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. Contract farming carries with it numerous risks that compromise the long term well-being of producers themselves. Furthermore, vertical grow room 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, 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.Farm Service Agency Lending Programs and the Farm Bill 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 com-position 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. It was not until early 2012, however, that federal regulations were made consistent with legislative changes. Because of the historic discrimination against farmers of color, and other structural barriers to land ownership for people of color, cannabis racks the population of agricultural producers is already heavily skewed toward white men. Thus, such measures to guarantee FSA committees are representative of agricultural producers in any particular region fall short in their attempts to address the acutely historical causes and outcomes of structural racialization that have upheld white land ownership in particular.The second major channel among the Farm Bill and other federal food and agricultural policies that have played a historic and ongoing role in structural racialization is the Farm Bill’s commodity programs, which have undergirded white farmland ownership at the expense of farmland ownership by people of color. While the FSA lending programs have upheld white farmland ownership amidst increasing consolidation and specialization, the Farm Bill commodity programs uphold white farmland ownership by way of increasing consolidation and specialization. Specifically, increasing agricultural specialization and consolidation—due in part to federal agricultural policy and corporate control, and increased mechanization, fertilizer use, and genetic modification—have upheld white farmland ownership because of both the historic access to prime farmland afforded to white farmers as well as the commodity support programs that are most applicable to the crops grown on such farmland. Limited access to prime farmland, and thus limited access to commodity support programs in conjunction with limited access to federal lending programs as outlined above, has compromised the possibility of farmland ownership for people of color. Historically, people of color were not only excluded from land ownership, but when land ownership was in sight, access to the best farmland was largely out of reach. 

After Emancipation, for example, chronic indebtedness kept the primarily Black population of sharecroppers tied to the same land, neither able to resist the demands and directions of their employers nor able to accrue enough wealth to buy their own land. Although some were able to garner the financial means to break such predatory cycles of debt and purchase their own land, few Blacks could afford to achieve ownership of land with the richest soil, including the notorious “Black Belt” itself, between Georgia and Arkansas. Rather, most Black-owned farms were on more marginal lands in the upper and coastal South, where Black farmers often had to supplement the low yields and profits with sharecropping on more substantial white-owned lands or with outside labor. The best opportunities available to farmers of color, Black or otherwise, on such land tended and remain to be specialty crops and livestock. As of 2012, for example, 63.6% of Asian American farmers, compared to only 8.5% of white farmers, grew fruits and vegetables. Moreover, as of 2012, 46.8% of Black farmers, compared to 29.1% of white farmers, raised beef cattle. Conversely, as of 2012, white farmers grow 98.6% of all grain and oilseed crops. Furthermore, livestock and specialty crops, including fruits and vegetables, are not eligible for these commodity programs, leaving farmers of color with less government support. Specifically, the current agriculture funding structure, from research funding to crop subsidies, and to conservation programs, as will be outlined in Part IV, is heavily weighted to support the large-scale production of commodity crops—among them, wheat, corn, soybeans, and others—crops that are primarily grown by white farmers on the highest quality farmland. Thus, as a result, as of 2012, 40% of white farmers receive government payments while only 30% of Black farmers receive government payments. Furthermore, white farmers that do receive payments receive an average of $10,022 per farm, while Black farmers that receive payments receive an average of $5,509 per farm. Farmers of color, and new immigrant farmers in particular, often grow high-value, labor-intensive horticultural products on small plots of land, which also receive less government support. In 2012, small-scale farmers received an average of $5,003 per farm while large-scale farmers received an average of $47,732 per farm. Perhaps most significantly, as of 2012, 97.8% of all government payments are given to white farmers. According to a 2012 USDA Economic Research Service study, the distribution of commodity-related payments—including federal crop insurance indemnities—to US farmers has shifted toward larger farms as part of the trend of increasing consolidation of farming operations, ensuring that those who have historically benefited from exclusionary practices benefit further. Significantly, because the operators of larger farms generally have higher incomes than those of smaller farms, the shift of commodity-related payments to larger farms led to a shift of payments to higher income households. For example, in 1991, households with incomes over $54,940 received 50% of commodity payments, households with incomes greater than $115,000 received 25% of commodity payments, and households with incomes over $229,000 received 10% of commodity payments. Since then, the distribution of payments has increasingly favored higher income households: by 2009, households earning over $89,540 received 50% of commodity payments, households with incomes greater than $209,000 received 25% of commodity payments, and households with incomes of at least $425,000 received 10% of commodity payments.

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, 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, pipp rack 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 fear for their physical safety and safety of their family members if they are not able to repay their debts.

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. Toward this end, employers go to great lengths to unlawfully exclude qualified US workers in favor of H-2A workers, many of whom have themselves migrated to the United States during prior seasons. For example, employers schedule interviews at inconvenient times or locations; hire too early in the season, lead workers to arrive for work when there is none; limit their hours in order to discourage them from continuing to work; use employment contracts that demand that workers forfeit their right to sue a grower for lost wages and/or other illegalities; and impose productivity quotas and other unrealistic work demands on employees. These practices greatly discourage US workers from applying to these jobs, which then allows employers to “legally” hire H-2A workers. Additionally, the profits reaped by large agricultural employers and by corporations at all levels of the food system not only come at the expense of the food system worker’s livelihoods and US job loss, but are also subsidized by taxpayers themselves. For example, Walmart, which sells 25% of all the groceries in the United States and is the largest employer in the US and world, has among the lowest wages across the retail industry. Walmart workers cost US taxpayers an estimated $6.2 billion in public assistance that would counteract the consequences of their low wages, including SNAP, Medicaid and subsidized housing. Because 58% of food system workers surveyed reported having no health care coverage, more than one-third of workers surveyed have used the emergency room for primary care, which taxpayers help cover. Finally, corporations like Walmart are able to determine wages and benefits for workers throughout their entire supply chain, given their massive procurement power and ability to dictate purchasing prices to its suppliers.

This pressure and influence forces suppliers to lower their worker’s wages, multiplying the number of workers robbed of fair and livable wages and taxpayer subsidization of corporate profits. In short, when food system workers require public assistance, the onus rests on taxpayers and the federal government, rather than on those that are responsible for creating these unhealthy outcomes—corporations. After over thirty years of liberal trade policies beginning in the late 1970s and early 1980s, many developing countries have been left with a great dependence on the global market for basic food and grains. Developing countries had yearly agricultural trade surpluses of $1 billion in the early 1970s. Yet by 2000, the food deficit in such countries had grown to $11 billion per year. At the height of the 2007–2008 global food price crisis, Low-Income Food Deficit Countries import bills reached over $38 billion for basic cereal grains. Such systemic vulnerability is, in part, a result of international finance institutions, structural adjustment, free trade agreements, and a broader divestment of the state from agricultural development. Furthermore, pipp racking system not only are overproduction and US food aid to blame, but also corporate actors use such international crises as opportunities to make additional calls for emergency aid coupled with further trade liberalization and increased investment in agricultural productivity. For example, although the 2014 Farm Bill authorizes $80 million annually for the Local and Regional Procurement Program, which encourages greater use of food that is locally or regionally grown for food aid, it pales in comparison to the $1.75 billion Food for Peace Title II through which United States Agency for International Development provides food assistance. Furthermore, foreign economies are undermined not only by such efforts that directly shuttle surplus and heavily subsidized commodities—produced for the benefit of corporate entities—to developing countries, but also by production support programs themselves, such as commodity payments or crop insurance. For example, a 2012 International Centre for Trade and Sustainable Development report found that the shift from direct payments to crop insurance support for farmers is likely to have far reaching effects on global trade and prices because of the anticipated change to cropping patterns. Specifically, the likelihood that the new programs will influence planting decisions is greatly enhanced because payments in all the new programs are calculated using actual planted acreage. Ultimately, if planting decisions are influenced enough, then program-induced changes in US crop acreage will be reflected in trade flows that have the potential to harm farmers in developing countries and cause fluctuations in global food prices. Academic Research and Development: One major way corporations profit and exert their control with regard to education, research, and development is their influence over academic research and development. Agricultural research in the United States is carried out primarily by three entities: the federal government, largely through the US Department of Agriculture; academia, primarily through land-grant universities; and the private sector. Over the past several decades, corporate interests have co-opted publicly-oriented agricultural research and land-grant university research efforts in particular. The federal government created land-grant universities in 1862 by deeding tracts of land to every state to pursue agricultural research to support agricultural production in the United States. Although public investments have maintained agricultural research since the creation of these universities, over recent decades public funding has stalled, prompting land-grant universities to appeal to agribusiness to remedy such financial shortcomings. Significantly, the landmark 1980 Bayh-Dole Act pushed universities to take this particularly entrepreneurial role, generating revenue through producing patents from which the private sector could profit. The Bayh-Dole Act, as part of the neoliberalization of science and academic research itself, prompted greater industry influence over land-grant research, as university research agendas became oriented toward the needs of corporate partners. Major agribusiness donors to land-grant universities across the United States, including Syngenta, Monsanto, PepsiCo, Nestle, Dow Agroscience, Chevron, DuPont and others, now push research carried out by faculty and students toward developments in bio-fuels, commodity crops research, genetically engineered foods, and other areas of interest. Land-grant universities today not only carry out corporate-directed research but also depend on agribusinesses to underwrite research grants, endow faculty chairs, sponsor departments, and finance the construction of new buildings. Even USDA research and USDA-funded research itself reflects corporate interests. The USDA spends roughly $2 billion per year on agricultural research, which goes toward funding USDA researchers and researchers at land-grant universities. This money, however, is largely directed toward a corporate-friendly industrial agriculture research agenda: the National Academy of Sciences found that USDA research prioritizes commodity crops, industrialized livestock production, technologies geared toward large-scale operations, and capital-intensive practices. The Farm Bill does not prioritize funding for more sustainable farming programs, with programs such as the Organic Agriculture Research and Education Initiative and Specialty Crop Research Initiative accounting for only 2% of the USDA’s research budget. Most research funding is directed toward commodity crops research. In 2010, for example, the USDA funded $204 million to research all varieties of fruits and vegetables, and spent $212 million to research just four commodity crops: corn, soybeans, wheat, and cotton. Seed Patents: Another major way private industry continues to profit and exert their influence vis-à-vis relations of education, research, and development, is seed research and patents. Since the early 1980s, the global seed industry has grown substantially and is now worth an estimated $44 billion and is expected to grow to an estimated $85 billion by 2018. The cumulative effect of seed legislation has facilitated the massive consolidation of corporate power, thus securing corporate control of one of the most crucial agricultural inputs. This history of seed legislation began shortly before the New Deal, beginning with the US Plant Patent Act of 1930 and continued with the 1970 Plant Variety Protection Act. Significantly, seed legislation did not move into the judicial system until the 1980 Supreme Court decision Diamond v. Chakrabarty, which laid the legal groundwork for the privatization and commodification of the genetics of seeds. 

