Hemp Growing

In 1985, Ex Parte Hibberd, an administration decision by the US Patent and Trademarks Office, extended property rights to the individual components of organisms, including genetic information, thus anticipating some of today’s contentious Genetically Modified Organism debates. Ten years later, Asgrow Seed v. Winterboer denied the rights of farmers to save and resell patented seed products, marking the continuation of a series of legislation that progressively placed power in corporate hands. In 2001, J.E.M. AG Supply v. Pioneer Hi-Bred International, a legal dispute between a large seed company and small seed supply center, affirmed that newly developed plant breeds are covered by expansive utility patents. In 2013, furthermore, Bowman v. Monsanto held that patent “exhaustion doctrine” does not cover farmers’ reproduction of patented seeds through planting and harvesting without the patent owner’s permission, further reflecting and securing corporate profit and influence. Lobbying: Although inadequate disclosure laws make it difficult to determine the exact amount expended on the Farm Bill and on other pieces of legislation, during the two years preceding the passage of the Farm Bill on February 7, 2014, at least 600 companies spent over $500 million in lobbying. The largest spenders ranged from Fortune 500 leaders in banking, trade, transportation and energy to non-profit organizations. A joint investigation by Harvest Public Media and the Midwest Center for Investigative Reporting found that the top 18 corporations and groups spent at least $5 million each in total lobbying from 2012 to the First Quarter of 2014. These corporations and groups include: the US Chamber of Commerce, Exxon Mobil, Du Pont, the American Bankers Association, Pharmaceutical Research and Manufacturers of America, Grocery Manufacturers Association, Wells Fargo, AARP, Monsanto, Independent Community Bankers of America, Coca-Cola, Association of American Railroads, Nestle, Nextera Energy, BNSF Railway Company, PMI Global Services Inc., Bayer Corporation, and American Forest & Paper Association.

The commodities support programs outlined above make up one major set of Farm Bill issues influenced by such lobbying efforts. These direct payments have long received the attention of growers groups and other interest groups that are beholden to corporate interests. Specifically, alongside the Farm Bureau, the Farmers Union, pipp vertical racks and other general farm organizations, all major agricultural commodities are represented by a lobbying organization that aims to keep the Farm Bill’s commodity programs intact as per the supposed interest of the producers of such commodities. These organizations include: the National Cotton Council, the Sugar Association, and the National Corn Growers, among others. While indeed all industries are represented by lobbying organizations, the relative political and economic strength of actors within the US food system that are already oriented toward large-scale production, processing, distribution, and service—such as those above—highlights their significance, particularly concerning contemporary campaign finance reform efforts. With the change to crop insurance as the safety net centerpiece, banks and insurance companies spent at least $52.6 million in lobbying the 2014 Farm Bill and other issues in the two years prior to its passage. For example, Wells Fargo, the fourth-largest US bank, spent approximately $11.3 million in lobbying efforts, signaling the potential gain to be had by the company’s Rural Community Insurance Services, the largest crop insurance provider in the country. The American Bankers Association, another group that will benefit most from the boost to crop insurance, reported spending $14 million on lobbying, including advocacy for crop insurance and other rural lending plans. Other lobbyists for crop insurance included Independent Community Bank-ers of America, ACE INA Holdings and Zurich , the National Association of Professional Insurance Agents, and Deere & Co., the large equipment manufacturer that also has a crop insurance arm. 

Private Funding: Private sector spending on agricultural research has risen steeply since the 1970s and 1980s, exceeding public sector spending on agricultural research. From 1970 to 2006, private agricultural research expenditures—both in-house research and donations to land-grant universities—rose from $2.8 billion to over $8 billion, in inflation-adjusted 2014 dollars. Yet total public funding—directed toward land-grant universities and the USDA—rose from $3.1 billion to $6.1 billion in that same period. Federal funding of land-grant universities in particular reflect such trends: by the early 1990s, industry funding had already surpassed USDA funding of agricultural research at land-grant universities and by 2009, private sector funding had soared to $822 million, compared to $645 million from the USDA. Significantly, the economic recession substantially restricted research funding. Yet USDA land-grant university funding dropped twice as fast as private funding between 2009 and 2010, from 39.3% and 20.5%, respectively, reflecting the increasing dependence of university research on corporate funds, particularly during economic downturns. Strategic Mergers: During the 1990s there were numerous mergers between agricultural, pharmaceutical, and chemical firms tied to the global seed industry that aimed to take advantage of potential synergies and secure even greater corporate profit and strength. Because the mergers took place within the globalized market where most seed industry markets exist beyond one nation-state, however, these expected synergies were not realized and resulted in the spin off of numerous agricultural divisions: Monsanto, for example, merged with Pharmacia and Upjohn before a new Monsanto division, now focusing on agriculture, separated to form a new entity. Syngenta began with the merge between the agribusiness divisions of Novartis and Zeneca. However, AstraZeneca, which focuses on pharmaceuticals, remains a separate company. Bayer acquired the agribusiness operations of Aventis, yet Sonofi-Aventis remains a financially distinct pharmaceutical company. By 2009, six companies with pharmaceutical and chemical origins held control over 67% of the global seed industry. “Revolving Door”: Collectively, in addition to the lobbying strength they exert and the private funding they funnel into public institutions, corporations have also been effective in translating their economic power into political power by way of the “revolving door” between corporations and the government.

In 1999, for example, Monsanto was described as a “virtual retirement home for members of the Clinton administration.” The outcome of such tight relationships between corporations and governments is readily apparent in federal legislation that upholds agribusiness power. The “Farmer Assurance Provision,” for example—a provision of a bill that was signed into law in March 2013 by President Obama, yet only remained in effect for six months—undermined the Department of Agriculture’s authority to ban genetically modified crops, even if the court ruled that such crops posed human and environmental health risks. Significantly, Republican Senator Roy Blunt worked directly with Monsanto employees to draft the initial provision. Although supporters stated that the provision was necessary to protect farmers from endless legal complaints by opponents of GMOs that hold up critical research, the Farmer Assurance Provision would have ensured a lack of corporate liability. THE WORLD HEALTH ORGANIZATION defines food security as having consistent access to sufficient, safe, and nutritious food to maintain a healthy and active life. At its core, however, food insecurity is a matter of income and poverty. As such, programs that aim to remedy food insecurity—most notably, the Farm Bill’s Supplemental Nutrition Assistance Program —hold potential not only as key nutrition assistance programs, but also as part of the anti-poverty programs and safety net to support historically marginalized communities in the United States, including low-income communities and communities of color. This is especially the case during times of economic hardship. In this context, Part II first provides a brief snapshot of the state of poverty, food insecurity, pipp grow racks and public nutrition assistance in the United States . It then addresses the origins of SNAP and its supposed concretization as an anti-poverty program in the 1970s, while tracing key periods of the erosion of the program tied to corporate influence and larger trends in public assistance reform. It then addresses in greater detail ongoing corporate influence and gain, particularly in the context of neoliberal economic and political restructuring since the late 1970s and early 1980s, and the myths against public assistance that undergird such gain: anti-poor and racist “culture of poverty” stereotypes, and the stereotype that people on SNAP are “not in a hurry to get off.” Finally, Part II further challenges these and other myths against public assistance and investigates the relationship between SNAP and Unemployment Insurance , another major safety net program, by highlighting their role during the global recession that followed the 2007–2008 financial meltdown. The 2007 subprime mortgage crisis that triggered the “Great Recession” was caused in part by intense financialization: relaxed lending standards and problematic federal housing policies, massive household debt, and the infamous real-estate bubble, among other factors. By exploring the racialized impacts of this decline in economic activity as well as the support available to low-income communities and communities of color—most notably SNAP and UI—this part argues that safety net programs have become essential for such communities. These communities use most of their total expenditures on food and other basic necessities, and are the hardest hit during such economic downtowns. While it also argues that such safety net programs, particularly SNAP, are an important strategy in preventing and alleviating poverty in the United States, Part II ultimately argues that the strong ties between SNAP and corporate control undermine long term and structural work against poverty and structural racialization.The Food Stamps Program, which was later renamed the Supplemental Nutrition Assistance Program , originated in the rural relief and commodity support policies of the New Deal era and, in the wake of the Great Depression, was just as much a farm price support program as an anti-poverty one. As part of the 1933 Agricultural Adjustment Act, the Federal Surplus Relief Corporation facilitated farmer and consumer support by allowing the federal government to distribute farm commodities, purchased at reduced prices, to state and local hunger relief agencies. Spearheading President Lyndon B. Johnson’s “War on Poverty” was the 1964 Food Stamp Act, which gained notoriety as a national anti-poverty program. Under the Food Stamp Act, food stamp benefits were financed by the government and administrative costs shared with states. Only with the 1977 Food and Agriculture Act enacted under President Jimmy Carter was SNAP directly incorporated as part of Farm Bill legislation. Before then, despite the work of the Federal Surplus Relief Corporation, the Farm Bill had long been geared primarily toward commodity support programs. During a decade that saw Black unemployment rise from less than double that of whites to 2.5 times that of whites , this move by the Carter Administration was generally hailed as their principal anti-poverty achievement. Toward this end, in the 1970s alone, federal expenditure on food support grew by about 500%. In 1981, a series of corporate- and government-driven cuts to public assistance began, with SNAP undergoing severe budget cuts of about $1.8 billion, or 16% of its budget, along with cuts to other food and agriculture support programs under the Farm Bill. President Ronald Reagan, who ushered in the era of neoliberalism, made “welfare queens” an epithet, and turned SNAP into a symbol of the ills of big government, made severe cuts to SNAP and other domestic spending, which coincided with the deep recession of the early 1980s. Subsequently, food insecurity in the United States rose during the 1980s and poverty peaked with 15.2% of the population living under the poverty level, the highest since the end of the 1960s. These cuts also facilitated the rapid growth of food banks and grassroots hunger relief agencies—rather than federal public assistance programs—as an appropriate response to the rise in hunger: more than 80% of pantries and soup kitchens currently operating came into existence between 1980 and 2001. Significantly, these cuts mirrored the broader trends in the corporatization of the food system, as outlined in Part I, including scaling back of federal efforts to stabilize prices for farmers and cushion the impact of market volatility, corporate growth, consolidation, and influence in the food system more broadly. In order to combat the growing hunger crisis in the United States, funding was partially restored to SNAP in 1988 and 1990. Funding increases were accompanied by efforts to not only streamline administration of SNAP with an early form of the Electronic Benefit Transfer card, but also to expand eligibility for low-income communities. Yet SNAP’s growth in the early 1990s was countered in the mid-1990s with the conversion of funds into block grants to the states, and the enactment of more strict requirements on SNAP usage and eligibility.

Racial/ethnic inequity with regard to land access is a defining feature not only of the corporate-controlled food system, but also of the US government itself, which, even years after emancipation, has made it nearly impossible for Blacks and other communities of color to acquire and keep land in substantial numbers. For example, in 1920, 926,000 US farmers were Black and they owned over 16 million acres of land, and by 1997, fewer than 20,000 US farmers were Black and owned approximately 2 million acres of land. While white farmers were losing their farms during these decades as well, the rate that Black farmers lost their land has been estimated at more than twice the rate of white-owned farm loss.Though the Farm Bill itself does not deal directly with immigration, the impact of the Bill on farmworkers cannot go unnoticed. The combination of an immigration system easily exploited by employers, 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. 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. Given the fact that the Farm Bill supports many of those companies that employ farmworkers, connections must be drawn to highlight how the Farm Bill upholds and perpetuates structural injustice among farmworkers.In the US, vertical weed grow exposures to environmental hazards have disproportionately impacted low-income communities and communities of color.

As a major contributor to global climate change and the racialized distribution of its impacts, conventional agricultural production practices, in particular, have been instrumental in maintaining and upholding these disparities. Furthermore, low-income communities and communities of color in the United States bear the burden of the impacts caused by climate change. For example, these populations breathe more polluted air than other Americans, suffer more during extreme weather events, have fewer means to escape such extreme weather events, and disproportionately experience greater hardship due to rising energy, food, and water costs.This report found a number of structural barriers to addressing these racial/ethnic, gender, and economic inequities. First, the Farm Bill itself is increasingly imbricated in, and ultimately functions as a pillar of, neoliberalism. The long term shift from the subsidization of production and consumption to the subsidization of agribusiness has structurally positioned low-income communities and communities of color on the losing side of such shifts. This population has also been given fewer options for recourse, given the ways in which the Farm Bill has been designed to be insulated from democratic influence, particularly by way of countless layers of congressional committees. Second, under the current Farm Bill, supporting public nutrition assistance programs and fighting poverty and racial/ethnic inequality, are antithetical to one another, despite the evidence that suggests otherwise. Specifically, while such public assistance programs do provide support to some of the most marginalized communities, they ultimately maintain structural inequity, particularly in terms of wealth, by channeling profits to corporations such as Walmart and other large retailers, which benefit greatly from distributing benefits such as SNAP.

