same in 2002 as in 2017 . California and New York both increased the number of across each Census year. California had a 27% increase in female core operators from 2002 to 2017 and the share of female core operators in New York increased by 33%. Idaho, Texas, and Wisconsin all had a slight decrease in female core operators in 2007 and 2012, but an increase in 2017 relative to all previous years. Interestingly, when we look at the share of female operators it follows a similar pattern. California and New York both increases in the share of female operators across each Census. Wisconsin, Idaho, and Texas all had slight decreases in 2007 and 2012 relative to the 2002 share, but the share of female operators in 2017 was larger than in 2002 . However, the share of female operators in New Mexico had a small decrease from 2002 to 2017. This suggests that despite the addition of a fourth core operator in the 2017 COA the pattern is not substantially different from the trend in operators and that the trend was not only facilitated by capturing previously unmeasured management activities by women. From here characterizing the trend could be thought of in two ways: 1) this describes an actual increase in women operators playing a more prominent role and/or 2) an increase in their male associates being more likely to recognize and report female operators.

Disentangling exactly what characterizes these trends is impossible, trimming cannabis but it seems likely that the addition of a fourth core operator and the ability for more than one principal operator may have signaled a conversation about representation on the COA for many commercial dairies. Next, it is important to characterize the management characteristics of commercial dairy operators. These results are only characteristic of core operators as this data was not collected for all operators. The COA asked core operators whether their principal occupation was off farm. Overall, a larger share of female core operators had a principal off-farm occupation than male core operators . In California, less than 10% of the male core operators had an off-farm principal occupation, but about 30% of female core operators had an off-farm principal occupation with little variation over time. In other states, like Idaho and Texas, the share of core operators with off-farm principal occupation followed a similar pattern to California by gender. However, there was an 86.6% increase in male core operators with an off-farm principal occupation and an 18% decrease in female core operators in New Mexico. Along a similar thread, a very small portion of female core operators was labeled as principal operators. Now, the definition of a principal operator did change for the 2017 COA, but even with the 2017 addition of more than one core operator being labeled as a principal operator the share of female core operators that are labeled as a principal operator is relatively small. In California, 5% of female core operators are principal operators from 2002to 2012 with a jump in 2017 to 17% with the addition of the fourth core operator .

Idaho, New York, and Wisconsin follow a similar pattern as California with little to no change from 2002 to 2012 and a large jump in 2017. New Mexico and Texas, however, had a decrease from 2002 to 2012 and then a large jump in 2017. In 2017, most states had about 16- 20% of female core operators listed as a principal operator, but New Mexico only had 11%. This research would be incomplete without a description of the presence of spousalrun dairy farms in the U.S. A spousal-run dairy refers to a dairy that is managed by two operators that are married to one another. There is a historic assumption that many dairy farms are run by spouses, however, this research finds that trends in spousal commercial dairy operations does differ greatly by state . For some states, like Wisconsin, New York, and Idaho, a significantly large share of commercial dairy farms was being run by spouses, with over 40% of commercial dairy farms in each state being spousal run. In California, 31% of commercial dairy farms are run by spouses, but New Mexico had relatively few commercial dairies run by spouses and a decrease from 15% to 13% from 2012 to 2017. A large share of female core operators of commercial dairies was married to a principal operator in 2012 and 2017 . In 2017 Texas had the largest share with 80% of female core operators married to a principal operator and then Idaho and Wisconsin both had more than 75%. New Mexico had the smallest share of female core operators married to a principal operator with 48%, but that remains a significant share. Next, age of commercial dairy operators has been a point of discussion for because of the increasing age of dairy farm operators. Table 5.9 presents the share of operators by gender and age group for the Census year and state. Across all state the largest share of female operators was in the less than 50 years old age group with all states following a similar trend of a decreasing share of younger operators and increase in the share of older operators. For male operators the largest share was the less than 50 age group also had the largest share.

There was a significant share of male operators in the larger age group categories across all states with every state, but Wisconsin, have at least 10% of operators being male and over the age of 66. Finally, previous literature suggested that women may be more likely to adopt sustainable-minded practices. Regarding organic production, this seems to be true. In 2017, most organic commercial dairies have at least one female core operator, except in New Mexico for which only 17% of organic commercial dairies have at least one female core operator . The share of organic commercial dairies with at least one female operator is larger than the overall share of commercial dairies with at least one female operator. There was an increase in the share of female core operators that operated an organic commercial from 2007 to 2017 , but this was also with the addition of the fourth operator. There has been a slight increase in the share of organic commercial dairies across all states, but in 2017 all states had less than 15% of commercial dairies with organic production . Organic dairies do tend to have smaller herd sizes, in general and more milk sales revenue per cow. Organic commercial dairies have a larger share of female core operators than commercial dairies overall for all states, except New Mexico. In 2017, organic commercial dairies report at least a 30% or more share of female core operators, except New Mexico which only had an 8% share of female core operators . In every state, except New York, there was an increase in the share of female core operators that manage organic commercial dairy. The share of female core operators that manage an organic dairy decreased by 28% in New York but increased by 66% in Idaho. Next, I turn to explore the relationship between the farm size and gender demographics of farm operators and spousal-run operation. COA is panel data, gardening rack meaning that it is both times series and cross sectional in nature. For my analysis, I utilize a log-linear model with fixed effects in order account for cross-state and cross-time differences. The farm size variables, of the individual farm at time , are the logged dependent variables including Cowsit number of milk cows , TMDit total sales revenue from dairy or milk, and TVPit total value of production. I utilize farm-level operator characteristics variables including a binary variable for the presence of a female core operator , the share female operator on the individual farm , and a binary variable that indicates a spousal run farm variable .

Furthermore, I included a variable to control for a relationship between the age demographics of operators on farm size. MaxAgeit describes the maximum age listed by any given core operator on an individual commercial dairy. Table 5.13 shows the list of variables use in regressions and their corresponding definition. In addition, αi and λt represent the state fixed effect and the time fixed effect, respectively, and uit is an error term. Xit represents a vector of farm operator characteristics and farm management characteristics. logFarmSizeit represents a vector of the logged farm size variables listed above. Equations 1 is the regression equation used to show the relationship between the presence of a female operator and farm size, accounting for age, state, and year influences on farm size. Table 5.14 shows the relative coefficients and standard errors of each regression. Concerning the number of milk cows, the presence of at least one female core operator relates to a decrease in the herd size by about 12.9%, when holding constant for age, state, and year influences on farm size. With herd size, when accounting for the presence of a female operator, the max age corresponds to an increase in the herd size by 0.5%. The presence of at least one female core operator suggests a decrease of the total value of production by 31% and all milk or dairy sales by about 13.4% as well. So, across all farm size measures, there are relatively similar results. A one-year increase of the maximum age of any core operator relates to an increase in the total value of production by about 0.7%. Since 2002, there has been an increase in the share of female core operators and commercial dairies with at least one female operator. The trends in the share of the core operator and the share of operators suggest that these increases are not due to the increase in the number of core operators’ data collected by the COA, but, in fact, an actual increase in female commercial dairy farm management. Furthermore, both the presence of female operators and the share of female operators had significant negative relationship with farm size of commercial dairies across states and time. Furthermore, the presence of spouses running the commercial dairy also shows a significant decrease on the farm size. Due to the significant share of female core operators that are married to principal operators, it seems likely that this trend could be due to change in management and risk incentives of the operators resulting from both spouses’ income being likely determined by the success of the dairy. A garden may be understood as a place where the ‘geography of the mind meets that of the earth’, making it entirely apropos that the vertical garden finds its origins in the monumentally horizontal prairies and farmlands of the American Middle West, where the topography of Professor Stanley Hart White’s creative intellect meets a seemingly endless geography of flatness. White patented the first known green wall in 1938, prototyping the technology in the backyard of his Urbana residence, yet the concept emerges in his writings and drawings as far back as 1931 as a response to the problem of modern garden design. The significance of this invention has ‘still unrealized provocations’3 on the history of gardens and designed landscapes, having been conceived during a trajectory towards modernism in the same geographic region as the Prairie School and American Skyscraper. Although the provenance of this new technological garden is topographically uncanny, the invention itself is pure genius, synthesizing ideas from modern landscape and architectural theory, building sciences, horticulture, and industrial arts alike. White’s vertical garden finds its legal origins in 1937–38, albeit the technological and material precursors to the invention extend back to early horticultural experiments and industrialization of modern building materials.

New Mexico saw an increase in the number of farms with milk and/or dairy sales between 2002 to 2007, but then subsequent decreases in 2012 and 2017 . Overall, there was a decrease in farms with herd sizes between 200-999 milk cows. Figure 3.8 show the relative decrease in farms with milk and/or milk sales in New York and the decrease in the share of dairies with 1-199 milk cow herd size. There was an increase in the number of farms with a herd size greater than 1,000 milk cows. Figure 3.9 shows a decrease in the number of dairies with milk and/or dairy sales with significant decrease in the share of farms with a 1-199 milk cow herd size between 2002 to 2017 in Texas. There was also an increase in the number of farms with herd sizes greater 1,000 milk cows. Figure 3.10 shows that between 2002 and 2017 Texas farms with a herd size greater than 1,000 milk cows saw a significant increase in the share of milk or dairy sales revenue, from about 40% in 2002 to almost 90% in 2017. The majority of Wisconsin farms have a small herd size, although there has been a decrease from 2002 to 2017. There has been an increase in the number of farms with larger milk cow herd sizes . Figure 3.12 shows that the majority of milk and/or dairy revenue in Wisconsin used to come from farms with smaller milk cow herd size but has shifted overtime towards farms with larger milk cow herd sizes. The size distribution of farms in the U.S. has been a topic of economic research and discussion for decades. Changes in farm size along with reductions in farm numbers have raised concerns based on it the possible impact on rural communities, particularly movement out of certain regions leading to a possible decrease of employment opportunities in that region. Moreover, accurate and descriptive analysis of farm size is often used to inform agricultural policy and discussion, particularly in the dairy industry. In both industry discussion and policy-based decision-making, surrounding farm size, cannabis dryer the trend of consolidation is central to the discussion on the future of the dairy industry. Some suggest that the trend of farm size is characterized by consolidation with an increase in large farms, and fewer small farms remaining.