Given that about 7.1% of pork with be produced Prop 12 rules , this implies about 0.54 million of 7.6 million sows in North America will be confined under California’s housing standards. California pork consumers will pay about $188 million annually to provide four square feet more per sow on average for about 540 thousand sows in North America. Thus, California buyers of covered pork will pay about $87 per square foot of additional housing space. The passage of Prop 12 in California by a significant majority indicates citizens’ interests in improving animal welfare. Regulations such as Prop 12 and its counterparts in other states such Local jurisdictions have increasingly imposed regulations on agricultural production processes within the jurisdiction to address issues associated with pollution, animal welfare, and farm worker health and well-being. Several papers have studied the impacts of such regulations, with the work summarized by Sumner . These regulations differ considerably in their impacts from those that restrict farm production practices for products sold within a local jurisdiction. The first type creates heterogeneous production costs and alters the comparative advantage of different production regions but generally does not affect downstream operations. This paper explores the economic implications of the latter group of regulations, with specific application to the impact of California Proposition 12 on the North American pork supply chain. Key innovations of the model are allowing heterogeneity in the costs of farms to meet the mandate and incorporating that a mandate in many cases will only apply to a portion of the output of the live animal. The model incorporates capital conversion for compliance at farms and variable production proportions between covered and non-covered pork in processing farm raw products into finished consumer products.

The model shows how these aspects interact and drive substantial price and quantity adjustments along vertically linked markets and across geographically different markets. Simulations show that, rolling benches for growing despite significant industry opposition to Prop 12, its mandates do not impose much negative total effect on hog producers in North America. Most firms that elect to comply with Prop 12’s mandates will increase profits, and losses to non-compliers are slight. Prop 12 causes moderately higher prices in California for covered pork products and generates a consumer welfare loss of about $188 million annually.Prop 12 will not make stall housing operations adopt California’s standards. These pork products will be diverted for the California market under Prop 12. Because California’s standards are stricter than typical group housing, breeding pigs confined in converting operations will have slightly more space than before. Prop 12 and, more generally, the regulations on products sold in local jurisdictions represent only one policy instrument to improve welfare for farm animals. To illustrate this point, I considered a simple alternative policy under which the California government would raise a general fund to directly subsidize farms that convert their housing practices. I showed that, for the same cost to California, this alternative policy could incentivize conversion of about three times as much sow housing to compliance with Prop 12 regulations as Prop 12 itself will achieve if it becomes fully implemented. This example illustrates that Prop 12 and, more broadly, regulations imposed at the point of purchase are likely not efficient ways to influence conventional farming practices.Google Surveys provides inferred respondent characteristics based on internet use rather than reported demographics by respondents to represent the general population of internet users. Google collects demographic information of their users when they make an account or while they use Google’s service. Google identifies general demographic information about websites when sufficient Google users visit those websites.

Given this demographic information specific to websites, Google infers visitors’ demographic information. Google Surveys cannot collect data from non-internet users, but the share of internet users in the U.S. population was about 91% in 2020 . Google Surveys cannot obtain responses from non-internet users, but it is not rare that responses from part of the population are not collected in survey studies. For example, survey studies often collected responses from only college students , people in a local region , and customers in a store . Several papers have found evidence that the inferred demographics of Google Surveys provided representative samples of the U.S. population and reliable estimation results . Table 5.3 reports respondent shares by demographics. The numbers in parentheses are the corresponding 95% confidence intervals. Table 5.3 reports the results by three different samples: a subsample including only respondents who selected “I don’t buy carrots,” a subsample including only respondents who did not select that option, and the full sample. Table 5.3 shows important patterns in the data about inferring demographics. Google inferred gender, age category and region based on search patterns and other information about the URL of the respondent computer. The shares of respondents without inferred demographic information was about 20% for gender and age, but negligible for region. The gender was not inferred for about 18% of respondents in the full sample. The age group was not inferred for about 20% of respondents. The geographical location was inferred for more than 99% of respondents. The shares of “not inferred” are slightly higher in the respondents selecting “I don’t buy carrots” than the other respondents. Table 5.4 excludes respondents for whom Google Surveys did not infer the full set of demographics, presumably because they did not have sufficient information on that respondent. The overall sample shares are similar to the U.S. population shares. The share selecting “I don’t buy carrots” is more male and younger than those who responded to the carrots purchase choices and the U.S. population. The gender pattern in the total sample is very similar to the U.S. population. The age range is slightly more middle aged with fewer 25-34 and fewer over 65. In addition, a smaller share of respondents is in the Northeast and South and more are in Midwest relative to the U.S. population. Given the findings of this section, I consider the following points in the model specification of Chapters 6 and 7. First, I include demographic variables as explanatory variables. Second, I use sampling weights based on demographic groups to make the sample represent the population.

Google Surveys also provide information about how long individual respondents elapsed between when the survey was opened and when it was completed. I explore the response time because several prior studies using online responses found that the inclusion of response times as a control in statistical estimation reduced random responses and standard errors of estimated parameters . The concern is that respondents that are too quick may not be actually reading the questions, and respondents that take too long were likely interrupted in their responses. Table 5.5 reports descriptive statistics on the response time by the WTP question types, the choice of “I don’t buy carrots,” and whether inferred demographics were provided. The table includes ten categories. Three features are common across the categories. First, the average response time is slightly less than 30 seconds for most categories. Second, the standard deviation within each category is high relative to the average for all the categories in the table. Third, the min and max values are substantially different from the average in each category. Three points are noticeable in comparison with categories. First, on average, respondents took about the same time for the Yes-No questions as the Multiple-Choice questions . Second, respondents choosing “I don’t buy carrots” tended to spend less than the other respondents. Third, on average, respondents without inferred demographics spent more than those with demographics. Based on the findings of this section, I consider the following model specification in Chapters 6 and 7. First, I include the response time as an explanatory variable in regressions. Second, I compare the models with and without outliers in response time. The outliers include both those with a very short response time and a very long response time because the relationship between response time and response reliability is possibly not linear . A very long response time possibly indicates insufficient attention to the survey because respondents often do multiple activities simultaneously on the internet.Specifically, corporate control refers to control of political and economic systems by corporations in order to influence trade regulations, tax rates, and wealth distribution, among other measures, vertical air solutions and to produce favorable environments for further corporate growth. Structural racialization refers to the set of practices, cultural norms, and institutional arrangements that are reflective of, and help to create and maintain, racialized outcomes in society, with communities of color faring worse than others in most situations. In this light, the production of racial/ethnic, gender, and economic inequity in the United States is more so a product of cumulative and structural forces than of individual actions or malicious intent on behalf of private or public actors. In order to challenge and eliminate corporate control and structural racialization in the United States, therefore, it is necessary to analyze the ways that public and private institutions are structured. It is also necessary to analyze how government programs are administered and operate in ways that reproduce outcomes that marginalize low-income communities, women, and communities of color in terms of health, wealth, land access, power, and degree of democratic influence. Additionally, as this report aims to do, it is crucial to analyze the genesis and formation of critical institutions and structures themselves.Therefore, the US Farm Bill—the flagship piece of food and agricultural legislation since its inception in 1933, which informs the heart of public and private policies that make up much of the US food system—is the subject of this report. This report is of particular importance now for two reasons. First, the Farm Bill will be under consideration again in 2019, yet there is no comprehensive critique of the Farm Bill that addresses its underlying contradictions, particularly with regard to racial/ethnic, gender, and economic inequity. Second, it is imperative that campaigns by grassroots, community, and advocacy organizations—generally most active during the period of Farm Bill negotiations in Congress—have enough time to gather adequate information and conduct in-depth analysis for targeted yet comprehensive policy change. As such, the timing of this report is also imperative for coalition-building efforts and the growth of an effective broad-based food sovereignty movement.Corporate consolidation and control have become central features of the US food system, and of the Farm Bill in particular. As of 2014, large-scale family-owned and non-family-owned operations account for 49.7% of the total value of production despite making up only 4.7% of all US farms. As of 2013, only 12 companies now account for almost 53% of ethanol production capacity and own 38% of all ethanol production plants. As of 2007, four corporations own 85% of the soybean processing industry, 82% of the beef packing industry, 63% of the pork packing industry, and manufacture about 50% of the milk. Only four corporations control 53% of US grocery retail, and roughly 500 companies control 70% of food choice globally. At every level of the food chain, from food production to food service, workers of color typically earn less than white workers. For example, a majority of farm workers who receive “piece rate” earnings , and many of whom are migrants from Mexico, frequently earn far less than minimum wage—an exploitative practice deeply tied to immigration policy, as elaborated upon below. On average, white food workers earn $25,024 a year while workers of color make $19,349 a year, with women of color, in particular, suffering the most. Furthermore, few people of color hold management positions in the food system, while white people hold almost three out of every four managerial positions. One result of this racial disparity in food system labor is that non-white workers experience a far greater degree of food insecurity than their white counterparts.Food insecurity in the US disproportionately affects low-income communities and communities of color, and these communities are over represented in the lowest-paying sectors of the labor market. For example, as of 2013, 14.3% of US households—17.5 million households, roughly 50 million persons—were food insecure. The report also found that the rates of food insecurity were substantially higher than the national average among Black and Latino/a households, households with incomes near or below the federal poverty line, and single parent households.

Finally, sowmortality showed a significant increase in t + 1 with one more sow death than during the baseline period . In general, the indicators confirm that a PRRS outbreak affected several production stages for an extended period of time .The decline in weaned pigs marketed in week t − 1, although statistically insignificant, as well as changes in some performance indicators , suggest that the outbreak may have started in week t − 1, one week before it was reported. We therefore developed an alternative estimate of production losses that can be compared to the estimated loss if the outbreak is assumed to begin in week t. Eliminating t − 1 from the preoutbreak period led to estimation of a slightly higher baseline and, as a result, to a higher estimate of PRRS losses. Nonetheless, the difference between this estimate and our primary estimate is very small. Our primary estimate is that PRRS reduced weaned pig production per farm by 7.4% on an annual basis, leading to a decrease in output value per sow year of $86.6, or $367,521 per farm year for an average sized farm. If instead we assume the outbreak began in t −1 , the estimated reduction in weaned pig production was 7.6%, or $88.8 less per sow year and an average revenue loss of $376,773 among the farms studied.We analyzed the impact of a PRRS outbreak on weaned pig production in a set of sow farms that are part of the same swine firm in the US. We estimated the time profile of disease effects, identifying the weekly changes in output relative to a pre-outbreak baseline. We find that PRRS caused a 7.4% decline in production value measured over a one-year period. Correspondingly, PRRS reduced production by 1.92 weaned pigs per sow when adjusted to an annual basis.

