Cannabis Grow

Subsequently, under the 1938 Farm Bill, the federal government, and not a processor’s tax, would finance such subsidies, thus relieving corporations of any responsibility to maintain high commodity prices or profitable farms. Significantly, this funding structure was held in place during the shift in agricultural policy from the support of production to the support of prices by way of the doctrine of parity. The ongoing erosion of the doctrine of parity from 1952 onward, which included the lowering of price floors and reduction of supply management practices, sent farm prices crashing and ushered in a period of agricultural policy driven by agribusiness. Specifically, corporations such as Archer Daniels Midland and Cargill were instrumental in helping replace New Deal-era loan programs and land-idling arrangements with direct subsidies that supported low prices for corporate purchasers themselves. Anticipating the 1973 Farm Bill, for example, and alongside Secretary of Agriculture, Earl Butz, Cargill and the Farm Bureau argued that crashing farm prices would be a plus. They argued that not only would greater exports and new uses such as ethanol and sweeteners remedy the drop in price, but also that farms would remain profitable with the support of government subsidies. The winners and losers were clear under such policies: corporate buyers could acquire commodity crops for record low prices that were subsidized by the federal government while farmers continued to lose their lands and their income. Such policies, furthermore, cannabis racking systems constituted part of the larger trend in corporate growth, not limited solely to agribusiness.

For example, according to a 2013 Bureau of Economic Analysis, corporate profit as a percentage of GDP more than doubled between 1980 and 2013, rising from less than 5% to over 10%; before tax, corporate profit, as a percent of GDP, rose from less than 8% to over 12.5% between 1980 and 2013. Both periods, from the Great Depression and New Deal farm programs, to their erosion over the following decades, were characterized by structural racialization. Although New Deal-era legislation was geared toward pulling Americans out of poverty, it was itself a project of racial exclusion, with Black communities and other communities of color systematically barred from such supports. Southern committee members in Congress, for example, blocked efforts to include agricultural workers and domestic workers in the Social Security Act—the New Deal’s centerpiece legislation—largely because of the high concentration of black workers within those lines of work. In the 1930s, 60% of Black workers held domestic or agricultural jobs nationally while, in the southern United States, domestic and agricultural occupations employed almost 75% of Black workers, and 85% of Black women. Furthermore, although the National Recovery Administration set wages within the cotton industry at $12 a week, many Black workers had jobs that were not covered by the law and thus had their wages reduced by employers so that white workers could be paid more. Finally, Black agricultural workers were also left out of New Dealera agricultural union programs—namely the National Labor Relations Act, enacted and signed into law on July 5, 1935—while Black landowners in particular were excluded from federal farm support under the Agricultural Adjustment Administration. Significantly, the distribution of federal support during this period resulted in the dramatic decrease in the number of Black farms, from about 900,000 in 1930 to 682,000 in 1939. 

Although these programs were slowly eroded over the next few decades, farmers of color continued to face great hardship relative to white farmers. The period of agricultural mechanization and industrialization after World War II, marked by the widespread adoption of scientific and technological innovations is usually credited with weeding out supposedly “non-productive, inefficient” farmers. Yet farmers of color and particularly Black farmers, in the context of the uneven application of New Deal era supports and years of discriminatory practices, were at a great disadvantage during this period because they were prevented from attaining the requisite access to capital and thus economic stability for such a transition. The Emergence of the Neoliberal Corporate Food System From the late 1970s and early 1980s until today, corporations have taken on a new and more deeply entrenched set of relationships within the food system. In short, this period is defined by neoliberal capitalist expansion and corporate control that began with the global economic shocks of the 1970s and 1980s During the 1980s, and working for the interests of multinational corporations in securing markets abroad for agricultural commodities produced domestically, Structural Adjustment Programs broke down foreign tariffs, dismantled national marketing boards, and eliminated price guarantees in the Global South. Alongside this destructive guarantee of foreign markets, the 1950s-onward trend of dismantling domestic safety net programs for farmers, guaranteeing low prices for commodity purchasers , and making up the potential loss for farmers with government direct payments continued. Such trends culminated in the 1996 Farm Bill—the “Freedom to Farm” bill. This Farm Bill eliminated the structural safety nets that had long protected producers during lean years. Corporate buyers and groups such as the National Grain and Feed Association, composed of firms in the grain and feed industry, pushed the 1996 Farm Bill to completely eliminate price floors, the requirement to keep some land idle, and the grain reserves that were meant to stabilize supplies and therefore stabilize prices, while simultaneously encouraging farmers to plant as much as possible. The 1996 Farm Bill thus marked the culmination of the shift from the federal government subsidizing production and consumption to diminishing price supports and the subsidization of agribusiness itself.

The dismantling of such price controls drove prices down and allowed corporate buyers to profit off heavily subsidized commodities while securing their power over producers. Specifically, deregulation left farmers increasingly vulnerable to market fluctuations caused by speculation, price volatility, and the profit-motives of corporate buyers. The shifts under the 1996 Farm Bill were deemed a failure by both farmers and legislators, and by 1997, rapidly falling farm prices resulted in direct government emergency payments to farmers, despite the fact that the legislation was designed to completely phase out farm program payments. Between 1996 and 1998, expenditures for farm programs rose dramatically, from $7.3 billion to $12.4 billion. They then soared to $21.5 billion in 1999 to over $22 billion in 2001. From 1996 to 2001, US net farm income dropped by 16.5% despite these payments. Rather than address the underlying cause of the price drop—overproduction—Congress voted to make these “emergency” payments permanent in the 2002 Farm Bill. As outlined below, neoliberal corporate influence remains particularly salient within two domains: the first is food production, processing, distribution, and service, and the second is education, research, and development.Commodity Supports: One major way corporations continue to profit and exert their influence on food production, distribution, and consumption is through commodity support programs. Once the safety nets of the New Deal farm programs were cut back during the 1980s and 1990s, and completely eliminated in the 1996 Farm Bill, farmers began to produce much more corn, soybeans, wheat, and other commodity crops. Specifically, harvest drying rack the 1996 Farm Bill eliminated the requirement to keep some land idle, which encouraged farmers to plant far more than they had before. As a result, the higher supplies of these crops brought down their prices, which drastically hurt farmer incomes and greatly increased the profits corporate purchasers reaped from purchasing even cheaper commodities. These low prices undermined the economic viability of most crop farms in the late 1990s, and subsequently, Congress provided a series of emergency payments to farmers. Furthermore, because continued oversupply kept prices from recovering, Congress eventually made such payments permanent in the 2002 Farm Bill. The dismantling of direct payment support for farmers thus ushered in another form of federally subsidized cheap commodities for corporate buyers that still leaves farmers themselves relatively vulnerable: disaster assistance programs and other emergency aid. The 2014 Farm Bill in particular cut funding allocated to direct payments by about $19 billion over 10 years—the most drastic policy change in this Farm Bill—with much of this money going into other types of farm aid, including disaster assistance for livestock producers, subsidized loans for farmers, and, most significantly, the crop insurance program. Crop Insurance: As fundamental as direct payments and emergency payments have been for subsidizing agribusiness profits, under neoliberal political and economic restructuring, crop insurance has surpassed them as the most egregious and expensive subsidy for agribusiness. For decades, farmers have been able to buy federally subsidized crop insurance in order to protect against crop failure or a decline in commodity prices. However, private insurance corporations and banks that administer the program, such as Wells Fargo, benefit the most from crop insurance subsidies. In 2011, these corporations received $1.3 billion for administrative expenses with $10 billion in profits over the past decade. In order to help cushion the blow from the reduction of direct payments, under the 2014 Farm Bill, $90 billion over 10 years will go toward crop insurance, which is $7 billion more than the previous farm bill. However, much of this money will go to private insurance corporations and banks instead of farmers. On the production side, the increase in government support will be directed toward the deductibles that farmers have to pay before insurance benefits begin. In other words, unlike non-farm insurance policies , crop insurance insures not only the crops, but also the expected revenue from selling those crops. Thus, Agricultural Risk Coverage and Price Loss Coverage only pays out when prices drop below a certain threshold. As of early 2015, corn crops have already reached this threshold. There exists a risk that this insurance program could cost far more than expected depending on how crop prices continue to shift: therefore, this is one of the more contentious aspects of the 2014 Farm Bill. Another contentious part is the uneven distribution of benefits. A 2014 report by the Environmental Working Group estimates that 10,000 policyholders receive over $100,000 a year in subsidies, with some receiving over $1 million, while the bottom 80% of farmers collect only $5,000 annually. In short, under the guise of cutting subsidies by repealing unpopular direct payments to farmers, the 2014 Farm Bill instead increases more costly crop insurance subsidies. Food Chain Workers: The pressure for corporate profit and the history of corporate consolidation with regard to the food system, both vertical and horizontal, has driven corporations to continue to lower wages for millions of food system workers and accumulate more wealth. A 2011 national survey of over 630 food system workers conducted by the Food Chain Workers Alliance found that the median hourly wage was $9.65 per hour. More than 86% of food system workers were paid poverty wages while 23% of food system workers were paid less than the minimum wage. Despite their significant role in every part of the food system—from production to processing to distribution and service—food system workers experience a greater degree of food insecurity than the rest of the US workforce. For example, according to the Food Chain Workers Alliance report, food system workers use SNAP at more than one and a half times the rate of the remainder of the US workforce. Additionally, as of 2014, twice as many restaurant workers were food insecure compared to the overall US population; as of 2011, in Fresno County, the country’s most productive agricultural county, 45% of farmworkers are food insecure. The situation is even worse in other parts of the country: in 2011, 63% of migrant farmworkers in Georgia were food insecure. Women and people of color disproportionately feel the economic pressure experienced by food system workers as a result of corporate consolidation. A comprehensive 2011 study of food workers and economic disparity found that people of color typically make less than whites working in the food chain. It found that half of white food workers earn $25,024 a year while workers of color earn $19,349. The study found that women of color in particular suffer the most, earning almost half of what white male workers earn. Furthermore, workers of color experience wage theft more frequently than white workers. More than 20% of all workers of color reported experiencing wage theft, while only 13.2% of all white workers reported having their wages misappropriated. Significantly, the study found that such discrepancies exist in all four sectors of the food system: production, processing, distribution, and service. Furthermore, such trends hold across the overall workforce.

This model incorporates segregation costs in the processing and marketing sector under regulations. The regulations of interest are imposed at the point of purchase within the regulating jurisdiction, so the identity of compliance must be preserved at all stages along the supply chain. For traceability, segregation between the restricted products and the other products is required. The segregation costs will be transferred mainly to final consumers of covered products in the regulating jurisdiction through a higher retail product price. The model yields several interesting results. First, the more adoption there is, the higher the farm compliance cost at equilibrium. Second, restrictions raise profits for some inframarginal adopters whose compliance costs are low. Third, restrictions have spillover effects on the unregulated share of the market. Fourth, processing and distribution costs are higher for products covered by regulations, and the magnitude of incremental costs is affected by the size of the regulated jurisdiction. Fifth, competition implies that the cost increases must be borne fully by covered products, and the product coverage of regulations affects its magnitude.The parameters of the primary supply and demand functions were calibrated such that the functions fit the 2018 market values for North America, based on Canadian and U.S. government statistics, vertical air solutions and have the corresponding price elasticity given those values. I used a price elasticity of supply of hogs at the farm of 1.8 from Lemieux and Wohlgenant , which was used in subsequent work .

To parameterize the primary demand functions, I began with a base retail price elasticity of demand for all pork of -0.68 from Okrent and Alston , a value that compares closely to values of -0.69 and -0.79 used by Buhr and -0.65 reported by Wohlgenant and Haidacher . The demands for covered and non-covered pork products will be more price elastic than the demand for pork as an aggregate category based on consumers’ willingness to substitute between the two types of pork products in response to price signals. After reviewing the relevant literature, I chose a base value of -0.9 for covered pork and -1.1 for non-covered pork. Given Okrent and Alston’s estimate of the price elasticity of demand for all pork and the market shares for C and N pork, these values imply a cross-price elasticity of 0.36 for N pork demand in response to a change in the price of covered pork and a cross-price elasticity of 0.26 for C pork demand in response to a change in the price of non-covered pork.Based on the size of the California pork market relative to the total market for covered pork products produced in North America, about 7% to 8% of North American sow housing needs to be compliant with Prop 12 standards to meet California’s demand. Generally, compliance would be less costly for farms already using group housing than for farms using gestation stalls. Therefore, Prop-12 compliant farms will mostly come from the set already using group housing. Hence, the relevant one-time cost of conversion to Prop 12’s requirements is that which applies to group housing operations. Variable costs for group-housing operations that become Prop-12 compliant are also compared with those that remain non-compliant.