One assumption is centered around the idea of the disappearing middle, mid-sized farms, in agriculture with some arguing that the farm size distribution can be considered bimodal. This language can be vague and detailed analysis by state is needed for a clear characterization of farm size. Wolf and Sumner find that the argument of U.S. farms being bimodal is not the case for the dairy industry in 1989 and 1993. This thesis research aims to expand on this finding by discussing correlations related to farm size changes, kernel density plots of herd size and using parametric statistical density functions to characterize the herd size by state, utilizing recent Census of Agriculture data. The COA is a representative sample of all farms in the United States. This is individual farm level data across six states and four years which is a unique sample for research studies. This research looks at individual farm-level characteristics including farm size and operator characteristics and discuss the shifts across time and states. The trend of dairy consolidation in the United States has been characterized by a decrease in the number of dairies with the number of milk cows remaining relatively stable . Using the COA data, the number of milk cows on a commercial dairy has remained relatively stable with most states seeing slight increases in the number of milk cows, except New York . Whereas the number of commercial dairies has decreased significantly across all six states, except New Mexico which only decreased slightly . California and Idaho both had about a 36-37% decrease in the number of commercial dairies, while in New York, Texas, and Wisconsin the number of commercial dairies decrease by about 50%. Farm size distribution remains a prevalent agricultural policy issue, as characterization of the dairy industry’s farm size is used to inform legislation and often characterizes colloquial discussion about the state of the industry. This is in part due to firm size growth’s correlation with innovation and technology, as well as the firm’s ability to capture economies of scale. Although dairy farm size can be characterized for the U.S. overall, there are important distinctions by state, as the dairy farm size distributions differ greatly by state.

Macdonald et al. detail that larger dairy farms are able to capture economies of scale, more so than smaller dairies, resulting in a lower average milk production cost. However, the article does go on to specify that the distribution of dairy farm size differs greatly by state based on the specific financial and economic environment of the dairy industry in that state. Alternatively, some dairy farms lower the average milk production costs by capturing the economies of scope, i.e., diversification of sales. This could be characterized as raising and selling replacement dairy heifers, or other agricultural products such as grain to maintain economic viability. Finally, I consider the relationship that farm operator characteristics may have with farm size and the decision of a farm to exit. In Chapter Five, I detail a specific line of analysis related to the influence of female farm operators on farm size, but in this chapter, I will discuss the influence that the age of the farm operator may have on the farm size. Dairy farm size changes in response to these and other factors is important in considering future trends in farm size and their impact on milk production in the U.S. and the future structure of the dairy industry. This chapter aims to characterize the herd size distributions of the U.S. dairy industry, present evidence on the characteristics of the farm size distributions, and then finally discuss the correlation between farm level characteristics and farm size. This chapter will be structured as follows: a brief overview of previous literature on firm and farm size, a discussion about farm size distribution estimation, and then the results and discussion. Economic research and discussion have produced several theories on firm size and firm growth to characterize industries and the economy. This section will briefly review important studies related to firm size more generally and then will move on to research specific to the study of farm size and the economics of dairy farm size and size distributions. The study of firm size by economists can be best discussed chronologically, as much of the research builds off one another or finds results inconsistent with previously held theories. In 1931, Gibrat postulated what has come to be known as Gibrat’s Law that a firm’s growth rate is independent of its size.

This would mean that the growth rate of an individual firm over a particular time period should not be influenced by its original size. Ijiri et al. , using the foundation built by Gibrat’s Law, finds that firms that grew over 10% in the subsequent period are more likely to see above industry average growth, due to continued benefits of innovation that occurred in the subsequent periods. Viner theorizes that firm size distribution is based on the industry environment and that individual firms have a U-shaped average cost curve and will function at the minimum of this curve. He goes on to specify that firm entries and exits are determined by the quantity demanded by the market. Lucas used these previous works to build a new theory about the size distribution of firms in an industry that looks at size distribution as a solution for output maximization with a given set of production factors and managers with varied human capital levels. This model predicts the size distribution of firms based on the managerial ability of laborers and then subsequent resource allocation. Jovanovic finds that smaller firms will tend to have higher growth rates than larger firms, but that these smaller firms are more likely to exit the industry than the larger firms. Evans discusses growth relative to a firms age, finding that a firm’s growth can be tied to the age of the firm itself and that older firms have a slower growth rate. This theory is hypothesized to remain true for dairy farms. Stemming from foundation of Gibrat’s law, which claims that the firm size distribution follows a lognormal distribution, drying weed there has been significant literature on the size distribution of firms that looks at fitting parametric distributions to actual firm size data. Kondo, Lewis, and Stella evaluate recent non-farm panel data from the U.S. Census Bureau and find that the current U.S. firm size data best fits with a lognormal distribution, but there are differences in goodness of fit by industry. Akhundjanov and Toda use the original data, in Gibrat’s original paper, find that a Pareto distribution better characterizes the empirical size distributions. The distribution of firm size remains a fundamental part of research firm growth patterns and the literature on firm size has been directly applied to research on the growth rate of farms and farm size changes in different agricultural industries. Two common parametric distribution used in farm size distribution analysis are lognormal and exponential. Allanson evaluates farm size trends in England and Wales finding that the lognormal distribution fits farm size measures relatively well across time. Whereas Boxely uses an exponential distribution to evaluate farm size data from the Agricultural Census and finds that from 1935-1964 farm size shifted to the right, but that at the state level farm size does tend to follow the exponential distribution with some regularity. Before going any further in the analysis, it is important to outline the concept of farm size for this analysis.

Farm size measures across the whole agricultural industry tend to leave out key details that give better and more accurate accounts of the size of the farm for the commodity/industry. For example, when looking at the size of U.S. farms overall measuring the size of the farm based on acreage will lead to inaccurate or confusing results. The acreage needed to generate the same revenue for corn versus dairy milk or strawberries is substantially different. However, looking at the dairy industry specifically, many different characteristics shape a dairy’s economic footprint on the market, and therefore, defining how to characterize dairy farm size is fundamental to discussing changes in the dairy market. One can characterize the size of a dairy by the number of milk cows, or herd size, as one measure of dairy firm size. However, other characteristics such as the quantity of milk produced, the value of production, and value-added on the farm could also be considered as farm size measures . Different farm size measures allow us to answer different agricultural economic questions. While analyzing the dairy industry it is relevant to consider herd size, the milk and/or dairy sale revenue of the firm, and the total value of production, as we have already discussed in Chapter 2. Previous research on dairy farm size documents strong trends toward consolidation in the U.S. with a decrease of about 50% of all registered U.S. dairies from 2002 to 2019 . These trends in consolidation have differed by location with historically dairy producing regions seeing a large share of exits, these states were historically made up of smaller and mid-size dairies. MacDonald et al. detail the cost differences between larger and smaller dairies with cost advantages for larger dairies that drive the investment decision to increase herd size. This research suggested that there would continue to be a steady decline in the number of smaller and mid-size dairies and that the trend of consolidation would likely continue. This trend has raised research questions about what factors influence the distribution of farm size and the decisions of some farms to exit the industry. A common, albeit incorrect, assumption about the size distribution of the U.S. dairy industry is that it is bimodal. This assumption comes from news reporting and political commentary that there is a “declining” middle of farms in the U.S. and that there is this dichotomy between small, sometimes organic, farms and larger farms.

All farmers interviewed mentioned direct experience as being one of the most important modes for understanding their landscape, their farming system, and management practices essential to their farm operation. The farmers described this accumulation of experience as “learning by doing,” being “self-taught,” or learning by “trial and error” . These farmers added that in learning by experience, they made “a lot of mistakes” and/or faced “many failures” but also learned from these mistakes and failures – and importantly, that this cycle was crucial to their chosen learning process. More than half of the farmers interviewed maintained that no guidebook or manual for farming exists; while reading books was viewed as valuable and worked to enhance learning for individual farmers, to farm required knowledge that could only be gained through experience. Moreover, nearly all the farmers also explicitly commented on the fact that they have never stopped learning to farm . Overall, farmers in this study learned primarily through personal experience and over time, making connections and larger conclusions from these experiences. On-farm experimentation was a critical component of knowledge building as well. Experimentation consisted of methodical trials that farmers implemented at small scales on their farms, and most often directly on a small portion of their fields. Experimentation was often incited by observation , a desire to learn or to increase alignment with their own values, or a need to pivot in order to adapt to external changes. The farmers experimented to test the feasibility of implementing specific incremental changes to their current farming practices before applying these changes across their entire farm. For example, cannabis indoor greenhouse one farmer relied exclusively on trucking in urban green waste compost as part of the farm’s fertility program when she first started farming.