This decrease is substantially larger than the 1.44 decrease of weaned pigs per sow/year reported in another study . We note that total losses due to PRRS are likely to be greater than the revenue losses estimated in this study, how to cure cannabis fast as total losses must include cost increases associated with the disease, e.g., an increase in management expenses, bio-security investments, additional feed and veterinary inputs, plus a possible decrease in the weight or in the sales price of piglets . We found that weaned pig production declined in week t − 1, although statistically insignificant, as did several performance indicators. The data suggest that the average PRRS outbreak in this set of farms began at least one week before it was announced. This delay may be explained, at least in part, by the inability of producers to detect PRRS until animals begin to show explicit clinical signs, as well as the additional time needed to test and confirm the disease. The lag between the outbreak of disease and the appearance of clinical signs may be longer in farms using vaccination programs, as in our sample, where clinical signs may be subtle . It seems likely that some weaned pigs being shipped by these farms in week t − 1, when the disease was almost certainly present in these farms, but as yet unannounced, were infected with PRRS. The relatively slow identification of the disease means that animal movements out of infected premises must be a common source of disease spread. This is particularly important in sow farms that deliver wean pigs to different swine grower facilities each week. The rise in abortions was the strongest signal of PRRSV activity in our data. Increased surveillance, particularly to rising abortions, may allow farms to identify PRRS more quickly. Abortions were rising in the several weeks prior to the reporting of the outbreak in some of the farms in the sample.

Abortions rose significantly in t − 1 and then increased sharply in week t. The number of abortions declined rapidly and fairly monotonically following week t, with a slight uptick in weeks t + 10 to t + 13, and recovered to the baseline level by about week t + 20. Thus, to the extent that abortions are an indicator of an active virus in the sow herd, circulation of the virus appears to have ended about 20 weeks after it was reported. The uptick in weeks t + 10 to t + 13 suggests that the disease may have been infecting other susceptible cohorts of sows within the farms two to three months after the initial outbreak. The length of PRRS outbreaks, as well as their effects over time, is highly variable. For example, one study estimated effects of an outbreak during 12 weeks post detection , while another indicated that production of negative piglets was reached 27 weeks post infection . Our results demonstrate that PRRS has a negative effect on weaned pig production for a longer time than previously estimated. In our study, the estimated means of weaned pig production remained below the baseline throughout the 35 weeks that we are able to observe following the outbreak. Although the production of weaned pigs recovered to a level that is not significantly different from the baseline, we cannot definitively declare that there was no effect beyond week t + 35. Nonetheless, it appears that any continued effect is likely to be very small relative to the large effect occurring before week t + 35. We detected a consistent decrease in production until the 5th week after the outbreak report, followed by a non-monotonic recovery. All performance parameters followed a similar non-monotonic recovery pattern. Each indicator manifested a sharp worsening after the outbreak, followed by partial recovery and at least one mild period of deterioration. The dynamic up-and down impact of PRRS on weaned pig production was surprising. The precise causes are unclear, but the disease may spread more slowly and unevenly through the sow herd than anticipated, particularly on large units with multiple cohorts, in addition to possible incoming flows of replacement sows.

This effect might also explain the longer period of recovery in our study, versus another study that found production returned to the baseline in 16.5 weeks for cohorts vaccinated with an MLV and using herd closure as a control strategy . Other performance indicators provided consistent signals. Pre-weaning mortality increased sharply in weeks t − 1 to t + 1, declined to pre-outbreak levels by t + 10, and then oscillated about that level until about t + 24. Sow mortality increased in week t + 1 and remained above baseline levels until week t + 5. The increase in sow mortality could affect the age structure of the herd and consequently its production. Stillbirths increased until week t + 12, indicating that some infected sows carried damaged fetuses to birth. The number of stillbirths remained elevated through t + 36, suggesting that infected sows may have a higher probability of producing stillborn piglets for more than one pregnancy. The failure to conceive was followed by repeated services, which must have contributed to the lag in weaned pig production in later weeks. The numbers of pigs aborting or dying indicated that PRRS had its strongest effects on fetuses. PRRS kills relatively few sows and piglets, though the economic damage from sow mortality and/or their subsequently reduced productivity is important. Information regarding the strains of PRRS virus that affected each farm was not available for this study, as systematic sequencing of PRRS virus following outbreaks is still scarce. More than one strain might affect a given area, although in general genetic variation is more related to temporal rather than spatial variation . Using a sample of 16 farms may help capture the variability of PRRS outbreaks in the industry, assuming different strains may be affecting different farms. According to a number of studies, no vaccine prevents PRRS infection, but vaccination may reduce the risk of infection and may also reduce the intensity of outbreaks by reducing the amount of virus excreted by ill animals . Therefore, our results may show smaller damages than those that would be obtained for farms that do not vaccinate. Similarly, because the farms analyzed in this study belong to a firm with standardized protocols for disease management, our measure of PRRS’ impact could be smaller than would be measured on farms with poorer protocols. We developed and used a straightforward approach to quantify the dynamic effect of PRRS on weaned pig production within sow farms. We found that PRRS decreased weaned pig production for at least 35 weeks among the firms studied. The magnitude of PRRS’ impact, vertical growing weed as expressed in the duration and magnitude of the output decline, were both greater than anticipated. We found that recovery oscillated about a rising trend, i.e., recovery does not depict a clear monotonic increase in production, suggesting that farms suffered from a continuing circulation of the disease within the herd and/or a lingering effect on sows and piglets. Analysis of the underlying performance indicators provided additional insight regarding how PRRS affects farm output over time. Previous studies have utilized numerous assumptions to develop estimates of the total annualized losses to the swine industry due to PRRS . We have not attempted to replicate those studies. However, our results suggest PRRS may cause significantly higher losses on sow farms than has been estimated previously. Further, we believe that the losses identified in our farm sample are likely to be smaller than those on the average sow farm infected with PRRS. Nonetheless, we found substantial variation in performance among even a set of relatively standardized 16 farms. There is thus need for caution when using simple averages, as we often have done, rather than distributions across farms.Food companies have increasingly introduced products featuring farm practices as product attributes, with organic practices representing a leading example.

About 1,400 new organic products were introduced in 2009 and 3,000 in 2016 . To contribute to understanding the organic market, I explore econometrically buyer willingness to pay for carrots grown with organic practices relative to conventional carrots. I also export the demand for convenience and processing practices by exploring willingness to pay for fresh cut carrots relative to full sized carrots. Some food processing and marketing companies supply food products only from farm outputs produced with certain farm practices. For example, McDonalds and Walmart, have announced that within the next decade they will buy, use or sell only cage-free eggs . As of May 8, 2016, over 160 prominent food companies had announced that they will use only cage-free eggs, most by 2025 . Although not generally practiced by major retailers, many specialty markets and restaurants offer only or primarily organic food products.Governments also contribute to the demand shifts away from once conventional food products. For example, several U.S. states have introduced mandatory rules to eliminate conventional eggs from the in-state market. For example California and other states, including Massachusetts, Michigan, Oregon, and Washington, passed such laws . California has implemented mandatory cage-free housing for eggs consumed in California starting January 2022 as a part of the implementation of Proposition 12 . My model of government restrictions on food products that may be sold based on farm practices, which is applied to California’s Proposition 12 rules for pork products, shows how specific features of regulations affect market outcomes. Such product regulations may be imposed only on buyers within a specific jurisdiction but apply to farm practices outside that jurisdiction. Such regulations seem to be increasingly common and controversial, as reflected by the Hog industry challenge of Prop 12 before the U.S. Supreme Court . However, economists have not fully explored their impacts on prices and economic welfare, either within or beyond the regulating jurisdiction.The Prop 12 regulations on pork products allowed for sale in California specify mandates about how the breeding pigs are housed. The housing rules apply to sows that farrow pigs that produce pork to be sold to buyers in California. My model incorporates four empirical and regulatory features that determine economic impacts: California comprises about 9% of the market for North American pork; The regulations cover only some of the pork products from each hog. When a fraction of production becomes California compliant, the converting farms incurconversion costs and higher ongoing production costs; Segregation and traceability along the supply chain of hogs and pork destined for California is costly; and The quantity demanded for covered and non-covered pork products respond to relative prices, which are affected by costs of production, and pork demand may respond directly to the farm practice mandate.

These scenarios were then enacted using three Farm Personas. Much agricultural and environmental assessment literature utilize case studies and scenarios as a means to demonstrate problem areas, exemplify good practices, to provide guidance to the agricultural community, and to create public awareness. These materials provided supplements to the data and findings presented in Chapters 3 and 4 and were valuable substitutes for domain-expert guidance.The structure of each persona-scenario set, as articulated for the scenario-based evaluation, is outlined in Table 6.7 . The subsequent scenarios describe sustainable agricultural practices and environmental assessments that involve modeling farm components, activities, resources, and data. An overview table containing all persona-scenario sets is available in 6.8 .Scholars have published extensively on the multifunctional benefits of urban agriculture including: promoting urban sustainability, reducing air and water pollution, building social cohesion, promoting community health and nutrition, teaching food literacy, and creating radical economic spaces for resistance to the capitalist political economy and structural inequities embedded in the “neoliberal city” . Despite growing evidence of these diverse health, education, and environmental benefits of urban agriculture, these vibrant spaces of civic engagement remain undervalued by city policy makers and planners in the United States. Thriving urban farms and gardens are under constant threat of conversion to housing or other competing, vertical grow rack system higher-value land uses due to rising land values, and other city priorities.

This land use challenge and threat to urban farm land tenure is especially characteristic of U.S. cities like San Francisco, one of the most expensive land and housing markets in the country. Under the current urban agriculture paradigm in the U.S., food justice scholars and advocates either try to quantify and highlight the multiple benefits of UA  or pursue a critical theoretical approach, arguing that urban agriculture can yield unfavorable results if pursued without an equity lens, especially in cities with intense development pressures and gentrification concerns . A productivist focus is problematic, because, while urban agriculture can be an important component of community food security, its other social and ecological benefits are just as, and sometimes more, significant . In this article, we suggest that the current debates around “urban agriculture” in the U.S. often lead to an unhelpful comparison with rural farms regarding yield, productivity, economic viability, and ability to feed urban populations, most notably in the policy arena. Defined in these ways, the radical, transformative potential of urban food production spaces and their preservation often gets lost or pushed to the side in city planning decisions in metropolitan regions such as the San Francisco Bay Area, where the threat of displacement is ubiquitous given high levels of economic inequality and extreme lack of affordable land. In order to facilitate what scholars such as Anderson et al. 2018a refers to as the “agroecological transition,” already underway in many urban food ecosystems around the globe , we argue that applying an agroecological approach to inquiry and research into the diversity of sites, goals, and ways in which food is produced in cities can help enumerate the synergistic effects of urban food producers. This in turn encourages the realization of the transformative potential of urban farming, and an articulation of its value meriting protected space in urban regions. Urban agroecology is an evolving concept that includes the social-ecological and political dimensions as well as the science of ecologically sustainable food production .