Many of these corporations are then able to funnel profits back to their corporate headquarters outside their respective retail sites, while still paying workers low wages and granting few benefits at every level of the food system. Finally, this report found that supporting the inclusion of producers of color into current payment schemes, and fighting poverty and racial/ethnic [ii]Neoliberalism is a new period of capitalism, particularly since 1970s and 1980s, characterized by unparalleled global reach of economic liberalization, open markets, free trade, and deregulation. Such changes have been facilitated by a mix of high-tech globalized financial systems and labor markets, speculative financial markets, corporate control over the public sphere, increased commodification of human heritages , and increased consumerism. inequality, are also antithetical to one another, despite recent gains in terms of USDA Civil Rights settlements and slowly increasing participation in such programs by such producers. Specifically, while such disparities may be addressed in part by better outreach and assistance, these payment programs, and even crop insurance, ultimately maintain structural inequity, particularly in terms of wealth and land access. For example, producers, be they of any racial or ethnic background, are forced to cut costs wherever possible, which includes: deploying environmentally destructive practices and unjust hiring practices, cutting farmworkers’ pay and working conditions, and relying upon troubling international economies of migrant agricultural labor collectively, which result in regressive racialized outcomes.Socially, economically, politically, and environmentally, the US food system has become characterized by widespread inequity. While corporations control agricultural production and prices, and enjoy record profits, many farmers cannot make a living, are increasingly vulnerable to price fluctuations, and struggle for market access in increasingly concentrated commodity markets.

While corporations reap the benefits of an overworked and underpaid work force, both on and off the field, many consumers, including food system workers themselves, do not have access to nutritious and affordable foods. Additionally, soil degradation, water pollution, and global climate change continue to advance, in part due to large-scale industrial agriculture. The US food system today, however, is not only characterized by social, economic, political, and environmental inequity. Our research indicates that inequity within the food system—such as limited access to nutritious and affordable food, high quality land, or farmers support program benefits—cannot be addressed without addressing inequity within society as a whole, such as low income and limited employment benefits, unfair treatment by public institutions, and limited access to positions of power. Of central concern within this report, therefore, are corporate control and structural racialization within the US food system and society as a whole. Significantly, the production of racial/ethnic and economic inequity in the United States, particularly in terms of wealth, land access, access to positions of power, and degree of democratic influence, is more so a product of cumulative and structural forces than of individual actions or malicious intent on behalf of private or public actors. To challenge and eliminate corporate control and structural racialization in the United States, it is necessary to analyze the ways that public and private institutions are structured, and how government programs are administered and operate in such a way that that reproduces outcomes that marginalize low-income commu-nities and communities of color. Additionally, it is crucial to analyze the genesis and formation of institutions and structures themselves. The US Farm Bill has been the flagship legislation of food and agriculture since its inception in 1933 and is at the heart of policies implemented by public and private institutions that comprise most of the US food system. As such, structural change requires a strong and united movement that is capable of organizing and mobilizing at the state and national level, and that aims to produce conditions required for food sovereignty, including food access, health equity, fair and living wages, land access, just immigration policy, restraints upon corporations, non-exploitative farm labor conditions, and environmental well-being, among others, in particular, and racial/ethnic, gender, and economic justice more broadly. It also reflects a prime opportunity to address corporate structural racialization at multiple scales: from the scale of the food system to that of society itself.

As such, structural change requires a strong and united movement that is capable of organizing and mobilizing at the national level, and that aims to produce the conditions that would guarantee food sovereignty, including food access, health equity, fair wages, land access, just immigration policy, restraints upon corporations, non-exploitative farm labor conditions, and environmental well-being, among others. Such a movement would thus need to encompass grassroots and advocacy organizations that are anti-capitalist, new economy, anti-racist, and feminist, and that are oriented toward environmental justice, labor rights, immigration rights, food justice, climate justice, pipp horticulture racks cost and human rights, among other strategies and goals. Toward this end, the US Farm Bill is a challenging, yet promising, target for structural change within such a movement. This report is of particular importance 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.In terms of structure, the food and agricultural provisions and programs of the Farm Bill are divided into overarching categories called “titles.” These titles are not static and can change between Farm Bills during the re-authorization process. The 2008 Farm Bill had 15 titles, for example, while the 2014 Farm Bill has 12 titles: commodities, conservation, trade, nutrition, credit, rural development, research, forestry, energy, horticulture, crop insurance, and miscellaneous. In terms of scale, the 2014 Farm Bill provided $489 billion in mandatory spending for all titles over the next five years and $956 billion in mandatory spending until 2024. Among the titles of the 2014 Farm Bill, programs under the nutrition title are the largest, accounting for 80% of spending. Nutrition is followed by crop insurance, which accounts for 8% of spending; conservation, which accounts for 6% of spending; and commodity programs, which account for 5% of spending. The remaining 1% of spending includes trade subsidies, rural development, research, forestry, energy, livestock, and horticulture/organic agriculture. Finally, in terms of the process itself, the Farm Bill comes up for renewal approximately every five years. Congressional negotiations on the composition of the bill typically take between two to three years. Many interest groups and corporations shape the Farm Bill by way of lobbying, campaign donations, and other such efforts. Though they vary greatly by their degrees of influence, such actors include large retailers and food manufacturers , suppliers and manufacturers of agricultural inputs , members of government and special interest groups , as well as a diverse set of advocacy organizations . Typically, it is corporate interests and actors that have had the greatest influence in pushing for specific language and policies that advance their respective interests in the Farm Bill.The commodity title includes several programs that aim to protect farmers against sharp fluctuations in prices on primary commodity crops and to keep production relatively profitable. In previous years, the commodity title was primarily geared towards providing large “direct payments” to farmers regardless of how much they actually planted or for how much they would sell their crops. The 2014 Farm Bill cut most of these direct payments by about $19 billion over 10 years, which was the most drastic policy change in this current Farm Bill. Much of this money has gone into other types of farm aid, particularly disaster assistance for livestock producers, subsidized loans for farmers, and the crop insurance program. For example, the 2014 Farm Bill abandoned the 70-year-old practice of setting minimum prices for milk, cheese, and butter, and instead invested in insurance for dairy farmers to protect themselves against price volatility or rising feed costs. Significantly, the shift toward crop insurance programs has largely benefitted private insurance corporations, banks, and the largest producers more than small and mid-sized farmers. The conservation title includes programs to help farmers protect against environmental degradation and maintain their means of production through the use of sustainable management practices. The conservation title also includes programs that pay farmers to retire some of their land, such as the Conservation Reserve Program, the largest land retirement program in the United States. The $4 billion cut in the conservation title in the 2014 Farm Bill marks the first time Congress has voted to reduce conservation spending since the title first entered the Farm Bill in 1985. In every Farm Bill since then—1990, 1996, 2002, and 2008—funding for the conservation title has increased.Trade funding is used to promote US commodity crops and food aid abroad as well as technical assistance to farmers in developing countries. Although President Obama suggested an overhaul of the food aid program—aiming to replace the processes of selling US-produced food to developing countries with direct payments to developing countries—such reform efforts did not take hold and Congress kept the food-aid program intact.

My model projects the economic effects of Prop 12 on the North American hog/pork supply chain. It incorporates the vertical supply chain of representing farms, intermediaries, and consumers. The equilibrium is derived in the vertical market without regulations, which is then compared to the equilibrium after I incorporate the local jurisdiction limit on sale of pork products determined farm sow housing practices. The model includes two regions – inside and outside California – and three sets of agents along the supply chain – hogs farms, processors and marketers, and consumers in and outside California. Quantitative simulations calibrated on recent market characteristics and response parameters from the literature show that: compliant farrowing operations incur higher costs ; compliant processing and distribution operations incur higher costs ; covered pork products have higher retail prices in the regulated jurisdiction ; impacts on consumers outside the regulated jurisdiction and for the unregulated pork products are minimal, with higher prices, California consumers of uncooked pork cuts have substantial welfare losses , and producer surplus impacts are small because consumers in the regulated jurisdiction pay higher prices that cover compliance costs. Results are robust to reasonable ranges of response parameters. The major hog requirement of Prop 12 is that farrowing operations for which the meat from pigs is destined for California must provide group housing with more than the normal amount of space for sows.

Operations that already use group housing have a compliance cost advantage over those that use stall housing. Although California demands pork from less than 10% of North American hogs and 30% of sows in North America are already in group housing, a sufficient share of pork products is available to be diverted to California under Prop 12. However, because the space per sow in the California-compliant group housing was higher than the North American standard, commercial cannabis growers there remained significant costs of compliance at pig farrowing farms. Prop 12 and, more broadly, regulations imposed at a local point of purchase are unlikely to be economically efficient ways to farm practices because they raise costs all along the supply chain as well as at the farm. To highlight the importance of this point, I evaluate an alternative policy under which California would directly subsidize farms to change their housing practices to meet Prop 12 housing standards. The analysis shows that, for the same cost to California residents, the alternative policy would cause more than twice as many sows to be housed in ways that meet California’s standards than would under the Prop 12 regulations of California retail market standards. To explore willingness to pay for product attributes linked to two sets of carrot production practices, organic and fresh cut, I conducted a series of large on-line surveys of U.S. carrot buyers. Starting in December 2019, I asked on-line respondents about their willingness to pay for carrot packages of different attributes in 7 rounds of surveys over about 15 months until March 2021. In all more than 300,000 respondents provided data for my econometric estimation. Respondents face one of two types of survey questions.

The first type of question showed survey respondents a picture of a carrot package and asked which WTP interval represented the most they would be willing to pay for the displayed package. My analysis compared WTP responses from groups that saw packages displaying different attributes. In the other question framework, respondents were shown pictures of two packages side by-side that differed by a single attribute , each with a stated price. Respondents were asked which of the two packages they would be willing to buy at the state price for each. The results of this part of my dissertation are of two types: substantiative about willingness to pay for carrot attributes, and methodological about survey procedures. Main substantiative empirical findings are: Based on the questions for which respondents were shown side-by-side pictures of alternative packages, the median WTP for the organic attribute is estimated to be between $0.19 to $0.23 per pound . Based on the questions for which respondents were shown side-by-side pictures of alternative packages, the median WTP for the fresh cut attribute is estimated to be between $0.47 to $0.56 per pound . Willingness to pay results from the question when respondents faced a single picture of carrot package indicate a large response to price, suggesting that many respondents had a “baseline” market price for carrots in mind. However, this framework was less successful in eliciting differential willingness to pay for attributes in comparison with carrot packages that were not displayed. 