However, one year, she decided to allow chickens to roam in a few of the fields; within a few years, those fields were outproducing any other field on her farm in terms of crop yield. She quickly transitioned the entire farm away from importing green waste compost to rotating chickens on a systematic schedule throughout all fields on her farm. This form of experimentation allowed this farmer to move from relying on external inputs for fertility to cycling existing resources within the farm and creating an internally regulated farming system . For this farmer, this small experiment was monumental and shifted her entire farm toward a management system that was more in alignment with her personal farming values. As she described, “When you look at everything on the farm from a communal perspective and apply that concept of community to everything on the farm . . . it literally applies to every aspect of your life too.”Though this farmer had initially used direct observation to implement raised beds on his farm, as he learned the purpose of raised beds through his own direct experience, he slowly realized – over the course of decades – that raised beds served no purpose for his application. One year, he decided not to shape some of his beds. At the end of the season, he evaluated no real impact on his ability to cultivate or irrigate the row crops on flat ground, and no impact on yield or crop health. In fact, he observed less soil compaction and more aeration due to fewer passes with heavy machinery; and, he saved time and fuel. The transition to farm on flat ground took several seasons for this farmer, but over time, his entire farm operation no longer used raised beds to grow row crops. This breakthrough in farming for this particular farmer was informed by personal experience and guided by careful experimentation.Second to experience, observation also influenced the farmer learning process.

Whereas direct experience is usually immersive, and embedded within a larger social context, observation is a detached, mechanical form of knowledge production, where a farmer registers what they perceive to transpire . For example, farmers cited observing other farmers in a multitude of ways: “By watching other farmers, I really mean I’d just drive around and look. I’d see what tools they were using;” or “If I saw someone working in the field, I would stop my car on the side of the road to see what people are doing;” or “I really would just observe my father farm,” as well as making observations about the status of their land . Several of the farmers summed up their cycle of learning as a cycle of observation, trial, feedback, observation, trial, feedback, etc . However, several of the farmers clarified that this type of learning did not necessarily involve talking to fellow farmers. One farmer shared that he learned certain farming practices from a neighbor farmer through distant observation and then borrowed ideas he subsequently applied on his farm; to achieve this, he admitted that he had never really talked to the other farmer directly. Another farmer noted that he would “go back at night if they [another farmer] left their equipment in the field and just study how it was set up, so I [he] could see what was going on.” Based on interviews with other farmers, farmer-to-farmer knowledge exchange often consisted of detached observation rather than personal conversation or direct contact with another farmer.During the initial field visit, the farmers shared their definitions of soil health. Across all farmers interviewed , responses appeared mechanical and resembled language disseminated by government entities such as the Natural Resources Conservation Service . As such, most responses emphasized building soil organic matter, promoting biological activity, maximizing diversity, and minimizing soil disturbance. During the in-depth interview, farmers shared specific indicators used to evaluate soil health on their farms. These responses were varied compared to definitions of soil health and were generally based on observation and personal experience.

Generally speaking, the farmers relied heavily on their crops and on the health of their crops to inform them about the basic health of their soil. In fact, the farmers cited using their crop as their foremost indicator for gauging optimum soil health. One farmer shared, “Mostly, I’m looking at the plants, if the color of green on a particular leaf goes from shiny to matte, or slightly gray undertone to it. These subtle cues, I pick up from just looking at my crops.” The growth habit of weeds within and around fields was also cited as an indicator of soil health. For example, one farmer explained, “I’m looking at how the weeds are growing at the edges of the field; in the middle of the field. Is there a difference between what’s happening around the edges and what’s happening in the field?” Some farmers also frequently relied on cover crops as indicators for determining soil health and soil behavior. When acquiring new fields, for example, the farmers tended to first grow cover crops to establish a baseline for soil health and also understand soil behavior and/or soil type. The farmers also used cover crop growth habits to gauge the status of soil health and soil fertility for a particular field before planting the next iteration of crops. As one farmer elaborated, “I’m judging a field based on how a cover crop grows. It’s one thing if you’re planting a nutrient-intensive crop in a field, but if you have a cover crop in the field and there’s a swath that’s this tall and another swatch that’s only this short, then you know there’s something seriously different about that section of field and the soil there.” The organic farmers in Yolo County that were interviewed for this study demonstrated wide and deep knowledge of their soil and farming systems. Results show that white, first- and second-generation farmers that farm alternatively accumulate substantive local knowledge of their farming systems – even within a decade or two of farming. These particular organic farmers demonstrated a complex understanding of their physical environments, soil ecosystems, and local contexts that expands and complements other knowledge bases that inform farming systems. While the content and application of farmer knowledge may be locally specific , below we consider aspects of this case study that may be more broadly applicable. First, we discuss emergent mechanisms for farmer knowledge formation using existing frameworks in the social-ecological systems literature, and also summarize key features of farmer knowledge that coalesced from the results of this study.To further examine how farmers in this study acquire and incorporate their knowledge within their farm operation, cannabis growing equipment we first explore emergent mechanisms that underpin farmer knowledge formation. Because farmer knowledge encompasses knowledge of both social and ecological systems – and the interactions thereof – it is useful to draw upon existing frameworks from the social-ecological systems literature in order to trace the process of farmer knowledge formation among farmers in our case study. Briefly, social-ecological systems recognize the importance of linking social and ecological processes to capture interactions between humans and the environment; importantly, existing literature within SES studies also emphasizes the interactive and adaptive feedback among social and ecological processes that link social and ecological system dynamics . Boons offers a conceptual guide for identifying social-ecological mechanisms, which adapted to our case study provides a starting point for tracing aspects of farmer knowledge formation. Here, social-ecological mechanisms for farmer knowledge formation refer to – on the one hand, social and cultural phenomena that influence farmer knowledge and their personal values – on the other, farmers’ observations of and experiences with environmental conditions and ecological processes on their farms that influence their knowledge and their values – and the interactions thereof . Drawing upon Bar-Tal , we further define farmer values as a farmer’s worldview on farming – a set of social values or belief system that a farmer aspires to institute on their farm .

In our study, examples of social-ecological mechanisms for farmer knowledge formation among these farmers included direct observation, personal experience, on-farm experimentation, and inherited wisdom from other local farmers. Similar to Boons’ conceptual guide, our results suggest that social-ecological mechanisms may play a central role in producing a farmer’s values and in integrating ecological knowledge into their farm operation. At the same time, results also highlight that social-ecological mechanisms may contribute to a farmer’s local ecological knowledge base, and importantly, place limits on the incorporation of social values in practice on farms. It is possible that social-ecological mechanisms may also provide the lens through which farmer values and ecological knowledge are reevaluated over time. Moreover, farmer values may also mutually inform ecological knowledge – and vice versa – in a dynamic, dialectical process as individual farmers apply their values or ecological knowledge in practice on their farm. Social-ecological mechanisms may also be key in translating abstract information into concrete knowledge among farmers interviewed. For example, experimentation may codify direct observations to generate farmer knowledge that is both concrete and transferable; or, to a lesser degree, personal experience may enhance farmer knowledge and may guide the process of experimentation. In general, we found that farmers interviewed tended to rely less on abstract, “basic” science and more on concrete, “applied” science that is based on their specific local contexts and environment . This finding underscores that for these farmers, their theory of farming is embedded in their practice of farming, and that these farmers tend to derive theoretical claims from their land.For example, the farmers who possessed a stewardship ethos viewed themselves as caretakers of their land; one farmer described his role as “a liaison between this piece of land and the human environment.” Farmers that self identified as stewards or caretakers of their land tended to rely most heavily on direct observation and personal experience to learn about their local ecosystems and develop their local ecological knowledge. This acquired ecological knowledge in turn directly informed how farmers approached management of their farms and the types of management practices and regimes they applied. That said, farmer values from this study did not always align with farming practices applied day-to-day due to both social and ecological limits of their environment. For example, one farmer, who considered himself a caretaker of his land expressed that cover crops were central to his management regime and that “we’ve underestimated how much benefit we can get from cover crops.” This same farmer admitted he had not been able to grow cover crops the last few seasons due to early rains, the heavy clay present in his soil, and the need to have crops ready for early summer markets.

The symptom cluster with substantial evidence of CEDS is present for migraine, fibromyalgia, and irritable bowel syndrome. The endocannabinoid system regulates gut function, the CNS, and has a communicative relationship with the microbiome. Therefore, many other disorders and diseases are linked to a deficiency and dysfunction of the endocannabinoid system. Dysregulation of the endocannabinoids and CB2 receptors lead to many disorders affecting the liver, kidneys, CNS,neuromuscular, GI, immune system, lungs, bone, and mental health. Deficiency of endocannabinoids disrupts homeostasis. This provides an opportunity for the additional assessment of the therapeutic potential of phytocannabinoids, naturally-occurring cannabinoids in the cannabis, or hemp plants. These phytocannabinoids interact with the endocannabinoid system in the same way as endocannabinoids. There was a reduction in expression of the tight junction proteins occludin and claudin-5 on brain microvascular endothelial cells in germ-free mice . Expression of these proteins and BBB integrity was restored after gut colonization with the butyrate producing species, Clostridium tyrobutyricum or by administration of butyrate. Probiotics improved gut integrity and enhanced endocannabinoid signaling. Zebrafish were treated with a probiotic formulation for 30 days. Compared to untreated animals, histological analysis of gut Thissue from treated animals showed an intact epithelial barrier with increases in enterocyte length, villus length, and crypt depth. There was a reduction in epithelial and mesenchymal apoptotic cells, microgreen grow rack confirming molecular level changes of the pro-apoptotic factors casp3 and BCL2 associated X , and an increase in antiapoptotic signals such as B cell lymphoma 2 . Probiotics also decreased the gene expression for fatty acid amide hydrolase and monoacylglycerol lipase , which are involved in the degradation of endocannabinoids AEA and 2-AG.