UAE provides a more holistic framework than urban agriculture to assess how well urban food initiatives produce food and promote environmental literacy, community engagement, and ecosystem services. This paper presents a case study of 35 urban farms in San Francisco’s East Bay in which we investigated key questions related to mission, production , labor, financing, land tenure, and educational programming. Our results reveal a rich and diverse East Bay agroecosystem engaged in varying capacities to fundamentally transform the use of urban space and the regional food system by engaging the public in efforts to stabilize, improve, and sustainably scale urban food production and distribution. Yet, as in other cities across the country, urban farms face numerous threats to their existence, including land tenure, labor costs, development pressure, and other factors that threaten wider adoption of agroecological principles. We begin by comparing the concepts of UA and UAE in scholarship and practice, bringing in relevant literature and intellectual histories of each term and clarifying how we apply the term “agroecology” to our analysis. We pay particular attention to the important nonecological factors that the literature has identified as vital to agroecology, but seldomly documents . We then present findings from a survey of 35 diverse urban farm operations in the East Bay. We discuss the results, showing how an agroecological method of inquiry amplifies important aspects of urban food production spaces and identifies gaps in national urban agriculture policy circles. We conclude by positing unique characteristics of urban agroecology in need of further studies and action to create equitable, resilient and protected urban food systems.Agricultural policy in the United States is primarily concerned with yield, markets, monetary exchange, and rural development. The United States Department of Agriculture defines agricultural activities as those taking place on farms. Farms are defined as “any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the year” . Urban agriculture has been proliferating across the country in the last decade on both public and private lands, as both for-profit and nonprofit entities, with diverse goals, missions and practices largely centered on food justice priorities and re-localizing the food system.

Yet U.S. agriculture policy has been struggling to keep up. In 2016, the USDA published an Urban Agriculture Toolkit, which aims to provide aspiring farmers with the resources to start an urban farm including an overview of the startup costs, strategies for accessing land and capital, assessing soil quality and water availability, production and marketing, and safety and security . The 2018 U.S. Farm Bill provides a definition of urban agriculture to include the practices of aquaponics, hydroponics, vertical farming, and other indoor or controlled environment agriculture systems primarily geared towards commercial sales. In both the Toolkit and Farm Bill, non-profit, subsistence, and educational urban farming enterprises are not well integrated or included in the conceptualization of UA. While there are many definitions of urban agriculture in the literature from the simplest definition of “producing food in cities” to longer descriptions of UA such as that of the American Planning Association that incorporate school, pipp racks rooftop and community gardens “with a purpose extending beyond home consumption and education,” the focus of many UA definitions used in policy arenas continues to center around the production and sale of urban produced foods. Accordingly, food systems scholars have recognized that “Urban agriculture, [as defined], is like agriculture in general”, devoid of the many political, educational, and food justice dimensions that are prioritized by many U.S. urban farming efforts. Thus the social-political nature of farming, food production, and food sovereignty are not invoked by formal UA policy in the U.S. Many goals and activities common in urban food production, including education, nonmonetary forms of exchange, and gardening for subsistence are obscured by the productivist definitions and can be thus neglected in policy discussions. Furthermore, UA policy in the U.S. remains largely agnostic about the sustainability of production practices and their impact on the environment. While U.S. agriculture policy narrowly focuses on the production, distribution and marketing potential of UA, broader discussion of its activities and goals proliferate among food systems scholars from a range of fields including geography, urban planning, sociology, nutrition, and environmental studies. These scholars are quick to point out that UA is much more than production and marketing of food in the city, and includes important justice elements . In the Bay Area context, we continue to see the result of this dichotomy: thriving urban farms lose their leases , struggle to maintain profitability or even viability and encounter difficulties creating monetary value out of their social enterprises. In light of the ongoing challenge to secure longevity of UA in the United States, there is a need for an alternative framework through which food and farming justice advocates can better understand and articulate what UA is, and why it matters in cities.Agroecology is defined as “the application of ecological principles to the study, design and management of agroecosystems that are both productive and natural resource conserving,culturally sensitive, socially just and economically viable” , and presents itself as a viable alternative to productivist forms of agriculture.

Agroecology in its most expansive form coalesces the social, ecological, and political elements of growing food in a manner that directly confronts the dominant industrial food system paradigm, and explicitly seeks to “transform food and agriculture systems, addressing the root causes of problems in an integrated way and providing holistic and long-term solutions” . It is simultaneously a set of ecological farming practices and a method of inquiry, and, recently, a framework for urban policy making ; “a practice, a science and a social movement” . Agroecology has strong historical ties to the international peasant rights movement La Via Campesina’s food sovereignty concept, and a rural livelihoods approach to agriculture where knowledge is created through non-hegemonic forms of information exchange, i.e. farmer-to farmer networks . Mendez et al. describe the vast diversity of agroecological perspectives in the literature as “agroecologies” and encourage future work that is characterized by a transdisciplinary, participatory and action-oriented approach. In 2015, a global gathering of social movements convened at the International Forum of Agroecology in Selengue, Mali to define a common, grassroots vision for the concept, building on earlier gatherings in 2006 and 2007 to define food sovereignty and agrarian reform. The declaration represents the views of small scale food producers, landless rural workers, indigenous peoples and urban communities alike, affirming that “Agroecology is not a mere set of technologies or production practices” and that “Agroecology is political; it requires us to challenge and transform structures of power in society” . The declaration goes on to outline the bottom-up strategies being employed to build, defend and strengthen agroecology, including policies such as democratized planning processes, knowledge sharing, recognizing the central role of women, building local economies and alliances, protecting biodiversity and genetic resources, tackling and adapting to climate change, and fighting corporate cooptation of agroecology. Recently, scholars have begun exploring agroecology in the urban context. In 2017, scholars from around the world collaborated on an issue of the Urban Agriculture magazine titled “Urban Agroecology,” conceptualizing the field both in theory and through practical examples of city initiatives, urban policies, citizen activism, and social movements. In this compendium, Van Dyck et al. describe urban agroecology as “a stepping stone to collectively think and act upon food system knowledge production, access to healthy and culturally appropriate food, decent living conditions for food producers and the cultivation of living soils and biodiversity, all at once.” Drawing from examples across Europe, Africa, Latin America and Asia and the United States, the editors observe that urban agroecology “is a practice which – while it could be similar to many ‘urban agricultural’ initiatives born out of the desire to re-build community ties and sustainable food systems, has gone a step further: it has clearly positioned itself in ecological, social and political terms.” . Urban agroecology takes into account urban governance as a transformative process and follows from the re-emergence of food on the urban policy agenda in the past 5-10 years. However, it requires further conceptual development. Some common approaches in rural agroecology do not necessarily align with urban settings, where regenerative soil processes may require attention to industrial contamination. In other cases, the urban context provides “specific knowledge, resources and capacities which may be lacking in rural settings such as shorter direct marketing channels, greater possibility for producer-consumer relations,participatory approaches in labour mobilisation and certification, and initiatives in the area of solidarity economy” . Focusing on the social and political dimensions of agroecology, Altieri and others have explicitly applied the term “agroecology” to the urban context, calling for the union of urban and rural agrarian food justice and sovereignty struggles . Dehaene et al. speak directly to the revolutionary potential of an agroecological urban food system, building towards an “emancipatory society” with strong community health and justice outcomes.

A substantial effort is being made to connect LCI data across databases. To this end, both ecoSpold and ILCD aim to support “alternative modeling options and data exchange [with each other]”. The data from LCI databases are usually exported as collections of either XML or XLS files . Collections of XML data are used in most major LCA software tools, as will be discussed next. Many of the databases described in Table 3.4 have been created and modified to include LCI data for a larger variety of systems types: i.e. not just industrial production systems. The World Food LCA database focusses specifically on LCI data for agricultural production and processing and is intended asan open data project. Government-run LCI databases like the USLCI, ELCI, and the AusLCI also aim to incorporate more LCI data relevant to agricultural systems. The combination of data collection for agricultural LCI databases, and the continuous development of LCA tools means that the LCA methods as they currently stand are being incrementally improved and better supported. Still, there is a lack of domain specific LCI data, particularly for alternative agricultural systems.Four of the most popular tools that are used throughout all the LCA phases are spreadsheet tools , SimaPro, GaBi Software, and openLCA. Table 3.5 describes the basic properties and features of these software tools. The main differences between LCA software tools include modeling process, cannabis drying kit range of databases available, usability, data documentation formats, and cost. Spreadsheet tools are a natural fit for the data-intensive LCA process.

They have the capability to create inventories easily, perform impact calculations on raw data, and produce charts exportable to partner word processing software for reports. Not only can most LCI data be exported as XLS/XLST, but many plugins, templates, and guides on how to use Microsoft Excel to conduct LCAs are available. An example of a spreadsheet tool is the Athena EcoCalculator, a template that allows for getting snapshots of the environmental footprints of buildings.Pre International develops SimaPro, one of the most popular full stack proprietary LCA software tools. In direct competition is GaBi Software, a “product sustainability performance solution”, developed by PE International, that is also used to conduct LCAs . Both GaBi and SimaPro have a similar set of functionality, and are industry leaders. They are expensive, but have alternative limited access licenses for education and teaching. Many other proprietary LCA tools of varying complexity and capacities exist. These include: Sustainable Minds, Umberto NXT LCA, Quantis Suite, among others. openLCA is one of the few free and open source tools aimed at professional LCA and footprint analysts. GreenDelta, an environmental consulting group based in Germany, conducts core development for this tool. In addition to having LCIA capabilities with built-in methods, data connectivity with popular LCI data documentation formats, and reporting functionality, openLCA also allows for users to build their own plugins to extend it. GreenDelta is also responsible for the openLCA Nexus website, which aggregates LCI data from different databases and allows for them to be searchable in one interface . Modern LCA tools have provided some support for connecting to LCI databases, automated report production, basic versioning information to track changes, and simple localized user created libraries for reuse within a project . Development on each of these LCA modeling tools is ongoing with new and promising features being rolled out each year. while there is interest in bridging the gap between the need for domain-specific data, these tools are still designed for the domain-agnostic LCA modeling process.

The decomposition of an agricultural system into quantifiable unit processes, the assumed relationships between different data, and the means by which unit process data are brought together in a synergistic way in an LCA model to enable the calculation of the environmental impacts of the system of interest have been described previously. LCAs can be leveraged to do more than just retrospective evaluation, as described in Section 3.2. The current LCA modeling process can be scaffolded to enable more proactive evaluation, monitoring of systems, and to use LCA results as a decision making tool during the system design process. In this section, I present a single scenario, concerning the creation of an LCA model, as it exemplifies the modeling process and challenges that would be faced by a small- to medium scale sustainable farmer. In fact, it is part of a larger analysis in which I developed a series of scenarios describing hypothetical modeling activities enacted by potential LCA stakeholders. The goal of the full set of scenarios was to tease apart the core issues with the LCA modeling workflow and the capacity of these existing LCA data structures and tools to connect and compare agricultural system. Section 3.4.4 highlights the modeling challenges identified through the scenario presented in this chapter. The issues identified during the full scenario-based analysis, in concert with work presented in this chapter, are collectively discussed in Section 3.5.Consider the following hypothetical scenario: Alice Kidogo is the owner of a small urban farm growing an assortment of fruit and vegetables in Orange County. It is 2016, California is experiencing a drought, and she suspects that the state government will impose water rations. She currently supplies produce to certain farm-to-table restaurants in the Orange County region. She would like to apply to be a supplier at the Whole Foods in her geographic area. She is aiming to score “Good” to “Better” on the Whole Foods Responsibly Grown ratings. She wants to conduct some form of environmental assessment to help her meet these goals. Alice wants to be proactive and use this opportunity to also optimize her water usage and lower her water footprint. As the farm is composed of many different subsystems, she wants a reasonably fine-grained assessment that allows her to: identify major water sinks, detect inefficient water flows, and understand the farm’s overall relationship with water. Alice also wants to be aware of the effects of her water-saving choices with respect to other environmental issues. For example, one concern that she has is the relationship between the heavy use of plastics within her irrigation system and the farm’s carbon footprint. She wants to consider alternatives to reduce her water footprint in case of rationing. She needs to find ways to improve her water footprint without compromising the farm’s overall environmental performance. Alice begins by performing a Google search with the phrase water or carbon footprint calculator farm. She finds two online tools: The Water Footprint Assessment Tool, and the AgroClimate carbon footprint tool. They provide her with interesting information about her local watershed, and some geography based statistics regarding water use. Unfortunately, even after spending some time trying to model her farm using the tools, they only allow her to get a rough estimate of the water footprint. As she wants to use the results of the water footprint assessment to make decisions about how to reduce the water consumption of different systems on her farm, these online tools do not suffice. She then browses through the United States Department of Agriculture website, to see if they have any recommendations on conducting an environmental assessment of her farm. The website lists “Quantification Tools” in the “Environmental Markets” section, including water quality, vertical farming equipment carbon and greenhouse gas emissions, and energy estimation tools. Once again, they aim to provide a snapshot footprint of the environmental performance of the system. They are specifically geared toward enabling the farmer to participate in emerging environmental markets involving, for example, the trading of offsets. Alice decides that a Life Cycle Assessment would provide her with a potential means to quantify and understand the environmental performance and of her farm. However, LCA seems to be a complex and time consuming venture, and Alice worries that she may have to resort to hiring professionals to provide her with the most reliable water footprint.