Based on information from the industry, about 20 square feet of usable space per sow is allowed among typical operations using group housing, with some variation below that space per sow. Capital costs of housing per sow for those mostly likely to convert will, thus, rise by about 20% to increase the space allowance per sow from 20 to 24 square feet. Based on farm cost data , the implied increase in capital costs were assessed to be $3 per piglet produced in a farrowing operation, when converted to a marketed weight basis this corresponds to in my model. As noted, compliance costs vary across farms based on farm-specific characteristics such as housing facilities and managerial expertise. Given that less than 10% of North American hogs are destined for California consumption, I assumed that farms covering roughly 30% of the total North American sows might seriously consider the option to produce Prop 12-compliant sows. I use $2 per pig as the lowest conversion cost and $5 per pig as the cost for the 30th percentile of farrowing operations . The calculated value for group housing with 20 square feet per sow, therefore, is consistent with the lower 10th percentile of the uniform distribution. Note that hog farms with a higher cost of conversion, say those using gestation stall housing, are irrelevant to the calculations because they are far outside the range of farms that might convert to compliance.Prop 12 raises variable costs per pig produced in several ways. These include higher sow mortality, lower farrowing rates, fewer live pigs per sow, higher veterinarian costs, and higher farm labor costs all assessed on a marketable per pig basis. To compare costs, I used as the baseline costs calculated by university specialists .

Based on productivity information from producers, including declarations from dozens of producers included in the Petitioners Complaint in UNITED STATES DISTRICT COURT SOUTHERN DISTRICT OF CALFIORNIA CASE NO. 19CV2324W AHG National Pork Producers Council and the American Farm Bureau Federation v. Ross Dated: December 5, 2019. Given that California comprises less than 10% of the North American retail pork market, many primary processing operations will choose not to acquire the costly Prop 12-compliant hogs. These plants will avoid added costs of identifying, segregating, tracing, and labeling the compliant pork separately from the rest of their production. Most primary processing operations that do acquire and process the more expensive compliant hogs will also continue to utilize non-compliant hogs to exploit economies of size,access hogs within a reasonable distance of the plant to reduce transport costs and utilize plant capacity efficiently. These firms thus incur additional costs for identification, segregation, and tracing to enable sales of compliant pork into the California market. Such costs include separate holding pens, more complicated and less flexible scheduling, interruption in plant operation between processing the compliant and non-compliant hogs, additional storage capacity so that the up-to-double SKUs of fresh pork can be kept in distinct lots, a more complicated labeling process, and more complex shipping of labeled products. The costliest among these factors is likely to be the interruption of plant operations and reduced throughput during the change-over from handling compliant to non-compliant hogs. Compliant hogs will be processed on different days and/or at different times on a given day from other hogs to assure that non-compliant pork is not comingled with uncooked cuts of pork that are destined for California.  Further, my simulation projects that the average price of uncooked cuts of pork in California will rise by 6.9%, or about $0.23 per pound . California consumers will buy 6.2% less of the covered pork products as a consequence, given the baseline price elasticity of demand. Accordingly, vertical weed grow the share of North American hogs that provide pork products destined for California will decline from about 7.6% to 7.1%.6 This reduction in the share of market hogs destined for California drives much of the small impact of Prop 12 on the rest of the North American market. Because covered pork products cost more in California post Prop 12 and consumers buy less of it, less of North American pork production is used to feed California with Prop 12 than without Prop 12 regulations in place. This means that more of the pork production capacity is available to supply the rest of the market, causing non-compliant hog prices to fall by about 0.3% . Retail prices for non-compliant pure pork products to decline by about 0.2%. The lower consumer price causes a small percentage increase in quantity demanded, but, given that the non-California share of the market exceeds 90%, this increase largely offsets the decrease in consumption in California, so that the model predicts only a 0.2% decline in hog production due to Prop 12.

As noted, the more efficient operations that convert to Prop 12 compliance can expect to increase profits from conversion, while marginal converters should on expectation break even from conversion. The model estimates that converting operations gain about $0.2 million annually in producer surplus in 2018 dollars from converting to compliance with Prop 12 and supplying pork to California. Those that continue to produce for the unregulated market will lose a small amount of surplus due to the slightly lower hog price. I estimate that this aggregate loss to those that do not supply the California market about $44.8 million annually. The resulting in a net annual loss to producers of about $44.6 million or about $0.16 loss per hog. Despite significant industry opposition, Prop 12 will not impose much negative impact on producers on average. The California covered pork price increase implies that California consumers of covered pork products will have a $258 million consumer surplus loss annually through paying more for less covered pork. However, the higher price of covered pork causes an increase in the California demand of the substitute, non-covered pork. California buyers of non-covered pork are now willing to pay more for non-covered pork and quantity demand rises by about 2.4%. With our base-case parameters, the consumer surplus gain from the shift in demand for non-covered pork is about $69 million annually. Therefore, the total annual consumer surplus loss for California consumers of the two types of pork is about $188 million or about $4.70 each if all Californians were to eat pork. The per capital impact of Prop 12 on pork consumers outside California will be minimal due to the tiny projected decline in prices uncooked pork cuts outside of California and essentially no change in the price of non-covered pork products.The model assumes that the implementation of Prop 12 does not shift California’s demand for covered pork products. The projected decrease in consumption arises from movement along the static demand curve due to higher prices. The resultant decrease in California quantity of pork demanded causes the small decrease in hog prices for non-compliant producers and loss of producer surplus. It is possible that Prop 12 or animal welfare regulations more generally could increase demand for the covered products. For example, some non-consumers of pork in California could become consumers and some who consume pork only occasionally could become more regular consumers upon implementation of Prop 12 because they believe pork for sale California is now more humanely produced. I explore this potential demand expansion by considering a rotation of the demand function of covered pork in the California market. This is implemented simply by adjusting the coefficient on the covered pork product quantity term in equation . I rotate this demand curve enough to generate sufficient increases in prices and quantities at the new equilibrium such that the producer surplus of non-compliant hog producers is unchanged under Prop 12. The full results from this exercise are shown in Table 4.2. The large and important changes are that the quantity of California uncooked pork now rises by about 8.3% rather than falling by about 6.2% as was the case in the Table 4.1 results. Also, the quantity of non-covered pork in California falls by 3.2% rather than rising by 2.4% because of the shift in preferences for covered pork production under the assumption that consumers now believe such pork is more humanely produced. Notice also that producers that supply the California market for covered products gain even more producer surplus so that producers as a group gain about $4.6 million per year.Although one major requirement of Prop 12 is no use of gestation stalls for sows that produce pigs destined to supply the California covered pork market, my work shows that Prop 12 will have negligible effects on the conversion of stall housing operations. Given that about 30% of breeding pigs in North America are already confined in group housing, and only about 7-8% of the North American hog production is needed for the California market, operations converting to Prop 12 requirements will come from this group of producers. Prop 12 will provide more space to breeding pigs in those operations that convert to compliance because the space allowance per sow in typical group housing is smaller than California’s 24 square feet minimum requirement.

Urban planners have an opportunity to address food insecurity and other urban food system challenges including production, consumption, waste management and recycling by co-creating solutions with urban farmers through participatory processes and investing in community-led solutions. In our systematic review of the literature on whether urban agriculture improves urban food security, we found three key factors mediating the effect of UA on food security: the economic realities of achieving an economically viable urban farm, the role of city policy and planning, and the importance of civic engagement in the urban food system . A radical transformation toward a more equitable, sustainable and just urban food system will require more responsible governance and investment in UA as a public good, that is driven by active community engagement and advocacy.The effects of natural events such as a disease outbreak are typically difficult to measure since simultaneous shifts can occur along several dimensions. The analysis of longitudinal data may reveal dynamic change that would be hard to recognize when only cross sectional data are used . With panel data, one can examine when depression on production occurs -if there is any-, vertical grow weed either at the time of the outbreak, soon after or even before, and for how long such depression occurs.

Previous studies have addressed the impact of porcine reproductive and respiratory syndrome , but have not provided detailed information regarding how the disease affected farm performance. Here, we used longitudinal data routinely collected from sow farms from a US firm between 2014 and 2015 to explore the intensity and extension of outbreaks of PRRS. We then evaluated the effects on revenue due to a decrease on output production using the pre-, during, and post-outbreak periods. This approach allows us to evaluate if outbreaks were reported on time, as well as the extension and length of the impact on production. Endemic animal diseases can affect farm profit by reducing output, increasing production costs, and reducing product price . For example, PRRS, which was first identified in the 1980s, has become one of the most important endemic animal diseases in the US . It affects the swine industry and disease control is difficult due to factors inherent to the disease and the nature of the swine production system. The causal agent is an RNA-virus from the Arteriviridae family that is highly mutagenic and resistant to the low temperatures registered in Midwestern areas of the US, where a significant proportion of the US swine industry is located. On the other hand, the disease is highly transmissible and can persist for long periods in chronically infected animals and in the environment, if contaminated through secretions and excretes . Because PRSS has no effect on humans and has little impact on international trade, PRRS is a non-reportable disease in the US. There are no official programs for its control, but producers in some regions have begun collaborative programs to exchange information on PRRS outbreaks in the hope that coordinated action might reduce disease effects .

PRRSV spreads between and within farms via airborne transmission, the introduction of infected animals and contaminated fomites, often associated with the failure of bio-security protocols . PRRS may increase abortion and mortality rates in pre- and post-weaning pigs, lead to reproductive failure in sows, and lower feed conversion in feeder pigs , thus affecting several stages of the swine production cycle. However, the severity and length of the impact at each production stage are still unclear. In high farm density areas, PRRSV eradication is not the main target. Indeed, farmers prefer to maintain homogeneous levels of immunity in breeding herds using vaccination or, although less common, exposing animals to live virus . Herd closure and rollover is one of the most common strategies to eradicate PRRSV from sow farms. It consists in stopping introducing new sows as replacements in addition to remove seropositive animals for at least 24 weeks . A study showed that production of PRRSVnegative pigs was reached 27 weeks after herd closure started, although an important variation between farms was observed . Alternatively, whole-herd depopulation and repopulation strategy is the most effective strategy described but in many cases is financially impracticable . Two studies have estimated the economic impact of PRRS using data from a set of farms and then extrapolating their results to the entire US swine industry . They reported total annual losses of ~$560 million and ~$664 million, respectively . Although the two studies estimated losses similar in magnitude, they significantly differed in the proportion attributed to losses on sow farms. While Holtkamp et al. estimated that 46% of total losses occurred on sow farms , Neumann et al. estimated that only 12% of total losses occurred on sow farms. The causes of the differences in their loss estimates are not explicitly explained, but may occur because of differences in the epidemiology of the disease at different times, the diversity of clinical outputs in infected animals and/or differences in types of farms. We observe that the effects of disease vary slightly across the farms in our study despite a common management approach, but the availability of data from multiple farms is likely to provide a better estimate of impact than would the use of data from only one farm.

