Horticulture

Farms in our sample had an average of $1.4 million in assets and $0.6 million in debts. The average debt-to asset ratio was close to 0.5. This ratio is much higher than the 0.16 debt-to-asset ratio reported by the United States Department of Agriculture for all American agriculture in 2003. When viewing assets and debts as financial inputs necessary to generate revenue, the ratio of financial input to gross sales was highest for vegetables and lowest for orchard crops.This study provides a detailed statistical profile of an important segment of California agriculture, the horticultural crop industry. The information provided is based on a unique survey of growers of horticultural crops, also known as specialty crops, that was conducted during the spring of 2002 at the request of the Risk Management Agency of the United States Department of Agriculture . This report presents data about horticultural industries in California and about the risk management attitudes, approaches, plastic growing bag and needs of farmers producing these commodities. Specialty crops are diverse. These crops can best be defined by exclusion—as all agricultural crops excluding grain crops , oil seeds , cotton, peanuts, and tobacco.

The bulk of specialty crops consist of fruits and nuts, vegetables, and ornamental crops . The industries featured in this study accounted for more than $16 billion of gross farm revenue in 2001. This value was more than 90 percent of the state’s total crop value and 60 percent of total agricultural value produced in California at the farm level. These industries are also important nationally. California accounts for 37 percent of the total value of horticultural crop production in the United States. In the past, these industries have expanded steadily in California, adding more than 300,000 acres between 1992 and 1997 . In the future, California’s horticultural industries are expected to continue to expand in size and importance. For the most part, horticultural growers have not been major recipients of farm program subsidies and have had relatively little government support compared to growers of commodities such as grains, oil seeds, cotton, sugar, and dairy products. Some horticultural crops have been eligible for USDA crop insurance programs and ad hoc disaster assistance, promotion assistance, and miscellaneous support, but the degree of subsidy has been small—typically around 5 percent of total value, compared to 30 to 50 percent and higher for grains, oil seeds, and cotton . Horticultural crops differ from other kinds of crops in their product characteristics, production processes, and market environments and thus in their risk characteristics. The design of public policy for these crops must reflect management of their unique risks.

Knowledge of market variables and grower risk behavior is essential to developing effective risk management tools for horticultural crops. Unfortunately, while studies on traditional crops abound, little research has been done on horticultural crops. The objective of this survey was to generate wide-ranging statistical information that can be used broadly to better understand the horticultural crop industry, its sources of risk, and typical responses to those risks. The statistical profile of California’s horticultural producers presented here is the most exhaustive ever undertaken for this group. It draws on survey data collected from approximately one-third of all horticultural crop producers in the state. This report presents a large volume of information concisely. To do so, we summarize the methodology used to collect and tabulate the data; provide an overview of the seven topics addressed; and discuss the primary results. The discussion is organized by issue and includes a narrative describing the main findings for each topic. Selected figures and tables are included. The narrative is supplemented with a data section in the Appendix, which is organized into three parts. The first provides the response rate for each question in the survey. The second contains data tables organized by commodity category. The tables supplement the information presented in the narrative section with further disaggregated analysis. The last part of the Appendix provides the actual survey instrument.The first stage of the study, the survey of specialty crop growers, involved developing a questionnaire. The questionnaire was developed specifically for specialty crop growers based on the format of a survey instrument used previously , with input from RMA and from researchers who conducted an identical study in Florida, Pennsylvania, and New York. The California Agricultural Statistical Service assisted in formatting the questionnaire to facilitate its implementation. The final version of the survey instrument is presented in Appendix 3. We established the sample frame by defining a minimum number of acres required for a farm to qualify for the study using information from CASS’s database.

To be included in the study, a farm had to have at least five acres of perennial crops or at least two acres of annual specialty crops . This limit was designed to exclude very small farms that were unlikely to be commercial operations. The acreage criterion was applied to CASS’s database, which contains information on more than 60,000 farms in California . A total of 31,864 farms met the acreage limit with the crops selected for the survey. CASS conducted two rounds of mailings and one round of telephone interviews to collect completed surveys. In total, the two survey mailings garnered 7,391 responses. Those mailings were followed by telephone interviews of growers who had not responded by mail, which collected an additional 7,746 responses. In total, 15,137 responses were received . Relatively few farmers answered all 25 survey questions, which required responses in 192 cells. Under some “usability” criteria on the completeness of the DATA COLLECTION AND AGGREGATION answers, some responses were discarded.1 In total, 10,410 observations were entered into an electronic database file that was then transferred to the authors. Our primary analysis used only the horticultural-crop based sample, which consisted of 10,200 observations. Among non-crop categories, aquaculture producers provided the largest number of observations, allowing some statistical analysis of that industry. We provide data tables for aquaculture in Appendix 2 but omitted aquaculture from the narrative analysis. Note that sample size used in our analysis varies depending on the question being analyzed. Survey responses varied in degree of completeness, and valuable information could have been lost if only fully completed responses were used. Thus, to maintain the maximum sample size, different sub-samples were used, depending on the usability and appropriateness of the data provided, in analyzing particular issues. Information on sample size is included in most of the table presentations.Several mountain ranges in California create the dominant Central Valley and smaller coastal valleys where much of the state’s agricultural production is concentrated. The large Central Valley consists of the Sacramento Valley, wholesale grow bags which lies north of the San Francisco Bay Delta, and the San Joaquin Valley, which lies south of the delta. The Central Valley is encircled by the Cascade ranges and Klamath Mountains to the north, the Sierra Nevada Mountains to the east, the coastal ranges to the west, and the Tehachapi Mountains to the south. The coastal ranges also create a long strip of valleys, including, for example, Napa Valley and Salinas Valley. . Climates in the region are affected by the cool currents of the Pacific Ocean and various mountain ranges. Temperatures in coastal regions are relatively mild while inland areas are hotter. Almost all of the state’s rain and snowfall occurs during late fall and winter . The majority of California’s water supply originates in the northern mountain regions of the state. Land for specialty crops is nearly all irrigated via ground water and various district, state, and federal water storage and distribution systems . California has 58 counties. In our analysis, we aggregated the counties into 11 regions with similar geographic and climatic characteristics as shown in Figure 1. The Sacramento Valley and San Joaquin Valley are together referred to as the Central Valley.California’s specialty crops include more than 200 individual crops. To facilitate a manageable analysis, crop aggregation was needed. First, all the commodities were assigned to one of five basic categories: field crops, fruits and nuts, vegetables, ornamental crops, and non-crop commodities. The last category included a small number of apiary and aquaculture farmers, but for category-specific analyses, we considered only aquaculture farmers because there were too few apiary farmers for any statistical analysis.