One online guide to LCA informs Alice that it could cost from $10,000 to $60,000 to outsource the LCA to a consulting company. Due to financial constraints, Alice chooses to try and conduct an LCA of her farm on her own.Alice begins by creating a flow diagram. Since no dedicated LCA flow diagramming tool is available, she uses Microsoft PowerPoint to create a simple block and arrow diagram to represent the major systems in the farm: the irrigation system, solar power system, grey water reclamation system, vermicomposting boxes, the nursery, and the farm grow beds themselves. As no formal guidelines regarding flow diagramming are available, she simply connects these blocks with arrows to represent directionality and types of flows within the system. The boundary of the system can be scope in many ways. For example, Alice chooses to include the build of the solar power system and the irrigation system, as she custom built many of the components. In contrast, her gray water reclamation and vermicomposting systems are direct from vendors. She creates an initial flow diagram, as shown in Figure 3.10.Alice goes through a variety of LCA educational materials, hoping to answer the following questions: how should she break down these subsystems, and what level of granularity is needed to calculate a useful water footprint? She converts her original flow diagram to the process based LCA flow diagram shown in Figure 3.11, created based on an introduction tutorial to LCA. This represents her systems as a series of high-level processes: these would later be decomposed into unit processes, with relevant data potentially available in existing LCI databases.No standard or generic LCA models are readily available to be explicitly built upon. Alice essentially begins from scratch when creating the LCA model, with minimal guidance on how to collect her data, what kinds of things to consider, and how to connect unit processes. Alice tries to create an LCI for just the irrigation system to try and see how far she can get. She has two options, pull data from an LCI database, or manually collect the data required. Unit process data are contained in several LCI databases. Alice chooses to use the USLCI database, as it would likely contain a geographically appropriate dataset for her southern California based farm. She uses Microsoft Excel to create a basic LCI. She tries to source much of her equipment and materials used on the farm from local vendors, and hopes that relevant data will be available in the USLCI.For example, the irrigation system on the farm is based on Harmony Farm Supply & Nursery’s sprinkler irrigation setup. The most complex part of the system is the “system head or manifold assembly”. It is responsible for distributing water among the main lines , and shown in Figure 3.12. Each of the sprinkler lines would result in a network of yet more tubes, fittings, and other parts. A complete accounting would require the knowledge of the environmental impacts of each of these sub-components of the irrigation system, and potentially even background information on their origins. Ideally, manufacturers of these parts would provide these data. Alice looks up irrigation in the USLCI to see what kind of data is available. Figure 3.13 shows the list of data available to her. None of the available data is relevant to her specific setup. The publicly available USLCI appears to mainly contain data for large-scale industrial processes, and the farming data is therefore also of that scale. She does note that the USLCI has three phases of data under development: field crop production data, Irrigation, manure management, and farm equipment operation unit process data, and mineral, fertilizer, herbicide, insecticide, and fungicide data. However, these data are not available yet. The USDA crop LCI database contains some of these data, but as with the USLCI database, it is missing certain kinds of data relevant to her system.Each modeling step required a different tool. While this alone may not be problematic, the modeling effort put into one step is lost in the next. The largest gap is between flow diagram and inventory, as no current tooling can support the connection of the two. openLCA does have the capability to import an entire external LCI database, as well as spreadsheet based inventories.

Due to small numbers in each category, organic, natural, NHTC and/or ASV status were combined to represent how management specific to a target consumer may influence AMR patterns overall. Pasture-based forage is common in California, with livestock grazing being California’s most extensive land use , but details on dryland versus irrigated pasture for beef cow-calf herds have not been reported. The types of diseases most treated with antimicrobials reported in our survey, namely pinkeye, respiratory disease, foot rot and scours, concur with prior data reported in a large survey on antimicrobial use on California cow-calf operations . Use of antimicrobials in feed is an uncommon practice in cow-calf herds and mastitis is not nearly as common in beef as in dairy production systems, so it is not surprising that these practices were not common amongst the farms surveyed. In addition, in California, veterinary oversight is required for the purchase and use of all medically important antimicrobials, which may explain the high percentage of farms that reported having a veterinarian-client-patient relationship . Of the E. coli isolates, approximately 36% were resistant or non-susceptible to at least one antimicrobial, excluding ampicillin, to which all isolates were resistant. AMR of E. coli in cattle or ruminants to various antimicrobials has been observed by other authors to varying degrees, but it is not always clear how resistant status is established. For example, vertical cannabis one study from Malaysia found 61.9% of E. coli isolated from diseased ruminants to be resistant to trimethoprim sulfamethoxazole compared to 4.9% in our study, 69% resistant totetracycline compared to 13.1% non-susceptible in our study, 54.1% resistant to amoxicillin, compared to 100% resistant to ampicillin in our study .

Discrepancies may be due to the choice of breakpoint to establish resistant status, meaning that a breakpoint can be chosen based on the species or it can be chosen based on the most similar bacteria for which there is an established breakpoint, both of which are routine practices and depend on the context of the study. Variations in results can also stem from the fact that diseased animals are more likely to have been treated with antimicrobials before isolation of the pathogen or because antimicrobial drug use patterns may vary between countries or regions. Ampicillin had the highest proportion of resistant isolates of E. coli , which was surprising, especially since none of the participating ranches reported any ampicillin use. In this study, a breakpoint of ≥0.25 μg/mL indicating resistance was chosen based on the veterinary literature for ampicillin resistance for treatment of metritis in cattle due to E. coli from VET CLSI. By contrast, the human breakpoint is ≥32 μg/mL indicating resistance. The most common MIC for ampicillin in this study was 4 μg/mL . The lowest prevalence of resistance or non-susceptibility for E. coli of all included antimicrobials was for ceftiofur , which was also only used by one of the enrolled farms. Restrictions were placed on extra-label cephalosporin use by the Food and Drug Administration in 2012 which aimed to decrease their use in livestock . The ampicillin breakpoint used in this study for Enterococcus was ≥16μg/mL indicating resistance. Although dramatically different from the breakpoint for E. coli, this human breakpoint was selected due to available data and differences between antibacterial spectrum and bacterium type. As no veterinary breakpoint is available for this bacterium/antimicrobial combination, the human breakpoint wasselected as outlined in the methods, resulting in only 1 resistant Enterococcus isolate to ampicillin. Second to ampicillin, the most common drugs for which E. coli was resistant or non-susceptible were sulfadimethoxine and trimethoprim-sulfamethoxazole.

A 2016 study of AMR in beef cattle found no associations between the prevalence of resistance of E. coli isolates to tetracycline, third generation cephalosporin, or trimethoprim-sulfamethoxazole and history of antimicrobial treatment with either ceftiofur or other antimicrobials . The authors conclude that mixing of treated and non-treated cattle may mask the effect of treatment or that animal-level effects due to treatment are short-lived. However, both ceftiofur and sulfa treatments were uncommon in our study population so that neither hypothesis would explain the prevalence of AMR to this class of antimicrobial observed. Florfenicol is a relatively new antimicrobial and limited published, peer-reviewed data exist on resistance profiles. It was first approved for use in cattle in 1998 . In this study, 2 animals had a history of being treated with florfenicol and 9 farms indicated that they use it on farm. For E. coli, there were 47 non-susceptible isolates to florfenicol, which was the drug with the third highest AMR prevalence, behind ampicillin and sulfadimethoxine. Reports of increasing AMR to florfenicol in Enterobacteriaceae exist in the literature. In addition to antibiotic use, mobile genetic elements and horizontal gene transfer are speculated to play a role in the replication of AMR genes resulting in the observed trend of AMR to florfenicol . Future research should also investigate genetic elements linked with phenotypic resistance to AMR to florfenicol in enteric bacteria from cow-calf operations to increase understanding of potential factors resulting in higher prevalence of AMR to this drug. Historically, bacterial resistance to tetracycline has had a high prevalence . In the present study, E. coli and Enterococcus isolates had a similar, relatively low proportion of isolates resistant to tetracycline, but Enterococcus isolates showed the highest proportion of non-susceptibility to this drug. No biologically relevant survey variables regarding farm description, animal management, or herd level antimicrobial use were significantly associated with AMR in our models while accounting for correlation between isolates from the same animal. Additionally, controlling for correlation between isolates from the same farm led to unstable models with non-positive G matrices indicating a lack of variation in the additional random effect. However, given the high prevalence of AMR at one of the farms, there may be exposures at farm or animal level associated with AMR that were not captured by this survey.