We measure the effect of disease using data prior to the outbreak as the baseline and find that disease impact varies over time, with output declining rapidly initially following the outbreak and then recovering slowly and non-monotonically. We gain additional insights into the progression and recovery of disease by measuring changes in seven other performance indicators. Our methodology can be used to characterize disease impact at the farm and/or firm level, as it provides information on the timing of disease effects, the pathways through which PRRS affects production, and the total time needed for recovery. We anticipate that the results presented will help in the development of more accurate models for evaluating alternative PRRS prevention and control strategies in the US.We screened production records from a large, vertically integrated swine firm that includes farms in each stage of the swine production cycle, i.e., breeding and growing. All of the farms are located in the Midwestern region of the US. Numerous of the sow farms experienced PRRS outbreaks during 2014–2015. An outbreak was reported when animals showed PRRS-compatible clinical signs that were subsequently confirmed through PCR testing. We chose for analysis only sow farms that had not experienced a PRRS outbreak for at least one year prior to the outbreak studied in this analysis. In addition, we excluded from the analysis any farm that experienced cases of porcine epidemic diarrhea virus during the eight months before the PRRS outbreak to avoid confounding disease effects. Thus, all farms were classified as positive-stable without undergoing elimination at the time of this study. This firm also used a common disease management protocol for all its farms.f weaned pigs , which subsequently was used to estimate the decline in the value of output due to a PRRS outbreak. Likewise, we used weekly data for seven statistics, drying rack cannabis referred here as performance indicators, to more comprehensively assess how the disease affected weaned pig output. These indicators are: the number of live births per litter -or litter size- , the number of stillbirths per litter , the number of pre-weaned pigs dead , the number of sows dead , the number of sows aborting , the number of sows with repetition of service , and the number of sows farrowing .The estimated baseline is used to measure PRRS’ effect on production after the outbreak, i.e., between t and t + 35. We then used the same procedure to analyze, separately, the baseline values and post-outbreak effects for each performance indicator . Using longitudinal data allows us to reveal PRRS dynamics that might be difficult to identify if using cross-sectional data. In this case we evaluate the net effect of the outbreak on production within a selected set of farms. The use of fixed effects also permits us to manage the unobserved heterogeneity within farms whose omission could bias the estimated coefficients. We assume that time invariant effects are unique to each farm and are not correlated with effects on other farms. In addition, the expectation that individual farms have stable characteristics over time and the recognition our sample set has not been selected randomly led us to prefer a fixed effects rather than a random effects model. We used the Hausman test to determine whether the unobserved effects are distributed dependently of the regressors . We used Stata Statistical Software V13.1 to perform all these statistical computations and graphic designs .Dummy coefficients for weeks were statistically different than 0 indicating that the inclusion of Tt as fixed time effects in equation is appropriate. Although we found no significant trend in weaned pig production during the pre-outbreak period, holding the number of sows and season constant when using equation , we observed a consistent decrease in weaned pig production relative to the baseline in the week t − 1, immediately before the outbreak was reported in week t . The decreases ranged between 1 and 12%. As our regressions based on equation 1) showed no significant trend in weaned pig production even when week t − 1 was included, we did not remove week t − 1 from the baseline period.

Had we done so, the baseline would have been very slightly higher and the estimated damages from PRRS slightly greater, as discussed subsequently. We estimate that aggregate weaned pig production for the 16 farms decreased from the baseline production of 2,094 per week to 1,600 in week t + 5, when output was a full 23% lower than the baseline. Table 1. The results show that farm production decreased monotonically from t − 1 to t + 5, and then began to recover . Output recovered moderately from t + 5 until t + 11, at which point another significant decline in production occurred to t + 17 . Eight of the 16 farms then recovered monotonically to their baseline production levels by t + 33, but a slight drop occurred again in t + 34 and t + 35 with 15 farms producing lower than the baseline. In the aggregate, observed production approached the baseline value by the end of t + 35, when our sample ended. Estimated output appears slightly lower than the pre-outbreak level, but the difference is not statistically significant . Similar to the production of weaned pigs, the seven performance indicators did not fully recover to their pre-outbreak means. Week-to-week comparisons revealed changes in all performance indicators, with some variation in timing and intensity. For each performance indicator, the recovery of each farm fluctuated around a rising trend estimated for all farms, and again showed a non-monotonic recovery . As expected, some performance indicators presented a lag with respect to the trend observed in weaned pig production. A significant increase in the number of preweaned pigs dead was detected at t with an average expected rise of 79 deceased animals relative to the baseline, reaching a maximum increase at t + 1, with 143 expected extra losses . While litter size did not show a significant decline at t, the expected number of live births decreased by around 1 animal between t + 1 and t + 18, reaching a maximum decline at t + 2 and t + 3 and a new deterioration at t + 14. The number of stillbirths increased between t and t + 16, reaching a maximum at t + 12, with 2 stillbirths per litter . Although there was no immediate increase in the number of sows designated for repeated service the week of the outbreak report, by t + 6 the number of sows that were designated to repeat service increased from 11 sows in the estimated baseline to 31 sows. Likewise, the number of pigs farrowed declined after t + 1. The number of abortions significantly increased at week t− 1, doubling the number of sows that aborted prior to t − 1. The number of sows with abortions peaked in the week of the outbreak report at a level five times higher than the baseline level.

While these may be useful in providing novice farmers with a starting point as to how to even begin putting together information regarding environmental, resource, and operational data, most of the farmers we spoke with end up restructuring their previously collected data to fit policy and/or certification requirements.The population of farm workers includes a large Spanish speaking population and a diverse range of literacy levels, as well as a varied set of communication needs across stakeholders. Such diversity is not always accommodated. Models may include multiple languages or a visual focus to overcome barriers to communication in the social context of the farm.There is a lack of consistency and technological capacity across the spectrum of sustainable agriculture as a result of the sparse attention that has been paid to the information management needs and requirements of sustainable farmers. Their agricultural systems cannot be adequately represented and analyzed using current modeling tools and methods, and many environmental data sources have not been designed to integrate with the tool kits that such farmers currently use.Artifact trails spanned across disparate data structures: from hand-drawn maps containing crop locations, rolling benches spreadsheets varying in structure from year to year even within a farm, to custom databases to handle portions of the farmer’s workflow.

Maintaining coherence and consistency across data structures is key to stitching together the information workflow of sustainable farmers; the heterogeneity of data structures within farms provides a significant stumbling block in effective workflows. One potential avenue for future work involves the design of better models to encapsulate the types of data that are collected and fit with the data collection techniques that farmers engage in. Models are apt devices for communication of a system’s composition and performance, as well as useful artifacts for reflection. In particular, a domain-specific modeling language would enable system stakeholders to gain perspective on their systems, perform environmental analyses, and create abstract yet grounded models that they can manipulate before changing the real world systems that are represented.The switching between data structures is accompanied by several transitions between different mediums of technology, resulting in a messy information management experience. Five genres of technology were used across one or more farms: physical or paper-based artifacts; communication technologies like email; external regulation- or certification-specific software; farm-specific custom software; and office productivity software such as Microsoft Word and Excel.Farm models varied in level of abstraction, formality, and granularity, both within an individual farm and across agricultural system types. For example, on some farms, resource use was tracked at the whole farm level, while on others there were intricate interconnections between subsystems requiring tracking at a granular level.

The commodity and process complexity of the farm also affected the level of abstraction, formality, and granularity of models. Models need to be flexible enough to capture whole-farm activities as well as fine-grained data about specific farm components. Ideally, farmers should be able to create both informal and formal models depending on the type of data they have and analyses they plan to conduct. Consequentially, relevant tooling must be capable of representing the varying spatial and temporal complexity present throughout the spectrum of sustainable agriculture.Many of the models used for internal assessments by farmers are created in an ad hoc manner and for a specific purpose For example, a map created for communication to visitors may be opportunistically used for coordination among farm workers. However, this is a one-off reuse, and is not inherently supported by the models. Representations cannot always be easily repurposed, resulting in a significant reusability gap.Further, data is often isolated in purpose-specific models. For example, input logs used to track resource application for organic certification are not necessarily connected to inventories that are used to track expenditure on materials. Data and effort are duplicated as data is tracked separately for inventory management and organic certification. Farm data needs to be captured in a general enough form such that it can be manipulated and transformed on demand. There is an opportunity to reduce the reusability gap by enabling farmers to create modular component-based farm models. There is also a significant amount of publicly available environmental data, such as data on soil composition throughout the United States, global weather data, and California-wide water quality and availability. These data can and should be incorporated into information management tools for sustainable farmers to reduce their data collection burden.The transience of agriculture means that not all data collected is necessarily archived. This is further exacerbated by the rate of change on farms: the more often the farm changes, the easier it needs to be to update the model. This also affects the formality and reusability of farm models. Use of model configuration management may allow for farmers to track changes in their farm models just as they use models to track changes in their farms. This would also allow farmers to compare models over time and reduce the effort involved in updating models to reflect changes. We found workarounds implemented by farmers to mimic such a workflow. By designing with the intent to archive historical data and track changes, the inherent dynamism of the farm can be captured.There is a mismatch between the causes and goals of data collection, particularly as the farm evolves. While initial causes for data collection may be for regulatory reporting and system understanding, eventually farmer goals can expand to include communicating to diverse stakeholders , environmental analyses, and monitoring.

The mismatch between the collection trigger and emergent goals results in a lack of coherence in farm models and data workflows. We must explicitly consider both the causes and goals of data collection in the design of information management tools for sustainable farmers to ensure that appropriate data is aggregated and connected. Supporting easy transformation of the information management structures and practices would facilitate synchronicity among causes and goals.Farm stakeholders are constantly using, sharing, and communicating various data for daily decision-making and problem solving. However, many of the technologies we found in use were not designed to meet the coordination and collaboration needs of varying farm stakeholders. Any redesign of the information workflow of sustainable farmers must provide these stakeholders with the capacity to conduct environmental assessments and other forms of analysis; coordinate among farm workers and other stakeholders; and communicate with regulators, certifiers, and consumers as needed. Explicit attention to the differing privacy and access characteristics of data would be critical.They have been used in software design as stand-ins for real users, allowing people to engage in human-centered design, where the user experience and interaction of the human with the system is the key focus. In this dissertation, I flip the traditional persona, and instead use it to describe a system , that a human , is interacting with, cannabis drying room the result of which is a Farm Persona. These Farm Personas were created based on the data and findings of Chapters 3 and 4. Methodological detail on the design of these personas is available in Section 6.1 of Chapter 6, as the Farm Personas are explicitly presented during the evaluation of MoSS. The Farm Personas were used to engage in Persona Driven Design. Partial MoSS models were created to represent various activities, components, and aspects of hypothetical farms. These models were then used to refine the Pseudo-Software Models and thus refine MoSS. For example, Figure 5.8 below shows how a Farm Persona called Blackbird Gardens was used to explore the representation of crop layout and field management. Persona Driven Design thereby allowed for the farm-centered design of MoSS.Personas are detailed constructions of fictitious yet archetypal users of a software system that allow for human-centered design. There is growing interest in using personas to engage in design that considers other forms of system interaction. For example, collaboration personas have been used for the design and evaluation of tools for use by groups of humans. Non-human animal personas were proposed in recognition that the human stakeholder should not entirely dictate prototype development: a cow is a user of a robotic milking device, and the cow’s needs, welfare, and experience in mind, should dictate the design of the tool. Farmers are integral actors in agricultural systems. Often, in addition to being the farm’s owner/operator, a farmer is also: a manager of staff, the primary decision maker regarding on-farm activities, a stakeholder of farm data, and a farm data collector. While future work involves the design of a modeling tool to allow the farmer to interact with MoSS, the scope of this dissertation is to design a mechanism for modeling sustainable agricultural systems. I therefore consider farmers as primary actors in a System Persona. I define a System Persona as a detailed construction of a fictitious yet archetypal system. As the focus of this dissertation is on the holistic representation of the characteristics of small- to medium-scale sustainable agricultural systems, I created a set of Farm Personas, where each persona represents a hypothetical small- to medium-scale sustainable farm in California.The goal of this activity was to flesh out the Farm Personas with enough details so as to be used as part of the Persona Driven Design of MoSS, as described in Section 5.1 of Chapter 5. The Farm Personas needed to be grounded in both the academic literature regarding formal modeling in sustainable agriculture , as well as the findings regarding sustainable farms in reality . Overview tables were created to map out the key characteristics of the 16 farms from Chapter 4, Farms Atwood to Pullman . These led to the creation of design parameters for the MoSS Farm Personas. These parameters reflect greater nuance regarding the characteristics of the farms. Once the base Farm Personas were created, they were augmented through the addition of characteristics from the 16 farms resulting in artificial yet data-rich composite farms. Two primary characteristics were used to augment the Farm Personas to engage in the design work that resulted in the MoSS framework: complexity and dynamism. This process is briefly described next.A scenario is a hypothetical set of activities to be enacted by actors interacting with a system of interest in the real world. Scenarios have been used in software engineering and HCI research both as a means to explore potential designs of a system, as well system evaluation. For example, Sutcliffe describes how scenarios may be used in requirements engineering, in particular, to check abstract models through use of scenarios as a substitute for formal verification. Scenarios are often implemented when the problem domain is “squishy”, i.e., the problem boundaries are not distinct, the interactions are complex, and the problem does not lend itself well to linear design work or structured evaluations. It follows that there is evidence of the use of scenarios for design and evaluation in the environmental assessment and sustainable agriculture communities. In LCA research, scenarios have even been used to represent hypothetical agricultural systems to explore, for example, the environmental impacts of various pig production systems. The University of California Cooperative Extension has been conducting “Sample Cost of Production Studies” for various farm commodities since 1928. While the early reports are written in the style of a handbook, later reports in the mid-2000s begin to list study assumptions in the style of a scenario. For example, the 2009 cost production report for organic leaf lettuce in the central coast region of California, begins with a description of hypothetical farm with specific produc-tion activities; labor, interest and equipment; and how cash and capital are spent and obtained. It is these characteristics of a fictitious farm that provide context for the various cost estimates that are subsequently listed. This structure is powerful as it allows the reader insight to the rationale for listed costs, contextualizes the calculations, and provides a human readable example that a reader may work through. These cost production studies also proved an invaluable model for tailoring the software-style scenarios for sustainable agriculture.There are eight scenarios described in this chapter that were used to evaluate MoSS. The primary basis for scenario creation were the findings of the qualitative study involving 16 sustainability-oriented farms in California, described in depth in Chapter 4. To ensure scenario accuracy, interview data were also checked against relevant literature prior to being incorporated into a scenario. To avoid overspecialization and simply designing for those 16 farms , I created a set of scenarios, applicable to farms throughout a spectrum of sustainable agriculture.