Fruits/nuts, vegetables, and ornamentals, which were our focus, were then further divided into subcategories of similar types of crops . The third level of classification identified specific crops. Our data analysis used mostly the first two levels of classification. See Table 1 for a detailed description of the classifications. While classification of fruits and nuts into the second level is self-evident, such classification of vegetables needs discussion. A wide variety of vegetables appears in the data and choosing transparent and intuitive yet manageable groups was difficult. Following USDA guidelines, nine botanical classifications of vegetables were aggregated into six groups, guided by climatic growing conditions and by the number of observations available.To highlight the results, we limited our analysis to the three primary crop categories—fruits/nuts, vegetables, and ornamental crops. The basic data set used in this analysis included only specialty crop farmers by excluding respondents whose primary commodity was listed as a non-crop or a field crop. With this exclusion, our basic data set consisted of 10,200 observations. Note, however, that much smaller samples were used in the analysis of many of the issues . In the following discussion we highlight only the major results for each topic. A fuller description of the data used for most charts and figures in this narrative can be found in Appendix 2.As a starting point, we present an overview of our sample and distributions of acreage and farms by region and by crop category. At the end of this section, we compare these distributions of survey respondents to those reported in the 1997 Census of Agriculture to illustrate the representativeness of the farms surveyed. Table A1 presents the share of farms and mean acres per farm by region and by crop category. Standard deviations are provided to give readers some sense of the variation in acreage. The three San Joaquin Valley regions accounted for 47 percent of the sample, the Sacramento Valley added another 13 percent, and the four coastal regions added 33 percent. The Far North, Sierra Nevada, and Desert regions comprised a substantial portion of the state’s land area, but only 7 percent of specialty crop growers in the sample were located in those regions and the average acreage per farm in those regions was below the state average. Fruit/nut growers represented about 86 percent of the sample; therefore, any data analysis on all crops tends to be dominated by the characteristics of fruit and nut farms. As shown in Table A1, mean acres varied considerably across crop categories but much less across regions. The average acreage for vegetable farms was substantially larger than the averages for fruit/nut and ornamental farms. On the other hand, average farm acres across regions were within the narrow range of 100–280 acres . The standard deviations for all acreage distributions reported in Table A1 were relatively high, meaning that the distributions were spread widely. To compare the degree of spread between distributions, the ratio of the standard deviation to the mean was calculated. The CV was seven for the whole sample and much higher in some regions. The South Coast’s CV of 15 was the largest. Of the crop categories, ornamentals had the largest variation in acreage. Table A2 provides the distribution of farms across finer crop classifications for each of the three main crop categories. Observations were classified into a subcategory based on farmers’ responses on their primary crops.3 Some facts stand out. Grape farms and nut farms each comprised more than 30 percent of all fruit/ nut farms, and nurseries comprised 67 percent of all ornamental farms. While almost one-third of vegetable farms grew tomatoes , the rest of the subcategories of vegetables were fairly evenly distributed. Table A3 provides the cumulative distributions by acreage class, which indicated that median per-farm acreage was between 21 and 30 acres for fruits/nuts and about 70 acres for vegetables. The same distributions are provided pictorially in Figure A1. About 40 percent of both fruit/nut and vegetable farms were concentrated around the land classes of 20 acres or less. Such high density of relatively small farms was common in the farm acreage distributions.

Kaplan’s support—and further pressure on Planning staff by City Council President Jane Brunner—was essential for getting the Planning Department to begin integrating urban agriculture into the current zoning update. The passage of SF’s urban agriculture ordinance also provided a significant boost to urban agriculture advocates in Oakland. Pesticide Watch, one of the NGOs active in the SFUAA helped to found the East Bay Urban Agriculture Alliance in February 2011. The organization, made up of a combination of “urban homesteaders” and food justice activists, has been engaged with the OFPC and Oakland-based NGO Bay Localize to finalize recommendations to the city for its integration into the zoning update. Many of the involved urban agriculture activists were also motivated by the highly publicized case of urban farmer and author Novella Carpenter who was cited for non-compliance with city permit requirements . Under mounting pressure both from City Council and the public, the Planning Department launched a plan to update urban agriculture zoning, a process that has galvanized community members, plastic nursery plant pot as evidenced by the July 2011 meeting I discuss in the dissertation’s introduction.