Calves have been shown to carry more AMR bacteria than cows in previous studies , however calves in two of those studies were less than 4weeks old. In contrast, calves in our study were up to one year old, and the bacterial AMR profile in neonates may differ from that of older calves. In dairy calves, antimicrobials may be used more often to treat and prevent disease, but in beef calves, the link is less clear. One hypothesis to explain AMR bacteria shed from calves is that AMR is acquired through other routes, such as genetic linkage or direct transfer from cows and may not be associated with antimicrobial use on farm. Another study found the frequency of water trough cleaning and size of operation were significantly associated with AMR prevalence . Although water trough cleaning was not significantly associated with AMR in our study, MFA analysis showed both water trough cleaning and whether or not farms used bleach to clean water troughs to be two of the variables contributing most to data variability. Other factors that have been found to be statistically significantly linked to AMR in other studies but were not explored in this study include spring versus fall born calves and proximity to dairy farms . None of the farms in the present study were within one mile of a dairy farm and spring versus fall calves was not examined. Antimicrobial use on farm has been suggested as a contributing factor for the development of AMR, but several studies have indicated that resistance is multifactorial and develops regardless of exposure or use of particular antimicrobials on farm , findings that may be substantiated by the results of this study. In addition, there is evidence that some AMR genes may be co-selected or have genetic linkages , in which resistance to one antimicrobial is genetically linked to resistance to a different antimicrobial and transferred either vertically or horizontally together . Alternatively, antimicrobial use in the cow-calf sector may not exert high enough selective pressures on bacterial populations to drive AMR. Multiple factor analysis showed that herd information and nutrition related factors accounted for approximately 52% of the total variance in the data. Several studies have shown herd health in farming systems, herd management,bio-security, population density, weed curing and external pressures to be linked to antimicrobial use . Previous studies reported an association between farm management factors and the prevalence of AMR in E. coli isolates . Markland et al. found that regular cleaning of water troughs and the addition of ionophores to feed were associated with a reduction in prevalence of cefotaxime resistant bacteria in fecal samples of beef cattle on grazing farms in Florida. Beef cattle require several minerals for optimal growth, health, and reproduction. Mineral deficiency may result in anemia, depressed immunity and increased opportunity for bacterial growth and dissemination of resistant bacteria . On the other hand, elevated heavy metal supplementation may co-select for antimicrobial resistance of fecal E. coli and Enterococcus spp. . A recent scientific report showed that synthetic smectite clay minerals and Fe-sulfide microspheres have antimicrobial properties and kill antibiotic resistant bacteria including E. coli and Enterococcus spp. but we did not inquire about the use of these products.

In addition, MFA in this study showed that farm level antimicrobial use, disease treatment, and antimicrobial dosing and record keeping practices accounted for 46% of the total variability in the study data. Similarly, a survey study of antimicrobial use in adult cows on California dairies found that antimicrobial stewardship practices, antimicrobial usage information, and producer perceptions of AMR on dairies accounted for 32.3% of the total variability in the survey data. On the other hand, the sampled animals’ life stage and antimicrobial treatment history and in particular the antimicrobial resistance data contributed to a lesser degree to data variability. Given that AMR seemed less variable than other factors describing the animals and farms in the data set, it is not surprising that statistical models were unable to find associations between AMR and animal or farm related factors. Overall, the MFA analysis identified important differences between herds that can be considered in studies that investigate the risk and the associations between farm practices and AMR of fecal bacteria. Cluster analysis identified some potential regional differences in management practices and antimicrobial use information among cow-calf operations in northern California since the Coastal Range was only represented by two clusters. The cause of the differences could be due to variable access to information or rancher education or due to the influence of veterinarians in the Coastal Range. One limitation for this study includes the use of a convenience sample of farms that could have introduced bias because the group of farms that are associated with the University of California teaching hospital or extension agents may have similar management tendencies. They could represent farms that have more progressive management, are more attentive to animal health and/or more willing to treat or may be more likely to adhere to legislation regarding antimicrobial use and antimicrobial stewardship. This is a significant factor to consider and, if true, could have biased the study either toward the null because of less antimicrobial use overall or away from the null because these producers may be more likely to watch carefully, identify, and treat any disease conditions that warrant antimicrobials. In addition to selection of farms, selection of animals for sampling was not random, as sampling is logistically challenging in a cow-calf setting. The animals sampled were either being put through the chute for another reason , were physically closest to the chute, or were the easiest animals to collect for sampling . Some other challenges associated with sampling in this system include limited animal identification, treatment records, and animal restraint. Many of the animal health records were based on the farmer’s recollection and therefore are subject to recall bias. In this case, those that were identified as treated were very likely actually treated; however, if a treatment was forgotten, that animal did not have any treatment to associate with AMR isolate status. In addition to logistical challenges, none of the farms put antibiotics in the feed which may have biased this study toward the null; however, it should be noted that this practice is not common in cow-calf operations in California. Finally, the use of human breakpoints for the determination of AMR status when no veterinary breakpoints were available is another limitation, underlining the need for further research into AMR in livestock species. A metagenomic analysis of isolates would have provided further information but was not possible at this time due to financial constraints.Food is essential; its infrastructure, complex.

A volunteer cares for the chicken coop, which houses four hens who provide eggs for the restaurant. A cat named Jolene resides at the farm and chases away pests .Through my field observations of 26 active sites, I found that community gardens, school gardens, urban farms had many unique and shared qualities. UA sites ranged in size from 0.06 to 8.5 acres, though exact measurements were not available for some sites . Sites that did not report an exact size were estimated to be less than one acre. Except for the two largest UA sites, LBCG and the Growing Experience most UA sites were smaller than two acres. Nearly all sites were secured by a chain-link fence or gate. In this study, the VA Hospital Patient Garden and the Michelle Obama Neighborhood Library Learning Garden were the only UA sites without a gated front entrance. However, the Patient Garden was protected by video surveillance, locked buildings, and fenced areas. Each site had decorations, reflecting the population using the garden . All UA sites offered communal areas for resting, in the form of logs, chairs, or picnic tables and benches. Trees, gazebos, and arbors covered in vine plants provided shade. UA sites typically implemented policies for membership, etiquette, and growing. LBO’s nine community gardens have a $55 minimum membership fee per six-month season, while LBCG’s membership fee is $160 per year. Though LBCG members pay more, they complete less service hours: LBCG requires four hours per year, grow rack compared to at least 20 hours per year for LBO members. Both organizations prevent gardeners from selling produce for profit. Even UA sites without membership fees enforced certain rules. Most UA sites prohibited the use of tobacco, cannabis, alcohol, or other illegal substances.

Ground Education’s gardens have signs with a list of rules for students. At the beginning of each class, Garden Educators ask students to respect others and “celebrate nature” by leaving insects alone and only picking plants with permission. Some UA sites enforced rules for the types of products and plants allowed. LBO and the Peace Garden only allow gardeners to use organic fertilizers and insecticides. To avoid plant diseases that occur in winter, LBCG only allows nightshade family plants from March 1st to November 15th. LBCG also has a common area for herbs, including rosemary and mint, which can quickly spread throughout plots and are difficult to remove. Several UA sites created food donation programs to distribute to community members, local pantries, and organizations. Every week, the LBO Director harvests from Zaferia Junction Community Garden, collects any produce donated by gardeners, washes the produce, then weighs and organizes donation bags. Connecting to my first research question, many UA sites were built by community members to revitalize vacant spaces and increase food access. For example, Santa Fe Community Garden and Farm Lot 59 were established by community members on littered, empty lots. In some cases, UA sites were intended for a specific population, such as the VA Hospital Patient Garden, which primarily serves veterans receiving long-term care at the hospital. Touching on my second research question, communities created UA by drawing on both agricultural knowledge and leadership skills. Typically, UA sites were built and maintained through community volunteers, who constructed raised beds, dug in-ground plots, weeded, and planted crops. UA sites were managed by an organization, city department, or at least one lead person. The partnership between UA leadership and landowners was critical for creating and maintaining sites. Over half of the active community gardens included in this study established a lease agreement with the City of Long Beach. School gardens were built with the permission of LBUSD.

Other sites were built on land owned by individuals or entities such as CVC, VA, and the LA County Housing Authority. The next section on former sites will discuss the significant influence that landowners hold over UA.The table is organized alphabetically, as there was limited information on when each former site was originally founded. Some websites provided outdated information, and many of the phone numbers listed were no longer in service. Gladys Avenue Urban Farm, originally owned by LBO founder Captain Charles Moore, was sold to another private landowner with the condition that the land must be used for UA. The site was rented to Heritage Farm in 2022, described previously in this chapter. Another urban farm, Long Beach Farms, is now a community garden managed by Puente Latino Association. Three Sisters community garden, owned by the City of Long Beach and located at Orizaba Park, was managed by a community group, and is now managed by LBO. In 2015, the landowner of former LBO community garden, Top of the Town, began renting the space to Organic Harvest Gardens. That same year, LBO opened Zaferia Junction Community Garden, which was built on the land of Wild Oats Community Garden. Wild Oats, which was constructed on a former Pacific Electric railroad, was closed due to construction of the Termino Avenue Storm Drain Project, a nearly two-mile long storm drain system designed for flood relief . These formerly closed sites demonstrate how UA can change over time due to changes in ownership and management, as well as public construction projects.As of 2023, most of former UA sites including Fifth Street Garden, Foodscape Garden, Hill and Atlantic Garden, New City Urban Farm, and Wrigley Village Community Garden, are empty lots. Former UA sites were forced to relocate or close due to the city or private owners choosing to not renew the lease. Figure 28 is a collage of Google Map images of the original Fifth St Garden , from 2008 to 2022. According to a blog written by Jon Rosene, the community garden was nearly 8,000 square feet. In 2009, the landowner offered a lease of up to three years for $1 a month . After the lease ended, gardeners built a school garden at Franklin Classical Middle School, which is now managed by Ground Education.Other former UA sites were repurposed for use by the city and other entities.

The Firehouse Community Farm was located at a former fire station, and featured beehives, community gardening classes, and monthly crop swaps. A neighborhood association managed the farm with a right-to-entry permit, but the permit expired in 2019 without the city’s renewal. The space is currently used by District 9 council office . Another city-owned property, the Civic Center Edible Garden, was removed when the previous civic center, city hall, and library were demolished. The buildings were “seismically deficient” according to a 2015 press release from the City of Long Beach. The Spring Street Farm Project, led by the Long Beach Community Action Partnership , planted an orchard, created an aquaponic system, and raised chickens, ducks, and a goose. The organic urban farm was part of the LBCAP Youth Opportunity Center’s Green Jobs program . According to an LBCAP employee, the space was leased from the Salvation Army. The Salvation Army asked LBCAP to relocate due to renovations, but LBCAP was unable to find a new location for its farm. Overall, it appeared that no matter how successful UA sites were, their longevity was determined by landowners, not the community members using the space. Regardless of the UA site’s scale or amenities, they could be demolished due to the construction of a new building or reverted into a vacant lot for sale. As of 2024, four of the nine nonoperational sites identified are still unused. The space that formerly hosted Wrigley Village Community Garden, which was closed in 2016 after a real estate investor sold the property, is covered in litter . Nonoperational sites highlight the precarious nature of UA. Without the support of landowners and policies to protect sites, UA benefits of food access, green space, drying cannabis and neighborhood beautification can be reversed. The next chapter will share insights from UA leaders and provide a deeper analysis of study findings in relation to CCW and SDOH.In the summer of 2023, I had the opportunity to visit Captain Charles Moore Urban Community Garden, which was constructed in April 2023 and officially named on February 18th, 2024. Before the garden received its name, gardeners would call LBO’s ninth garden, “the new garden,” or simply “3121,” from the address, 3121 Long Beach Boulevard. I interviewed a UA leader who rented a plot there, named Nana . Though Nana had just started renting her 10’ x 10’ in-ground plot at the new garden, she had decades of experience, as she had been growing plants since the age of six. With a smile, Nana reminisced how her grandmother “hauled [her family] into the garden.” As the descendant of slaves, her grandmother wanted to “pass along the skills of growing your own food.” Out of 11 grandchildren, Nana is the only one who continues to garden. She estimated that she grows 80% of the food she eats. Growing food was essential for Nana to take control of her own health after being diagnosed with cervical cancer. She used to take 27 different medications per day. At that time, Nana felt that she was “a zombie [who] woke up to sleep.” Growing her own food and medicinal plants empowered Nana to take control of her diet and improve her physical and mental health. She explained that if she has a bad day she goes into the garden. Nana said, “It just brings me so much happiness to grow.” With pride, she described her recent crops: watermelon, winter squash, onions, chives, fava beans, stringless string beans, snow peas, black-eyed peas, moringa, borage, chamomile, and mint. Watermelons were her biggest success.