Given the large sample sizes, parameters are precisely estimated, and differ little in response to large economic, supply chain, and social disruption over periods before and during the pandemic. Overall, the research demonstrated that reasonable and useful willingness to pay information can be gathered from cost-effective surveys . I documented stability of parameter estimates over time and found that showing respondents displays of relevant comparisons may be particularly important in framing the question.The first part of the dissertation, dealing with the California Prop 12 regulations of hog and pork regulations, makes three main contributions. The first contribution is to show how economic implications of consumer regulations that apply in a limited jurisdiction have implications for producers that depend on their cost of compliance, and for consumers that depend on whether they are within the jurisdiction of the product regulations. The second contribution is to evaluate how such consumer product regulations that apply in local jurisdictions likely create incentives for only the producers already close to compliance to change their practices. This reduces the costs of the farm practice shifts, but also means that relatively little change occurs in farm practices. The third contribution is to show that consumer product regulations tied to upstream production practices are especially costly ways to achieve changes in farm practices because they impose significant cost on processing and marketing services because of the need for segregation, certification, and traceability. The second part of the dissertation, on consumer demand for carrot attributes, makes several broad contributions. First, although carrots are a widely consumed, staple vegetable in the American diet, very little economic research has been devoted to carrot demand broadly or on demand for organic and fresh cut attributes. My dissertation research begins to fill this lacuna. Second, I find that WTP parameter estimates were constant over periods of massive economic, supply chain, and social dislocation. Third, I show reliable and robust ways to elicit useful estimates from a large and cost-effective online survey. My sampling approach and my empirical procedures offer guidance to empirical research on consumer demand.Foie gras is a food product made of the liver of a duck or goose. Although foie gras can be produced using natural feeding, foie gras production is usually conducted by force-feeding. Force-feeding, growing racks often called gavage, is feeding a duck or goose with more food than they voluntarily eat, fatting the liver. Animal rights activist groups, including the Humane Society of the United States, claim that force-feeding is inhumane treatment of animals . Several countries attempted to prohibit force-feeding practices in production within their jurisdictions. For example, the Israeli Supreme Court ordered the Israeli Ministry of Agriculture to prohibit geese force-feeding to produce foie gras in 2003 . The United Kingdom banned foie gras production under the Animal Welfare Act 2006. However, these examples do not restrict selling foie gras products sold within the regulating jurisdiction. This subsection provides three examples of banning foie gras products sold within the regulating jurisdiction.In 2004, California passed Senate Bill 1520, which changed the California Health and Safety Code. Section 25981 prohibits force-feeding in foie gras production: “a person may not force feed a bird for the purpose of enlarging the bird’s liver beyond normal size” . Section 25982 prohibits selling foie gras products in California: “a product may not be sold in California if it is the result of force feeding a bird for the purpose of enlarging the bird’s liver beyond normal size” . Farms had a seven and one-half year period to modify their production practices. The regulations were implemented on July 1, 2012.

To overturn the foie gras ban, in 2015, the California attorney general appealed to the Ninth Circuit. However, in 2017, the District Court favored the ban, and the law was upheld .In 2006, the Chicago City Council passed an ordinance banning foie gras, City Ordinance PO- 05-1895. The ordinance prohibited selling foie gras in all food dispensing establishments in Chicago. Food dispensing establishments were defined as “any fixed location where food or drink is routinely prepared and served or provided for the public for consumption on or off the premises with or without charges.” The ordinance became operative on August 22, 2006. Soon after the ordinance was passed, the city was sued by the Illinois Restaurant Association and a local Chicago restaurant in the state court, claiming that the ordinance violated the Illinois constitution. However, in 2007, the district court concluded that the ordinance did not violate the Illinois Constitution or the United States Constitution. However, after lobbying by restaurant owners, in 2008, the Chicago City Council repealed the foie gras ban.In November 2019, the mayor of New York City signed the bill banning the sale of force-fed poultry products. The New York City Council introduced the bill in January same year. After a series of hearings and amendments, the council approved the bill in October 2019. The bill is scheduled to take effect three years after it was enacted in November 2022. The new law prohibits selling force-fed poultry products, stated as follows: “No retail food establishment or food service establishment, or agent thereof, shall store, keep, maintain, offer for sale, or sell any force-fed product or food containing a force-fed product.” . According to the definitions in the law, retail food establishment includes supermarkets, grocery stores, specialty food stores, and farmer’s markets. Also, food service establishment includes any type of food service providers, stated as follows: “a place where food is provided for individual portion service directly to the consumer whether such food is provided free of charge or sold, and whether consumption occurs on or off the premises or is provided from a pushcart, stand or vehicle.” .Traditionally, fishers have used dolphins to harvest tuna. Because mature tuna swim below dolphins, fishers use dolphins to locate tuna schools. Drift netting was a widely used fishing practice to harvest tuna. The nets are drawn around located tuna schools, and the bottom of the net is tightened. Then, the fish are trapped inside and hauled onboard. Because dolphins swim above the tuna schools, drift netting catches those dolphins, which frequently kills those dolphins. In response to the reduced number of dolphins by drift netting, consumers boycotted canned tuna in the 1970s and 1980s . One type of consumer response was legislation. In Portland, Oregon, a group of consumers petitioned for an initiative to ban selling canned tuna caught by drift netting in 1990. However, their attempt did not result in legislation .Subnational jurisdictions, e.g., U.S. states and municipalities, increasingly impose farming practices regulations within their jurisdictions . Examples include restrictions on farm organizational structure, regulation of farming practices that cause pollution, setting of minimum wages and working conditions for farm labor, and limiting the use of inputs such as chemicals and fertilizers in crop production and hormones and antibiotics in livestock production . Although such regulations impact the cost of production and competitiveness of farms located within those jurisdictions, the products produced under these various regulatory regimes are eventually commingled in the supply chain without identity preservation and sold to consumers in integrated markets. Such regulations differ significantly in their economic impact from an emerging body of laws and regulations that control production practices for food products sold within the regulating jurisdiction regardless of where the products were produced . A key example is California’s Proposition 12 that was approved by voters in November 2018 and set to be implemented fully in January 2022. Prop 12 sets specific housing requirements for egg-laying hens, breeding pigs, and calves raised for veal and prohibits the sale in California of specified products derived from covered animals maintained in housing that does not meet these standards, regardless of where the covered animals were located. Other examples of such regulations are presented in the previous section.

Our research builds upon this emergent body of work that employs urban agroecology as an entry point into broader policy discussions that can enable transitions to more sustainable and equitable city and regional food systems in the U.S. . This transition in UAE policy making is already well underway in many European cities . As noted, there are many dimensions of agroecology and ways in which it is conceptualized and applied. We employ the 10 elements of agroecology recently developed by the UN FAO in our discussion of urban agroecology1 . These 10 elements characterize the key constituents of agroecology including the social, ecological, cultural, and political elements. Despite the emancipatory goals of agroecology, a recent review of the literature by Palomo-Campesino et al. found that few papers mention the non-ecological elements of agroecology and fewer than 1/3 of the papers directly considered more than 3 of the 10 FAO-defined elements. In an effort to help guide the transition to more just and sustainable food and agricultural systems in cities across the U.S., we propose that food system scholars and activists consider using the 10 elements as an analytical tool to both operationalize agroecology, and to systematically assess and communicate not only the ecological, but also the social, cultural and political values of urban agroecology. “By identifying important properties of agroecological systems and approaches, as well as key considerations in developing an enabling environment for agroecology, the 10 Elements [can be] a guide for policymakers, practitioners and stakeholders in planning, managing and evaluating agroecological transitions 2 .n San Francisco’s East Bay region, urban food production proliferates in schoolyards, vertical growing racks in half-acre lots converted to urban farms, on rooftops, and in backyards reflecting a diversity of participants, goals, impacts and challenges .

The San Francisco East Bay region is also experiencing rapid gentrification and a worsening affordable housing crisis coupled with high rates of income inequality and food insecurity. The challenge of urban soil contamination creates tradeoffs for aspiring growers between vacant lot availability and siting on the most heavily polluted plots . Specific city policies vary in the degree to which they support or discourage urban agricultural activities, and availability of arable land across the East Bay is uneven. Our case study focuses on urban farmers in the East Bay spanning over 28 miles from El Sobrante in the northeastern edge of the bay, to Hayward in the southern East Bay as shown in Figure 1. We include both for-profit and non-profit farms ranging from educational school gardens to roof-top farms marketing microgreens.We employed a participatory and collaborative mixed methods approach, involving diverse stakeholders from the East Bay Agroecosystem. We held two stakeholder input sessions involving over 40 urban farmers and food advocates to co-create the research questions, advise on the data collection process, interpret the results, and prioritize workshop topics for the community. We administered an online Qualtrics survey to 120 urban farms in the East Bay that had been previously identified by the University of California Cooperative Extension Urban Agriculture working group and additional outreach. The survey launched in Summer 2018, which is a particularly busy time for farmers, and in response to farmer feedback was kept open until November 2018. 35 farmers responded in total, representing a 30% response rate.

While there are limitations in our ability to generalize findings to the East Bay urban farming landscape as a whole due to the relatively small sample size, we obtained a fairly representative sample of the diversity of farm types in the East Bay based on our typology of the original 120 farm types . Survey questions fell into nine categories: 1) Background Info, 2) Farm Description, 3) Operating Expenses and Revenues, 4) Land Access and Tenure, 5) Production and Soil Health, 6) Distribution, 7) “Waste” and Compost, 8) Food Access, and 9) Training, Communications, and Follow Up. There were a few open-ended questions allowing farmers to express what they saw as the three largest challenges facing urban agriculture operations in the area, and policy-relevant suggestions for securing spaces for urban farms and increasing community food security. In addition, we interviewed five urban farmers to deepen our understanding of the social, political, economic, and ecological constraints under which their farms operate. These farmers are particularly involved in networking efforts to strengthen urban farm viability in the East Bay. Four out of five represent locally prominent non-profit farms and one subject represents an alternative cooperatively-run urban farm; three interview subjects are women and two are men. Our study complied with UC Berkeley’s Institutional Review Board protocol for the protection of human subjects and all participants gave consent for participation.Most farms including the UC Oxford Tract and Gill Tract Farms, distribute food to a diverse array of community organizations. The two aforementioned farms together distribute food to over 50 community organizations, ranging from food pantries to community health groups to native land trusts seeking to feed and reclaim land for those of indigenous heritage. 52% of respondents distribute all food within 5 miles of their farm, while 70% distribute within 10 miles.

Produce from each farm site reaches approximately 250 people per week on average during the peak growing season, or approximately 7,000 people from all surveyed farms. Customers reached is moderately correlated with total revenue suggesting a growing impact on CFS as farms access additional income. Farmers reported diversified distribution methods including volunteers harvesting and taking food home , on-site consumption , on-site farm stand distribution, CSA boxes at pick up sites, and volunteers delivering produce directly to distribution sites . Some gleaning and second harvesting occurs at urban farms and gardens with potential for growth given reported “unharvested” and “wasted” food percentages. Backyard produce is also exchanged through crop swaps and neighborhood food boxes . Eight operations reported having access to a refrigerated truck for food deliveries, and two are willing to share their truck with other farmers. There is no universally used or city-organized process for distributing produce off of urban farms and into the community, yet there exists great interest in aggregating produce or distribution channels , an unrealized goal of urban farmers in the East Bay. All of the food system stakeholders involved in our study are working towards transformative food system change, focused on increasing equity, food security, and access to healthy, locally sourced food. See Box 1 for a description of one of the non-farmer stakeholders engaged in the food recovery and distribution system, who has recently established an aggregation hub to serve as a network for reducing food waste and channeling excess food in the urban community to those who are food insecure.Farmers in our study stressed the importance of producing non-food related values on their farms, including education and community building. One farmer in particular emphasized their organization’s mission of “growing urban farmers growing food,” or teaching other people how to grow a portion of their food basket, vertical farming racks thus unlocking food sovereignty and food literacy while increasing healthy food access. Another respondent reported that their farm is “highly desirable for adults with special needs that need a safe place to be outside,” echoing respondents who point out the intimate connection between food and health . Farms frequently reported hosting educational and community-building workshops, cooking and food processing demonstrations, harvest festivals, and other open-to-the-public community events enhancing the resilience and connectivity of people, communities and ecosystems. Social networks emerged as an important theme for enabling the establishment of urban farms , and sustaining operations through social connections between urban farmers and other food justice and health advocates.Farmers identified three primary challenges: revenue, land, and labor inputs. Half of all respondents reported farm earnings of $1,500 annually or less, and all four operations receiving over $250,000 in annual revenue are well-funded non-profit operations . Regardless of for-profit or non-profit status, most farms reported multiple sources of revenue as important to their continued operation , with an average of 3 revenue streams per farm. All non-profit farms reported multiple revenue streams except for three, who were sustained entirely by either board donations, membership fees , and grants. The most important revenue sources for non-profits include grants, grassroots fundraising, and unsolicited donations rather than sales. In addition to these monetary sources, all farms reported receiving substantial non-monetary support , which adds to the precarity of operations when these informal support channels disappear.