The first phase of the zoning update, approved by City Council in October 2011, was the legalization of sales of produce grown in home gardens . While these changes at the policy level to scale up urban agriculture are only beginning in Oakland, they signal a transition from lip service to implementation on the part of municipal government. Indeed, Planner David Ralston captures the shift in the receptivity of city officials, “Now they won’t laugh you out of town when you talk about urban agriculture” . In a modified version of the People’s Grocery logo that briefly appeared on fundraising website for non-profits, a white male in a baseball cap stands to the left of the other three young urban farming activists, one hand on a shovel, the other on the Asian male’s shoulder . This addition seems odd at first, an apparent afterthought, or perhaps a nod toward politically correct multicultural inclusiveness, or simply a more accurate representation of Oakland’s demographic make-up. But the addition also befits the story of the rise of the contemporary urban agriculture movement in Oakland. At each historical moment, from the Black Panthers to the EJ campaigns, to the rise of garden-based community food security and job training programs and urban agriculture’s current food justice-oriented incarnation, the success of urban agriculture activism has depended on multiracial, cross-class coalitions; indeed, as history sadly tells us, such alliances are necessary because the efforts of the poor acting alone are likely to be crushed. In addition to capturing the demographic of the 21st century urban agriculture movement , grounded in the ideology of food justice, the alternate logo pays homage to the radical groundwork underlying the food justice movement.

In the cases of the Black Panther Party, the EJ movement, and Urban Habitat, activists challenged the racial, political, economic, and ecological disparities between the flatlands and the hills. The struggle for healthy food, clean air, and green space mobilized community members at these different moments. Their successes depended on the discursive rescaling of the language of struggle in a way that helped cultivate multiracial and cross-class alliances. Using the language of Cox , these groups were able to expand their spaces of engagement through this politics of scale, to defend and improve their spaces of dependence, their neighborhoods and the food they eat. These coalitions, in turn, were able to marshal the resources necessary to grow the movement, tilling up vacant lots for food production, education, and youth employment. As organizations grew with the slow trickle of public and private funding, they became legitimate in the eyes of funders, who then opened the spigot further. While the specific goals of the urban agriculture organizations varied, their gardens nevertheless served as training grounds and/or inspiration for the current generation of food justice-oriented urban agriculture activists, intent not only on teaching nutrition and science, but also on creating an alternative provisioning strategy in Oakland’s flatlands while raising awareness of the structural inequities of the corporate food regime. Returning to the logo, the rays of sunlight beaming upwards, silhouetting the urban skyline and raised fists of the activists, embody the hope and vision of the food justice movement, the dawn of a just and equitable food system that contributes not only to the health of the city’s inhabitants, but also to broader goals of environmental sustainability and economic justice. On one level, these urban agriculture organizations have helped to move Oakland closer to these goals, as the growing patchwork of gardens and food policy attest.

On another level, however, the increasing institutionalization of the urban agriculture movement begs the question: what has been lost as these efforts have been formalized, as funding ebbs from one urban agriculture initiative and flows to another, as cross-class, multiracial coalitions are formed, as action in the streets and vacant lots and gardens is translated into grant proposals and zoning codes? Furthermore, can we consider urban agriculture to be radical? To what extent does urban agriculture actually function as an alternative provisioning system and what is the extent of its reach? I conclude by highlighting a few key considerations. First, let me reiterate the absence of the city’s majority urban farmers in the contemporary urban agriculture movement, the immigrant and migrant populations who continue to grow food for home consumption and maintenance of cultural traditions . Food justice activists use this form of urban agriculture as symbolic capital to strengthen their claims, frequently proffering it as an example of urban agriculture’s contribution to food security, neighborhood beautification, cultural value, and ecological sustainability. As urban agriculture has become a movement, however, largely dominated by a multiethnic group of young, educated, middle class activists, these urban farmers play a limited role in defining the urban agriculture movement as a movement. While some reap the benefits of urban agriculture programs—garden space at a new community garden, for example—many are simply unaware that a movement even exists. Second, the institutionalization of the urban agriculture movement has depended on funding. Organizations frequently compete for the same modest grants and end up fighting for proverbial crumbs. Moreover, these crumbs, in turn, can ultimately define the missions of the organizations. If the funding “flavor of the month” happens to be school gardens, seedling starter pot then school gardens become a central focus of the activity of these organizations . Many urban agriculture activists are quite aware of this dependent relationship, as well as the dependence of communities on outside NGOs for the implementation of urban agriculture and other programs. The centrality of the “non-profit industrial complex” is, in many ways, simply an outgrowth of the so-called neoliberal turn, where NGOs have rolled out to fill in the gaps in the social safety net left by the roll back of the Keynesian welfare state . The ability of such a movement, so dependent on relatively small flows of public and private funding, to effect structural change or create a just alternative to the corporate food regime , much less to sustain itself, is doubtful. Finally, the scalar politics employed by urban agriculture activists and their radical antecedents exemplify the power of coalition building and the ability to slowly shift the dominant paradigm surrounding the food system, slowly revealing its connections to city planning and public health. Ultimately the story of urban agriculture in Oakland is one of urban agriculture’s de-radicalization and its institutionalization into the mainstream. But rather than a story of its urban agriculture’s appropriation by the mainstream, it is a story of change arising from within the system due precisely to urban agriculture’s new place within the system.97 Changes are taking place on some structural level as food policy is slowly drafted, adopted, and implemented. The extent to which these changes, piecemeal and limited in reach, coalesce and evolve into a robust framework of incentives and regulation that truly challenges the corporate food regime remains to be seen.The ratio of workers to full-time-equivalent jobs in an industry is one important measure of the nature of the labor market.