Nana grew multiple watermelons per year in different varieties, like Klondike , moon and stars watermelon , and a sweet variety from Louisiana. She also loves mint tea because it soothes stomach aches and helps with her liver issues. As Nana showed me around her garden plot, she expressed her joy at seeing new life come from the seeds she planted, “like a mother birthing a child.” Following in her grandmother’s footsteps, Nana hopes to pass down her gardening skills and knowledge to her kids. She said that growing food was her grandmother’s “longevity”: a lasting contribution to her family. Nana’s story demonstrates how UA provides not only a space for growing food, but for community members to enhance their overall health and environment. Through interviews with UA leaders and field observations of gardeners, school garden educators, students, interns, and volunteers, I learned that UA allows people to connect with others, transform their neighborhoods, and share knowledge. Building on the previous chapter, which provided context for the UA sites, Chapter 4 will discuss recurring themes from interviews and field observations. While Chapter 3 provided a broad overview of how community members create, maintain, and engage in UA, this chapter will focus on the specific forms of skills and knowledge fostered by UA, and how UA addresses health. I analyzed transcripts and field notes by using ATLAS.ti to identify phrases and words that were emphasized by participants or related to CCW and SDOH. Following the primary-cycle and secondary-cycle coding procedure described by Tracy , I developed a codebook to organize qualitative findings.UA brought Long Beach community members together through the collective experience of being involved in a garden or farm, often by volunteering and sharing food. One UA leader explained that they were interested in creating “more venues for more classes [and] more activities.” They remarked, “It’s not just about people growing stuffany more. There’s more community involvement or community building.” Another described a “sense of social connectedness” by getting to meet community members at volunteer days. These are examples of social capital, described by Yosso as social networks and community resources. At LBO’s nine gardens and LBCG, gardeners socialized during Saturday “work parties.” Gardeners completed community service by weeding and cleaning common areas, but also mingled with one another, swapped crops, and enjoyed potlucks together. Though LBO only required a minimum of 10 community service hours per six-month season, one person had set a record of 102 hours. Others reached over 90 hours per six-month season. A volunteer had 70 hours completed at the time of writing. He volunteered every week “to beat that 102 record.” In this scenario, community members used social capital to maintain gardens, even if they were not growing food.

Tracy describes “the mind and body of a qualitative researcher” as a literal research instrument, “absorbing, sifting through, and interpreting the world through observation, participation, and interviewing” . I am a Social Ecology doctoral candidate and the Assistant Director of a Long Beach UA nonprofit, Adventures to Dreams Enrichment . AtDE empowers youth by providing the resources and education for youth to grow their own food. The organization’s mission is to engage youth in hands-on enrichment activities, create a safe environment to learn and play, and provide mentorship . While collecting and analyzing data, I will be mindful of how my subjective experiences may influence the research . I began volunteering at AtDE in 2017 while completing my Bachelor’s in Dietetics and Food Administration at California State University of Long Beach. Since then, I assisted AtDE in building and maintaining a youth garden, harvesting produce, coordinating volunteers and interns, and raising funding. These experiences allowed me to build rapport with not only AtDE, but with community members of Long Beach, which is essential for recruitment and data collection for this evaluation. For this study, I could be considered a “complete participant” as a researcher studying a context in which I already am a member . My role as the Assistant Director of a nonprofit engaged in youth gardening offered the advantage of insight into the world of UA. Subjects of a study may act more open and candid around a complete participant, as if a colleague or friend is visiting, rather than a researcher .This research investigated UA sites in Long Beach that fit the United States Department of Agriculture’s definition of cultivating, processing, industrial grow and distributing agricultural products. UA sites in Long Beach include community gardens, school gardens, and urban farms.

An important distinction between community gardens and farms is that community gardens are collectively maintained by a group of individuals to grow food, compared to farms which may be privately owned . Although rooftop farms, hydroponic, aeroponic, and aquaponic facilities, and vertical production qualify as UA, these UA types were not identified in Long Beach . Equipment facilities, distributors, and green spaces that do not grow food, such as botanical gardens, were excluded from this study. This study also collected information on UA sites that are no longer in operation, such as the community gardens deemed “no longer operational” by Ban et al. in 2013 . IRB approval was granted by the University of California, Irvine in May 2023. I used publicly available contact information to request a site visit and interview with the main person responsible for the site . Additional participants were recruited through snowball sampling, or recommendation by initial study participants . UA site leaders were contacted via email with a study information sheet. The interview was conducted with the interviewee’s verbal consent, and interviewees received a $25 gift card as compensation for their time.Data collection took place from June 2023 to December 2023. I interviewed 19 people in a leadership role at a Long Beach UA site . Interviewees were aged 22 to 69 years old, with the average age being 49. In addition to the broad range of ages, interviewees had varying levels of experience. The longest amount of time a UA leader held their position was 13 years, and the shortest amount was one month. About 58% of interviewees were female, and 42% of interviewees were male. I conducted field observations at 27 sites, about 39% of 66 sites that were actively operating in Long Beach at the time of writing, in 2024. I observed, interacted with, and volunteered with over 60 adults engaged in UA and over 200 LBUSD students.

Additionally, about 68% of interviewees identified as White, which is higher representation compared to the city’s population. Only less than a third of Long Beach residents are White . Therefore, the demographics of UA leaders who participated in this study may greatly differ from that of all gardeners, students, and farmers engaged in UA.During the site visits, I wrote field notes as a qualitative method for collecting descriptive information about the UA sites and people involved. Field notes allow researchers to understand others by immersing themselves in events, experiencing and interpreting those events as participants, and “transforming witnessed events, persons, and places into words” . This provides description that may otherwise be missed by a survey questionnaire with prefixed questions . Using a field observation protocol , I recorded the date, time, and duration of sites visit and notes of my initial impressions, including sights, tastes, smells, and sounds. Based on this protocol, I first created a “raw record,” my first, unprocessed writing of the site . Within 36 hours of the site visit, I typed the raw record and saved it as an electronic document . I recorded any site characteristics that corresponded with the SDOH domains of social and community context, economic stability, education access and quality, neighborhood and built environment, and health care access and quality . For example, while observing the Neighborhood and Built Environment, I noted the type of land that the site is located on and its size, any amenities , crops, plants, and vegetation, agricultural techniques, animals and/or insects kept at the site, nearby public transportation, and security features. I took notes on the UA site’s Social and Community Context by recording the number of people present, their visible characteristics , any ongoing activities, programs, or events, and any rules or policies followed at the site.

In my reflections, I documented key events or incidents that are perceived as “significant” or “unexpected” by myself and/or those at the site . I also noted any aspects of the UA site that demonstrate CCW, which will be further explored in interviews with UA leaders .The interview guide included 31 questions organized into 7 sections: 1) Description of Urban Agriculture Site, 2) Management, 3) Transportation, 4) Description of the Community, 5) Community Engagement, 6) Relationships and Partnerships, and 7) Successes and Challenges. Interviewees were asked to describe their role, their UA site or organization, participants at the UA site , the surrounding community and environment, types of community outreach and programs, collaborators, obstacles related to maintaining the site, and any notable accomplishments. Interviews lasted about 45-60 minutes and were audio recorded. During the interview, I wrote short notes to record my observations . Interviews were transcribed with the assistance of Otter.ai, a speech-to-text transcription tool. The interview guide was designed to address the five SDOH domains and CCW forms of capital . SDOH interview questions served to collect detailed information about the community from the perspective of the UA leader, that may not be available from field notes and external sources. For example, Question 15 asked interviewees to describe the UA site’s social and community context: “In your own words, how would you describe the community where your site is located?” Question 17, “How would you describe your community’s environment?” specifically asked for information on the neighborhood and built environment domain of SDOH. CCW questions focused more on abilities, resources, skills, or knowledge actively contributed or gained by the community through participation in UA. For example, Questions 21 “How do community members contribute to the site?” and 22 “How do community members benefit from your space?” were created with social capital in mind. Questions 26, “What are some of the biggest challenges in sustaining a space for urban agriculture?” and 27, “How have you overcome these challenges in the past?” touched on aspirational capital , navigational capital , and resistant capital .Rigorous qualitative research requires care and effort to ensure that there is enough data to support claims, the context or sample is appropriate, and methods are valid and reliable . Tracy outlines eight criteria for producing qualitative work with rigor: 1) worthy topic, 2) rich rigor, 3) sincerity, 4) credibility, 5), resonance, 6) significant contribution, 7) ethical, and 8) meaningful coherence. As mentioned in the previous chapters, this study’s topic is relevant, timely, significant, and interesting due to emerging UA literature, and limited research on UA in the City of Long Beach. The study design addresses rich rigor by carefully explaining how survey and interview questions were developed, based on the SDOH and Yosso’s CCW model. I aim to be sincere and transparent in this dissertation, by reflecting on my own involvement with UA in Long Beach and providing clear details on methodology. Regarding credibility, open-ended interview questions invited participants to describe their narrative on their own terms. This research incorporated thick description, an in-depth illustration that will allow readers to understand context and come to their own conclusions about the data. The study’s major contribution is that it will offer a new lens on the topic of UA in the context of communities addressing health inequities, by exploring the perspectives of UA leaders in Long Beach. Findings may resonate with others involved with UA and community health. The results of this study will be informative for community groups or organizations who are operating, developing, or interested in creating similar UA sites. Additionally, equipment for weed growing readers may be able to replicate methods to transfer findings to another context, such as another city or region . The following chapters will provide meaningful coherence by explaining how the study achieved its goals and connecting findings to previous literature.In this chapter, which explains the dissertation’s theoretical significance, I will describe the social determinants of health and how they apply to the City of Long Beach. Then, I will explain how Yosso’s community cultural wealth model is applicable to addressing health inequities through community-led urban agriculture . From a social ecology perspective, UA can influence multiple, interrelated factors, such as individual attributes and behaviors, interpersonal relationships, the surrounding environment, organizations, corporations, government, and culture .