As some of these foods are produced primarily outside of the regions of interest in this paper , they were cross referenced with the top ten most produced agricultural commodities in each of those regions to create a list of commodities to be considered in this paper.Table 3.1 shows which of the globally most produced food and agricultural commodities are also in the top ten most produced commodities of the United States, Europe and Australia. Note that some commodities, such as rice, vegetables, and cassava, are not widely produced in any of these geographic regions. Therefore, commodities of interest were those that existed in the both the global top ten most produced and the top ten list of at least two out of three regions. Figure 3.3 shows that enteric fermentation and three manure related activities , contribute 66.1% of agriculture’s emissions, resulting in significant interest in better understanding the environmental impacts of livestock farming. Beef is one of the top ten most produced commodities in both the United States and Australia. Similarly pork is one of the most produced commodities in Europe. As they are therefore responsible for a large proportion of global agricultural emissions, these two livestock farming commodities, vertical grow beef and pork, were added to the list of commodities of interest in this paper.Most of the LCA studies found, 18 of the 25, were comparative studies. Many looked at the differences between conventional and organic farming systems.

As there is a current push toward both growing and buying more organic food, it makes sense that the community is interested in finding empirical evidence to support the environmental benefits of organic food. Comparisons of different cropping systems that result in a similar processed product is also common: For example, three cropping systems are compared, as all three result in the production of vegetable oil. In all of the studies in this category, the agricultural systems are manually compared. Surprisingly, there were only two connective studies and one update study found. It may be the case that updates to LCI data are not commonly published in academic literature, and are instead directly updated in LCI databases . The lack of papers in the connective category may be evidence that there truly is a lack of connectivity across the plethora of LCA models created. Four methodology studies were found. There is substantial literature on improving LCA methods. The search criteria that were used aimed to find papers that specifically involved one or more LCA studies and their details. The papers in this category did conduct an LCA study, but the purpose was to test proposed methods.LCA Methods allow for the modeling of different types of agricultural systems . Some methods, such as EIOLCA, have been developed to try to reduce the overhead inherent in LCA, by allowing analysts to calculate estimates. However, the process is very involved, requires expertise in the method, and it is difficult to reuse models, thereby taking advantage of effort already expended. In general, accurate, thorough, and rigorous LCAs are effort-intensive. Each of the LCA studies presented in this chapter provide further insight into how the LCA technique is customized for and commonly used in the assessment of agricultural systems,such as the creation of hybrid LCA methods and streamline LCAs.

In this section, I articulate these findings as seven observations regarding LCA for agriculture.The most common type of LCA used in the analysis of agricultural systems is a cradle-to gate analysis, since once the product is ready for shipping, the storage, variety of packaging, distribution methods, preparation, and consumption, among others, vary widely. Table 3 lists the LCA types used in the representative sample of LCA studies, highlighting those that involved a cradle-to-gate LCA. The scope and boundary of the cradle-to-gate agricultural system LCAs are very similar to each other. The final product of the agricultural system is usually some form of raw product, such as a meat, grain, fruit or vegetable. The amount of processing this food product undergoes within the system of interest also varies widely in some cases, the final product is frozen, ground, deboned, packaged, or transformed into some derivative transportable product such as sugars from sugarcane.Table 3 overviews the functional units that are used in each LCA study. For instance, “1 ha of land used” is a popular metric, which allows for the calculation of energy intensity with respect to land use, demonstrating how much strain the system puts on the land. For single product systems, a functional unit is often in terms of produce weight, as it allows for the calculation of energy, emissions or impacts per unit weight of the product at the gate. The decision of where the gate lies depends the system boundary, i.e., which of these processing techniques occurs on the farm. For example, in beef production, the farm-gate may be pre-slaughter or post-slaughter. This also determines the functional unit: for pre-slaughter the functional unit would be live-weight, while post-slaughter, a common unit is Hot Standard Carcass Weight.Sometimes, midpoint functional units are used to analyze system subcomponents or to allow for the discretization of processes. For example, HSCW , is used.

It represents the end of the production process in the pork supply chain, i.e., the weight of the product at the slaughterhouse gate. This makes the unit incomparable to other pork or meat product LCA studies that may define the endpoint at the consumer side To address this issue, the analysts in the Australian Pork study also use two midpoint functional units: 1 live piglet and 1 live slaughter pig at the farm gate. These units allow the findings to be used in comparative studies. For reference, another pork production study, by Basset-Mens & Van der Werf, has a functional unit of 1kg of live slaughter pig as well, in addition to a land use unit . However, not all LCA studies have a midpoint functional unit or a functional unit that can be used to compare the models produced in the study with other studies, even if they are ISO compliant. Functional units can also be highly specific to the system of interest, product, or location. For example, the functional unit is “1kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor”. The level of granularity is non-negotiable. While there must have been intermediate steps in the LCA that separated the different processes , these numbers are not always released or easily accessible. Various levels of detail are lost to the reader, and more importantly the system cannot easily be broken down into reusable components. Unfortunately, while the functional unit is meant to make LCA studies more comparable and reusable, it is not always the case.Renouf et al. perform a comparative assessment of the production of sugars for fermentation in Australia, corn production in the US, and sugar beets in the UK. The product of interest was a sugar suitable for fermentation, as its bio-products have wide use, including as an alternative energy source. Here, in addition to systems based in different locations, the initial crop is different too. The functional unit in this study is 1 kg of monosaccharide , as this enables comparability across sugarcane, corn and sugar beets. As opposed to conducting a separate LCA study based on the specific sugarcane farms, the researchers used data from a variety of Australian inventory databases, local survey data, and other academic publications that have looked at different processes within the sugarcane production system . Similarly, for the U.S. corn, best way to cure cannabis and the British sugar beet impact numbers, the researchers looked at two sets of studies for each case, and converted their functional units into 1 kg of monosaccharide, based on the yield numbers reported. As all the U.S. corn and British sugar beet studies had a high level of detail available in the report, the resulting analysis is precise comparison between the three sugar production systems.Heller et al. perform a very broad review of the United States food system by using a life cycle perspective to connect systems within different sectors of the industry. They use a product life cycle approach to analyze sustainability indicators across different life cycle stages: resource origin, growing and production, food processing, packaging, and distribution, preparation and consumption, and end of life management. This study is unique in that it attempts to address the entire US food system, connecting different agricultural systems without resorting to a sector based approach like EIO-LCA. Heller et al. did not conduct a new LCA, instead opting to review LCAs in published literature and connect information about the impacts that occurred at each stage to provide a holistic view of the food system.

It is still one of a small number of papers that attempts to connect impacts across products and agricultural sectors over a large region, thereby encompassing a sizable portion of the industry.Another massively-scaled LCA study is available in a report by the Center for Environmental Strategy at the University of Surrey by Mila i Canals et al.. The paper details a series of comparative LCAs, which combined aim to compare the environmental impacts of domestic versus imported vegetables. They compared broccoli production in the United Kingdom and Spain, salad in the UK, Spain, and Uganda, and finally, legume production in the UK, Uganda, and Kenya. The life cycle of each product is geographically disparate, therefore they break it down into three projects/reports chunked as follows: “cradle-to-central-depot”, “retail-to-plate”, and “consumption-to-waste” , The report highlights the importance of connecting LCAs across products, production systems, regions, up to connecting the entire industry. It is because of this highly detailed, connected set of LCA models that they can come to the surprising conclusion that local is not always more environmentally friendly.Once a flow diagram has been created, and the analysts have a feel for how resources move within the system, they gather all the data required to calculate different environmental impacts. This process involves decomposing the high level steps in the flow diagram into individual sub-flows or processes. The basic unit of LCI data that is collected is the “unit process”, defined by ISO 14044 as: “the smallest element considered in the life cycle inventory analysis for which input and output data are quantified” . For each unit process, inputs and outputs , and the associated environmental impact with it are listed. The question to be answered is: how does actually performing this step affect the environment? The data may be collected in several ways: primary data collection , data obtained from published literature , data obtained from the results of simulations of approximately similar systems, or through lookup in a Life Cycle Inventory database. Due to the scope of the LCA, the number and size of the agricultural systems under study, the type and level of detail of the LCA to be conducted, and the availability of existing data, the LCI phase can consume the most time, money, and effort. They contain structured collections of objects representing unit process data. An overview of some LCI databases used in Agricultural LCAs is available in Table 3.4 .Within the LCA studies surveyed in this chapter, the national or regional LCI databases that analysts interact with are: the United States Life Cycle Inventory database, the Australian National Life Cycle Inventory database, and the European Life Cycle Database. Additional databases are listed in Table 3.4. Although this list is not exhaustive, others exist, many of which contain smaller, specialized datasets. Many proprietary databases are populated via primary data collection performed by consultants in partner organizations. These proprietary databases often aggregate existing free databases, and/or resell other proprietary databases as part of a package deal. ecoinvent is such an example, and is popularly used to supplement data regarding machinery, infrastructure, or capital goods in an agricultural LCA. These data are often international in scope. Most databases , contain data gathered during process-based LCAs. Some new database initiatives focus specifically on agricultural and food systems, some of which are also included in Table 3.4. Some databases, such as USLCI and ecoinvent, only release data in the ecoSpold format, ELCD and GaBI only use ILCD. Whereas others, such as AusLCI,, have versions of their data in both formats.

Contaminated or no-growth inoculated samples were not read and repeated. In addition, quality control strains were run weekly alongside the test samples. For anti-microbials in which the BOPO7F Vet AST Plate dilutions included the established breakpoint, “resistant” status was assigned if the isolate grew in or beyond the breakpoint dilution . For antimicrobials in which the testing plate included only dilutions below the established breakpoint, “non-susceptible” status was assigned and included isolates in the intermediate range according to CLSI guidelines or isolates that grew in the highest dilution available. Resistance or non-susceptible status was only assigned to antimicrobials for which breakpoints were available and for which in-vivo activity and antimicrobial spectrum were applicable. For antimicrobials that were assigned non-susceptible status , it was not possible to establish resistance because the drug dilutions did not reach the threshold breakpoint; hence growth or no growth at or beyond the breakpoint could not be established. Antimicrobial breakpoints used and dilution ranges for the BOPO7F Vet AST Plate can be found in Table 1.Data from the ranch survey, individual animal data, pipp racking and AST results were entered into a spreadsheet and combined using a relational database . Descriptive statistics for ranch demographics and prevalence of resistance or non-susceptibility for antimicrobials with existing breakpoints were prepared. Univariable generalized linear mixed models with a logit link were prepared for the outcome of resistance or non-susceptibility status of isolates to each antimicrobial with available breakpoint data using the GLIMMIX procedure in SAS .