Land tenure arrangements range from land accessed without payment through contracts with City or School District officials, to arrangements where a token fee is paid , to more formal leasing arrangements at the utility-owned Sunol Ag Park, where land tenants pay $1000/acre/year for their plots, ranging from 1-3 acres. Only five of the respondents owned their land , representing a mix of for-profit and nonprofit operations . Challenges around land access, security, and tenure were the most frequently occurring theme in the survey long response and interview analysis process, including consensus that land access is the largest barrier to scaling UA in the East Bay. The cost of labor, and relatedly, access to capital and grant funding to pay living wage salaries, were also extremely significant challenges identified by survey respondents. The majority of respondents stated that most of their labor is volunteer rather than paid, with nonprofit respondents reporting this more frequently than for profit enterprises . The maximum number of paid staff at any operation is 20 , while the average is 4. Many farms reported the desire to be able to hire and pay workers more, but not having sufficient revenue to accomplish that goal. Annual volunteer labor participants on farms ranged from 0 to 1542 with an average of 97 volunteers, representing a significant public interest in participating in local food production. Not surprisingly, amount of paid labor and total farm income are positively correlated . However, volunteer labor is also positively but more moderately correlated with total farm income .The farmers in our study acknowledged many challenges facing urban agriculture, stemming both from the high economic costs of production and land rents, and insufficient monetary returns from produce sales. They also framed these challenges through a food justice lens, arguing that the current political economy does not fully compensate farmers for the social-ecological services provided from their farms. Farmers articulated many solutions that could improve the viability of their farm operations including: conversion of city parks into food producing gardens with paid staff, government and institutional procurement goals for urban produced foods, municipal investment in cooperatives or other community based food production , and establishment of aggregation hubs and distribution infrastructure.Our survey results describe a highly diversified East Bay Agroecosystem comprising urban farmers and other food system stakeholders that are growing food as well as food literacy, civic engagement, connectivity, and community. Applying an agroecological lens to interpret our findings of East Bay urban agriculture operations reveals the many agoecological practices farms have long been engaged in, as well as the important distinctions of UAE that still need to be explored, and specific threats to agroecology in urban areas. Pimbert suggests that “agroecology’s focus on whole food systems invites urban producers to think beyond their garden plots and consider broader issues such as citizens’ access to food within urban municipalities and the governance of food systems.” We argue that applying an agroecological lens to the urban context also invites researchers and urban planners and policymakers to think beyond garden plots and singular benefits of food production, to consider these sites as part of a larger agro-ecosystem with synergistic social, cultural and ecological dimensions. We reference the 10 elements of agroecology to illustrate the dynamics of how these elements manifest in practice in this urban context.All of the farms in our survey follow agroecological production practices which include a focus on building soil health through, most commonly, cover cropping, compost application, and no-till practices. These practices produce synergistic effects of adding fertility to the soil through organic matter amendments and boosting water holding capacity. Soil building practices are a response to the impetus to remediate toxins present in urban soils , a prerequisite to intensive cultivation and unique consideration of the urban farm environment. Overall, production practices on our urban farms seek to conserve, protect and enhance natural resources. Our survey respondents described numerous strategies for enabling diversified, intensive production of fruits, vegetables, and other agricultural products.

The inventories, however, must then be structured according to the formats usable by openLCA. This modeling process also requires an abrupt switch from a very high level diagram — the flow diagram — to a detailed inventory requiring low-level information about her farm. There is no process connectivity between the two steps and information does not flow between the models. Two: Time and effort overhead. Models must be manually inspected to ensure completeness and correctness. This may result in a suite of errors, such as models that misrepresent the system, models that are missing portions of the real world system, or models that have incorrect connections between processes. Further, the modeling effort expended to create the flow diagram is not utilized to reduce effort in the LCI creation process. The majority of LCI databases are focused on industrial production systems. Those that are concerned with food are oriented toward the production of processed foods as a resultof partnerships with food processing stakeholders. Data required to conduct an LCA of any alternative agricultural systems is unavailable and puts the onus on the system owner to collect primary data. Three: Lack of flexibility. Only one type of granularity, the unit process, can be modeled. There is no support for creating logical groupings of unit processes in the form of components and no capacity to create hierarchies of such components. Alice is locked into two disconnected levels of abstraction: the high-level flow diagram, vertical grow and the low-level inventory. Even once an LCA model is created, current mechanisms only support the sharing of unit process data is shared.

No other reusable portions of the model are explicitly supported. This also means that as things on the farm change, an entirely new LCA model would need to be created.Life cycle assessment has enabled people to investigate, quantify, and understand the environmental impacts of agricultural and industrial systems. On the whole, LCA is useful, but it is not without its limitations. LCA requires meticulous and tedious data collection for every single process within a system. A good LCA is comprehensive and results in detailed models, however it can be a time consuming and cost-prohibitive process, depending on the size, complexity, and novelty of the system under analysis. The development of software also involves modeling, and there has been substantial research into improving the software modeling workflow. In the following analysis, I call on software engineering research and practice to tease apart some of the modeling challenges faced in modeling the environmental impacts of agricultural systems, as well as to propose opportunities for future work. Fred Brooks discriminates between essential difficulties with software — those relating to intrinsic characteristics — and the accidental difficulties — those relating to temporary or circumstantial characteristics. He goes on to state that to address accidental difficulties, one must promote incremental improvements, but to solve essential difficulties, one must promote revolutionary improvements. The challenges faced in the modeling of the environmental impacts of agricultural systems involve addressing both the essential and accidental difficulties. LCA is one of many environmental impact assessment methods that aim to address these difficulties. LCA methods, tools, and data have undergone immense incremental improvements . However, several essential difficulties remain.

Essential difficulties faced when modeling the environmental impacts of agricultural systems involve the representation, or capture, of complexity, change, and context of such systems. Interestingly, some of these mirror the essential difficulties or characteristics of software as defined by Brooks: complexity, conformity, and changeability.Only some of these attributes are represented in current LCA models: the flow diagram informally represents the scope and boundaries of the system and data documentation formats provide structure when representing unit processes. Dependencies, subsystems, and other potentially relevant attributes are linguistically represented, i.e., they are often described in reports that accompany LCA models. Through the structuring of unit processes as prescribed by DDFs and the list-of-unit processes LCI inventory, LCA results in a powerful declarative model: one where within the system boundary, unit processes represent all major flows of material and energy withinthe system, and are connected through the functional unit.Current LCA modeling is similar, in that it is effective at retroactively assessing static systems. Agricultural systems have a symbiotic relationship with the environment, and are closely tied to the health of the environment due to the interconnectedness with many natural systems. These assessment of agricultural systems is unique as not only is one assessing a system that is directly dependent on the natural environment, but that also has a large component of it that are not of human construction, i.e., they are a part of nature: plants, animals, and the land. The quality of the food produced on a farm is related to the quality of the land, air, and water used. These are seasonal systems that are constantly producing, consuming, and evolving over time.

The global warming potential of a farm in Northern California over the Spring of 2014, when they were growing peas and corn, will not be the same five years from now. For this reason, LCA models are time, location, and system sensitive. Dynamic models of agricultural systems that represent changing resource flows, however, are not available in the LCA modeling paradigm to date. Existing LCA models and tools do not capture change effectively. They provide functionality to increment a model version number, but updating and maintaining models to keep up with changes in the real world is difficult. Excel has the “track changes” functionality,which is a helpful collaboration aid that provides different authors with awareness of how a document is changing, but is not explicitly geared toward capturing changes in LCA models. GaBi does something similar. The tool logs all changes to each object at every save . Once again, the goal is to keep track of which user changed the model to support collaboration and not necessarily to augment the capacity of the model to reflect changes in the world. openLCA simply provides a version number for each object in the file that can be incremented as changes are made.State of the art LCA modeling tools do not provide the ability to revert to previous models, compare models over time, or maintain a model history that reflects the changing world. If one is to update and maintain a model over time, it would involve a manual process of backing up and continuing from where one left off. While this is a valid approach, grow vertical given the scale to which some LCA models can grow and the increasing complexity of agricultural systems, more advanced tool support is required.We are constantly producing and trying to connect different types of information in the real world, as well as in our models. A model has two aspects: the representation of a system, and perspective on the system. When these models are treated in isolation, they provide representation without context. Such models cannot be connected with each other to produce meta-models, nor can existing models be reused. When a model is created of an agricultural system, an artificially created system boundary sharply closes off the rest of the world from the system under analysis. The flow diagram tries to capture some contextual information by showing materials and energy flowing into and out of the system as a whole, but, as discussed earlier, the flow diagram is quite disconnected from the eventual LCA model. Trying to capture the context within which a system exists is a difficult problem. After all, one must scope a modeling problem appropriately lest they end up trying to simulate the world. The question remains, however, how does one capture the context of a system? Standalone LCAs are still common as they are conducted when stakeholders of a particular system are interested in self-evaluation and improvement. Comparative LCAs are conducted when stakeholders in an industry are interested in pitting one set of production techniques against another, to analyze whether or not a new or alternative concept is actually better for the environment than an older or standard approach, or any of the other reason discussed previously in this paper. Conducting an LCA that is wide in scope is difficult, and so, when people are interested in understanding wider or more far reaching impacts, existing LCA studies are used to conduct retrospective meta-analyses. Environmental consulting agencies also tend to conduct these analyses, as part of their business model is LCA as a service , where they charge for their expertise, time, and effort. Depending on the intellectual property issues at play, they may own the model details and are not beholden to reveal the raw data to anyone else.

LCA models provide perspective on a specific system without any context. They are difficult to connect with each other to provide more holistic environmental impact assessments, and it is difficult to reuse partial models. People all over the world are producing extremely detailed models, with a lot of time, money, and effort going into inventorying complex systems. A vast amount of data and a large number of models are produced through LCA. We are unfortunately, producing, but not connecting, environmental impact models. No explicit interfaces or connectors are available to connect entire LCA models.LCA models are intended to provide efficient and convenient access to information about the environmental performance of production systems, such as agricultural systems. However, due to the mismatch between the current LCA modeling language, workflow, tool, and the systems represented, I believe that an important opportunity has been missed to capture the complexity, change, and context of agricultural systems. In this section, I describe a potential avenue through which to address these modeling difficulties. In software engineering, the Object-Oriented paradigm came about as an attempt at capturing the essential complexity of software. Booch describes two kinds of decomposition: Algorithmic and Object-Oriented decomposition. The algorithmic results in a top down structured model, declarative and directed. On the other hand, in the OO approach, one decomposes the systems using the key abstractions. Booch notes: “[In object oriented modeling,] we view the world as a set of autonomous agents that collaborate to perform some higher level behavior”. Current LCA modeling utilizes a hybrid of the two, where the flow diagram is created using an OO approach, but is then quickly abandoned for a mostly algorithmic decomposition in subsequent models. Given the modeling challenges described in this chapter, I propose that a potential means to capturing the inherent complexity of agricultural systems is through a new modeling language that allows for a more object-oriented approach.A new modeling language would not be a silver bullet. It would not necessarily capture all of the complexities, changes, or context of agricultural systems, nor would it remove all accidental misrepresentation of systems. What it would hopefully do, is make the process of representing the environmental performance of such systems easier and less tedious, providing clear and distinct ways to represent and connect modular system models. They are prone to change over time due to rising environmental issues , advances in agricultural practices and technology, changing needs of an ever-growing human society, and a dynamic economic context. Our farms are part of the highly interconnected web of industrial civilization, with dependencies on many other systems. Environmental impact assessments are conducted, regulations written to govern production, tools built to guide the flows of materials and products through supply chains, and food labels created to assist in consumer decision making. At the core of these issues is a mismatch between existing environmental assessment tools and the needs of small- to medium-scale farmers , who do not engage in large-scale industrialized agricultural practices. In Chapter 3, I described the essential difficulties faced when modeling agricultural systems.The Grounded Theory Method is methodology used to develop theory about phenomena through iterative interrogation of data. GTM offers a means to explore new territory, particularly when there is a lack of dominant theory. While the goal of this study was domain understanding, I used GTM as a means of structuring the design of this study and utilize GTM techniques for subsequent data analysis. Through use of GTM, my broader goal was to develop theories of design for sustainable agriculture. Muller writes that one must “remain faithful to the data, and to draw conclusions that are firmly grounded in the data” . I do this by iterating between recruitment, data collection, and analysis, exploring the different concepts at play, while gradually developing a theory of how to design a consistent mechanism for modeling sustainable farms and their interactions with the environment.