Although the research questions of this study focus on the community’s role in UA, this dissertation will later explore UA impacts on interconnected, social-ecological systems. The SDOH framework offers specific dimensions to further study health factors health within systems, such as access to education and health care . SDOH provides context to study health inequities, which are systematic differences in the health of different population groups . Health inequities result from socioeconomic inequality, not natural causes or harmful behaviors . This dissertation will build on existing UA research by connecting SDOH to the CCW model, which highlights communities’ cultural knowledge, skills, and abilities . CCW is necessary for developing and maintaining UA spaces, often converted from lots that were abandoned or not designated for growing food . UA is the cultivation, processing and distribution of agricultural products in urban and suburban areas, including tribal communities and small towns . Examples include community gardens, rooftop farms, hydroponic, aeroponic, and aquaponic facilities, and vertical production. UA can improve health by increasing access to fruits and vegetables, which prevent disease and supply nutrients . However, UA provides more than just dietary benefits. I developed Figure 7 to illustrate the conceptual framework for this study, which analyzed how CCW is used to develop UA, which in turn bolsters CCW. Both CCW and UA can address inequities in different facets of SDOH .Rather than focusing on disadvantages faced by lower SES populations, the CCW model highlights their cultural knowledge, skills, and abilities . Yosso’s CCW model describes six forms of capital: 1) aspirational, 2) linguistic, 3) familial, 4) social, 5) navigational, and 6) resistant. Aspirational capital is the resilient ability to maintain hopes and dreams for the future, despite barriers. Linguistic capital includes communication skills in more than one language or style. Familial capital refers to cultural knowledge nurtured among kin, which fosters commitment to community well being. Social capital includes networks of people and community resources. Navigational capital is the ability to maneuver through social institutions not created with communities of color in mind, and resistant capital is built from oppositional behavior that challenges inequality . Social capital, resistant capital, and other aspects of CCW can be seen in community led initiatives to create UA . Urban areas are complex systems and networks , which the United States classifies as “densely developed territory” that has “residential, commercial, and other non-residential urban land uses.” Any areas not included within this definition of urban are classified as rural.1 Because access to agricultural land is less common in urban areas, urban dwellers typically purchase their food from stores .

The results of this proposed work would help us to determine if 1) there is variability in vertically transmitted seed microbiomes across location and host genotype and 2) what these differences arise from. By fully describing the bacterial community of the leaves and fruits as well, we could determine seed colonists’ origin and address if transmission of leaf epiphytes is possible via the seeds. A similar culture-based approached could be used to determine the protective ability of vertically transmitted microbes, as it is possible that taxonomic identity may differ- but function may be the same. In conclusion, the work described in Chapter 2 makes an important step in filling in the gap in the literature as to the existence and importance of vertically transmitted symbionts in this system. Future work will help us understand the origin of these symbionts and if genotype specific microbes are physically heritable through seed transmission.Breeding for agriculturally beneficial traits, such as disease resistance, is an area of ongoing research in the tomato industry, and agriculture in general. Many resistance genes in modern tomatoes originate from their wild Peruvian relative, S. pimpinellifolium, which has a much larger genetic diversity than modern cultivars. Whether or not there are differences in tomato microbiota due to domestication and the presence/absence of resistance genes is a relatively unexplored topic. My work on adult phyllosphere microbiomes originally began as a way to test the hypothesis that breeding for disease resistance genes in tomatoes would ultimately impact the microbiome of those plants. I was, indeed, able to show that host genotype shaped the phyllosphere community for the first two passages of the experiment. When genotypes are classified “resistant” or “susceptible” , there is no overall effect of resistance in microbiomes that had been fully selected on various tomato varieties.

This lack of an overall effect of resistance leads me to conclude that it may not be the resistance genes themselves, drying and curing bud but rather other genetic differences that existed between the genotypes that drove genotype specificity of the bacterial communities. Although I chose two pairs of near-isogenic lines and one out-group, it is likely that other genetic differences existed between near isogenic lines due to the introgression breeding technique that was used to generate the lines. Furthermore, the pairs of near isogenic lines are different tomato cultivars, further contributing to genetic differences amongst hosts. Future work by others will further test the degree to which differences in host genetics impacts the phyllosphere community, and indeed, some evidence for heritable taxa already has been produced using genome wide association studies in corn and Arabidopsis. Moving forward, if there are taxonomic differences between microbiomes, whole genome metagenomics sequencing will help us determine if the functional capability of the microbiomes differed as well. One of the puzzling results from the microbiome passaging work that we were not able to fully explain is that host genotype had a significant impact on bacterial community at the beginning of the experiment, but this declined over time. Through identifying the specific taxa that were significantly associated with the five genotypes in P1 and P2 it’s clear that it is not only the rare taxa associated with particular genotypes that drove such a genotype effect on the microbiomes, and thus the decline in genotype effect cannot be fully explained simply by an overall decrease in diversity. It seems likely that the microbiome underwent environmental selection driven by three factors: 1) the greenhouse, 2) the tomato phyllosphere, and 3) specific tomato genotypes. It seems reasonable that the relative strength of each selection pressure would change over time, whereby host genotype is important early on, but the community experiences progressively more time in the tomato phyllosphere in the greenhouse, the pressure of those environments overshadows a genotype effect.

Even with well-designed and well-controlled experiments, it is difficult to disentangle the selection pressures at play.Through our microbiome transplant and passaging technique that is biologically relevant to how the phyllosphere is naturally colonized, we were not only able to select upon entire host-associated microbial communities, but we could also experimentally test hypotheses regarding microbiome adaption in subsequent experiments. Again, this is due to the physical accessibility of the phyllosphere community and the ease at which it can be inoculated onto hosts. These findings also shed light on a notable challenge in microbiome research. Our data suggest that when describing the microbiome of an open environment, such as plant surfaces, many of the taxa found there may be transient visitors. In the case of the phyllosphere, there are microbes on leaf surfaces that may have emigrated from air, soil, surrounding plants, or other non-plant habitats and do not necessarily represent an adapted community that is capable of growth and persistence. Passaging of microbiomes on a particular host seems to be a powerful way of differentiating those taxa that are, or can become, well adapted to a plant host environment and those that were present upon sampling, but are not well adapted to the environment. Across all systems, much of the work in microbial ecology is highly descriptive: the community associated with a particular host or ecosystem at a given time is described to be its microbiome, implying strong selection for a particular interactive community- rather than a context-dependent assemblage with many recent immigrants, for example. Our findings raise the question as to if a microbiome should be defined as the community that is merely found there upon sampling, or alternatively, if a true microbiome is only one that is adapted to its host or environment. The latter definition might prove hard to establish in many habitats, but fortunately can be readily addressed in the phyllosphere. Thus we expect that our phyllosphere studies will provide important conceptual contributions to the field as a whole.The final chapters of my dissertation explore the importance of bacteriophages in the phyllosphere community.

There are many challenges facing phage research, and studying environmental phages is an especially difficult field due to lack of a universal marker gene for sequencing, a lack of cultivability, and our nascent understanding of phage genetic diversity. Thus, we are unable to describe the abundance and diversity of phages within our samples without shotgun metagenome sequencing. Some of these challenges I was able to overcome, and others I was not. My work primarily depended on the assumption that there were phages on the leaves used to generate the initial inoculum. I took a “black box approach” in which I isolated the size fraction of the microbiome that should contain most lytic phage particles. I treated this as the “phage fraction”. I then looked for an effect of this fraction on bacterial abundance, composition, and diversity. This approach allowed me to overcome the difficulty of identifying and quantifying phages. My findings show that there is, indeed, an important effect of the phage fraction on the microbiome as a whole. This work also provides empirical support for the theory that phages mediate prokaryotic diversity and contribute to temporal population size dynamics. In order to measure phage abundance in starting samples, I attempted to use both fluorescence microscopy and transmission electron microscopy. Although both approaches yielded images of “phage-like particles”, it was impossible to quantify these particles, primarily due to the amount of background fluorescence that interfered with microscopy and the sheer difficulty of identifying low-abundance phages using electron microscopy. Another alternative for quantifying phage particles is flow cytometry, but this method suffers from the samelimitations due to the presence of intrinsically fluorescent contaminating particles. In Appendix 1, I describe a method that I was able to develop for quantification of known phages. If I were to continue the work described in Chapter 4, I would do so with a defined synthetic community of bacteria and phages, from which I could sensitively measure both bacterial and phage abundance throughout the course of the experiment. There remains much to learn about lytic and temperate phages. My findings in Chapter 5 attempt to disentangle the effect of lytic versus temperate phages on the bacterial community on leaves. This work is an important extension from Chapter 4 in a number of ways. First, I wanted to test if the patterns that I observed of the effect of lytic phages on the bacterial community after only a short time together on leaves were persistent over time. I found that patterns of the effect of phages on bacterial communities differed when examined after 3 weeks compared to 1 week. This has important implications in how we think about the issue of timescale in microbial community interactions. It also begs the question: how many lytic phages were present at the start of the experiments, cannabis drying and how long did they persist on the surface of the plants? If lytic phages do not persist, can selection for lysogenic phages produce some of the same patterns in the bacterial community as well? Interestingly, I did observe that treatment consisting only of bacteria that were passaged on leaves for several weeks, i.e. the treatment in which lysogenic phages would have been selected, had a qualitatively higher alpha bacterial diversity than the other treatments. This may suggest that lysogenic phages are capable of promoting bacterial diversity over longer time scales We were not, however, able to find conclusive evidence for an increased presence of lysogenic phage in the communities of bacteria passaged on plants in the absence of lytic phage.

The questions that I attempted to address in Chapters 4 and 5 are difficult to answer with current, common bacteriophage techniques. Moving forward, the best way to uncover both lytic and temperate phage abundance and diversity in the phyllosphere is likely through shotgun metagenomic sequencing. Sequencing the phage fraction would better describe the lytic phages in the system. Sequencing the bacterial fraction should reveal the prevalence of temperate phages integrated into the bacterial genomes. This approach is not devoid of challenges, however, as it can be difficult to sequence microbiomes associated with plants because of the presence of abundant contaminating plant genetic material. Improvements in high throughput sequencing are allowing us to overcome this limitation, if only by the sheer amount of sequences that can be obtained in environmental samples. I see this approach as the most promising way to comprehensively understand the abundance, diversity, and importance of bacteriophages in the phyllosphere.That the microbiome is an entity that fundamentally influences host health and function has caught the attention of researchers, medical doctors, nutritionists, and every other person interested in the microbial world that exists around and within them. Next generation sequencing and other “omics” approaches have enabled us to address the diversity and complexity of various microbial communities, but there are limitations to these approaches. For example, many labs frequently use 16S rRNA amplicon sequencing to describe bacterial communities. This is the most accessible approach due to reasonable cost, accessibility to protocols, and ease of use of sequence analysis pipelines. However, 16S amplicon sequencing only gives us coarse taxonomic resolution of the community, and it does not give any idea of function . The movement of the field away from the division of bacterial diversity within Operational Taxon Units to Exact Sequence Variants should provide more resolution of bacterial diversity, but still does not provide much insight into the functions of these taxa. It seems likely that as the cost of sequencing continues to fall and more labs have access to both the sequencing and analysis technology required, shotgun metagenomics will surpass 16S amplicon sequencing in popularity. Even so, these advanced sequencing approaches must be coupled with hypothesis-driven experiments and highly controlled experimental design. This, in addition to culture-based approaches and the use of synthetic communities when possible, will enable us to move the field beyond observational and correlational findings.Reconstructing the timing and magnitude of changes in human population size is important for understanding the impact of climatic fluctuation, technological innovation, natural selection, and random processes in the evolution of our species. With census population sizes estimated to be only in the millions during most of the Pleistocene, it is obvious that human population size has increased dramatically towards the present.