One must take into consideration these metabolism pathways. Thus, probiotic treatment improved gut integrity and enhanced endocannabinoid signaling. The influence of cannabis on host Thissues, particularly gut permeability and its subsequent indirect effects on the gut microbiome, suggests significant potential therapeutic applications in HIV. Cannabis has been used for its medicinal properties for thousands of years in ancient cultures. Being a novice to cannabis use can be an intimidating issue for providers making recommendations to their patients. There are hundreds of strains that have names that are not based on structured nomenclature that clinicians are normally familiar with. Only recently, in the 1960s have scienThists begun to explore the properties of cannabis and even more recently the medicinal application in conjunction with Western medicine. Understanding the general physiological mechanism of endocannabinoids will support the framework in forming strategies to strain selection for symptom management. Despite suppressive ART, PWH maintain a high symptom burden with GI disorders, HAND, depression/ anxiety, pain, and fatigue. In addition, CD4 + T cell depletion and gut microbiota dysbiosis promote dysfunction of the gut epithelial barrier, resulting in a positive feedback loop sustained by increased microbial translocation of pro-inflammatory antigens such as LPS and subsequent immune activation and chronic inflammation. Consequences of these events in PWH are associated with poor health outcomes, including organ damage, cognitive decline, and decreased quality of life. Phytocannabinoids may be a viable supplement to accommodate for deficiencies in the endocannabinoid system. Components of cannabis have an anti inflammatory and antioxidant effect addressing problems on a molecular and cellular level. Responsibly used, cannabis can be given as an antidepressant and for relief of post-traumatic stress disorder, sedative, and anticancer benefits and relief of obsessive behaviors. Benefits extend to symptomatic relief for symptoms like fatigue, poor appetite, depression,anxiety, insomnia, pain, nausea/vomiting, and cognitive changes.

Clinicians will find confidence in educating themselves on the effects of cannabis to support a conversation with patients during office visits. One caveat to widespread adoption by the medical community is that as of 2019, the Department of Justice Drug Enforcement Agency holds that cannabis is a controlled substance with no evidence of medical benefit and high potential of abuse, even with 33 of 50 states and the District of Columbia currently having state-legislated approval to dispense cannabis for medical purposes. Furthermore, the federal government enforces barriers and restrictions on studies investigating the benefits of cannabis due to federal restrictions.104 Regardless of the discordance of laws between federal and state governments, patients are in fact using or interested in using cannabis to manage aspects of their health. Providers should have a working understanding of cannabis and its various effects on the body, including benefits and potential risks. While effects of cannabis on gut barrier function have been studied in pre-clinical models, the translation to humans is uncertain. Evaluation of the gut microbiome in both PWH and HIV transgenic animals exposed to chronic cannabis is necessary to begin to test beneficial effects to correct gut permeability and dysbiosis. The additive effect of probiotics and cannabis may result in synergistic effects in terms of supporting healing of the gut and also the reduction of inflammation, immune activation, and neuropsychiatric disorders within the context of ART.Modern agriculture faces environmental concerns about the use of pesticides. Organic agriculture is an alternative production method that limits the use of synthetic pesticides and fertilizers. The literature has documented that organic crop production does has a lower environmental impact per unit of land than conventional agriculture . However, previous studies often concentrate on a small geographic or crop variety scoop. In essay 1, I use the California Pesticide Use Report database to examine the environmental impacts in conventional and organic crop production at a full scale. It includes all pesticide use in commercial production. I examine the period 1995 to 2015 and find that pesticides used in organic production had smaller negative environmental impacts on surface water, groundwater, soil, air, and pollinators than pesticides used in conventional production.

Over time, this difference has declined. I also investigate how farm size and farming experience are correlated with pesticide use. I find that farmers with more acreage use pesticides that have larger environmental impacts. Specifically, more experienced farmers use pesticides that have greater impact on surface water and groundwater, and less impact on soil, air, and pollinators. The environmental impacts of pesticide use in organic agriculture increased over my study period, which is an interesting observations that requires further investigation. In essay 2, I focus on organic crop production and try to quantify the change in pesticide use. I find that the pesticide portfolio has changed dramatically for organic crop growers, as illustrated by the decline in sulfur use and the increase in spinosad use. Pesticide use is correlated with farm size. The consolidation of organic cropland is another trend documented in essay 2. Historically, ebb and flow flood table consolidation in agriculture as a whole has manifested as an decrease in the number of farms while the total cropland remains stable . In the organic sector, in contrast, both the number of farms and acreage have grown significantly for the last two decades. Nonetheless, consolidation has occurred because the share of large farms in total acreage had increased. In 2015, 56% of organic cropland was operated by growers with at least 500 acres of organic cropland, up from 15% in 1995. At the other end of the spectrum, growers with 10-50 acres accounted for 18% of organic cropland in 1995, which dropped to 8% in 2015. The average organic farm size increased from 46 acres in 1995 to 103 acres in 2015. The median organic farm size increased from 15 to 17 from 1995 to 2015. Farms with larger organic acreage, holding other variables constant, applied sulfur and fixed copper pesticides more frequently than those with smaller acreage. As a result, they had greater impacts on surface water and smaller impacts on soil and air because those ingredients are more toxic to fish and algae, and less toxic to earthworms and have lower Volatile Organic Compound emissions than other ingredients used in organic fields. The composition of organic crop has changed in California with the acreage share of vegetables increasing from 30% in 1995 to 50% in 2015. However, pesticide use patterns and the correlation with farm size do not differ between vegetables and other crops. The consolidation of cropland has not been limited to the organic sector. MacDonald et al. documented that the consolidation of acreage and value of production into a smaller number of larger operations has characterized U.S. agriculture for decades. In essay 3, I adapt and extent the endogenous growth model introduced in Lucas to explain changes in the size distribution of farms and specialization over time. In the theoretical model, farmers have knowledge regarding the production of each crop, and this knowledge grows only through learning from other farmers. Increased knowledge increases the profitability and knowledge can be apply across crops to various degrees. In my modeling framework, the opportunity cost of producing crops that farmers know less about increases as specialized knowledge accumulates, which reduces the number of crops produced by each farmer. The evolution of the farm size distribution in equilibrium and simulation results are presented to demonstrate how model parameters including learning rate, budget share, and elasticity of substitution alter the distribution of farm size and specialization.

The food system has faced concerns about its use of pesticides since even before Rachel Carson published Silent Spring . Today, concerns about environmental impacts from pesticide applications continue to grow . In this context, organic agriculture is proposed as an alternative farming system as it prohibits the use of most synthetic substances . With strict modeling assumptions, Muller et al. presents sim- ulation results that support organic agriculture as an alternative production system capable of providing food for the world population by 2050. Consumers’ perception that organic agriculture is more environmentally friendly has facilitated its growth . According to the Organic Trade Association, U.S. organic food commodity sales reached $39 billion in 2015 in real terms, up from $4 billion in 1997, the base year. The share of organic food sales in total food commodity sales increased from less than 1% to 5% during the same time period . In 2002, the National Organic Program was launched. It established national standards for organic certification and took enforcement actions if there were violations of the standards. Organic growers are prohibited from using certain production practices that have significant negative environmental impacts. However, the regulation of organic agriculture is process-based, not outcome-based, and the regulatory agency does not monitor or enforce standards on environmental outcomes such as biodiversity and soil fertility . Another source of concern comes from the way organic farming practices may change as the sector grows. As pointed out by Läpple and Van Rensburg , late adopters of organic agriculture are more likely to be profit driven and care less about the environment than early adopters. And, the prices of organic products remained at least 20% higher than their conventional counterparts in 2010 , which could encourage additional entry. Therefore, unintended consequences might emerge and organic agriculture could be less environmentally friendly than commonly perceived. There is some evidence of this in the scientific literature. Organic agriculture has been reported to have higher nitrogen leaching and larger nitrous oxide emissions per unit of output than conventional agriculture . Certain pesticide active ingredients used in organic agriculture have been found to be more toxic than conventional AIs in laboratory environments and field experiments . For example, Racke reviewed the discovery and development of spinosad, a natural substance used to control a wide variety of pests, and observed that spinosad was approved based on its low mammalian toxicity. However, Biondi et al. found that spinosad is more harmful to natural predators than pesticides used commonly in conventional agriculture. As the case of spinosad demonstrates, pesticide use in organic agriculture could impose more environmental impact than conventional agriculture in one or more dimensions. Therefore more evidence is needed to evaluate the environmental impact of organic farming practices and its determinants. In this essay, I provide novel evidence regarding the impact of pesticide use in organic and conventional agriculture on different dimensions of environmental quality, and quantify the difference between the environmental impacts of pesticide use in the two production systems in California. In addition, I examine the relationships between farmers’ pesticide-use decisions and their experience and farm size. California is the leading state for organic agriculture in the U.S., accounting for 12% of certified organic cropland and 51% of certified organic crop value nationally in 2016 .

To test irritability-like behavior after WIN exposure, we used the bottle-brush test, based on the experimental method that was designed previously for mice and slightly modified to better monitor rat behavior. Currently, this model is increasingly used by both our laboratory and others as a measure of negative emotional states in animal models of addiction. This method has advantages over other behavioral paradigms that measure aggressive/ defensive behaviors, such as the social dominance/subordination paradigms and resident/intruder confrontation paradigm, because the experimenter has greater control over the mechanical stimulus and thus better precision in ensuring uniform provocation. Furthermore, the “social” factor in eliciting agonistic behavior and the risk of physical injury during an agonistic encounter are both circumvented in the bottle-brush test. The mechanical stimulus of the moving bottle-brush has also been found to be more effective in provoking these behaviors compared with either deceased or stuffed animals. In the present study, the animals were randomized, and three trained observers scored the rats’ behaviors in real-time as described below. Te observers were blinded to treatment of the animals. Testing consisted of ten 10-s trials with 10-s intertrial intervals in plastic cages with clean bedding. A bottle-brush was rotated rapidly toward the rat’s whiskers. Both aggressive responses and defensive responses were recorded. The behavioral responses were chosen based on Riittinen et al. and Lagerspetz and Portin. Total aggressive and defensive scores were calculated for each animal based on the average score of the observers. Both aggressive and defensive behaviors were summed to calculate the total irritability score. Irritability-like behavior reflects a composite measure of aggressive vs. defensive responses. Irritability-like behavior was assessed 6 days afer the last injection of WIN/vehicle in adolescence and again in adulthood 18h into withdrawal afer the escalation of cocaine self-administration .