Agricultural systems are a critical component of this infrastructure, providing crucial food products as part of the human food system, but also providing vital agricultural by-products as inputs for other industrial systems. They are deeply coupled with the natural environment and undergird human civilization. As this web of systems — industrial, agricultural, environmental, human — grows more intricate, it becomes increasingly difficult to understand and map out the consequences of our actions. Agricultural systems are, thereby, commonly assessed to improve economic performance, reduce environmental impacts, and improve social sustainability. System assessment begins with the representation of a system through some modeling process, and a subsequent evaluation of the model with respect to certain attributes. This process involves the collection, structuring, manipulation, and storage of a variety of data. System assessment often involves multiple modeling processes as each model is created to represent a particular aspect of a system and its performance. While instrumental in im-proving our understanding of these complex systems, these efforts have led to a fragmented field with tensions and pulls in different directions, and have resulted in a duplication of effort, and moreover, data and processes that are disconnected. Over the past decade, small- to medium-scale sustainable farms have been growing in popularity. There is an increasing demand for food that is grown sustainably, raised humanely, and produced with fair and just labor practices. This has spawned many efforts to curtail environmental impacts: including, numerous regulations, eco-labels, indoor cannabis grow system and certifications. Such efforts often require farmers to engage in additional record keeping that can be both tedious and time consuming.

Farmers, in essence, pay for the privilege of growing food sustainably. Despite growth in this sector, there is a lack of appropriate technological tools, arising in part from a mismatch between existing tools — which are typically designed for large-scale industrialized agricultural practices — and the needs of farmers who work at smaller scales. With this comes a growing need for systemic mechanisms to understand, analyze, manage, and further improve, sustainable agricultural systems. There is no dearth of analyses of the environmental impacts of our food systems, but there is a lack of connectivity across the plethora of models created and a lag with the changing real world that is represented.Agricultural systems are composed of heterogeneous subsystems with varied environmental impacts. For example, Figure 1.1 shows Alegria Fresh Farms, an urban farm in Orange County, California. It is a small-scale farm that produces fruits, vegetables and a variety of agricultural byproducts such as organic soil. It is not a single product system. A number of subsystems are present within the farm for irrigation, solar energy production, hydroponic cultivation, hydroponic vertical cultivation, certified organic cultivation, vermicomposting, and a nursery, among others. To understand which subsystems are more responsible for certain environmental impacts, one would need to be able to attribute impacts to particular subsystems and processes.This would show the farmer which cultivation method, for example, is more environmentally friendly. The hydroponic vertical cultivation subsystem shown in Figure 1.2 exemplifies the complexity of modern agriculture, even at this small scale. Many of the materials described are sourced externally from other industrial systems. Many external data are therefore required to accurately assess the environmental impact of the farm.

Availability and access to the environmental impact data of these external components is not guaranteed. Agricultural systems are also constantly evolving: equipment is upgraded, cultivation methods are refined to optimize certain metrics , subsystems grow and shrink, and food types grown change according to supply and demand, in addition to season. Figure 1.3 shows two satellite images of Alegria Fresh Farms: the image on the left is the farm shortly after it was set up. Alegria Fresh Farms was one of a set of small experimental and demonstration farms that were introduced to the Orange County Great Park in around 2009. The image on the right was taken after several years of farm activity. The farm has since been relocated. As changes are made in an agricultural system, any models that were initially created become outdated. When models are constructed for agricultural systems, they are a static representation of a dynamic system. Once data have been collected and things change, the model is no longer an accurate representation of the system. Over the last six years, the Alegria Fresh Farms has grown, systems have changed, and as a result, the relationship of the farm with the environment has been affected. However, many models, from satellite images to layout diagrams, cannot necessarily capture the change in the system and its en-vironmental impacts. While there are many techniques that allow for detailed analyses of an agricultural system’s environmental performance, they are primarily expert-driven technique, which means that once a model is created, it is difficult for the farmer to maintain or update it for continued use.Software systems for agricultural modeling are primarily designed for activities such as farm simulation and yield maximization.

These can be both computationally expensive. Such work, while interesting, relevant, and timely, primarily addresses the modeling and simulation of agricultural systems to improve crop yields and system management, with only a marginal focus on environmental assessment of these systems. Agricultural Ontologies: The Food and Agriculture Organization of the United Nations has maintained the AGROVOC project since the 1980s. It is a controlled vocabulary, designed for, and used by, information management professionals . It consists of over 32,000 agricultural concepts, gleaned from publications, research artifacts, and external thesauri. Similarly, the United States Department of Agriculture’s National Agricultural Library has provided a more America-centric glossary and thesaurus service since 2002. These initiatives are part of a broader goal within the agricultural information management community to standardize agricultural terms, concepts, and data. There are also several efforts — in early stages of development — to extend tradition agricultural thesauri into fully developed ontologies for use at the farm level to inform crop production, “foods-for-health” knowledge systems in the nutrition space, and to augment precision agriculture with plant-driven decision making capa-bilities. The AGROVOC team is also developing an ontology service in anticipation of semantic web requirements. Crop Modeling: Work in crop modeling tends to focus on operational and yield optimization. For example, Honda et al. present a service platform that uses a network of field sensors to obtain real-time field data to plan large-scale field operations e.g. fertilizer application. Meanwhile, Ponti et al. used statistical models to assess the yield gap between organic and conventional agriculture to obtain a deeper understanding of the range of performance in agriculture. Agricultural Simulation: Agriculture researchers often use simulations to understand and make predictions about the performance of various agricultural systems, while computation researchers apply their expertise to improving the performance of simulations in agriculture. For example, the Agricultural Production Systems sIMulator project is a modular simulation tool that allows for the investigation of relationships between plants, animals, soil, climate, and management involved in agricultural systems. Papajorgji et al. present different ways in which model-driven architecture, in particular the use of the Unified Modeling Language, can be leveraged to improve crop simulation models. Miralles & Libeourel study how Geographic Information System models can be brought to bear on crop simulation to allow for better integration of weather data. Life Cycle Assessment: LCA is a modeling technique used to assess the environmental impacts of products and the processes by which they are constructed. Farmers, along with environmental analysts, can conduct LCAs to quantify the environmental impacts of resource flows in a system, vertical grow racks and subsequently make improvements in the farming processes to reduce undesired impacts. Many software systems and databases exist to support LCAs, most of which are domain agnostic . Models of agricultural products and production systems are commonly created in the academic community using LCA. Examples of systems that have been successfully analyzed using LCA include: a large American feedlot where beef is produced; a soybean meal production chain that spans from Argentina to Denmark; and an Australian corn chips production chain that begins at the corn farm, and ends at the consumer-ready packet. While LCA models are intended to provide efficient and convenient access to information about the environmental performance of production systems, such as agricultural systems, there is a disconnect between the current LCA modeling process, the needs of sustainable farmers, and the systems represented.

The Bruntland report presents an umbrella definition of sustainable development as, “the ability of humanity to ensure that it meets the needs of the present without compromising the ability of future generations to meet their own needs”. While this definition is commonly used in sustainability research, it is too broad to allow us to engage in grounded, sustainable, human computer interaction work, or other forms of sustainable design activities. We often come away from such discussions with difficulty envisioning specific applications to design and actionable interventions to pursue. There have been many explorations of sustainability in the context of HCI, ICT for Development, and the coordination literature. However, just as most “sustainability-oriented work takes place outside HCI” , much design work for sustainable agriculture also occurs at the periphery of applied computing research. In this section, some of the more promising explorations within this periphery are reviewed.HCI and Agriculture: Interest in the intersection of HCI, design, and agriculture is growing. For example: Raghavan et al. recently suggested use of computation to design better agro-ecological systems, and Frawley & Dyson created non-human animal personas to enhance welfare in animal agriculture. For the most part, design for agriculture tends to be for specific subsets of agriculture. For example, Chinthammit et al. ran a Software Interest Group meeting, looking at “HCI in Food Product Innovation”. Whether it is design ideations specifically for urban residential gardeners or the design of platforms to assist in the creation of backyard permaculture systems, Di Salvo et al. point out that “there is a significant gap between the professional fields of industrial and interaction design and design research in sustainable HCI” . In an attempt to bridge this gap, my collaborators and I ran a workshop on at the ACM CHI conference in 2017 on “Designing Sustainable Food Systems”. We aimed to bring together HCI researchers, designers, and practitioners to explore, design for, and reflect on, opportunities for the HCI community to engage in creating more a sustainable food system. Coordination and Collaboration in Agriculture: Food production is an inherently collaborative process, with many stakeholders involved and varying organizational configurations across system types. Examples include: a qualitative study looking at coordination challenges in organic farm families, and an early warning management technique to enable collaboration among rice farmers participating in small-scale precision agriculture. Such work provides valuable context for understanding how the interplay between stakeholders on farms affects tool design. Farm Management: At the time of this writing, the Information Technology startup community began focusing on the design of tools for agriculture. Earnest examples include: precision farming tools, precision agriculture tools requiring specialized hard-ware ; daily farm management tools aimed at agribusiness; inventory management tools; and tools that provide analytics.Agricultural Environmental Policy: Environmental regulations have serious implications on farm-level assessment, data collection, and record keeping that farmers in California must engage in [38]. Regulations, both state-imposed, and federal, typically require some form of record keeping and form filling, and in some cases, the presentation of these documents during site inspections. Environmental policies that California farmers are subject to include: water, pesticides, and emissions regulations. In addition, farmers may participate in voluntary programs to demonstrate commitment to environmental protection, such as the environmental stewardship program. Environmental Labeling and Certification: The primary goal of environmental labeling and certification agencies is to provide farm quality assurance. These labels and certificates provide the purchaser, whether broker, retailer, or consumer, with the validation that a farm meets a particular standard set out by the certifying agency. The efficacy and usefulness of labels has long been debated from the early days of dolphin-safe labels to the present-day quandary of GMO-labeling. Nevertheless, many farms actively pursue labels and certifications, necessitating an additional layer of record keeping and data collection. There are four main types of labeling and certification in sustainable agriculture: government regulated National Organic Program; non-profit regulated ; retailer specific ; and product-specific .

By meeting others and forming new relationships, community members also increased their social support, an objective of the social and community context domain . When asked to describe their proudest success, one UA leader responded, “seeing friendships form that wouldn’t have happened otherwise.” They believed that the garden was a space for people to “meet people from different cultures, hear different languages, [and] try different cuisines.” Farmers described several types of outdoor community events, such as “Farm Friday,” for people to “have a drink and hang out during the summer.” One gardener said that they “run into good people” at their community garden, and another replied that “garden people are always fun to talk to.” Acknowledging Long Beach’s diversity, one UA site created promotional materials in English, Spanish, and Khmer, demonstrating linguistic capital, or skills from communicating in more than one language or style . During field visits to community gardens, I observed multiple occasions when people would walk by and ask questions about the garden. Gardeners welcomed outsiders by sharing their harvest and providing information on how to join.All interviewees described how UA had changed the land in some way. Community members worked together to enhance their neighborhood and built environment, which influences safety, health behaviors, and risk of disease . The UA sites in this study were built on former oil properties, dumping sites, vacant lots, and in some cases, previous UA sites. Social capital was crucial for building new gardens and farms. UA leaders collaborated with volunteers, the Conservation Corps of Long Beach, pipp racking local troops from Boy Scouts of America, and the UC Master Gardener Program to construct raised beds, sheds, and other amenities.

Ground Education typically constructed raised garden beds and nature paths on existing green space, such as playground fields. In other cases, UA leaders had to build on land that was previously unintended for planting. This passage illustrates how community members used social capital to collectively build a UA site. The garden director explained that they thoroughly tested the soil to ensure that it was safe, and that the process of organic gardening would further improve the soil over time. Gardeners’ determination to turn the former oil property into a green space demonstrates resistant capital, knowledge and skills fostered through oppositional behavior. By repurposing the once “lifeless” soil to grow food, gardeners also demonstrated aspirational capital, the ability to maintain hopes and dreams for the future . UA leaders emphasized the importance of protecting the environment for future generations, a further example of aspirational capital. Though most sites were located near freeways, UA sites planted trees to provide shade and oxygen for fresh air. Trees also served as a noise barrier and gave a “park-like feel” to UA sites. Gardeners and farmers used natural pest control methods and practiced composting to repurpose their food waste. One UA leader harvested seaweed from the beach to make kelp extract, which they used as a fertilizer.UA leaders shared how their sites directly benefitted community health by increasing access to fruits and vegetables, promoting physical activity, and supporting mental health. One UA leader was inspired to donate produce to the local university food pantry after reading articles on food insecurity among college students. Gardeners also relied on growing food for their own food security. For example, a gardener who lived alone and did not own a car relied heavily on their community garden plot. They said growing food helped them eat more fruits and vegetables “loaded with fiber.” Another mentioned their grandparents “save some more money” by eating the food their family grows. In this way, UA supported the SDOH domain of economic stability by allowing community members to reduce food expenses.