Over the last several decades, seasonal industries such as construction have restructured in ways that have reduced the ratio of workers to FTE jobs. To evaluate this aspect of the agricultural labor market in California, we analyzed data collected by the California Employment Development Department in 2016 and compared key findings with our earlier analysis of similar data from 2015.How many people work for wages in California agriculture? Answering this question has been surprisingly difficult, largely because most farm jobs are seasonal, lasting from several weeks to several months, and there is high turnover, with many workers trying farm work and soon quitting. EDD publishes data on farm employment for the payroll period that includes the 12th of the month; in 2016, EDD data indicated that average monthly farm employment was 425,400. This 425,000 average is not a count of all individuals employed in agriculture, because some workers were employed but not during the payroll period that includes the 12th of the month. Including these not-on-payroll during the 12th of the month workers provides a count of all workers employed in agriculture. EDD does not report the total number of unique farm workers. This article fills this information gap, finding that there were about 2.3 workers for each average or FTE job. All California employers who pay $100 or more in quarterly wages are required to report each quarter their employees for the payroll period that includes the 12th of the month and the wages paid to all workers during the quarter, and to submit appropriate unemployment insurance taxes. In 2016, some 16,150 California agricultural establishments — North American Industry Classification System code 11, including farming, forestry, fishing and hunting — hired a monthly average 425,400 workers and paid them a total of $13.7 billion. The data also show that over the past decade, the number of agricultural establishments fell over 10%, average employment rose over 10%, and total wages rose 50%. Over 99% of the agricultural establishments that report employment are farms or firms supporting farms such as farm labor contractors . There are very few workers who had their maximum earnings in forestry, fishing and hunting, only 0.8%. We use “farm worker” in this paper to mean all workers employed in agriculture, including supervisors and accountants employed by farms, acknowledging that a few are employed in forestry, fishing and hunting. The average monthly employment of 425,400 reported by EDD represents 12 monthly snapshots of persons on the payroll during the payroll period that includes the 12th of the month. As such, it is a measure of the number of FTE positions in agriculture in California. Employers do not report hours of work, so some of the workers on the payroll may have worked full time and others part time. The $13.7 billion total wage figure represents payments to all workers, including those who were employed at other times of the month but not during the payroll period that includes the 12th. Dividing $13.7 billion by 425,400 gives $32,316, which would be the average annual salary of a full-time farm worker. However, since many farm workers are employed fewer than 2,080 hours a year, average earnings for the individuals who do farm work are significantly less; our analysis of earnings by individual workers indicates that the average earnings from all jobs of all workers with at least one job in California agriculture was $19,762 in 2016. To investigate this difference, we captured all workers reported by an agricultural employer, tallying a total of 989,500 individual workers in 2016. This process allows us to compare the total number of farm workers with the monthly average number of farm jobs. Figure 1 shows that this ratio has been rising from two workers per average job in 2014 and 2015 to 2.3 workers per average job in 2016, suggesting more workers tried farm work. The analysis is based on Social Security numbers reported by agricultural employers when paying UI taxes. Because we had data on all of the California jobs associated with each individual SSN reported by an agricultural employer, we could assign each worker to the NAICS code in which he or she had their highest earnings. This procedure identified 804,200 workers who worked primarily in agriculture .

A 1910 promotional booklet published by the Oakland Chamber of Commerce features a world map with all shipping lines leading to “Oakland Opposite the Golden Gate, The Logical Port and Industrial Center of the Pacific Coast” . 47 Worker housing emerged primarily in West Oakland, between the downtown business district and the rail and shipping terminus. The displacement of San Francisco residents following the 1906 earthquake was a boon for Oakland, bringing in a new workforce and new demands for housing. With population and industry growing at a rapid pace and aided by the extension of horsedrawn and electric streetcar lines, Oakland expanded to the north and east, annexing previously autonomous communities such as Temescal, Claremont, Brooklyn, Fruitvale, Melrose, and Elmhurst by 1909 . World War I saw a massive influx of military capital into Oakland. Automotive manufacturers such as the Durant Motor Company, Hall-Scott Motor Company, Chevrolet, and General Motors expanded considerably during these years, bucket flower earning Oakland the moniker “Detroit of the West.” Shipbuilding dominated the port, and employed upwards of 40,000 in 1920.

Drawn by the promise of jobs, new workers, many of them African Americans and immigrants, flooded in by the thousands. Wartime industrialization and the boom that continued through the ‘20s saw the expansion Oakland’s residential development alongside the construction of new factories eastwards into the orchards and pastures of the annexed townships . Integrating the pragmatism of locating industry where land was available with the reformist planning vision of Ebenezer Howard and Lewis Mumford, planners and developers in Oakland embraced the paradigm of the “industrial garden”: the dispersal of industry away from the mixed-use downtown core but closely tied to nearby, semiautonomous residential neighborhoods. In these industrial garden suburbs, factory workers would return home by bus or rail to a neighborhood of small, single-family homes, each with a yard or garden. Proponents pushed “garden living” in these quiet and tranquil respites far—but not too far—from the factory grind as a cure to the social and health risks already well documented in the mixed-use urban slums of the Northeast, Chicago, and to a lesser extent in the older downtown cores of San Francisco, Oakland, and Los Angeles . Urban and rural modes of survival came together here, as workers clocked out and headed home to tend vegetables, chickens, and goats in their yards .