A random effect was added to account for correlation between isolates from the same animal, since 2 isolates from each fecal sample were required for inclusion in the MIC analysis. A second random effect of farm with animal nested within farm was attempted but led to non-positive G matrices and not explored further. The independent variables were created from the questionnaire data on herd demographics, antimicrobial practices, treatment history, and management practices on the farm. Multivariable generalized linear mixed models were attempted by including all variables from the univariable analysis with p<0.2. A multiple factor analysis was conducted for survey data and antimicrobial susceptibility testing results of the 244 E. coli isolates. MFA was conducted to reveal the most important variables that explain the variation in the data set . The dataset consisted of 63 data variables which were organized into 6 groups based on relatedness as follows: herd information: a group of 7 categorical variables specifying farm number, the location of farm, breed distribution, herd size, certification status , type of pasture, and type of production; sampled animals’ life stage and treatment history: a group of 7 categorical variables specifying sampled animal life stage, method of fecal sample collection , date of fecal sample collection, whether animal was treated with antimicrobials, and antimicrobial used for treatment ; antimicrobial resistance group: a group of 8 variables describing AMR for E. coli ; AMR for E. coli; farm antimicrobial use and disease treatment group: a group of 17 categorical variables describing the different injectable and intramammary antimicrobial drugs used in farms and type of treated diseases ; antimicrobial dosing and record keeping practices: a group of 12 variables describing methods used for determining treatment duration and dosage, and information recorded regarding antimicrobial treatment ; and nutrition related factors: a group of 12 categorical variables specifying the provision of byproducts and mineral supplement to calves, and cows .

The groups with loading weights of 0.5 or higher on the first two principal components were retained for interpretation . The percentage of variability contributed by each group of variables to the principal components and the correlation coefficients for the component variables within each group were estimated . Variables within each group with loading weights of ≥0.5 on the first two principal components were also retained for interpretation. The function MFA in FactoMiner package was used to perform the MFA on the dataset. The function get_mfa_var was used to extract the results for the groups and variables. Hierarchical clustering was performed on the MFA principal coordinates using the principal component methods at the animal level . The identified clusters were described based on the variables that contributed the most to the data variability. Both MFA and hierarchical clustering were performed in R software using FactoMineR for the analysis and factoextra for data visualization . MFA analysis was not performed for Enterococcus data due to the limited number of resistant and non-susceptible isolates.A total of 18 cow-calf farms were surveyed and sampled during this study. General descriptive data including the major breed, herd size, pasture type, location, antimicrobial practices, and the number of injectable or oral antibiotics used on farm is shown in Table 2. Other management survey results of interest revealed that most farms had at least one beef quality assurance certified employee, one farm fed byproducts, 7 farms had submitted samples to a diagnostic lab in the past year, and 17 had an established veterinarian-client-patient relationship. Oxytetracycline was the most common antimicrobial used on farm , followed by tulathromycin , florfenicol , sulfas including sulfadimethoxine and sulfamethoxazole , penicillin , enrofloxacin , and ceftiofur . No farm reported using danofloxacin or ampicillin.

Regarding the types of diseases that had been treated with antimicrobials in the past 12months in any cattle on the farm, 13 farms reported treating infectious bovine keratoconjunctivitis , 13 reported treating bovine respiratory disease, 10 reported treating foot rot, 7 reported treating scours, 6 reported treating wounds, 5 reported treating navel infections, 3 reported treating metritis, and 2 reported treating mastitis. There were 2 farms that reported no antimicrobial use because no disease identified as needing treatment was observed during the past year. Only one farm had routine prophylactic use of antibiotics where all calves received an injection of oxytetracycline between 1week and 1month of age, and all farms that used antimicrobials recorded at least one form of information after antimicrobials were administered such as date, dose, route, withdrawal, and/or product name.In total, fecal samples were collected from 187 animals and plated for growth and recovery of E. coli and Enterococcus isolates. A total of 244 E. coli isolates and 238 Enterococcus isolates were recovered and tested for antimicrobial susceptibility using broth microdilution method. Of the 104 cow samples plated, 50 samples grew at least 2 isolates of E. coli and 50 samples grew at least 2 isolates of Enterococcus. Of the 83 calf samples plated, 72 samplesgrew at least 2 isolates of E. coli and 69 samples grew at least 2 isolates of Enterococcus. Details regarding the number of samples and resulting isolates can be found in Figure 1.The distribution of isolates within various drug dilutions tested for each antimicrobial can be found in Table 3. Resistance or non-susceptible data is only shown for those antimicrobials for which established breakpoints by CLSI were available, including ampicillin, ceftiofur, florfenicol, sulfadimethoxine, tetracycline, and trimethoprim-sulfamethoxazole . Among the 244 E. coli isolates, 88/244 were resistant or non-susceptible to at least one antimicrobial excluding ampicillin, to which all isolates were resistant. Similarly, a large proportion of isolates showed antimicrobial resistance or non-susceptibility to sulfadimethoxine followed by trimethoprim-sulfamethoxazole, while the lowest proportion of isolates showed antimicrobial resistance to ceftiofur. More isolates were classified as non-susceptible to tetracycline than florfenicol. Neither univariable nor multivariable generalized linear mixed models revealed any statistically significant associations between any of the risk factors considered, vertical grow racks including record of antimicrobial therapy with the same antimicrobial in the past 6 months, and resistance or non-susceptible isolate status. Although none of the farm-specific variables captured in this study were significantly associated with differences in resistance or non-susceptibility, there were numerical differences between farms in terms of their antimicrobial resistance profile for E. coli isolates. Specifically, the highest percentage of resistant or non-susceptible isolates for florfenicol , tetracycline , and trimethoprimsulfamethoxazole at the farm level was found on Farm 6, which contributed 14 isolates. Interestingly, Farm 6 did not report the use of any antimicrobials on farm.The distribution of isolates within MICs tested for each antimicrobial can be found in Table 4. Resistance or non-susceptible data is only shown for those antimicrobials for which established CLSI breakpoints were available, including ampicillin, penicillin, and tetracycline . Only a small proportion of the total 238 Enterococcus isolates, 35/238 were resistant or non-susceptible to at least one antimicrobial. Amongst all isolates tested, antimicrobial non-susceptibility was highest to tetracycline, followed by non-susceptibility to penicillin, and lowest resistance to ampicillin. Similar to the statistical models for the E. coli isolates, no significant associations between any of the risk factors and AMR status for Enterococcus isolates was found.The first two principal component dimensions of the multiple factor analysis explained approximately 8.5% of the variability in the data, i.e., 4.4 and 4.1% of the variance for the first and second principal component dimensions, respectively. The MFA analysis of 63 variables identified four components and 16 variables with a correlation coefficient≥0.5 on both first and second dimensions that accounted for 98.7% of the variability in the data . Herd information accounted for 27.7% of the total variability in the data, while antimicrobial dosing and record keeping practices accounted for approximately 25% of the total variability in the data.

Nutrition related factors and farm antimicrobial use and disease treatment accounted for 24.2 and 21.6% of the total variability in the data, respectively . The sampled animals’ life stage and treatment history as well as antimicrobial resistance data were groups of variables where correlation stayed below the threshold of 0.5.Hierarchical clustering was performed on the MFA principal coordinates to aggregate homogeneous clusters. The hierarchical tree suggested clustering into six clusters . The identified clusters were described based on the 16 variables that contributed the greatest to the data variability from the MFA analysis . Cluster 5 represented the majority of sampled animals and ranches . Most animals represented in cluster 5 were on ranches that reported estimation of the dose of antimicrobial drugs based on estimated animal weight, reported recording the date of antimicrobial use , reported feeding free choice minerals to calves , reported cleaning of water troughs , and did not use antimicrobials to treat mastitis . However, 91.8% of animals in cluster 5 were on ranches that also reported that withdrawal periods are not recorded when animals are treated with antimicrobials. Cluster 4 represented two ranches in our study. The farms represented in cluster 4 mentioned that they were not recording the date, route, and withdrawal period of antimicrobial use . One farm represented in cluster 2 mentioned routine use of antimicrobials for prevention of disease and use of antimicrobials for treatment of mastitis. Clusters 1, 3, and 6 represented one herd each. Farms represented in clusters 3 and 6 reported that they were not using antimicrobials for treatment of mastitis and the farm in cluster 6 reported dosing antimicrobials according to veterinarian’s orders. The beef operations located in the coastal range were only represented by clusters 5 and 6. The majority of beef ranches in clusters 5 and 6 reported several antimicrobial stewardship or herd health practices including estimation of the dose of antimicrobials based on estimated animal weight, recording of the date of antimicrobial use, feeding free choice mineral to calves, and cleaning of water troughs once a month, and did not use antimicrobials to treat mastitis in comparison to beef ranches included in clusters 1, 2, 3, and 4. A complete description of the six clusters is available in Supplementary Table S1.Antimicrobial resistance is a global problem , and while much of the attentions is focused on human health implications, the effects of AMR on livestock health may be similar,including treatment failures requiring the use of newer and often more expensive antimicrobials . For our study, the distribution of herd sizes closely represented what has previously been reported for cow-calf operations throughout the state of California . Considering the state’s number of beef cow farms, however, our study included a higher proportion of larger herd sizes for the state, since approximately 77% of beef cow farms in California are reported to have fewer than 100 cows, not including hobby farms with less than 10 cows . Information about the percentage of different breeds, type of production , and/or Age and Source Verified versus conventional) for beef cow-calf herds or specific antimicrobial practices have not been previously reported.

As a volunteer mentioned, patients are “prescribed by psychiatrists to come and garden.” Additionally, healthcare workers and veterans participating in the VA’s Compensated Work Therapy Program use the garden for growing food and taking breaks. Visitors are welcome to enjoy the garden at any time. Unlike most other UA sites that will be mentioned in this chapter, there is no gate at the front entrance, though there are security cameras monitoring the property. Signs instruct visitors not to pick crops without permission. There are accessible pathways for those with mobility-aiding devices , a restroom, benches, a barbecue grill, a building with a refrigerator, and a large gazebo and seating area. Eagle Scouts from the Boy Scouts of America installed raised garden beds and a flagpole for the garden. Figure 13 shows a large greenhouse surrounded by California native plants, shade-providing trees, chairs, and rainwater barrels.In 2023, the Surfrider Foundation donated six 50-gallon rain barrels to collect rainwater, to water plants and prevent storm water pollution from runoff. The rain barrels were up cycled using mosquito netting, barrels used to ship food items, and PVC pipes and spouts . These amenities helped gardeners plant a variety of edible crops and about 70 fruit trees, such as bananas, pears, and persimmons. The Patient Garden is also decorated with cacti, roses, bird feeders, a rock garden full of painted stones, cannabis drying curing and a sensory garden with plants meant to see, smell, and touch.The Long Beach Community Garden was first established in 1976 at the abandoned Honor Farm .

Originally, LBCG was 6.5 acres with 218 garden plots, located between the Interstate 605 and East Carson Street. However, city officials asked LBCG to relocate in 1996, following the demolition of a Naval hospital and construction of a shopping center, which included Walmart and other stores . On December 1, 1997, LBCG opened its new 8.5-acre location, which is between the 605 freeway and El Dorado Park, on Spring Street in the 90815 ZIP Code. With 300 plots each measuring 20’ x 30’, LBCG is the largest community garden in the city. LBCG is managed by the Long Beach Community Garden Association , a not for-profit organization with no paid staff. A board of eight members oversees the LBCG’s budget, which goes toward the maintenance and enhancement of the garden. Gardeners pay an annual membership fee of $160 and are required to complete four hours of community service per year, to maintain common areas of the garden. Only LBCG members and authorized individuals can enter. The garden is protected by a locked gate, which features a remote-controlled entryway for cars and large trucks for trash collection and mulch delivery. The photograph in Figure 14 shows a tree in front of one of the many paved roads inside LBCG, which allows accessibility for both vehicles and gardeners.LBCG features a large gazebo, benches for sitting, a storage shed, and a fruit tree orchard. Gardeners may donate food from their garden plots and the fruit orchard to the Food Bank Collection Station. The food from this station is distributed to a variety of local charities, such as Long Beach Rescue Mission. LBCG’s fruit orchard has over 100 trees, including but not limited to: avocado, banana, cherimoya, grapefruit, lemon, lime, loquat, kumquat, orange, peach, pear, persimmon, pineapple guava, and pomegranate. There are several varieties of fruit and hybrids such as aprium, nectaplum, and pluerry . The QR codes direct to the official LBCG website with information on each tree.As shown in Table 3, five LBO community gardens are located on city property and four are located on land owned by a private owner .