UA leaders believed that “if you grow your own vegetables, you’re more likely to eat vegetables” and that food grown from a garden or farm was “fresher,” “tastier,” and more “flavorful” than produce from grocery stores. During my observations, people at UA sites were consistently engaged in outdoor physical activity, usually with their hands, shovels, hoses, wheelbarrows, and other gardening equipment. When I asked an interviewee if they used any power tools for gardening, they replied, “We like to burn calories, not fossil fuels.” They added that “some of the younger folks really get a workout with the pickaxes and stuff,” while gardening for “older folks” is “slow, gentle exercise that [they] can do for prolonged period.” Others agreed that gardening helped them stay “healthy and active.” A gardener who suffered with joint pain from fibromyalgia said, “[Gardening] gets me out of my pain.” Another gardener had to stop gardening at one UA site because of chronic lower back pain but was able to begin gardening again at a different site with raised beds. He sat on his walker to pull weeds and said the raised beds help with accessibility. Participating in UA helped community members cope with physical discomfort, as well as negative feelings and emotions. The phrase “mental health” was repeated by multiple interviewees, such as one who stated, “I think that the biggest crisis that we’re facing in health care other than nutrition, is mental health.” UA sites provided a space for the community to “de-stress,” “heal,” have “peace of mind,” and “manage depression and anxiety.” One UA leader described their community garden as “therapeutic” and a “safe space” that benefitted their family’s physical health and mental health. Another said, “People told me it’s their lifeline… A lot of people told me that they went through a rough time, maybe they lost a spouse, or they had broken up, or they lost their job, and the garden was their focal point.” From my interviews and observations, many others seemed to share this sentiment. During this study, I met three gardeners who had experienced homelessness. They all expressed how gardening helped them stay calm during stressful times. A UA leader who previously lived in their car and struggled with addiction issues said that gardening helped them with sobriety. They said, “The garden is important to me because it gave me purpose.” The act of gardening allowed community members to shift their attention away from negative thoughts and focus on the present moment.UA presented a multitude of learning and teaching opportunities for people of all ages. Ground Education’s lessons throughout the LBUSD are one example that ties directly to the SDOH goal of increasing educational opportunities and helping children and adolescents succeed in school . In October 2023, I had the opportunity to assist third grade students with an herb-picking activity. A lively group of about 30 students walked from their classroom to the school garden. Since it was also Picture Day, the students wore their best clothing. In their suits and dresses, the third graders cheerfully ran up to the Garden Educator and greeted her with hugs. Once the students took a seat on the outdoor benches, the educator began the lesson with a mindfulness exercise. Students placed a hand on their heart and stomach, so they could feel their body move as they inhaled, held their breath, exhaled, and repeated. After the exercise, the energetic class of third graders appeared calmer. The educator asked the students to name plants that were herbs. Then, she discussed how herbs were used as medicine by native peoples, and how herbs are still used today in teas and topical treatments. The educator used a whiteboard with a diagram of the limbic system to teach students about the olfactory bulb. As the educator explained to students, the olfactory bulb in the brain perceives smells and sends signals to the body, which is why smelling different herbs can make people feel more focused, calm, or alert. After the lecture, I helped distribute sachets for the students to collect herbs. We walked from the main school garden to a “Secret Garden,” where the next part of the lesson would take place.Students explored the garden and collected calendula, lavender, mint, rosemary, vertical grow racks and thyme to add to their sachet. Though it was not part of the lesson plan, students also gathered yellow roses, nasturtium, and other flowers, and one student picked up a tiny lizard.

After returning to the benches for a closing discussion, students walked back to their classroom. However, the garden was not empty for long. Many returned during recess to water the plants, or simply enjoy the space while sitting, eating, and chatting with others. This lesson was unique because it focused on biology and the medicinal qualities of herbs. Other lessons I observed related to nutrition and tasting fruits and vegetables, connecting to the previous themes of “Improving Health.” Students also learned about food supply chains, the structure of plants, and using fractions to plant seeds in a garden bed evenly. School garden lessons are examples of organized educational curriculum at UA sites. A few sites offered youth education programs, such as LBO’s Gateway to Gardening program taught by a horticultural therapist. The Martin Luther King Jr. Peace Garden offered garden-based education classes. There were also informal opportunities for teaching and learning through hands-on experience. It is likely that students may share their garden experiences at home with their parents and siblings. Many adult gardeners and farmers had learned agricultural skills from their families. This is an example of familial capital, cultural knowledge nurtured among kin . A UA leader admitted that when they started, they “literally knew nothing about gardening” but now “[are] trying to learn as much as they can” while gardening with their family. During field visits, I saw that gardeners were often working together with children, parents, grandparents, and siblings. UA leaders recalled learning how to grow food with their family or a significant other. One UA leader, who had learned agricultural techniques from their mother, felt it was important for their children to learn that “the growing experience is normal and natural.” They believed that if “kids are more attuned to things growing and dying, and then going into the compost, and then creating life again, it makes the idea of death, less onerous.” For this interviewee and others, educating children was crucial for preserving UA skills and knowledge.UA leaders identified conflicts with landowners, lack of funding, engaging local community members, and language barriers as major challenges of maintaining gardens and farms. In addition to social capital, UA leaders exercised navigational capital, the skill of maneuvering through social institutions , to use land owned by the city or private owners. An interviewee said that ideally, this was a “win-win situation” because “[landowners] don’t have to take care of these odd properties and they save money and time, and [community members] get a garden.” However, there was often “red tape” obtaining permits from the city, which delayed UA leaders from starting construction. Even after UA sites were constructed, their operation was not guaranteed. The City of Long Beach could temporarily lease land to UA leaders, then choose to not renew the lease in the future. Private landowners had the power to reclaim their land or sell it to a buyer. An interviewee reflected, “We’re all on borrowed land.” UA leaders also felt the constant pressure to acquire funding through grants or fundraisers to keep their sites operational. Due to inadequate funding to hire full-time staff, UA leaders were tasked with “doing everything,” from fundraising and coordinating volunteers, to cleaning bathrooms and dealing with animal pests. Although UA sites generated income from produce sales, community garden memberships, donations, and grants, revenue was unpredictable. Oftentimes, UA sites were spearheaded by one main person, with either a few staff, volunteers, and/or interns. Ground Education was the exception to this, with a team of over 20 employees; however, the two co-founders worked for seven years without pay to grow their nonprofit organization. They gained financial support through the LBUSD, parent teacher associations, and Long Beach Gives, a citywide fundraiser. A common challenge that community garden leaders identified was “a lack of people who can consistently come and maintain the garden.” They would repeatedly contact gardeners who neglected their plots, despite policies requiring regular upkeep. Schools similarly struggled to maintain their gardens, which was why Ground Education was founded. Before they had the resources to construct new gardens, Ground Education cared for existing school gardens that were untended during the summer or completely abandoned.

While UA can increase access to food, it also has the potential to bolster CCW through cross-cultural social interactions and educational and employment opportunities . Communities can potentially use UA to address health inequities through social and environmental changes .While SDOH may be broadly defined as “the conditions in which people are born, grow, live, work, and age,” Hahn clarifies that SDOH encompasses social resources and health hazards controlled by societal systems which, in turn, have consequences on health outcomes and risks . SDOH can include health-related knowledge, attitudes, beliefs, and behaviors, but these factors are often the result of social factors that are uncontrollable by individuals, such as discrimination . Populations that experience wide disparities in SDOH are affected by health inequities, such as higher risk of disease and earlier mortality . Research suggests that low income and education levels are strong predictors of physical and mental health problems . Socioeconomic status , which can be broadly defined as the combined total measure of a person’s social and economic position in relation to others, is also positively correlated with health outcomes . The following sections will describe SDOH in the context of Long Beach, based on the domains described by The Healthy People 2030 initiative: 1) social and community context, 2) economic stability, 3) education access and quality, 4) neighborhood and built environment, and 5) health care access and quality.The social and community context domain refers to the relationships people have with others. Factors like social support, self-esteem, pipp horticulture and self-efficacy may protect against health risks from adverse social conditions . These protective factors may be fostered through the CCW model . This is especially important for marginalized groups targeted by discrimination .

Those who have a low SES due to social disadvantages, such as discrimination due to race/ethnicity, gender, sexual orientation, and disability, are more likely to suffer from health inequities . Children born into low SES have greater risk of experiencing sudden infant death, infectious diseases, exposure to lead poisoning, household smoke, accidents, and child abuse, which may explain why low SES children have higher rates of asthma, developmental delay, and avoidable hospitalizations . Children from low SES neighborhoods face greater barriers to health-promoting behaviors and often experience stressors from family conflict and economic instability . Additionally, they are at greater risk of being exposed to intimate-partner and community violence. Low SES adolescents report worse health than their peers, experience higher rates of obesity, pregnancy, sexually transmitted disease, depression, and suicide, and more likely to be sexually abused, drop out of high school, or be killed. Compared to those who are more economically advantaged, low SES adults experience higher rates of mental illness, food insecurity, coronary heart disease, and other chronic health conditions, and experience earlier mortality . When discussing SES, it is important to note that in the United States, “race, socioeconomic status, and health have historically been inextricably intertwined” . Federal agencies collect data primarily by race due to Statistical Directive No. 15 of the Office of Management and Budget , originally adopted in 1977 . As of 1997, the directive requires federal agencies in the United States to report statistics for one ethnic category, Hispanic, and five racial groups: American Indian and Alaskan Native, Asian, Black, Native Hawaiian or Other Pacific Islander, and White . United States census data is based on how individuals self-identify to one or more groups, and reflects a general, social definition of race, independent of biological, anthropological, or genetic factors .

For clarification, when this dissertation describes “minorities,” “people of color,” or “communities of color,” this typically refers to non-White racial groups. There is no internationally agreed definition for minority , but in the United States, racial and ethnic minorities are groups of non-European descent: American Indian/Alaska Native, Asian, Black or African American, Native Hawaiian/other Pacific Islander, and Hispanic/Latino . As of 2020, most United States residents identify as White . The U.S. Census Bureau predicts that the nation’s population will become more racially and ethnically diverse, as immigration is projected to surpass birth as the primary driver of population growth. People who identify as more than one race are projected to be the fastest growing racial or ethnic group over the next several decades, followed by Asian Americans and Hispanic/Latino Americans . Over 70% of Long Beach’s population identifies as a racial/ethnic minority, or people of color . About 44.1% of the population is ethnically Hispanic or Latino2 .A stable income is necessary to afford food, housing, and health care. Steady employment can prevent poverty, which is experienced disproportionately among most non-White populations. Compared to 8.2% of non-Hispanic White persons, poverty rates are over twice as high for Black and Hispanic people . Unemployment is strongly associated with worse health and higher mortality, and those who live in poverty are unable to afford health-promoting living conditions . Higher education often leads to employment in jobs with higher compensation, better health care benefits, and safer working conditions. Conversely, those with a lower education are at greater risk of being injured and exposed to hazardous chemicals while working . As mentioned previously, historical redlining practices shaped the neighborhood demographics of Long Beach. For example, housing lenders imposed deed restrictions to prohibit non-White residents to purchase, lease, or occupy property .