As Mike Davis writes, the industrial garden was “a new kind of industrial society where Ford and Darwin, engineering and nature, were combined in a eugenic formula that eliminated the root causes of class conflict and inefficient production” ; in essence, by keeping the worker happy, productivity could increase while nipping a restive labor movement at the bud. During the New Deal the vast expanse of small homes that had cropped up as part of the industrial garden expanded rapidly. Beginning in 1934, a flood of highly subsidized, low-interest mortgage loans from the newly created Federal Housing Administration fed the growing suburbs; East Oakland soon filled in with suburban developments of small Mediterranean-style single-family homes. As in other California industrial centers, developers consolidated land purchase, subdivision, construction, and sales in order to maximize efficiency and minimize costs. Vast tracts of small houses, mostly prefabricated or built from kits with nearly identical floor plans, created an economy of scale that dovetailed nicely with the contemporary planning vision of neighborhood cohesion, mixed use, and garden cities to create quintessential industrial gardens. In order to expand homeownership, housing production had to be reorganized into a quasi-Fordist system of on-site assembly of prefab components to perfect the “minimum house”: a small, single-family home constructed as cheaply as possible but comfortable and unique enough to satisfy the dream of home ownership . The newly subdivided suburban landscape was rapidly filled in with these small, single-family homes erected virtually overnight. However, market forces alone were not responsible for the shifting landscape. While the social idealism of Ebenezer Howard’s garden cities and Lewis Mumford’s inclusive “eco-topian” regions undergirded the vision of many suburban planners, the pragmatism of industrial location, the whims of individual developers, and the rising power of racist homeowners’ organizations soon elided their utopian vision.

Indeed, the flows of capital defining Oakland’s urban landscape were clearly racialized. The federally-subsidized dream of homeownership in the industrial garden was not available to everyone; people of color rarely qualified for FHA loans because these were to be applied only to newly constructed homes and, contrary to Howard’s vision of universalist garden cities that welcomed and nourished all workers, new home developments in the suburban industrial gardens were racially exclusive. Until 1948 racial covenants established by developers and homeowners’ associations prevented people of color from moving in and disturbing social divisions seen as “natural” . Even after the Supreme Court made racial covenants illegal via Shelley v. Kraemer in 1948, such obstacles remained in practice. Contractors were rarely able to secure loans for construction for non-whites in a “Caucasians only” neighborhood and realtors feared “the wrath of white homeowners” . The racialized demarcation of urban space taking place between the wars was not new in California. For decades the labor movement in California had already laid the groundwork for the formation of a virulent form of white class-consciousness via their aggressive exclusion of Asian, Latino, and African American workers . Easy access to low-cost, single-family homes in close proximity to East Oakland’s factories simply fueled racist and exclusionary sentiments by creating a sense of bootstrap entitlement. Homeownership thus helped heterogeneous European and Euro-American populations of workers consolidate as a spatially and racially homogenized labor force of “whites,” geographically distinct from the radicalism of recent European immigrants and African Americans in West and North Oakland and along the estuary.48 Suburbanization of industry and housing was thus a way to escape from the working class and “to attract a better brand of labor, removed from the ‘bad moral atmosphere’ of the inner city, and promising the stability of homeownership for the ‘better class’ of workers” .If industrial relocation and FHA-funded residential development were the source of capital flows that irrigated East Oakland’s industrial garden from the 1920s to the ‘40s, homeowners associations, zoning, and redlining were the dikes that initially prevented this capital from flowing back towards West Oakland, and then effectively quarantined its devaluation to the few areas where people of color were allowed to live. New capital continued to flow in. Between January 1945 and December 1947 roughly $300 million was spent on the expansion of new industrial plants in the Bay Area . Within the city itself, however, devalued fixed capital—a landscape of aging housing stock and obsolete factories— left little room for new industry to take root. A highly coordinated growth machine of industry, developers, boosters, cut flower bucket and white laborers driven by the promise of homeownership and jobs diverted this latest flow of capital to the green fields of the newly incorporated industrial suburbs—San Leandro, Hayward, Fremont, San Lorenzo, Newark, Union City, Milpitas—that flanked the East Bay between Oakland and San Jose. Vast tracts of agricultural land were incorporated into these pro-business municipalities, zoned as industrial, and sold for prices below industrial land prices in Oakland. National companies such as General Motors and Caterpillar built branch plants on these fertile green fields, and defense contracts showered the new industrial suburbs with federal capital, ensuring rapid growth. As the data in Table 2.1 illustrate, manufacturing nearly doubled in Alameda County between 1948 and 1967. Here at the urban edge of the new suburbs, industry was given a tabula rasa. In essence, these new suburban municipalities provided a more favorable business climate, spatially removed from the pressure cooker of the urban center’s working class and the grip of recalcitrant city politicians . In the words of the Bay Area Council, which helped drive industrial suburbanization, suburban employees were “more loyal, more cooperative, more productive workers than those in big cities” . The implicit message to future investors was that this suburban workforce was largely white.Just as in East Oakland during the interwar years, industry and housing in the new suburbs went hand in hand, part of a concerted planning effort to disperse industry and the suburban residential developments that followed in its stead. These industrial shifts and the prosperity of the post-war era further fertilized the American dream of homeownership.