The rats were anesthetized by isofurane inhalation, plant growing stand and intravenous catheters were aseptically inserted in the right jugular vein using a modified version of a procedure that was described previously. The right jugular vein was punctured with a 22-gauge needle, and the tubing was inserted and secured inside the vein by tying the vein with suture thread. The catheter assembly consisted of an 18 cm length of MicroRenathane tubing that was attached to a guide cannula . The guide cannula was bent at a near right angle, embedded in dental acrylic, and anchored with a mesh . The catheter exited through a small incision on the back, and the base was sealed with a small plastic cap and metal cover cap. The catheters were flushed daily with heparinized saline in 0.9% bacteriostatic sodium chloride that contained 20mg/0.2ml of the antibiotic Cefazolin .Self-administration in adulthood was performed in operant conditioning chambers that were enclosed in lit, sound-attenuating, ventilated environmental cubicles. The front door and back wall of the chambers were constructed of transparent plastic, and the other walls were opaque metal. Each chamber was equipped with two retractable levers that were located on the front panel. Cocaine was delivered through plastic catheter tubing that was connected to an infusion pump, which was activated by responses on the right lever. Responses on the left lever were recorded but did not have any scheduled consequences. Activation of the pump resulted in the delivery of 0.1 ml of cocaine . A computer controlled fluid delivery and behavioral data recording. The rats were first trained to self-administer cocaine under a fixed-ratio 1 schedule of reinforcement in daily 1-h sessions. Each active lever press resulted in the delivery of one cocaine dose. A 20-s timeout period followed each cocaine infusion. During the TO period, responses on the active lever did not have scheduled consequences. This TO period occurred concurrently with illumination of a cue light that was located above the active lever to signal delivery of the positive reinforcement. The rats were trained to self-administer cocaine in 14 sessions until a stable baseline of reinforcement was achieved .

The criterion for the acquisition of cocaine self-administration was defined as the intake of at least 2.5 mg/kg cocaine in the 1-h self-administration session, requiring at least five lever presses. This criterion was adapted from previous publications. After the 14-session acquisition period, the rats were subjected to fourteen 6-h cocaine self-administration sessions to allow them to escalate their cocaine intake. To study the motivation to seek cocaine, a progressive-ratio schedule of reinforcement was used, in which the response requirement began at one lever press/infusion and increased exponentially according to the following equation: lever presses/infusion=[5×e] − 5. The session duration was limited to 6h or ended when a rat failed to achieve the response requirement within 1h. The PR sessions were conducted after the training/acquisition phase and again after a stable level of escalation was achieved. In the course of the experiment, which lasted for more than 3 months, some rats were excluded at different stages of the experiment. Two rats were excluded during the acquisition phase because of the failure of catheter patency. In the course of the escalation phase, three rats were excluded from the study because of the failure of catheter patency at the end of the study, and one rat in the vehicle group died unexpectedly during the day of from cocaine self-administration before the study was completed, thus leaving n=6 rats/group for the final analysis. The present study found that adolescent WIN exposure increased irritability-like behavior in adolescence, which persisted into adulthood, induced cross-sensitization to the locomotor-stimulating effect of cocaine in adolescence, which did not persist into adulthood, decreased the speed of acquisition but not the rate of cocaine self-administration in adulthood, and had no effect on the escalation of cocaine self-administration in adulthood.

Overall, these results demonstrate that although cannabinoid exposure in adolescence induces irritability-like behavior and cross-sensitization to the psychostimulant effect of cocaine during adolescence, it does not promote cocaine self-administration once the animals reach adulthood. However, the effect of adolescent WIN exposure on cocaine self-administration in adolescence was not investigated in the present study because the animals reached adulthood by the time they had recovered from the surgeries that were required for self-administration. Reductions of both body weight and food intake were observed during WIN treatment. Although the activation of cannabinoid receptors typically produces an increase in food intake in adulthood accumulating evidence suggests that adolescent exposure to THC or WIN in rats decreases food intake and body weight. The increase in water intake during WIN exposure in the present study confirms the role of cannabinoid receptors in homeostatic responses that regulate not only energy homeostasis but also fluid balance. Irritability, anxiety, and dysphoria are key negative emotional states that characterize the withdrawal syndrome in humans, which arises when access to the drug is prevented and contributes to drug relapse. Irritability has also been reported to be greater in adolescents at higher risk for substance use. Irritability-like behavior has also been shown to increase during withdrawal from alcohol and nicotine in rodents. However, to our knowledge, whether early exposure to cannabinoids affects irritability-like behavior has not been studied in animal models. In the present study, we found that WIN exposure induced irritability-like behavior in adolescence and adulthood, suggesting that cannabinoid exposure in adolescence induces long-lasting neurobehavioral adaptations that can persist months after WIN exposure. However, further studies are needed to investigate whether this finding has translational relevance. An alternative explanation is that, despite blind randomization of the subjects to the two groups, the increase in irritability-like behavior that was observed in WIN-treated rats may be attributable to preexisting differences in irritability-like behavior. Further studies are needed to investigate whether this fnding has translational relevance. Numerous human studies demonstrate that early cannabis use is associated with greater vulnerability to the later development of drug addiction and psychiatric illness. A recent study reported a pivotal role for cannabinoid receptors as molecular mediators of adolescent behavior and suggested that cannabinoid receptors may be important in adolescent-onset mental health disorders. Chronic adolescent exposure to WIN has also been shown to induce anxiety-like behavior in rats. However, plant grow table contradictory findings have also been published, with either no change or even a decrease in anxiety-like behavior after cannabinoid exposure in adolescence. Rats that were exposed to cannabis smoke were also reported to exhibit a decrease in anxiety-like behavior. Interestingly, a previous study also demonstrated that long-term cognitive and behavioral dysfunction that was induced by adolescent THC exposure could be prevented by concurrent cannabidiol treatment. Importantly, WIN acts as a full cannabinoid receptor agonist, in contrast to THC, which only acts as a partial agonist. Moreover, cannabis is known to consist of dozens of additional phytocannabinoids apart from THC.

Furthermore, different strains of cannabis differ in their THC content, and THC levels in cannabis have increased year after year because of consumer demand, thus making direct comparisons of human data across time and across studies difficult. Nevertheless, we chose this model of early cannabinoid exposure and followed it precisely because it has been shown to induce cocaine cross-sensitization, thus supporting the gateway hypothesis. Further studies are needed to investigate whether the long-term irritability-like behavior that was observed in the present study can be prevented by concurrent cannabidiol treatment or whether adolescent exposure to cannabis smoke induces long-lasting irritability-like behavior in rats. Epidemiological data consistently document that cannabis exposure precedes the use of other illicit drugs. However, epidemiological data cannot provide causal evidence of this sequence. Animal models are particularly useful for studying effects that are related to cross-sensitization because they allow sequential administrations of the studied drugs while controlling for confounding variables. Several studies have reported behavioral cross-sensitization between cannabinoids and stimulants in rodents. WIN treatment during adolescence in rats induces long-lasting cross-tolerance to morphine, cocaine, and amphetamine, potentiates amphetamine-induced psychomotor sensitization, and induces cocaine-induced psychomotor sensitization in adolescence. WIN exposure also leads to increases in methylenedioxymethamphetamine-induced and cocaine-induced conditioned place preference. In the present study, WIN exposure in adolescence induced cross-sensitization to the stimulatory effect of cocaine in adolescence. However, this effect was no longer present in adulthood when the rats had self-administered cocaine for several weeks, suggesting that cannabinoid exposure in adolescence may increase the psychomotor effects of cocaine during the first exposure to cocaine, but this effect is not necessarily long-lasting. Cannabinoid exposure increased irritability-like behavior and the psychomotor effects of cocaine, but it did not promote the acquisition or escalation of cocaine self-administration. Indeed, we observed the slower acquisition of cocaine self-administration with 1-h short-access to cocaine in male rats with prior exposure to WIN compared with controls. In contrast, a previous study reported a trend toward an increase in cocaine self-administration during the short acquisition phase in female rats with prior exposure to the cannabinoid receptor agonist CP55,940 but not in male rats. However, this study did not discriminate between inactive and active levers, and no diference in cocaine self-administration was observed during the 14-day maintenance phase in either sex. A recent study showed that adolescent WIN exposure caused impairments in an attentional set-shifing task, a measure of cognitive fexibility, in adulthood. An alternative hypothesis is that the slower acquisition of cocaine self-administration in adulthood that was observed in the present study may be attributable to cognitive impairment that slows the acquisition of operant responding. In humans, several studies have indicated that the adolescent use of cannabis can lead to long-term cognitive deficits, including problems with attention and memory. During escalation, no differences were observed between the rats that were exposed to vehicle in adolescence and the rats that were exposed to WIN in adolescence. This suggests that if cognitive impairments affected the initial acquisition of self-administration, then they did not produce long-term deficits. Te model of long-access to cocaine self-administration is one of the most validated animal models of cocaine use disorder and drug addiction in general. This model has been shown to result in all seven of the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition , and seven of the 11 DSM-5 criteria, including most of the criteria that are required for severe use disorder: tolerance, withdrawal, substance taken in larger amount than intended, unsuccessful efforts to quit, considerable time spent to obtain the drug, important social, work, or recreational activities given up because of use, and continued use despite adverse consequences.