The largest LBO garden was Zaferia Junction, which is 1.4 acres, over 20 times larger than Orizaba Park, the smallest garden . All LBO gardens provided hoses, a communal shed for tools, a compost area, and a picnic arbor for gardeners to sit in the shade and enjoy meals, and most of the gardens are accessible for those with disabilities. A few gardens feature special amenities, such as a hive for beekeeping, portable toilets, fruit trees, and herb beds. Zaferia Junction has a sensory garden and an earth oven for cooking food outdoors. At the time of writing, 250 of 281 plots were rented. Six of the gardens have a waitlist for renting a garden plot, with the wait time ranging from six months to four years. LBO prioritizes renting plots to low-income families who may otherwise lack access to organic produce, Long Beach residents or those who live near an LBO garden, and those who have no other opportunity to garden. Requirements for plots vary on the property owner. For example, gardens located on land owned by Long Beach’s Department of Parks, Recreation & Marine prioritize gardeners who live within walking distance. Gardeners pay a minimum of $55 per six-month season for plots smaller than 10’x8’. The fee for larger plots is $0.70 per square foot . LBO gardeners are required to sign a rental agreement and waiver of liability, maintain their plot, and complete a minimum of 10 hours of community work per six-month season. Each garden is secured by a locked fence, which gardeners receive the code for when they rent their plot. The gardens are open to volunteers and community members on Saturday workdays, which involve weeding, construction projects, and maintaining spaces outside the garden plots, such as walkways or areas near the fence. For example, for the Captain Charles Moore Urban Community Garden located on Long Beach Blvd, which began construction in April 2023, volunteers built the shed, arbor, and raised garden beds, as well as a swale to capture rainwater and direct moisture to the garden .

Gardeners may also complete their volunteer hours on Wednesdays at Zaferia Junction to assist with harvesting, washing, and sorting produce, which is donated to California State University, Long Beach’s student pantry .Though most gardeners grew the previously mentioned crops that thrive in Southern California’s climate , there were many notable crops unique to each garden site. For example, one gardener at Zaferia tended to a variety of fruit trees, including peaches, apples, and hybrids like nectaplum , pluot , and bubblegum aprium . Pacific and 6th had banana, papaya, and loquat trees, as well as perilla and other crops planted by Asian gardeners. Gardeners at Grace Park grew longevity spinach, Malabar spinach, and hoja santa , an herb native to Mexico. Many LBO gardens also had herbs such as cilantro, basil, sage, thyme, and oregano, and grew grapes from their picnic arbor.Three small community gardens are managed by Century Villages at Cabrillo, Inc., an independent nonprofit entity affiliated with Century Housing Corporation. Founded in 1997, Century Villages at Cabrillo is a 27-acre campus community that aims to provide permanent housing to veterans, families, and individuals to prevent homelessness. CVC is located at 2001 River Avenue Long Beach, CA 90810. The Magic Garden, David’s Garden, and American Indian Changing Spirits Garden at CVC are free for residents, vertical growing systems with a “first come, first served” policy. Over 1,797 people reside at CVC, including 669 veterans . The Magic Garden, which was formerly known as the CVC Veteran’s Gardening project, was originally built by the late veteran and former Navy SEAL Tony M. in 2013. Due to CVC construction, the garden was moved in 2017, and is available to all residents. The Magic Garden offers 22 raised garden beds, where gardeners planted several edible crops. There is also a common area with papaya and banana trees. The Magic Garden also offers a sensory garden and meditation labyrinth .A CVC Occupational Therapist , who helps residents with disabilities or changes in their physical and mental capabilities, oversees the garden. To recruit volunteers, they partnered with PATH and OT internship programs from University of Southern California and California State University, Dominguez Hill’s Occupational. David’s Garden and the American Indian Changing Spirits Garden are adjacent to each other, next to two barbecue grills and five picnic tables with benches. David’s Garden, which has four raised garden beds and in-ground plots, is maintained by Sowing Seeds of Change, which operates its own urban farm about three miles away from CVC. Produce from David’s Garden supplements CVC’s weekly farm stand, which provides fresh, locally grown produce to residents. American Indian Changing Spirits, an agency which served 132 adults in 2022, provides culturally appropriate alcohol and drug education, counseling, and recreation for American Indian men and women. Their garden supplements social and cultural activities.I visited two gardens managed by the City of Long Beach: the health department’s Peace Garden and the Michelle Obama Neighborhood Library Learning Garden. Information on the exact size of each garden was unavailable, but they are roughly the size of LBO’s smallest gardens . The Peace Garden is inside Martin Luther King Jr. Park at 1950 Lemon Ave, Long Beach, CA 90806. It is adjacent to Long Beach’s Black Resource Center and the Central Facilities Center of the Nutrition Services Division WIC Program, which provides public services related to the Special Supplemental Nutrition Program for Women, Infants, and Children . LBDHHS established the Peace Garden in 2010 as part of the Healthy Active Long Beach Project, to promote healthy eating and physical activity through educational activities.

The garden has eight plots, a greenhouse, compost bin, and several fruit trees: plantains, figs, mulberries, and pomegranates. Local community residents can register for a garden plot at no cost. LBDHHS prioritizes households that reside in the 90806 or 90813 ZIP Codes, are eligible for federal nutrition programs , and/or have children who attend a school served by Healthy Active Long Beach. The garden is open on weekdays from 8:00 AM to 5:00 PM. LBDHHS staff provide free plants and grow produce around the perimeter of the garden for community members to harvest. For example, Figure 18 shows a table with free cacti and seedlings, next to a patch of squash.To learn more about UA sites located at schools, I visited gardens built by the nonprofit organization, Ground Education. Ground Education provides gardening curriculum to the LBUSD, which has 47 elementary schools and six K-8 institutions . In 2024, Ground Education operated in 24 LBUSD schools, over half of all schools in the district. The nonprofit intentionally selects schools in areas with less access to green space, and so their gardens are “mostly concentrated in North, Central and West Long Beach” . Founded by Holland Brown and Karen Taylor in 2008, Ground Education was originally created to revitalize former school gardens that were abandoned or needed maintenance. Originally a two-woman team, Ground Education now employs 29 staff members, including Garden Educators at each school site and a team that builds and maintains school gardens . Ground Education designs and builds new school learning gardens, teaches monthly outdoor lessons for transitional kindergarten through 8th grade, develops educational programming for after-school and summer programs, and provides workshops for partner community gardens. All Ground Education gardens feature the following amenities: a portable hand washing sink, rotating compost bin, storage shed, bird feeder, and benches for students.The gardens also have a small nature path with tree stumps and native California plants for students to explore. Ground Education’s Garden Educators deliver hands-on educational activities for TK-8 students to plant wheat, peas, lettuce, fava beans, carrots, beets, green onions, and other crops. Each class participates in monthly one-hour lessons during their usual class time. In addition to learning about where food comes from and tasting the food they grow, students hatch chickens in their classrooms, discover the importance of decomposers, and gain skills such as pickling vegetables, making pesto from carrot tops,milling flour from wheat, and using a solar oven to melt cheese on nachos with heat from the sun. Figure 22 shows Fremont Elementary’s main garden, which has 14 raised garden beds.Heritage Farm was once Gladys Avenue Urban Farm, previously owned by LBO founder Captain Charles Moore. The 8,000-square-foot parcel of land was sold to the current owner with the stipulation that it would continue to be used for UA. As of June 2022, Heritage Farm is managed by Chef Lauren Pretty, owner of the restaurant, Heritage. The farm supplies Heritage with a variety of herbs, flowers, fruits, and vegetables. Some notable crops included cucamelons, passion fruit, longan, Brazilian cherries, mulberries, “1,500-year-old cave beans” , and cardoons .

The City of Long Beach, located in Los Angeles County of Southern California, is home to a diverse population of about 451,000 people . Long Beach is 20 miles south of downtown Los Angeles and borders the west side of Orange County . Out of 88 cities in LA County, Long Beach is the second most populous after Los Angeles, which has 3.8 million residents . The land area of Long Beach accounts for just 1.2% of land in LA County . However, the coastal city is well-known for the Port of Long Beach , the Queen Mary , California State University, Long Beach, and the Aquarium of the Pacific . This dissertation focuses on the City of Long Beach due to its high prevalence of community gardens compared to other cities in LA County. A 2013 comprehensive report of LA County urban agriculture identified the city as having 19 community gardens, one-sixth of the county’s total . As of 2024, Long Beach has about 28 community gardens according to multiple sources . This suggests that about 22% of the county’s estimated 125 community gardens are in Long Beach . There is evidence that UA can increase communities’ access to green space and food, while also creating opportunities for socialization and the exchange of resources and knowledge . The high prevalence of community gardens in Long Beach may result from local efforts to increase access to food. Compared to LA County, Long Beach has higher rates of food insecurity, pipp horticulture meaning that residents “lack consistent access to enough food for an active, healthy, life” . The food insecurity rate in Long Beach is 10% more than that of the county .

Communities of color who live in North, Central, and West Long Beach are at highest risk of being food insecure . These areas include the 90805, 90806, 90807, 90810, 90813 ZIP Codes , where communities are disproportionately burdened by disease and health conditions, such as asthma, diabetes, and hypertension .Existing health inequities in Long Beach may be explained by housing practices enacted nearly a century ago. According to the City of Long Beach’s Historic Context Statement, neighborhoods were shaped by redlining, the restriction of housing loans based on race . Federal Housing Association and private banks implemented redlining in the 1930s, preventing people of color from purchasing or renting in certain areas. Although the U.S. Supreme Court ruled in 1948 that such housing restrictions could not legally be enforced, redlining continued into the 1970s. Housing discrimination even affected professors of color at Long Beach State College, presently known as California State University, Long Beach. Despite being recruited to teach at the university, they were not allowed to buy homes near the campus, located on the East side of Long Beach . Figure 2, a census tract map from 1950, shows that most African American and other non-White residents were concentrated in Central and West Long Beach. The purple tracts represent areas where both groups resided .Before Long Beach’s incorporation as a city, its demographics were impacted by colonization. Present day Long Beach was originally the traditional and ancestral territory of the Tongva/Gabrieleño and the Acjachemen/Juaneño indigenous peoples . Puvungna, once a large settlement, was located where California State University, Long Beach stands today. It continues to hold spiritual and cultural significance to several tribes .

The Tongva , consumed a variety of meats, insects, and plants . Instead of maize, which was more popular in the Colorado River region, the Tongva favored nutrient-rich acorns, high in both fiber and fat . They also consumed a meal made from ground seeds of islay , cholla cactus seeds, wild sunflower seeds, chia seeds and shoots, and clover . In 1542, Spanish explorer Juan Rodriguez Cabrillo seized land from the Tongva tribe . European colonization resulted in the spread of new diseases, death, and displacement of the Tongva and other indigenous peoples. Over time, Spanish settlers established ranchos throughout the region to raise horse, cattle, and other livestock. One of the largest ranchos in California was owned by retired soldier Manuel Nieto, who received 300,000 acres of land in 1784 as a reward for his military service . Nieto’s rancho spanned from the Los Angeles River to the Santa Ana River. After his death in 1804, Nieto’s children inherited the land. During their joint ownership of the rancho, California became part of Mexico in 1821 after the country gained independence from Spain . In the early 1830s, Nieto’s family divided their land into six parcels. The United States claimedCalifornia as a territory in 1848, and in 1950, California became the 31st state . As California assimilated into the U.S., Nieto’s rancho became further divided and sold to American ranchers. For example, the 27,000-acre Rancho Los Cerritos, Ranch of the Little Hills, was owned by Nieto’s daughter Manuela Cota, then sold after her death to Massachusetts-born John Temple in 1843 . Temple used the land to raise cattle for their hides and tallow. Although he mainly resided in Los Angeles for his mercantile business, Temple lived at Rancho Los Cerritos during the summer, and built a two-story adobe headquarters and garden. After retiring, Temple sold Rancho Los Cerritos to Flint, Bixby & Co. in 1866. The Bixby family kept 30,000 sheep to provide wool. However, the sheep industry declined during the late 1870s, and so Jotham Bixby began to lease and sell portions of the property. Over time, the cities of Long Beach, Bellflower, Paramount, Signal Hill and Lakewood were founded on Los Cerritos lands .