Large scale housing developments in the urban periphery and the expansion of automobile ownership cultivated suburban development and white flight, draining urban areas of their tax base. Just as the industrial garden of East Oakland was watered with a strong mix of industrial and residential capital during the World War I and 1920s boom years, and with capital available through FHA loans in the ‘30s and ‘40s, the new industrial garden suburbs grew rapidly in the post World War II era as a result of this same combination of industrial capital and federal housing subsidies. As Oakland de-industrialized and new factories sprouted in the suburbs, working class white Oaklanders followed, lured by homeownership and proximity to jobs, just as they had done in the previous wave of inter-war and wartime suburbanization. Between 1949 and 1951 only 600 units among the 75,000 constructed in the Bay Area were open to blacks . Upwardly mobile whites left the East Oakland flats to join the downtown ruling elite in their Oakland foothills and hillside neighborhoods, taking their cash with them. In Elmhurst, for example, white residents made up 82 percent of the neighborhood’s population in 1960 and median income was $6,154, only about 2 percent lower than the citywide median income; a decade later whites made up only slightly more than a third, while on the other side of the city boundary in San Leandro, people of color were excluded. As capital was channeled into the industrial suburbs, it began to dry up inside the city’s boundaries, leaving the once-verdant urban economy parched of tax revenue. By the mid 1960s, the number of manufacturers within Oakland had begun its steady decline. Between this downward trajectory and the steady growth of manufacturing in the new industrial suburbs, Oakland’s share of Alameda County’s industrial productivity dropped from more than half to less than a third in the four decades following World War II .55 More than 130 factories shut their doors and nearly 10,000 manufacturing jobs were lost by 1977 . Unemployment skyrocketed as a result. The unemployment rate in 1964 was 11 percent but for blacks was almost twice that high. Business ownership was absentee for the most part; by 1978, only 25 percent of businesses in East Oakland were locally-owned . This trend continued in the ‘80s as jobs shifted from the traditional manufacturing and warehousing sectors to a service-based industry. The Bay Area on the whole benefited from a boom during this period, with a 15 percent growth in jobs between 1981 and 1986. Oakland, however, reaped little in the way of this regional bounty; employment grew only by 1.5 percent during these same years. The flatlands bore the brunt of job loss during this period. West Oakland and Fruitvale lost eight to ten percent of jobs. In the Elmhurst and San Antonio districts, employment decreased by roughly a third . As East Oakland’s industrial garden withered and whites fled to the suburbs and hills, housing there became available to upwardly mobile people of color for the first time. The Oakland border with San Leandro truly became a color line. Just as East Oakland’s industrial garden communities had excluded people of color via racial covenants, new housing developments in places like San Leandro and San Lorenzo excluded people of color using racial covenants and informal “gentlemen’s agreements” between realtors and homeowners’ associations. Creating a class alliance with developers, increasingly conservative white homeowners in the new suburbs helped to exert political pressure to further confine devaluation to the Oakland flatlands. Proposition 14, a 1964 ballot initiative sponsored by the California Real Estate Association and supported by 65 percent of voters statewide, essentially overturned the federal Fair Housing Act, passed the year before. In 1978 this same alliance was able to pass the infamous Proposition 13, which severely limited cities’ ability to raise property taxes. The resulting decrease in property taxes took a toll on Oakland’s already impoverished flatlands, as inflow of revenue was squeezed by more than $14 million, leading to facilities closures and cuts to public services .

While seasonal droughts are known to be a natural and regular occurrence in arid and semi-arid regions, the increased frequency, duration, and intensity with which they have occurred in recent years is highly unusual . Such extreme droughts, referred to as “global-change type drought” , are predicted to continue, and even become the norm, as a result of human-induced climate change . Consequently, species that are typically capable of withstanding regular drought stress may be susceptible to canopy dieback, and mortality, as a result of shifts in drought regimes . Chaparral shrublands, which occupy approximately 7 million acres throughout California , are a dominant vegetation community in southern California, composed primarily of evergreen, drought tolerant shrubs and subshrub species including manzanita , ceanothus , and chamise . These species are well adapted to the seasonal variations in temperature and precipitation typical of mediterranean climates where hot, rainless summers are the norm . However, mediterranean-type regions like southern California are predicted to experience rapid increases in temperature , procona London container and increased drought occurrence and severity ; IPCC, 2013, resulting from human-caused climate change.

These regions have thus been designated as worldwide global change “hot spots” . Indeed, recent studies have reported extensive mortality of chaparral shrub species resulting from global-change type drought throughout southern California . Thus, climate change represents a significant threat to native plant community persistence in this region. A critical topic for ecological research is understanding where, how, and to what extent plant communities will change as a result of increased drought . Studies aimed at understanding the physiological mechanisms behind drought-related plant mortality – and why some plants suffer mortality from drought while others survive – have elucidated a variety of complex mechanisms of plant mortality . These include loss of hydraulic conductance , exhausted carbon reserves , and susceptibility to pests and pathogens due to being in a weakened state from drought . Measuring xylem pressure potential can be a useful index of soil water availability , and dark-adapted fluorescence can be a quick and accurate indicator of plant stress, as values drop significantly in water-stressed plants, . Together, these may be useful tools for predicting plant vulnerabilities to drought and biotic invasion. Landscape variables such as elevation, slope, and aspect have also been shown to correlate with plant water stress and mortality , and can be useful for predicting vulnerabilities during drought.