These strict county measures, which discarded and replaced publicly developed regulations, stoked reaction. When the Siskiyou County Board of Supervisors met in December 2015 to vote on these measures, advocates and cultivators presented 1,500 signatures to forestall its passage, a super majority of attending residents indicated opposition, and supervisors had to curtail 3 hours of public comment to vote. Despite this showing, supervisors passed the restrictive measures, prompting cannabis advocates to collect 4,000 signatures in 17 days to place the approved ordinances on the June 2016 ballot. Meanwhile, the Sheriff’s Office enforced the new stricter regulations . The Sheriff’s Office assumption of code enforcement blurred the line between noncompliance with civil codes and criminal acts. Stricter ordinances, still in effect in Siskiyou, created a broad, nearly universal category of “noncompliance.” No one we interviewed, including officials at the Planning Division and Sheriff’s Office, knew of a single cultivator officially in compliance. One interviewee estimated that growing 12 indoor plants would cost $40,000 in physical infrastructure, in addition to numerous licensing and inspections requirements, effectively prohibiting self-provisioning. The Sheriff’s Office notified the public that it would initiate criminal charges against “non-compliant” cultivators, specifically those suspected of cultivation for sale , child endangerment  or suspected drug trafficking . Since the county regulations produced a situation where no one could comply, law enforcement could effectively criminally pursue any cultivator. The slippage from civil noncompliance to criminality was mirrored in enforcement practices. Investigations were “complaint driven,” meaning not only that warrants could be issued in response to disgruntled neighbors upset about a barking dog on a cultivation site, indoor plant table as one person reported, but that police officers could serve as a kind of permanent, general complainant and take “proactive action” when they spotted code violations .

Administrative warrants allowed deputies to enter properties with a lower evidentiary bar than they would have needed for criminal warrants, leading one patients rights group — Siskiyou Alternative Medicine — to file a lawsuit alleging county violations of Fourth Amendment protections against unreasonable search and seizure . In effect, cannabis’s criminal valences in the county endured through California’s shift of cannabis from criminal to civil provenance. Formerly illegal activities continued to be formally or informally treated as criminal matters, as researchers have noted with other stigmatized activities and groups, for example, after the decriminalization of sex workers in Mexico . Also, enforcement of civil matters can lead to substantive criminalization when those matters are stigmatized, as in the regulation of homelessness . While it is not unique for police officers to enforce civil codes, what is unique in Siskiyou County is the assumption of the entire civil process under the sheriff’s authority. To understand how this civil process became criminally inflected, in a county that voted for statewide cannabis legalization in 2016, one must first understand significant contextual shifts in who was growing cannabis where — and the challenge this posed to dominant ideas of land use, agriculture and culture. Since 2014, cannabis gardens have emerged on many of the county’s undeveloped rural subdivisions in unincorporated areas of Siskiyou. Subdivided into over 1,000 lots each in the 1960s, these subdivisions contain many parcels that are just a few acres in size and relatively inexpensive. Previously populated mostly by white retirees, squatters and a few methamphetamine users and makers, the parcels were often bought sight-unseen as investments or potential retirement properties, with most remaining unsold and undeveloped until the mid-2010s. In 2014, these subdivisions became destinations for Hmong Americans from several places, including Minneapolis, Milwaukee and Fresno; many of them cultivated cannabis. The inexpensive, sparsely populated, rural subdivisions enabled Hmong-Americans to live in close proximity to ethnic and kin networks, which multiple interviewees expressed was especially important for elders who had migrated to the United States as refugees after the Vietnam War.

The county sheriff estimated that since the mid-2010s around 6,000 Hmong-Americans had moved to Siskiyou, purchasing approximately 1,500 parcels . In an 86.5% white county with just 745 non-cannabis farms and fewer than 44,000 people , this constituted a major demographic shift. Hmong-American residents found themselves susceptible to scrutiny by white neighbors and officials. The subdivisions are often sparsely vegetated, dry and hilly, making them not only unproductive as agricultural lands but also highly visible from public roads, horseback, neighboring plots, helicopter and Google Earth. Green screen fencing, wooden stakes, portable toilets, generators, campers, plywood houses, or water tanks and trucks often signal cannabis cultivation but would be necessary for many land uses, especially since many lots are sold without infrastructure like water, sewer or electrical access. If detection of code violations depends upon visibility, Hmong Americans on subdivisions have been made especially visible and vulnerable to detection. One lawyer, for instance, reported that 90% of the defendants present at administrative county hearings for code violations in fall 2015, when the first complaint-driven ordinance was put in place, were Hmong-American. One Hmong-American resident reported being stopped by police six times in 3 months and subjected to unfriendly white neighbors patrolling on horseback for cannabis — one of whom made a complaint for a crowing rooster, a questionable nuisance in this “right to farm” county. Numerous Hmong-Americans and sympathetic whites echoed these experiences. County residents confirmed their antagonism toward Hmong-Americans by characterizing them in interviews and public records as dishonest, thieves, polluters, negligent parents and unable to assimilate, and making other racializing and racist characterizations. While written regulations and enforcement profess race neutrality, in a nuisance enforcement regime based on visibility, Hmong Americans were more visible than others, leading many to argue that they were being racially profiled. Rhetoric emerging from the county government amplified racial tensions and visibilities.

Numerous Sheriff’s Office press releases located the “problem” in subdivisions and attributed it to “an influx of people temporarily moving to Siskiyou” who were “lawbreakers” from “crime families” with “big money” and who threatened “our way of life, quality of life, and the health and safety of our children and grandchildren” . Just 2 days before the June 2016 ballot on the strict cannabis ordinances, state investigators responded to county reports that newly registered Hmong-American voters might be fraudulent or coerced by criminal actors and visited Hmong-American residences to investigate, accompanied by sheriff’s deputies . The voter fraud charges were later countered by a lawsuit alleging racially motivated voter intimidation; the suit was eventually dismissed for failing to meet the notoriously difficult criteria of racist intent. The raids may have discouraged some Hmong-Americans from voting, charges of fraud may have boosted anti-cannabis sentiment, and, one government official explained, “creative balloting” measures enabled some municipal voters in conservative localities to vote while others in more liberal places could not. The voter fraud charges, raids and legal contestation drew widespread media attention that further linked Hmong-Americans and cannabis. Amidst these now-overt racial tensions, the restrictive June 2016 ballot measure passed, allowing the Sheriff’s Office to gain full enforcement power over the “#1 public enemy to Siskiyou citizens … criminal marijuana cultivation” . Shortly after the June 2016 ballot measure affirmed stricter regulations, the Sheriff’s Office formed the Siskiyou Interagency Marijuana Investigation Team with the district attorney to “attack illegal marijuana grows” “mostly” around rural subdivisions . Within a month, SIMIT had issued 25 abatement notices and filed 20 criminal charges, in addition to confiscating numerous plants. Meanwhile, the Planning Division’s role had diminished — code enforcement officers were relegated to addressing violations not directly related to cannabis . The November 2016 state legalization of recreational cannabis prompted Siskiyou to examine a possible licensure and taxation system for local growers . Amidst sustained, vocal opposition, the proposal stalled for several reasons that further aggravated cultural and racial tensions: A key proponent of licensure was discovered to be running an unauthorized grow, three Hmong Americans died of carbon monoxide poisoning due to heaters in substandard housing, and a cannabis cultivation enterprise run by two Hmong-Americans attempted to bribe the sheriff. These developments were interpreted not as outcomes of restrictive regulations and criminalizing strategies, but as proof that, in the words of one supervisor, regulation was impossible until the county could “get a handle on the illegal side of things.” The sheriff encouraged this interpretation, arguing in an interview that statewide legalization was “just a shield that protects illegal marijuana” and efforts to regulate it would always be subverted by criminals. This anti-regulatory logic prevailed in August 2017 when the county placed a moratorium on cannabis commerce. Still, hydroponic flood table the sheriff argued for stronger powers, citing an “overwhelming number of cannabis cultivation sites,” which, according to the Sheriff’s Office, continued to “wreak … havoc [with] potentially catastrophic impacts” across the region . Just 1 month later, at the sheriff’s urging, the Siskiyou Board of Supervisors declared a “state of emergency” aimed at garnering new resources and alliances to address the cannabis cultivation problem. Soon, the Sheriff’s Office enlisted the National Guard, Cal Fire and the California Highway Patrol in enforcement efforts, and, by 2018, numerous other agencies joined, including the Siskiyou County Animal Control Department, California Department of Toxic Substances Control, State Water Resources Control Board, California Department of Fish and Wildlife and a CDFA inspection station.