Agriculture was an important part of Long Beach’s early economy that drew long-term settlers. 19th century farmers raised cattle and sheep, and grew flowers, fruits, and vegetables such as corn, beans, barley, and alfalfa . During the 20th century, the city placed a stronger focus on tourism and industry, advertising Long Beach as a seaside resort town. This attracted seasonal tourists but marked an overall decline in agriculture. The city’s population increased due to national and regional immigration and the discovery of oil. Landowners sold large tracts of real estate for industrial, commercial, and residential expansion . Many of Long Beach’s UA sites from the last century were developed from vacant land, either by city officials or local organizations. One example, Rancho Esperanza, was originally created to extract labor from Long Beach’s jail population, which overflowed with a “staggering number of drunks” . The idea was initially proposed in 1949 by city prosecutor Kenneth Sutherland and endorsed by court judge Charles Wallace police chief William Dovey. Rancho Esperanza became known as the “Honor Farm.” In 1954, “42 alcoholics and assorted vagrants and traffic law violators” were sent to the Honor Farm, and after eight months, grew enough food to feed themselves and the inmates back at the city jail . Though the farm was abandoned in the 1970s, it was later converted into a community garden. In 1974, the Long Beach Parks Department was requested by the city council to study how vacant city properties could be converted into private vegetable plots . In January of 1976, the City Council approved the Honor Farm community garden, which was described as “heaven to a gardener,” featuring 218 garden plots, compost bins, rustic building, and a small orchard. The Long Beach Community Garden Association was organized in May of that year to supervise operations and maintenance. In 1997, drying curing weed cityofficials relocated LBCGA’s garden to be adjacent to El Dorado Park, on 8.5 acres of land. LBCGA currently has a food bank which donates fresh produce to local charities, and its tree orchard “provides a visual and sound buffer between the gardens and the 605 Freeway” .Ban et al. and Tijerina both studied The Growing Experience, a joint collaboration between the Housing Authority of Los Angeles County and the University of California Cooperative Extension, which transformed a neglected lot into a community resource. Ban et al. , who studied the impact of a Community Service Agriculture program, found that The Growing Experience offered organic produce at a more affordable cost than supermarkets, and made a conscious effort to include ethnicallyappropriate crops targeted to the local primarily Latino and African-American populationof the Carmelitos Housing Project. CSA subscribers and Carmelitos residents who used The Growing Experience’s Farmer’s Market ate more fruits and vegetables than residents who did not participate in The Growing Experience. However, the CSA boxes were more expensive than the majority of non-organic produce from local supermarkets, so some residents were unwilling to participate due to financial and time restraints. One resident described that it was difficult to buy fresh produce then cook it after working all day .

The Carmelitos Housing Project was originally designed for low-income families, specifically serving soldiers who had just returned home from World War II. Unfortunately, Carmelitos became notorious for violence, drug, and gang culture . Tijerina , who focused on types of environmental justice offered by The Growing Experience, found that the area surrounding Carmelitos was a food desert located near 309 hazardous waste sites, 48 sites that released toxic chemicals, and 15 sites that reported air emissions to the Environmental Protection Agency. The Growing Experience resisted environmental injustices and aimed to uplift locals from poverty by offering a CSA program, farmer’s market, community garden, community kitchen, business and job training, and other educational and economic opportunities . However, nearly a decade after Ban et al. and Tijerina’s case studies were published, the Los Angeles County Development Authority nearly shut down The Growing Experience due to budget constraints. In 2021, they laid off all but one staff member . As of 2022, The Growing Experience is managed in partnership with the MAYE Center, a nonprofit that helps Cambodian residents cope with trauma from the Cambodian Genocide .This document listed a total of 22 UA sites, suggesting that attempts to map UA in Long Beach may be inconsistent. More recent sources from 2017 to 2023 indicate that there are potentially 66 UA sites in Long Beach, including 28 community gardens, and 30 public school garden sites, and 10 urban farms . Some UA sites are managed by one entity. For example, as of 2024, nine community gardens are managed by the nonprofit organization, Long Beach Organic . As of March 2024, the nonprofit Ground Education manages 24 school gardens in the Long Beach Unified School District . Additionally, Ground Education provides gardening activities to other UA sites, including Adventures to Dreams Enrichment, Farm Lot 59, and Sowing Seeds of Change . An undated webpage from the City of Long Beach claimed there are 30 LBUSD gardens. In a 2021 article from the local newspaper The Grunion, assistant public information director Evelyn Somoza reported that LBUSD has 20 district-approved gardens that receive mulch and weed pickup . Murray elaborated that while 20 school gardens completed the district’s application process, there may be additional self-maintained gardens. Information on Long Beach school gardens was limited to those within Long Beach Unified School District and did not include private or charter schools. In general, there is a lack of research on Long Beach school gardens . This dissertation will contribute to scholarly knowledge on gardens and urban farms in LongBeach, by investigating community engagement in UA through field observations and interviews.This study was guided by the social determinants of health framework and the community cultural wealth model, which relate to the dissertation’s focus on communities uniting to improve their health and environment through UA. UA can positively impact SDOH, which are socioeconomic and environmental factors that influence individual or community health . The Healthy People 2030 initiative, developed by the Office of Disease Prevention and Health Promotion of the U.S. Department of Health and Human Services, groups SDOH into five domains . These domains provide context for why health inequities occur, as well as how they can be addressed . By targeting SDOH, UA can potentially improve physical and mental health outcomes . For example, many studies reported that UA fosters social capital . Social capital is part of Yosso’s CCW model, which highlights communities’ cultural knowledge, skills, and abilities. Additionally, social capital connects to the “social and community context” domain of SDOH.In this section, I will disclose my background to provide context for qualitative data collection and analysis. Self-reflexivity is an important consideration of qualitative methods, as researchers’ experiences affect their interpretation of data.

Plaques, a zone of clearing on the bacterial lawn, were construed as evidence for the presence of phage. Overall, we found very little evidence for the presence of either lytic or lysogenic phages that attacked any of our bacterial isolates. We did find some evidence for lysogens: 20% of bacterial isolates showed evidence for the presence of lysogenic phages in the B only lines. There was evidence for lysogeny in only 7% of isolates in the BP lines where both types of phages were passaged. With regard to lytic phages in the original phage fraction, we also did not find evidence for phage predation of any bacterial isolate using this starting inoculum. The results are reported in Table 2.Overall, disruption of co-passaging of bacteria and phage on leaves over time and between plants was found to have an impact on both the composition and diversity of the resultant epiphytic bacterial communities. The bacterial communities resulting from passaging bacteria with their ancestral phage appeared to be the most dissimilar as compared to the other passaging treatments. This microbiome also had lower alpha diversity than that of both only bacteria passaged only and bacteria passaged along with any potentially evolving phage . BPa lines also have lower beta diversity than all other treatments, and Pseudomonaceae dominated the communities. All of this taken together suggests that the original phage present in the inoculum is capable of having the largest impact on the community, drying and curing buds even to a bacterial community that may have changed in composition by passaging on plants.

The most parsimonious explanation for this finding is that the phages present in the initial inoculum have low persistence in the phyllosphere, at least in a growth chamber, and as such, there were both more, and perhaps more diverse phage present in the initial inoculum than what remained after attrition on the leaf surface during passaging in the growth chamber. This is, however, not fully supported by our phage-isolation attempts, as we were unable to isolate phages from the original phage-fraction on any bacterial isolates. However, this may be explained by the apparent decay of phages during refrigeration as observed by others, and it does not preclude the possibility that there were lytic phages present at the time of the passaging experiments . In treatments in which lytic phages were passaged for three weeks, it is likely that the phage fraction contained very little, if any, active phage particles by the end of the experiment. This is supported by the fact that we were unable to recover any phage isolates from the final time point of the experiment, except for one isolate from plants exposed to a BPa microbiome. Poor persistence of phages in the phyllosphere is a finding supported also supported by the work of others. Phages, in general, are found in very low incidence on the surface of leaves compared to that in endophytic compartments. This may be due to phage’s sensitivity to UV on the surface of leaves, or may be due to low replication of bacteria in the phyllosphere, which is a nutrient-deplete environment that may limit bacterial growth, and hence lytic phage replication. The strong effect of the ancestral phage fraction on the bacterial community is consistent, in some regards, to the findings described in Chapter 4.

There, we found that after one week, plants that received bacteria and phage together had lower beta diversity than plants receiving only bacteria . Plants receiving passaged bacteria and ancestral phage have significantly lower beta diversity than both B and BP treatments. In both cases, it may be that the impact the original phage fraction had on the bacterial community shaped it in a way that made all the microbiomes similar to one another. Interestingly, alpha diversity was also the lowest in this treatment, yet there are no differences in alpha diversity between the B and BP treatments. The alpha diversity finding may be explained by the taxonomy of the bacteria that was most impacted by the phage fraction. Here, Pseudomonaceae was in highest relative abundance in the BPa treatment- the treatment with the lowest alpha diversity. In previous work, the Bacteria-only treatment had a significantly higher relative abundance of Pseudomonaceae as well. Again, this treatment had the lowest alpha diversity. Thus, it is possible that the phage fraction has an initial impact on a dominant bacterial family in the phyllosphere, and this lethality subsequently has a ripple effect on alpha diversity of the rest of the community. Future work using rationally designed synthetic communities of bacteria and phages could address with hypothesis with more clarity. One of the primary goals of this work was to disentangle the effects of lytic and lysogenic phages on bacterial communities in the phyllosphere. Through our experimental design, we attempted to include a variety of lytic versus lysogenic phage challenges to the bacteria. Visually, it appears that the bacterial communities in which only lysogenic phages were passaged are different from those in which lytic phages were passaged. Furthermore, 20% of bacterial isolates showed evidence for the presence of lysogenic phages in the B only lines in which only lysogenic phages were passaged.

In contrast, there was evidence for lysogeny in only 7% of isolates in the BP lines where both types of phages were passaged. However, these findings are not statistically significant, and thus no conclusions can be drawn. This work highlights the importance of time-scale when studying the effects of phages on the phyllosphere bacterial community. Predictions about alpha diversity based on results after only a one week experiment are not entirely consistent with our findings from the study of communities that were passaged between plants for three weeks. Specifically, we did not find an increase in alpha diversity in the lines in which both bacteria and phage were passaged together compared to bacteria-only lines. This may indicate that temperate phages are able to mediate long-term bacterial diversity as well as lytic phage, or it may be unrelated to the presence of phages and reflect other microbial dynamics occurring in the community. Future work that involves more rigorous identification of lysogenic phages, such as bacterial genome sequencing, may help address the question of their importance in maintaining diversity. We also found that the ancestral phage fraction has the strongest impact on both composition and diversity of the bacterial communities – probably because it was more abundant. Finally, our lack of ability to culture phages after passaging on plants suggests strongly that lytic phage particles do not persist well in the phyllosphere of plants grown in the growth chamber This supports work by others indicating that the feasibility of using phages as bio-control agents in agriculture may be largely dependent on their ability to persist in the phyllosphere. Overall, these findings are an important extension of previous work , and they underscore many of the unanswered questions that remain regarding the abundance, persistence, growing tray and importance of bacteriophages in the phyllosphere.A diverse field inoculum was generated using field-grown tomato plants. Above ground tomato plant material was collected from two fields from the UC Davis Student Organic Farm in June 2018. The material was stored on ice for transportation to the lab. One hundred grams of plant material was submerged in 1.5L of 10mM MgCl2 and sonicated for 5 minutes, vortexed for 30 seconds, and sonicated again. This was repeated with an additional 500 grams of plant material, 100 grams at a time. The leaf wash from above was filtered using 8 µm filter paper to remove large pieces of plant debris. The flow-through containing all microbes was then filtered using .22µm filter units. Microbes collected on the filters, which should be most bacteria, were sonicated off the filter paper into sterile buffer for 10 minutes. To concentrate the phage fraction of the microbiome, .22 µm flow-through was then concentrated using 100Kda MWCO Millipore filter units. Both the bacterial and phage fractions were split into 8 aliquots to account for the 3 weeks of inoculation and the need for ancestral phage and ancestral bacteria for weeks 2 and 3. Bacterial fractions were stored at -80°C in 1:1 KB glycerol, and phage fractions were stored in the dark at 4°C. On each day of inoculation, bacterial aliquots were re-suspended in 3mL of MgCl2 without the addition of any other fractions for the “B only” treatment. A bacterial aliquot was combined with a phage fraction aliquot for “B and P”. For the first week, three treatment groups received B and P. Inoculum was spray inoculated onto each plant individually. After 1 week, entire plants were harvested individually. Bacterial and phage fractions were recovered from the plants as described previously . The bacterial and phage fractions were re-combined and inoculated onto the plants. For evolved bacteria and ancestral phage, the bacterial fraction from the end of the passage was combined with an ancestral phage aliquot from frozen storage. For the ancestral bacteria and evolved phage treatment, the phage fraction from the end of the passage was combined with an aliquot of the ancestral bacterial treatment from frozen storage.