However, major knowledge gaps still remain, and studies combining field mortality patterns with physiological data on plant water stress are rare . Plants employ a variety of complex strategies to cope with drought stress, but generally fall along a continuum of “drought avoiders” or “drought tolerators”. Drought avoidance, also known as “isohydry”, refers to plants that regulate stomatal conductance to maintain high minimum water potentials as soil dries out . While this strategy reduces the risk of xylem cavitation and subsequent hydraulic failure, it may increase the likelihood of carbon starvation, as C assimilation is greatly reduced . Conversely, drought tolerant plants maintain higher Gs, even at very low water potentials, which allows for continued C assimilation but with greater risk of xylem cavitation . These different strategies can have significant implications for ecosystemlevel consequences of severe drought ; indeed, recent studies have linked anisohydry with greater levels of mortality in chaparral systems . An historic drought in southern California provided an opportunity to simultaneously measure physiological stress and dieback severity along an elevational gradient in a classically drought-tolerant evergreen chaparral shrub, big berry manzanita . A. glauca is one of the largest and most widely-spread members in a genus consisting of nearly 100 species. Its range extends as far north as the Cascade mountains and south into Baja California, though it is most dominant in southern California shrublands .

They frequently occur on exposed ridges and rocky outcroppings. In the chaparral shrublands of Santa Barbara County, it occurs from elevations of about 500- 1200m. A. glauca are obligate seeders, and must recruit from the seedbank following fire . Compared to resprouters, which regenerate from a carbohydrate-rich burl at their base following fire, seeders tend to be fairly shallowrooted , and are thus less able to access deep water sources . Seeders are generally considered to be more tolerant of seasonal drought than resprouters , possibly a mechanism for shallowrooted seedlings to survive summer drought in an open post-fire environment following germination . However, this strategy has also been linked to higher mortality during extreme drought . A. glauca are also known to exhibit anisohydric mechanisms of drought tolerance , and can exhibit extremely low water potentials and high resistance to cavitation during seasonal drought . In 2014, we observed sudden and dramatic dieback in A. glauca in the Santa Ynez mountain range of Santa Barbara, California during an historic drought . The drought that lasted from 2012 to 2018 in southern California was the most severe to hit the region in 1,200 years , with 2014 being the driest year on record . Preliminary field observations indicated greater levels of canopy dieback at lower elevation stands compared to higher elevations. Dieback also seemed to be more prevalent on exposed and southwest-facing slopes, which in this region experience direct sunlight for most of the day. Other studies have reported significant Arctostaphylos spp. dieback and even mortality during periods of extreme drought stress, further suggesting species in this genus are vulnerable to drought-related mortality. Additionally, we observed widespread symptoms of fungal infection – including branch cankers and brown/black leaf discoloration – later identified as members of the opportunistic Botryosphaeriaceae family , suggesting multiple factors may be driving canopy dieback in this species. Drought-related mortality has previously been associated with opportunistic fungal pathogens in A. glauca and other chaparral shrubs , yet few studies have sought to understand the relative levels of drought stress incurred by plants infected with these pathogens, cut flower transport bucket or how stress is related to canopy dieback and/or mortality. A. glauca shrubs are important members of the chaparral ecosystem, providing habitat and food for wildlife through their nectar and berries . Their structure and fire-induced germination strategies also make them significant components of the chaparral fire regime and post-fire successional trajectories . Large-scale mortality of this species could reduce resource availability for wildlife, as well as alter fuel composition and structure in the region, resulting in an increased risk of more intense, faster burning fires. Therefore, the potential continued dieback of A. glauca is of great concern for both ecosystem functioning and human populations alike. Yet because of the heterogeneity of landscapes in this rugged region, it is possible that portions of the landscape will act as refugia for drought-susceptible species. We hypothesized that A. glauca dieback severity is associated with areas of increased water stress across the landscape. To better understand the patterns and trajectory of A. glauca stress and dieback across a topographically diverse region of coastal California, we asked the following specific questions: How severe is drought-related stress and dieback in this region? How do plant stress and dieback severity vary with elevation and aspect across the landscape? How does dieback change across the landscape as a multi-year drought progresses? We chose xylem pressure potential as an indicator of plant water availability, and measured dark-adapted fluorescence and net photosynthesis as proxies for drought-related plant stress and physiological function.