Such restrictions were common in East Long Beach and Bixby Knolls, which, decades later, still report lower minority populations than other parts of Long Beach . The National Association for the Advancement of Colored People advocated against such discriminatory housing policies, and worked toward local policy reform in education, employment, economic development, and law enforcement. Their work resulted in the end of racial deed restrictions during the 1960s, though a 1975 study found that unfair housing practices still continued . That same year, Cambodians escaping civil war found refuge in Central Long Beach, which lenders considered risky for investment. Despite this, Cambodians created a commercial district there, and Long Beach became home to the largest Cambodia diaspora. In 2006, the Long Beach City Council officially designated a portion of the city as “Cambodia Town” . Recent data shows that communities of color are still concentrated in North, Central, and West Long Beach. Figure 9 shows a map of Long Beach . The dark purple areas represent where communities of color are the most concentrated.Education increases access to economic opportunities and resources which can influence health . Persons with less than a high school education are expected to live six years less compared to those with a college education . Almost half of all deaths among working-age adults in the United States can be attributed to potentially avoidable factors associated with lower educational attainment, including discrimination in health care settings . In addition to racial/ethnic groups and women, lesbian, gay, bisexual, and transexual groups and persons with disabilities also experience health inequities, which can be attributed to reduced education and employment . Higher education is associated with increased social support, which may benefit physical and mental health by buffering the effects of stress, enhancing health knowledge, and encouraging healthy behaviors . Education level is also highly correlated with health literacy, the ability to comprehend and use information to manage medical care and make informed health decisions . According to Long Beach Unified School District enrollment data from 2021-22, 37,952 students were socioeconomically disadvantaged. The California Department of Education states that socioeconomically disadvantaged students meet at least one of the following: neither parent received a high school diploma, eligible for the Free or Reduced Price Meals Program, are a migrant, homeless, or foster youth, or were enrolled in a Juvenile Court School. In Long Beach, over 40% of residents are Hispanic or Latino, and 12% are Black. Hispanic/Latino and Black families are more likely to have lower educational attainment and quality, due to living in neighborhoods with under-resourced schools . LBUSD enrollment data from the California Department of Education shows that most socioeconomically disadvantaged youth identified as Hispanic or Latino , African American , or Asian/Pacific Islander .Residential segregation forces communities of color into hazardous areas, resulting in detrimental effects on mental and physical health . Neighborhoods with increased social disorder may heighten anxiety and depression . Particularly for poor and Black neighborhoods, health risks are exacerbated by increased exposure to polluted air and contaminated water, because toxic waste facilities, industrial plants, best way to dry cannabis and landfills are often intentionally sited in low SES neighborhoods . Pollution also increases risk of COVID-19 , which, after its discovery in December 2019, became the nation’s third leading cause of death after heart disease and cancer. Compared to non-Hispanic Whites, those who identified as American Indian or Alaska Native, Black, and Hispanic or Latino were about twice as likely to become hospitalized and die from COVID-19 . According to Sprainer , this is because “low-income communities and communities of color across the country are exposed to higher long-term concentrations of an air pollutant that makes COVID-19 more deadly.”

Low SES communities are historically exposed to pollutants due to perceived lack of political power to control zoning, which is controlled by largely white governance structures for industrial development . In urban areas, low SES communities also have less access to green space . Additionally, not all green spaces are equally healthy and well maintained. Data from Su et al. suggested that low-income and minority residents with access to city parks have greater exposure to air pollutants such as nitrogen dioxide , fine particulate , and ozone . Therefore, low SES populations have less opportunity to experience green spaces and their associated benefits, such as improved physical and mental health, and even safer neighborhoods . These patterns of pollution exposure and access to green space can be clearly seen throughout Long Beach. The Long Beach Airport was ranked by the Environmental Protection Agency as having the second highest lead emissions of airports nationwide . Air pollution is further exacerbated by emissions from cars and trucks, particularly those transporting goods from the Port of Long Beach. As seen in Figure 10, areas closest to the port and 710 Freeway, toward the west, have pollution levels of 70- 100%. In comparison, areas of East Long Beach, where the minority population is lower and household incomes are higher, pollution levels are less than 40% .The areas with the highest concentration of minorities, air pollution, asthma, and diabetes are furthest from the largest parks in Long Beach . West Long Beach only has one acre of green space per 1,000 residents . Low SES communities in Long Beach are also susceptible to contamination from industries. For example, an empty lot of upper West Long Beach was used as a sludge dumping ground for oil companies in Long Beach and Signal Hill . This has resulted in years of toxic waste build up, including lead and arsenic. In 2021, local residents opposed developer plans to build a storage facility and requested officials to conduct an environmental impact report. They also argued that the empty lot, which became an area for homeless encampments, should be used to develop a park .The multitude of health risks associated with low SES cannot be addressed by health care alone and may even be exacerbated by discrimination in health care settings . Discrimination perpetuates health inequities by increasing health risks, lowering health care quality, and disrupting the economic opportunities available for low SES populations . There is evidence that people of color in Long Beach, who live in areas with higher air pollution and lower access to green space, have higher hospitalization rates. Long Beach’s hospitalization rates for asthma are higher than that of LA County and California . Data from the Long Beach Department of Health and Human Services also reveals that age-adjusted emergency room rates due to adult and pediatric asthma are over eight times higher for Black residents , than White residents . Asthma rates for the Asian and Pacific Islander population Hispanic/Latinx population were also higher . Figure 11 displays adult and pediatric rates of asthma by ZIP Code.Similar to asthma, hospitalization rates for diabetes are also higher in Long Beach compared to the county and state . In 2016, 9.7% of adults in Long Beach reported being diagnosed with diabetes.

A major unanswered question is whether expansion began with hunter-gatherer groups, perhaps as a result of the invention of particular technologies or behavioral innovations, or much more recently with the advent of agriculture. Early mtDNA studies suggested that humans experienced a burst of population growth between 30 and 130 thousand years ago —well before the start of agriculture. More recent results have extended the time frame for sub-Saharan African growth to 213–12 kya, depending in part on mtDNA haplogroup. However, it is populations—not haplogroups— that are subject to growth, and many present-day hunter-gatherer groups, including those in Africa, do not exhibit any mtDNA signal of demographic expansion at all. On the other hand, Y chromosome sequence data are compatible with a model of constant size for both hunter-gatherer and farming populations in Africa. Autosomal microsatellites tend to indicate an early start to population growth, but there is disagreement among studies on the time of expansion and whether or not the expansions involved African populations. Zhivotovsky et al. examined a large autosomal microsatellite dataset in 52 worldwide populations and concluded that African farmers, but not hunter-gatherers, exhibit the signal of population growth. Unfortunately, inferences of demographic parameters based on the above mentioned loci may be unreliable due to the possible confounding effects of natural selection or evolutionary stochasticity , best way to dry bud or uncertainty in our understanding of mutation rates or the underlying mutation process. A more reliable source of information regarding past population size change comes from multilocus nuclear sequence studies.

Once polymorphism data from multiple X-linked and autosomal loci began to appear, clear discrepancies with inferences based on both mtDNA and microsatellites emerged. For example, most non-African populations tend to have positive Tajima’s D values— reflecting possible contractions in Ne—while most African populations tend to have only slightly negative values. Indeed, the largest re-sequencing study to date that targets unlinked autosomal non-coding regions finds that patterns of neutral polymorphism in non-African populations reject the standard constant size model, and are most compatible with a range of bottleneck models invoking a large reduction in effective population size some time after the appearance of modern humans in Africa. In contrast, data from the sole African population examined, the Hausa of Cameroon, were compatible with demographic equilibrium, as well as with a set of recent population expansion models. In this paper, we expand upon the work of Voight et al. by analyzing a re-sequencing dataset comprised of 20 independentlyevolving autosomal non-coding regions in 7 human populations. Our sub-Saharan African populations include the San from Namibia, Biaka from the Central African Republic, Mandenka from Senegal, and Yorubans from Nigeria. Our multilocus analysis, which focuses on two summary statistics with power to detect population growth , follows a two-step approach. We employ a simulation-based method to test the hypothesis that populations experienced exponential growth after a period of constant size. When the hypothesis cannot be rejected, we then fit parameters of this two-phase growth model to our data using approximate Bayesian computation. As in previous studies, we find that the non-African data are not consistent with a simple growth model. On the other hand all four sub-Saharan African samples fit the two-phase growth model, and we are able to infer a range of onset times and growth rates for each population.

We sample sub-Saharan African populations that practice different subsistence strategies and then ask whether the inferred signals of population growth are shared between, or specific to, food-gathering or food-producing groups.Our understanding of population size changes in human prehistory has improved as our genetic datasets and analysis methods have become more sophisticated. Early studies of the pairwise mismatch distribution in mitochondrial DNA suggested dramatic increases in population size between 110 and 70 kya in sub-Saharan Africa. More recent coalescent studies have also favored 50- to 100-fold growth occurring between 213 and 12 kya. Conversely, modern surveys of nuclear sequence variation at unlinked loci have not provided clear evidence for rapid population growth from small ancestral size. For example, African populations usually exhibit slightly negative Tajima’s D values, while non-African populations tend to have positive Tajima’s D values. Different patterns of polymorphism in African and non-African populations have been interpreted as reflecting a history of bottleneck in the ancestry of non-Africans. Therefore, the question of when anatomically modern human populations began to expand in size is better addressed in sub-Saharan African populations because more recent demographic events likely obscure signals of population growth in the ancestors of nonAfrican groups. Bottlenecks, in particular, can mask the effects of earlier, as well as later, population growth.However, thus far, very few surveys of nuclear DNA sequence variation have been performed in sub-Saharan African populations, and interpretations drawn by existing studies have been complicated by the different populations and loci analyzed, the kinds of analyses performed, and the different growth models assumed.

The earliest studies considered only the few existing nuclear sequence data available in the literature at the time, and explored only a small set of growth model parameters. Later studies adopted a more explicit hypothesis-testing framework, but focused on only a single African population. For instance, Pluzhnikov et al. analyzed a large resequence dataset of noncoding autosomal regions for the Hausa of Cameroon . They determined that while observed summaries of the site frequency spectrum did not statistically reject a null model of constant size, they were consistent with a range of alternative growth models. Consequently, Voight et al. turned to a goodness-of-fit approach to determine better estimates of the time of onset of growth and the growth rate in the Hausa. By generating approximate likelihoods for the mean of observed summary statistics over a grid of parameter values, they determined that the Hausa best fit a growth model beginning ,1,000 generations ago with a per-generation growth rate a of 0.7561023 . Assuming a generation time of 25 years, this corresponds to an overall ,2-fold growth rate from ancestral to modern size beginning ,25 kya. Here, we extend these sorts of analyses to a greater range of African populations: two hunter-gathers, the San of Namibia and the Biaka of the Central African Republic; and two food producers, the Mandenka of Senegal and the Yorubans of Nigeria. All four groups show depressed values of Tajima’s D and Rozas’ R2 coupled with a high proportion of singleton mutations . These patterns of sequence polymorphism are suggestive of population growth. We therefore tested our multilocus African dataset to determine whether we could reject models of population growth, and adopted the best aspects of previous hypothesis-testing and inference approaches. We first employed hypothesis-testing to determine, by coalescent simulation, whether a range of growth models could be rejected in favor of constant size using the method pioneered by Pluzhnikov et al.. When growth could not be rejected, we fitted parameters of the two phase growth model to our data using approximate Bayesian computation . Thus, we conditioned simulations on each locus individually , cannabis grow setup and explored a continuous range of parameter values rather than restricting our search to a set of predetermined grid coordinates. All of our African populations best fit models with relatively low population growth beginning in the late Pleistocene . Even with ,112-kb of sequence data per individual, a large range of growth models are consistent with our 95% credible regions for t and a. We cannot, for instance, statistically distinguish different rates and times of growth among our four sub-Saharan African samples. However, our hunter-gather populations show a tendency towards slightly older and stronger growth than our food-producing populations . Furthermore, we detect a strongly negative, non-linear association between t and a . This effect, which has been identified previously, implies that sequence data from our four African populations are consistent either with weaker growth beginning earlier in the Late Pleistocene, or with stronger growth commencing more recently. Interestingly, we can reject an onset of population growth for the San during the Holocene , and therefore, growth in this population is not linked to the development of agriculture. Although we cannot reject an onset of growth associated with agriculture for the Biaka, Mandenka and Yorubans, our best fitting models do not favor this interpretation. Indeed, the limited size of our dataset gives us more power to infer older rather than more recent growth. We see little effect from the increased size of the dataset obtained for Yorubans. Even though we increased both the number of samples and the number of loci , estimates of the rate and timing of growth are comparable to those inferred for the Mandenka, and our 95% credible region is not appreciably smaller. This is interesting given that, under a model of population growth, expected values of Tajima’s D depend to some extent on sample size. With regard to the small increase in the number of loci in our Yoruban dataset, recent power analyses by Adams and Hudson suggest that orders of magnitude more data may be necessary to obtain growth model parameters with substantially greater accuracy, especially in models involving recent growth. Furthermore, the modern effective sizes we infer – on the order of 105 – are much smaller than regional census sizes.