The experiment was continued for three passages in total, each consisting of one week.My thesis work began by demonstrating that vertically transmitted bacteria on the surface of tomato seeds are capable of protecting seedlings against a common bacterial pathogen. Vertical transmission is a well-studied process in other systems such as termites and aphids [192]. However in plants, it has been primarily limited to the transmission of pathogens and endophytes. Both are important fields of study- especially for the prevention of plant diseases. However, I felt that there was a significant lack of knowledge as to the functional importance of vertically transmitted commensals or mutualists in plants. Intuitively, vertical transmission of a microbiome or symbiont would allow for maintenance of key members of the microbial community across generations. Beneficial microbes would have primary access to both spatial niches and nutrients provided by seedlings. Interestingly, plants have a differential onset of resistance to pathogens throughout their life-stages, something described as age-related resistance or developmental resistance. However, much of the work on ARR investigates exposure and resistance to specific pathogens throughout the developmental stage of the plant and does not address if there is a crucial window of exposure to commensals, and whether these commensals ae contributing to ARR, as observed in other systems. To my knowledge, there are no studies to date that test the importance of timing of commensal microbial exposure on microbiome establishment or immune function in plants, although there is a wealth of literature on establishing biological control agents. For example, would a seedling exposed to beneficial microbes mount as strong of a response as an older plant? And would exposure of otherwise sterile adults result in the same successional dynamics of microbiome establishment as often observed in seedlings? Given that we know that resistance to pathogens can change throughout the life cycle of a plant, research focused on age-related tolerance and recruitment of commensals and plant-growth promoting bacteria has large implications in seed treatment and agricultural practices. One fundamental question that I was unable to answer is: are the types of bacteria transmitted on seeds merely a reflection of the parental plant from which the progeny was generated? If so, do transmitted microbes vary based on adult-plant microbiomes? If so, which anatomical portion of the plant is most influential in shaping the seed microbiome? In this workwe found that some seed microbiomes were more protective than others. This suggests that they may have differed in their composition, but we never confirmed that this was the case. Furthermore in my work on adult plants, I found that plant host genotype influences the phyllosphere microbiome. Are these differences between microbiomes heritable through vertical transmission? In order to address these outstanding questions, I would conduct a common garden experiment wherein multiple host genotypes would be planted in replicated field sites. Upon fruiting, I would not only collect tomato fruits, but I would also collect leaf, flower, and soil from each site. I would collect seeds from fruits using the same approach as described in Chapter 2. I would carry out Gyrase B amplicon sequencing in order to describe the bacterial communities of the seeds in addition to the adult plants from which they came.

The similarity of changes in community structure both across replicates and genotypes over the course of the passaging experiment led us to predict that these microbiomes were adapting to the local plant and greenhouse environment. To further determine if the community changes we observed from P1 to P4 were due to habitat selection rather than neutral processes, we employed a community coalescence competition experiment. In this experiment , phyllosphere communities from the end of P1 and the end of P4 were inoculated onto a new cohort of plants, either on their own or in an approximately 50:50 mixture of live cells . To ensure that our method for the mixed inoculum was effective, we sequenced multiple replicates of the P1, P4, and Mix inoculums and found that source of inoculum explains 88% of dissimilarity amongst samples . To ensure that the Mix inoculum was significantly different than both P1 and P4 separately, we compared P1 and Mix inocula directly and found that 75% of difference between samples can be explained by this variable . Similarly, when P4 and Mix are compared directly, 74% of variation in the community is explained . This consistent difference among the three inocula allowed us to compare the communities colonizing plants from each treatment. We first measured final bacterial abundance and found that colonization was lower on these plants than in previous experiments, pipp mobile storage systems but does not significantly differ among treatments , apart from control plants, where bacterial colonization was greatly reduced .

We then compared bacterial communities again using 16S amplicon sequencing and ordinated samples on a PCoA based on Bray-Curtis distances. Plants that received P1 inoculum had distinctly different communities than those that received either P4 or the Mixed inoculum. Plants that received the Mixed inoculum clustered together with those receiving P4 and were relatively indistinguishable. Using ADONIS tests, we determined that inoculum source can explain 45% of Bray-Curtis dissimilarity amongst samples , and there was no effect of plant genotype . In a pairwise analysis between P1 and Mixed, inoculum source explains 31% of the community dissimilarity . In contrast, inoculum source does not explain any significant variation in dissimilarity amongst P4 and Mixed inoculum plants . Together, these results suggest that the plants receiving the 50:50 mixed inoculum were indistinguishable in community composition from those receiving the pooled, P4 adapted microbiomes, and that these selected communities were not invadable by the microbial communities from the start of the experiment. Consistent with our results from the passaging experiment itself, alpha diversity was highest in P1 plants compared to both P4 and Mixed plants . Alpha diversity did not differ amongst communities colonizing plants from the P4 and Mixed inoculums, despite being different between the two inocula themselves. We also examined compositional makeup of the communities , and consistent with P1 to P4 passaging results, we see differentially abundant taxa between groups .

Again, two Pseudomonas OTUs are more abundant in P1 plants as compared to P4 and Mix, in which there was an unclassified Pseudomonaceae that was higher in relative abundance.The impact of a microbiome on host health and fitness depends not only on which microbial organisms are present in the community, but also on how they interact with one another within the microbiome. Unlocking the great potential of microbiome manipulation and pre/probiotic treatment in reshaping host health will therefore depend on our ability to understand and predict these interactions. We took a microbiome passaging approach, inspired by classic experimental evolution, to test how selection for growth in the tomato phyllosphere under greenhouse conditions would impact microbiome diversity and adaptation across genotypes that differ in disease resistance genes. Across independently selected lines passaged on five tomato genotypes, we observed a dramatic shift in community structure and composition, accompanied by a loss of alpha diversity . We also found that host genotype shapes community composition early in passaging , explaining over 24% of variation amongst samples, but diminishes over time. The relative importance of host genotype and environment in shaping microbiome composition remains highly debated. Our results suggest that the relative importance of genotype versus other factors, such as the growth environment or strength of within-microbiome interactions, changes over the course of passaging on a constant host background. We did observe that even in the absence of a strong genotype effect, there remains a legacy of genotype effect, in that OTUs found to be significantly associated with particular genotypes early on are more likely to be present at the end of passaging than those that did not exhibit any host preference.

In order to test if the phyllosphere microbiome undergoes habitat filtering, we chose to begin the experiment with a diverse inoculum. This starting community generated from field grown tomato plants likely contained microbes from other surrounding plant species, dust, soil, and other sources. In particular, neighboring plants have been shown to contribute to both the density and composition of local airborne microbes. We found that although the total number of these field inoculum OTUs decreased over the course of the experiment, the taxa that remained consistently made up 78-95% of the community. This provides evidence that the original spray inoculum underwent strong niche selection over the course of the experiment. To test the alternative hypothesis that community changes were due to neutral processes such as bottle necking or random dispersal, we first fit our data to neutral and null models, finding a poorer fit over time. We then tested this experimentally by conducting a community coalescence experiment to measure fitness of passaged microbiomes as compared to those from the start of the experiment. The results of this experiment strongly support the idea that these phyllosphere microbiomes adapted to the plant host environment over the course of four passages . Independent of overall bacterial abundance, P4 microbiomes were able to dramatically outcompete the less-adapted P1 microbiomes. This community coalescence approach allowed us to demonstrate non-neutral adaption of a bacterial community that is independent of host genotype and resistant to invasion by a more diverse, less-adapted community. This community coalescence approach was used by others in a study conducted on methanogenic bacterial communities. The authors found that when multiple methanogenic communities were combined, a single dominant community emerged from the mix. This emergent dominant community resembles the single community with the highest methane production that went into the combination, suggesting that the most-fit community is capable of reassembly, even in the presence of other bacteria.The impact of a microbiome on host health and fitness depends not only on which microbial organisms are present in the community, but also on how they interact with one another within the microbiome. Unlocking the great potential of microbiome manipulation and pre/probiotic treatment in reshaping host health will therefore depend on our ability to understand and predict these interactions. We took a microbiome passaging approach, cannabis growing systems inspired by classic experimental evolution, to test how selection for growth in the tomato phyllosphere under greenhouse conditions would impact microbiome diversity and adaptation across genotypes that differ in disease resistance genes. Across independently selected lines passaged on five tomato genotypes, we observed a dramatic shift in community structure and composition, accompanied by a loss of alpha diversity . We also found that host genotype shapes community composition early in passaging , explaining over 24% of variation amongst samples, but diminishes over time. The relative importance of host genotype and environment in shaping microbiome composition remains highly debated. Our results suggest that the relative importance of genotype versus other factors, such as the growth environment or strength of within-microbiome interactions, changes over the course of passaging on a constant host background. We did observe that even in the absence of a strong genotype effect, there remains a legacy of genotype effect, in that OTUs found to be significantly associated with particular genotypes early on are more likely to be present at the end of passaging than those that did not exhibit any host preference. In order to test if the phyllosphere microbiome undergoes habitat filtering, we chose to begin the experiment with a diverse inoculum. This starting community generated from field grown tomato plants likely contained microbes from other surrounding plant species, dust, soil, and other sources. In particular, neighboring plants have been shown to contribute to both the density and composition of local airborne microbes.

We found that although the total number of these field inoculum OTUs decreased over the course of the experiment, the taxa that remained consistently made up 78-95% of the community. This provides evidence that the original spray inoculum underwent strong niche selection over the course of the experiment. To test the alternative hypothesis that community changes were due to neutral processes such as bottle necking or random dispersal, we first fit our data to neutral and null models, finding a poorer fit over time. We then tested this experimentally by conducting a community coalescence experiment to measure fitness of passaged microbiomes as compared to those from the start of the experiment. The results of this experiment strongly support the idea that these phyllosphere microbiomes adapted to the plant host environment over the course of four passages . Independent of overall bacterial abundance, P4 microbiomes were able to dramatically outcompete the less-adapted P1 microbiomes. This community coalescence approach allowed us to demonstrate non-neutral adaption of a bacterial community that is independent of host genotype and resistant to invasion by a more diverse, less-adapted community. This community coalescence approach was used by others in a study conducted on methanogenic bacterial communities. The authors found that when multiple methanogenic communities were combined, a single dominant community emerged from the mix. This emergent dominant community resembles the single community with the highest methane production that went into the combination, suggesting that the most-fit community is capable of reassembly, even in the presence of other bacteria.Seeds were surface sterilized using TGRC recommendations as follows: seeds were soaked in 2.7% bleach solution for 20 minutes. Sterilized seeds were then transferred onto 1% water agar plates and placed in the dark at 21°C until emergence of the hypocotyl. At that point, seedling plates were moved into a growth chamber and allowed to continue germination for 1 week. Growth chamber conditions were 25°C, 65% humidity and 16 h daylight per day. After approximately one week, seedlings were transferred planted in sunshine mix #1 soil in seedling trays. After approximately one more week of growth, seedlings were transplanted into 8” diameter pots, making the plants approximately 2.5-3 weeks old at the first time of microbial inoculation. Age of inoculation varied slightly from experiment to experiment but was kept identical amongst genotypes within an experiment.Microbial inoculum for the first passage of the experiment was generated from field-grown tomato plants from the UC Davis Student Organic Farm collected in September and October of 2016. One-gallon Ziploc bags were filled with leaf, stem, and some flower material from tomato plants. One bag was collected from each of nine different sites, spread through four different fields. Plant material was collected from various genotypes of tomatoes. Other plant types, such as lettuce, eggplant, corn, and oak trees, surrounded the tomato fields. During the October collection, soil was also collected at each site. The top ~2cm of soil was brushed away, and a 50mL conical was pushed directly into the soil at the base of a plant which was in the middle of each collection site. Plant material and soil were transferred to the lab on ice and stored at 4°C briefly until processing. Sterile phosphate freezing buffer was added to the bags of leaves, and the entire bags were placed in a Branson M5800 sonicating water bath. Material was sonicated for 10 minutes. This gentle sonication washes microbes from the surfaces of the leaves but does not damage cells. The resulting leaf wash from each site was pooled. From the September collection, leaf wash was pelleted for 10 mins at 4000 x G, resuspended in glycerol freezing buffer, and stored at -80 for approximately one month. This was then thawed, re-spun to remove the freezing buffer, and combined with the October leaf wash.