These alliances multiplied the civil and criminal charges cultivators might face . Ironically, California’s cannabis legalization has enabled a kind of multi-agency neoprohibitionism at the county level, one that reinforces older criminal responses with new civil-administrative strategies and authorities. The need to “get a handle” might be regarded as a temporary emergency measure, but it may also propagate new criminalizing methods and institutional configurations. The more enforcement occurs, the bigger the problem appears, requiring more resources and leading to a logic of escalation symmetrical to the much-critiqued War on Drugs . And the more cannabis cultivators are viewed as criminal, the less likely they are to be addressed as citizens, residents and farmers.Given concerns about biased county policy and enforcement, the Sheriff’s Office held the first HmongAmerican and Siskiyou County Leader Town Hall in May 2018 to “foster a closer, collaborative relationship with members of the Hmong-American community,” exchange information about Hmong and Siskiyou culture and educate attendees on county policies . According to public records, racial tensions surfaced at this meeting when some white participants expressed that “our county” had been “invaded” and that Hmong-Americans were not fitting into local cultural norms . Meeting leaders — both government officials and Hmong-Americans — however, identified cultural misunderstanding, rather than criminalization and racialized claims by whites on what constitutes local culture, as the core problem to be addressed. “Misunderstanding” was an inadequate framing, given that Hmong-Americans had attempted to make themselves understood by attending public meetings, forming advocacy groups, signing petitions, demanding interpreters and administrative hearings, and registering to vote since their arrival in Siskiyou. At the 2018 town hall, and numerous prior meetings, they emphasized their status as legitimate community members — veterans, citizens, consumers of county goods, local property owners, “good” growers and medical users — not nuisances, criminals, foreigners or outsiders. In interviews and public forums many Hmong-American cultivators expressed a desire to comply with the rules. Their efforts, however, they said, were frustrated not only by linguistic and cultural differences, but also understaffed and underfunded permitting, licensing and community services agencies. Hmong-American cultivators routinely told us about their desires to settle down, build homes and plant other crops. “I’m growing watermelons, pumpkins and tomatoes,” one cultivator told us, but he was waiting for a permit to build his house, a process another interviewee reported took 3 years. Though the town hall meeting sought to address cultural misunderstanding, this framing overlooks how misunderstanding — of Hmong-Americans or cannabis producers generally — is produced by criminalizing enforcement practices. Properties given as gifts in the Hmong-American community were seen as evidence of criminal conspiracy, not generous family assistance; land financing networks evidenced drug trafficking organizations, not kin-based support and weak credit access; repetitive farm organization patterns suggested “organized crime” , not ethnic knowledge-sharing circuits. When Hmong-Americans, leery of engagement with government agencies and unfriendly civic venues, self-provisioned services, including firefighting teams, informal food markets and neighborhood watches, these actions were taken to confirm suspicions that they could not assimilate. Now that some Hmong-Americans are considering, or already are, moving away in response to county efforts, the sheriff’s prior description of them as temporary residents seems prophetically manufactured.

Rather than speaking about pathological forms, Douglas focuses on marginal conditions. Her basic premise is that objects, practices, behaviors, and ideas that do not fit the existing social classifications are considered polluting, impure, and even dangerous and thus should be separated . Managing spatiality is a technique of power that allows the legitimate authority to reject “inappropriate” elements and protect what it deems normal, natural, and right. In this paradigm, space is not value-free but constructed through politics and power relations. Take, for example, racial segregation, building the wall to isolate immigrants, hot spot policing, skid rows, mental hospitals, jails and prisons—in all these cases, devaluation of individuals involves their spatial separation. Socio-spatial stigmatization is a mutually constitutive process, in which places inherit the stigma of persons, but persons also can be stigmatized through their interaction with places . For instance, concentrating homeless shelters into specific areas of a town tends to reinforce the stigmatized understanding of such areas. In similar ways, the stigma attached to a homeless shelter extends to individuals using it. Those who live in areas with a high concentration of “disordered” facilities, practices, and individuals tend to oppose them physically, ideologically, and discursively . For example, in his research of addiction treatment clinics in Toronto, Christopher Smith shows that residents perceive these facilities as a threat to the productive places and try to enforce certain socio-spatial borders . Previous research showed that medical cannabis dispensaries were more likely to be located in less desirable parts of a neighborhood, characterized by high poverty level, unemployment, and homelessness . However, rolling flood tables we know very little about the recreational cannabis facilities: Are they perceived as a “matter out of place”? Do they blur, contradict, and otherwise confuse the moral and social order of the communities?

This study investigates the extent to which cannabis is normalized in California. Normalization is a barometer of changes in social behavior and cultural perspectives . Drawing from the Thomas theorem—stating that if men define situations as real, they are real in their consequences—I suggest that if cannabis is conceived as legitimate, it will not be pushed to the geographical and/or social margins. By the same token, if it is viewed as dangerous and illegitimate, then cannabis dispensaries will be regarded as sites of contagion, which are to be marginalized and isolated. I conduct a regression analysis to identify factors that explain variations in cannabis practices at the city level. In particular, I examine the relationship between the support of cannabis legalization in California cities and the number of cannabis licenses issued by local governments. Following the normalization theory, I expect that cities whose residents supported cannabis legalization are more likely to permit legal cannabis dispensaries within their borders. If residents view cannabis as legitimate and socially acceptable, local governments will favor cannabis-related activities on their territories. I also expect homogeneity in the characteristics of cities whose citizens supported cannabis legalization and those that permitted cannabis businesses. For example, if cities whose residents voted for cannabis legalization have a higher percentage of the middle and upper class, then cities that de facto legalized cannabis would also have a higher percentage of the middle and upper class. Since cannabis businesses create jobs and bring tax revenues to city budgets, local governments have strong incentives to permit cannabis-related activities, especially when most citizens favor legalization. But imagine situations in which citizens voted for cannabis legalization, but governments forbade any cannabis businesses, or, on the contrary, citizens did not support the legalization, but governments adopted pro-cannabis policies. These examples demonstrate the dissociation between the public’s wishes and the government’s deeds and cast doubts on the legitimacy of cannabis in a given jurisdiction. As I discussed earlier, cannabis users and distributors bear a stigma that can potentially extend to other people and places. Prosperous communities may decide to distance themselves from the possible harm of cannabis stigma and forbid any cannabis related activities .

In contrast, for economically disadvantaged communities, financial benefits may outweigh the harm of stigma and reinforce the marginalization of places with already limited resources. I look at the adoption of pro-cannabis regulation as an example of morality policies, through which local governments draw a boundary between “pure” and “polluted”, “ordered” and “disordered”, “safe” and “dangerous.” To get a more nuanced picture of the legalization and normalization processes, it is important to understand the moral-economic rationale behind decision making at the city level. The question is not only whether cannabis is legal, but where, how, and to what degree it is legal. exploring which cities are more likely to allow cannabis businesses, this research contributes to understanding the relationship between legitimacy and legality and helps determine the current status of cannabis in California. Moreover, the focus on city-level data provides an insight into how boundaries of normality vary across local contexts. Acting as moral entrepreneurs, local governments rely on principles of the politics of pollution and create a cognitive map of acceptable and non-acceptable places . Previous studies have highlighted the importance of religiosity, economic development, political competition, community composition, organizational perviousness, and historical legacies in explaining moral policy outcomes . This research takes a different path and sets out to clarify the relationship between changes in public attitudes and the adoption of morality policies . I posit that greater social and cultural accommodation of cannabis can explain permissive cannabis policies only to a certain degree. Licensing agencies collect information at the individual level. For the current project, I aggregate the number of issued licenses at the city level, which excludes any personal identification from the dataset . The reasoning behind aggregating data at the city level is that according to the Medical and Adult Use Cannabis Regulation and Safety Act , cities have the full power and authority to enforce cannabis regulation and complete responsibility for any regulatory function relating to the licensees within the city limits. Local jurisdictions decide whether cannabis businesses will be legal on their territories or not, define which types of cannabis activity to allow , and establish regulatory schemes for activities involving growing or selling cannabis.

Before applying for a cannabis license, an applicant has to obtain a permit from the city administration that would enable him/her to conduct commercial cannabis activity. The permit does not guarantee each applicant a cannabis license, but it gives him/her the green light to advance to the final stage and submit the application to a licensing agency. The dependent variable has three different measures: the total number of cannabis licenses, flood and drain tray the number of cultivation cannabis licenses , and the number of sale and distribution cannabis licenses . I suggest that factors explaining the permissiveness of local governments towards cannabis cultivation and cannabis distribution are not exactly the same. Cultivation primarily occurs in private spaces and thus is hidden from the public eye. On the contrary, retail is associated with public display: shop-windows, street signs, and adverThising boards make cannabis dispensaries visible and accessible. I expect that the public display of cannabis will be more stigmatized than its private cultivation. The normalization of cannabis is a gradual process, and we cannot expect it to progress at the same pace in different localities. But we can assume that cities whose residents supported cannabis legalization will be more likely to pass pro-cannabis laws. As seen in Tab. 2, 72% of California cities supported the legalization of cannabis in 2016, but only 45% of them legalized cannabis-related economic activities within their borders. Moreover, of those cities whose residents did not support Proposition 64, 22% eventually permitted cannabis companies, despite the lack of public support . There is an obvious gap between people’s preferences and governments’ actions, which should be explained. Before turning to the description of other independent variables, I should address the issue of moral hypocrisy. Greater cultural acceptance of cannabis does not necessarily translate into moral acceptance of its sale and use. In particular, we do not know whether people who supported legalization are amenable to cannabis dispensaries in their neighborhoods—i.e., we cannot exclude the NIMBY phenomenon . The general population may support the legalization of recreational cannabis for a variety of reasons. The willingness to legalize cannabis may follow a pragmatic logic: decriminalizing cannabis generates tax revenues, creates jobs, and diminishes law enforcement costs. People may also support legalization because it gives an opportunity to begin repairing the damages caused by the criminal justice system in the past. Moreover, it may be perceived as a progressive move that fits general liberalizing trends, including same-sex marriage, abortion, pre-marital sex, drinking, gambling, and so on. And yet, people may be moral hypocrites: they may support the idea of cannabis legalization and act in discord with it by opposing the location of cannabis dispensaries in their backyards. The statistical analysis cannot account for these nuances and, thus, simplifications are inevitable.