This discrepancy partly reflects the fact that effective size is not a simple proxy for census size. However, another explanation also seems likely: under a model of exponential growth, the bulk of the population increase is weighted towards the present, and for the aforementioned reasons [28], we are not likely to capture the effects of substantial increases population size in modern times. Although population growth seems like a reasonable demographic model for human groups on non-genetic grounds [1,2,34], humans have likely experienced both population growth and population structure at some time in the past. The question is whether and to what extent either or both of these aspects of population history left a signature on patterns of variation. To explore the effects of alternate models of population structure on patterns of genetic variation, we use a coalescent simulation approach. In particular, we examine how Tajima’s D and Rozas’ R2 respond under models incorporating low-frequency gene flow in a structured population, recent admixture, and cryptic population structure . We assume a two-deme splitting model with i) a constant low level of gene flow, ii) a single admixture event occurring ,3 kya , and iii) population structure collapsing ,150 years ago . All of these processes produce very slight reductions in Tajima’s D and Rozas’ R2, but the mean deviations never exceed 0.27 and 0.011, respectively. To put these values in perspective, such deviations represent no more than 10% and 12% of the variance naturally observed for Tajima’s D and Rozas’ R2 under the corresponding standard neutral models with no gene flow, admixture, or cryptic population structure. Although these confounding factors may have caused our growth estimates to appear slightly older or stronger than they actually are, their effects are minor. Similarly, biases in our estimates of per-locus mutation and recombination rates are unlikely to have major effects on our inferences. For instance, elevated recombination would lead to a lower variance of Tajima’s D and Rozas’ R2, which would return growth estimates with less uncertainty, while elevated mutation rates would shorten our time frames, and hence return younger growth estimates. Estimates of growth rates under the isolation-with-migration model, which simultaneously accounts for population structure and gene flow, are consistent with our inference of an increase in the effective size of sub-Saharan African populations. Although growth rates are lower than suggested by ABC, we still infer that African populations experienced ,5-fold growth from ancestral sizes. While a simple two-phase growth model is too simplistic to fully describe African population history, it is interesting to note that a more complex model incorporating an ancient bottleneck does not fit African resequencing data. This is in marked contrast to the large reduction in population size that the same studies inferred for non-Africans. We therefore suggest that our growth estimates genuinely reflect a substantial increase in effective size among sub-Saharan African populations beginning in the Late Pleistocene. However, we note that these inferences could be complicated by other forms of population structure not accounted for in our models. While some authors have speculated that human populations underwent sudden expansions in population size in response to dramatic climatic events, technological inventions, or behavioral changes that took place earlier than 50 kya, our data are more consistent with a model of exponential growth beginning after 50 kya, but certainly before the Holocene.

Our results emphasize that lytic phages are likely to be an important component of the microbiome and are capable of influencing both bacterial abundance and diversity over short timescales.In our first of multiple experiments , we conducted a proof of concept experiment. We used ddPCR to measure quantities of known phage and bacterial host in size fractions of our mock community , and we determined that our fractionation method effectively concentrates phages from the leaf wash, allowing us to deplete them from both the “bacteria only” and 100K MWCO filtrate fractions of the leaf microbiome . FRS and SHL bacteriophages were effectively depleted, although the ddPCR signal was not entirely eliminated in the 0.22- µm filter bacterial recovery fraction . Phage levels were concentrated from the 0.22- µm flow-through fractions in the 100K MWCO centrifugation unit, representing bacteria plus phage treatment. Lastly, we also measured decreased levels of phage in the 100K MWCO flow-through fractions, representing the additional phage-depleted inoculum: bacteria plus filtrate. FRS and SHL phages are approximately 60 and 80 nm in size, respectively, and we thus presume that most phages in the environmental samples that are that size or larger should be retained in the upper portion of the 100K MWCO centrifugation unit. Membrane pore size for the unit we used is 10 nm; therefore, curing and drying weed smaller phage particles should have been retained in the upper fraction as well. Overall, we therefore consider both the bacterial/fungal fraction and the 100K MWCO flow-through fraction phage-depleted, but not necessarily absent of all phage.

Lastly, levels of P. syringae pv. tomato abundance was measured in all fractions , and signal was also detected in the non-bacterial fractions. However, this is likely due to the detection of DNA and not the presence of live cells, as bacteria could not be cultured from those filtered fractions . As seen in Figure 4-1d,infectious phage particles were present in the initial leaf wash, and they were also sufficiently high in concentration to completely lyse the bacterial lawn in the 0.22 µm flow through and 100K MWCO concentrate fractions, as little to no bacterial growth is observed. By comparison, a solid bacterial lawn is seen in the 0.22- µm filter recovery sample, where most phages appear to be depleted. As evidenced by a small number of plaques, a few bacteriophages are present in the 100K MWCO filtrate. This further supports the possibility that the third treatment, bacteria plus filtrate, was phage-depleted, but not completely free of phages, in our subsequent field experiments.After rarefaction and filtering, there were a total of 200 OTUs present in the spray inoculum from field experiment 2 representing taxa from the four top phyla commonly found in the phyllosphere: Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. As expected, the bacterial composition of inoculum from the three different treatments, sampled after resuspension with/without phage but before growth, has similar rank order of relative abundance for the top OTUs . Observed differences in relative abundance of specific taxa may be due in part to concentrated free bacterial DNA in the 100K MWCO fraction. Given the way in which inocula was prepared , it is unlikely that the bacterial communities differed substantially between treatments at inoculation.Using a community-level phage depletion approach, we found that the phage fraction of the phyllosphere microbiome from field-grown tomato plants impacted bacterial abundance and composition during microbiome establishment on a new host.

When microbial communities were sprayed onto juvenile tomato plants after either phage depletion or resuspension with the depleted phage-fraction, we observed decreased abundance in the latter treatment after 24 hours across three different experiments : first with six independent leaf wash sources , then with one leaf wash source and six plant replicates per treatment , and finally with a constructed bacterial community and natural phage fraction . Using 16S rRNA Illumina MiSeq data from field experiment 2, we were able to further show that the phage-fraction of the phyllosphere affects microbiome composition, including relative abundance of specific OTUs . We observed an effect of phage depletion treatment on community dissimilarity between treatments after 24 hours, but not after 7 days . We also found some evidence for differences in both alpha and beta diversity between phage depleted and phage re-suspended communities after 7 days . Overall, these results support the idea that lytic phages can mediate bacterial dynamics within host-associated bacterial communities, as they have been found to do in free-living communities. Across these experiments we observed a decrease in overall bacterial abundance 24 hours after inoculation, suggesting that phages affected growth of the most common and/or fastest growing bacterial strains during colonization of a new plant host. However, it is important to note that decreased overall bacterial abundance is not necessarily an expected outcome of lytic phage action within a microbiome. This is both because phage-mediated lysis has been shown in some cases to increase population growth due to release of nutrients but also because other strains that are not being targeted by phages should be able to offset any decreased growth of susceptible bacteria.

That the impact of phages on abundance in our experiments was short-lived suggests either that phages are particularly impactful during initial colonization, as bacterial population are rapidly growing, or that resistant bacterial strains/species increased in density over time to utilize existing resources. Indeed, the Kill the Winner hypothesis predicts that phages should most commonly prey upon highly competitive bacterial species. Results of our sequencing efforts supports this model, as we found different relative abundances of the two dominant families when the phage fraction was versus was not present in the initial inoculum. After 24 hours, the bacteria plus phage treatment plants were observed to have lower abundances of Pseudomonads, but when the phage-fraction was depleted there was an overabundance of an OTU within the family Enterobacteriaceae. However, after seven days the differences in relative abundance of these two OTUs were no longer observed to differ among treatments. Although only marginally significant, the presence of phage in the inoculum also led to an increase in alpha diversity at seven days post-inoculation. Again, this result may have been driven by a decrease of Pseudomonads after the first 24 hours, perhaps allowing a richer community to develop after the first week. Interestingly, when comparing beta diversity among treatments using averaged Bray-Curtis distances between samples within a treatment, we found an interaction effect between day sampled and inoculum treatment. This suggests that the phage fraction of the microbiome may also be having an effect on among-host microbiome diversity, initially driving divergence among communities as the empty niches are filled, , but eventually leading to more synchronous community structure. It is important to note that the patterns we observed were based on the depletion of lytic phages from the microbiome at the point of inoculation, but there were almost certainly many temperate phages remaining and possibly some lytic phages contained within bacterial cells at the time of collection/filtration. As such, it is possible that differences in treatment effect observed between 24 hours and 7 days were due to the resurgence of phages in the phage-depleted communities rather than loss of phages in the bacteria plus phage treatment. The observed transience of phage-mediated impacts on abundance and diversity is intriguing, and longer-term studies with more time points are needed to better understand temporal effects of phages on bacterial communities. One question we were not able to directly address in this series of experiments is the constituents of the leaf wash filtrate . The molecules and small proteins found in this filtrate had a surprisingly large and variable impact on the phyllosphere microbiome, impacting both abundance and community composition and causing high variation among biological replicates. In future experiments, additional size fractionation of the leaf wash filtrate and/or mass spectrometry analysis of these fractions may help address this question. As observed in our proof of concept experiment, cannabis drying system it is also possible that some bacteriophages made it through the filtration step and were present in this treatment. We decided to eliminate this treatment from many of our analyses due not to the effect of the treatment itself but rather due to the high variances observed across replicate plants. In most cases, plants within this treatment spanned the variation observed in both the bacteria alone treatment and the bacteria plus phage treatment. It was therefore unclear to us how to interpret this treatment and what biological significance it might have, but further study is certainly warranted. Another limitation of this work is that we have not identified the specific phages in the phage-fraction of the experiment. We have taken measures to ensure that the method used for separation of microbiome fractions is effective at separating phage from bacteria, but in order to fully describe the diversity of phage, as we have done for the bacterial community, one would need to take a metagenomics approach.

Furthermore, there may be other entities that are phage-sized in thatfraction of the microbiome, such as extracellular vesicles or spores of bacteria such as Bacillus that impact upon microbiome colonization. However, given that the current estimates of phages largely outnumber bacteria in the environment, we expect non-phage particles to be far less abundant than phages in this size fraction. This was recently shown for outer-membrane vesicles, where they were estimated to represent less than 0.01-1% of SYRB DNA-stained phage-sized particles quantified in seawater. Furthermore, we cannot rule out the possibility that the presence of phage, but not their predation on specific taxa, is causing the effects we are observing. However, by recapitulating the results of decreased abundance in bacteria after 24 hours when a phage fraction was present in our constructed community, we were able to lend some insight to this question. In this case, our detection of a phage capable of lysing a member of the constructed community suggested that the phage fraction was most likely driving the observed decrease in abundance. This is further supported by the fact that the phage was found to lyse Pantoea agglomerans, a member of the family Enterobacteriaceae, which we have found to be in high relative abundance in 16S rRNA community data in both this experiment and other unpublished work. Another important note is that the ddPCR protocol used here relies on lysis of bacteria cells through a hot-start step in the PCR. Because of this, it is possible that our abundance measures do not take into account hard-to-lyse bacteria. Finally, we did not include any analyses of the fungal communities in these microbiomes, as it was outside the scope of the current work. However, it is possible that our filtration methods also impacted any fungal viruses that might have been present in this study. How fungal communities are influenced by viruses within the microbiome is certainly an open question in the field that warrants further study. Given the building evidence that the phyllsophere microbiome is a key component of plant fitness, influencing key functional traits and likely protecting host plants against disease, the idea that lytic phages impact these communities is of direct relevance to plant health. A better understanding of bacteria-phage dynamics within these systems may present opportunities for manipulating the plant microbiome and ultimately increasing plant health. These ideas can be extended to the human microbiome, where the role of phages is proving to be appreciable. With regard to using phages in therapeutics, their role in controlling bacterial community dynamics and local adaptation is an important consideration for both phage-therapy to target specific pathogens and full-microbiome perturbations or replacements via fecal transplants. Overall, our results make a significant contribution towards the empirically demonstration of the role that phages play in shaping bacterial community structure in natural systems. This may be through, but is not limited to, impacts on bacterial abundance, composition, competitive-dynamics, and/or diversity. These effects are ultimately likely to affect the overall stability and function of the microbiome, and consequently, host fitness. In conclusion, it is becoming increasingly clear that phages should be considered when seeking to understand the diversity, evolution, and ecology of any microbiome.With the goal of using a ‘natural’ microbiome for subsequent studies, we sampled tomato leaves from the UC Davis Student Farm between the months of August and October. For field experiment 1, inoculum was generated from each of six different sites from across three different fields . For the subsequent experiment with sequencing data, field experiment 2, leaves were pooled across fields into a single diverse inoculum source .