For the purposes of this analysis, “legitimacy” means tolerance of cannabis use rather than its total acceptance; it is what people are ready to declare publicly rather than act upon. Legitimacy is a necessary but not sufficient condition for legality. What other factors can explain the responsiveness of local governments to morally controversial issues?The analysis demonstrates that cities whose residents supported cannabis legalization are more likely to permit cannabis-related activities within their borders . It is not surprising since, as I mentioned above, 45% of cities supporting cannabis legalization allow legal cannabis companies. The main question is: What other city properties are associated with the adoption of procannabis legislation? Opinion polls show that the middle- and upper-class representatives, young adults, and non-Hispanic citizens support cannabis legalization at higher rates than other social groups. I ran a separate model regressing the percent of support for Proposition 64 on the index of economic prosperity, percent of people aged 20 to 29, and percent of the Hispanic population . The results confirm that the support of cannabis legalization is associated with a higher index of economic prosperity, a larger percentage of young adults, and a lower percentage of the Hispanic population. This association is significant at the 0.01 level. However, as we see in Table 4, cities that eventually allowed legal cannabis companies, on the contrary, are more likely have a lower index of economic prosperity , a lower percentage of young population , and a higher percentage of the Hispanic population . The disparity between the demand and supply offers an intriguing puzzle. Economically prosperous cities, on average, express higher support of cannabis legalization, but it does not mean that they are more likely to permit legal cannabis companies within their borders. Moreover, there are significant differences between licenses issued for sale and cultivation. Cultivation licenses are more likely to be issued in cities with a lower percentage of the young population, which can be explained by the fact that these are mostly rural remote areas, and young adults live in more urbanized places. Sale licenses are associated with three other factors: a higher percentage of the Hispanic population, a lower city’s fiscal score, and higher violent crime rates. There is substantial evidence in the findings that socio-spatial stigmatization of cannabis persists despite its legalization. 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.

Many studies provide evidence that the war on cannabis was a moral crusade that undermined the medical professionals, discredited scientific research, and spread fears among the population. In this constructivist approach to the drug problem, anti-cannabis rhetoric functions as a currency in political campaigns: the purpose of the war on cannabis was not to counter actual drug crime or drug abuse but to respond to white middle-class anxiety towards immigrants, minorities of color, or political activists. By performing a symbolic—rather than instrumental—role , anti-cannabis legislation drew a line between “normal” and “pathological” substances and between deserving and undeserving citizens. Sociology of deviance defines several essential characteristics of morality policies. First, moral laws are centered around controversial questions on which it is difficult to reach a compromise. Since different groups have opposing and even mutually exclusive concepts of virtue, moral issues tend to recur now and then and rarely come to an ultimate resolution. Second, moral laws seek to establish a set of values that dominate at their present time. According to James Hunter, laws are not just about what we can and cannot do; laws “contain a moral story that proclaims the ideals and principles of the people who live by them” . Through lawmaking, societies create “a particular nomos, a normative universe that draws distinctions, discriminates, judges, excludes and includes” . Third, passing moral laws involves the work of “moral entrepreneurs,” the rule creators and rule enforcers who invest their efforts in constructing a new meaning of goodness . Moral crusaders believe that what they do is good for others and that by improving morality, they help people live a better life . Moral entrepreneurs can have differing agendas—prohibitionist, abolitionist, humanitarian, traditionalist, liberal, etc.—but their ultimate goal is to change the existing rule. Fourth, morality policies are often difficult to enforce because the policy’s objective is symbolic and offers no clear operationalization . The application of moral laws does not affect actual behavior—for example, it does not decrease drug consumption rates—but instead shies it away from public display, encouraging the development of the black market We tend to think about moral politics as banning certain activities rather than allowing them.

This research, on the contrary, dry rack cannabis focuses on the legalization of cannabis as an example of permissive morality regulation. The moral dilemma that characterizes the current cannabis debate can be described as “social expectations vs. core individual rights.” As the history of cannabis criminalization shows, prohibition stood as a symbol of the general system of values with which the conservative white majority was identified. The Protestant ethics measured men’s moral worthiness in terms of productivity: any activity distracting an individual from being productive is a waste of time37 . Using cannabis for pleasure is immoral not only because it can affect an individual’s physical well-being but also because it represents a particular lifestyle and attitude toward work and social responsibilities. In turn, the legalization rhetoric centers on the discussion over the limits of state intervention in the private behavior of citizens . The US Constitution guarantees the right to privacy and non-interference by the state in personal matters, and the California Constitution lists a right to privacy among the inalienable rights .38 The history of cannabis criminalization shows that ideas and moral visions have been a decisive factor in the adoption of anti-cannabis legislation. Currently, California is in a transition period when cannabis use is slowly getting normalized in public perception but continues to be illegal and pathological from the federal government’s perspective. This ambiguity impedes the formation and establishment of a new universal meaning of legal cannabis. Analyzing the context of the war on drugs is important for understanding the departure point of this ideational change. However, as I argue in the next section, in order to apprehend today’s status of cannabis, we should turn away from the macro-level explanations and shift the attention to local actors and on-the-ground processes. The crime control paradigm is preponderant in the socio-legal literature on drugs. The social history of cannabis in the US is often recorded as a top-down, event-based analysis, which focuses on capstone legislations, elite actors, and political intentions. From this perspective, the war on drugs results from the deliberate actions of the state officials and the mass media against marginalized groups. Although these views are extremely valuable for understanding the deep historical forces of mass incarceration, the focus on top-down processes may sometimes overshadow other possible interpretations of the drug problem. By devoting much attention to the public debates over the dangers of cannabis, these studies tell us a great deal about political agendas but relatively little about the role of medical professionals, pharmacists, manufacturers, associations, businesses, schools, families, local authorities, cannabis users and distributors in defining cannabis.

Drugs are not only a criminal justice issue but also a societal problem, a medical problem, a moral problem, a market problem, and so on. As Joseph Spillane points out, much of the action happens “on the street, in the daily interactions among sellers, users, families, doctors, police, and jailers” . As I argue further, the prevailing orthodoxy often fails to understand criminalization as a complex dynamic process with several levels of action and thus casts aside many essential questions. One voice that is consistently missing in the constructivist approach to the drug problem is the voice of drug addicts and their immediate environments. As Michael Fortner argues, the existing perspectives on the war of crime tend to minimize the agency of African Americans who are typically portrayed as victims of the power of racial order and reactionary Republican politics. The New Jim Crow’s thesis focuses on white power and black powerlessness, which renders black politics invisible and obscures the causes of mass incarceration . According to Fortner, many scholars treat actual crime as fiction rather than lived experience. To cover the theoretical gaps, he investigates the role of the “black silent majority” in adopting punitive legislation, namely the Rockefeller drug laws in New York City in 1973. His study provides several critical analytical implications. First, we cannot study the drug problem out of context without exploring how local institutions and local processes influenced the framing of public concerns and policy responses . Second, aggregated staThistics give little information about attitudes to crime and drugs. As Fortner demonstrates, white districts in New York City experienced significantly lower violent crime rates, drug addiction, and deaths due to drugs than black areas. The devastating effect of the drug and crime problem on black communities raised great concerns among the black population and led to higher support of anti-drug and anti-crime policies. Evidence from California reveals similar patterns: in the 1970s, whites in Los Angeles were more concerned with pollution and noise than crime, while black citizens listed better crime control as the number one issue . This picture contradicts the popular notion in socio-legal studies that whites supported punitive policies more than blacks.

By focusing on white victimization and black criminalization, researchers neglected the activism of the urban black middle class, which created incentives for local politicians to respond to demands for greater punitive policies .James Forman advances a similar argument showing that working- and middle-class black communities did not support the decriminalization of cannabis. The history of cannabis is often presented as a part of the war on drugs. However, as Forman righty notices, the anti-drug campaign aimed at heavier drugs. Nixon’s declaration of drugs as the nation’s largest enemy coincided with the first attempts to decriminalize cannabis at the state level by making possession of small amounts of cannabis a civil fraction rather than a criminal offense. The widespread knowledge about the minimal risks of cannabis use boosted decriminalization movements in many states. Since the pro-cannabis movements’ leaders were overwhelmingly white, roll bench cannabis decriminalization was framed as a question of civil liberties and individual autonomy rather than racial justice. In Washington, D.C., the black community took cannabis decriminalization with skepticism as a policy that protects young whites and oppresses young blacks. White teenagers could smoke cannabis without jeopardizing their future because their middle-class cocoon could shield them from the consequences of drug use. But, poor black teenagers had much less room for error because they would risk graduating from school or finding a job . In other words, to the African American communities, the drug problem was not just a hypothetical threat. The 1960s the heroin epidemics had instilled a real sensibility for the drug problem and taught the members of black communities that addiction could destroy families, schools, and entire neighborhoods. Another particularity of the crime control perspective is the reliance on law and legislation as a sole historical marker of change . The “told” history of cannabis prohibition is often plotted as a sequence of events leadings from one capstone legislation to another. Such an approach overemphasizes the role of formal laws in triggering social and institutional change. In reality, legislation itself does not cause change; its scope, significance, and relevance are determined in the process of interpretation and further implementation. To understand when, where, and how change happens and what role the law plays in the process, we need to look at local phenomena and on-the-ground practices. The event-based perspective fails to recognize that the passage of legislation is the continuation of a process that begins before and lasts after its adoption .The legal prohibition of cannabis is not merely a political project organized by a cohesive group of elite actors but a multilevel, complex, and dynamic social process. Formal legislations affirm rather than cause social and cultural changes, especially when the primary function of law is symbolic. For example, the legalization of the medical use of cannabis in California in 1996 did not affect cannabis arrest rates. The number of arrests for cannabis possession was steadily growing until 2010 and decreased five-fold in one year after the passage of SB-1449, which reclassified the possession of small amounts of cannabis as a misdemeanor . Thus, the legalization of medical cannabis symbolized the public affirmation of new social norms and ideals, but it did not change law enforcement practices. The SB-1449, on the contrary, affected people’s behavior in a more direct and instrumental manner, but its adoption would not be thinkable without the preceding cultural shift. On Fig. 1, the legalization of the medical use of cannabis in California would be a constructed event, while the adoption of the SB-1449 would be on the level of observable occurrences that followed the event. 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 longlasting 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, political threats, and discursive opportunities.