To address Question 1, we conducted an initial survey measuring general levels of canopy dieback, shrub water availability, and stress in the region. To address Questions 2 and 3, we conducted a more in-depth study of how shrub water relations and dieback vary with aspect and along an elevational gradient, and tracked changes in dieback severity for the four final years of the seven-year drought. We expected to find areas of low XPP correlated with greater physiological stress responses, and more severe dieback in lower elevation sites and on southwest aspects. Alternatively, shrub stress and dieback may be dependent on a wider variety of variables, particularly in a landscape as heterogeneous as this. Additionally, we predicted that dieback severity and individual shrub death would increase over time in lower elevations and exposed slopes compared to upper elevations and more mesic slopes. The specific area chosen for this study is located in the Santa Ynez mountains of Los Padres National Forest . Stands of A. glauca occur from approximately 400m to 1200m elevation, and are frequently mixed with other co-dominate woody evergreen shrub species including Adenostoma fasciculatum, Ceanothus megacarpus and, at lower elevations, Malosma laurina. The landscape of this region is extremely heterogeneous, with unstable terrain composed largely of sandstone rock outcroppings and sandstone-derived soils , and steep slopes and ridges that are interrupted by deep canyons . These dramatic features, while common habitat for chaparral plant communities, were a limitation in our ability to choose field sites. Thus, we relied heavily on accessibility by road and trail in finding sites. The climate in this region is of a mediterranean-type, with cool moist winters and a hot, dry summer season. The majority of rainfall typically occurs from November to April, and mean annual rainfall, based on a 120-year average, is 47cm .Three weather stations, equipped with real-time, self-recording data loggers and maintained by the Santa Barbara County Public Works Hydrology Division, were chosen to retrieve precipitation data during the drought based on proximity and similar elevation to study sites. The Trout Club , San Marcos Pass , and El Deseo Ranch stations represented low, intermediate, and high elevations, respectively. Data from these stations were retrieved from the Santa Barbara County Public Works Hydrology website.Average rainfall at these stations, based on 54-69 year means, increases with elevation from 68.3cm to 90.4cm . Annual rainfall data for this study are presented in “rainfall years” from November 1 of one year to October 31 of the next, to reflect the seasonal wet period preceding each sampling period. Consistent with these historical trends, annual rainfall at Trout Club was lowest between the 2014-15 and 2018-19 water years . However, during this same time period, rainfall totals were generally lower at El Deseo Ranch compared to San Marcos Pass . In 2015, an initial survey was conducted to assess dieback, as well as shrub water demand and physiological stress as the summer dry season progressed during the drought. Five sites were chosen for this survey representing variable elevation, slope, and aspect, but were also limited by access, safety and proximity to roads. Sites were defined as being composed of greater than 50% A. glauca cover, except for site C, which had lower than 50% A. glauca cover but favorable access. Boundaries were delineated using a combination of on-the-ground visual assessment and polygons drawn using 1m National Agriculture Imagery Program imagery within ArcGIS® . Later, they were refined using a Phantom 4 Pro Drone .Each site was initially assessed for A. glauca stand dieback severity and site mortality in winter, before new summer leaf-out occurred . Stand dieback severity was defined as the percent of non-green or defoliated canopy cover within the boundaries of the site, and was estimated by the collective valuation of two-to-three people viewing the stand from different angles. If stands were not completely pure A. glauca , we did not include canopies of other species in our estimation of average percent dieback. Data on stand mortality were collected by counting the number of dead individuals within site boundaries using ArcGIS. Number of dead individuals per site was also recorded. To measure shrub water availability and physiological stress through the summer dry season, twenty individuals per site were selected based on similar size , accessibility, and representing different levels of health along a continuum. From these twenty, ten individuals were randomly chosen using a random number generator and tagged for collecting repeated data on stress and dieback as the dry season progressed. At one site only nine individuals fell within the size criteria and were readily accessible, therefore the sample size for this site was nine, for a total of 49 shrubs used in this initial survey. All selected individuals were measured for height and canopy volume. Measurements included basal diameter , height, and canopy width in two directions.

In eastern Chester County, the rural zoning is typically one house per two acres, with only a few municipalities requiring one house per 10 acres. Chester County’s master plan seeks to acknowledge and support agricultural economic planning through a variety of measures that focus economic development efforts on farm-related businesses, promote agritourism, transition younger farmers into employment, and allow construction of farm labor housing. The county has committed its own staff for agricultural economic development and local food marketing within the county, while offering county facilities as host sites for farmers markets. The county plan also recognizes the synergy between agricultural land uses and alternate energy or emerging bio-fuel markets- but does not go so far as to encourage model siting legislation for these industries. Going further than agricultural economic planning, Chester County ties its county plan to food security planning. In the effort to keep farms viable, plastic plant pot the county makes a commitment to work with the Delaware Valley Regional Planning Commission in regional food system planning.

The County plans recognize that nearly 25% of the county is food insecure and encourages local farms and citizens to volunteer in the local food bank’s gleaning program .Farming networks are represented by 754 unique geographic locations with 1087 connections between these nodes. The total farm network reach on average was 44 km, drawn to the east with a magnitude of 89 km, indicating that the majority of farming networks are drawn to the urban market of Philadelphia and surrounding suburbs . Despite using methods which were expected to reveal more direct-to-consumer relationships, findings are dominated by sales to wholesalers, representing medium and large-scale grocery chains, food hubs, and produce aggregators . Farm sales to farmers’ markets, CSAs and Buyer’s Clubs are the next most prominent networks. The longest reaching networks are farmers’ markets, farm-to farm sales, wholesale distribution, and sales to restaurants . Farm participation in farmers’ markets exhibits the direct network with the longest average reach, with farm employees travelling nearly 60 km to visit farmers’ markets, predominantly located in the south east toward urban populations in Philadelphia. Conversely, the most common relationship in farm to-farm networks are those where farms located in more rural western settings partner with Chester County farms for farm-gate sales. For example, one farm outside the county supplied milk to a Chester County farm that made cheese, which it sold further from its farm gate and to local wholesale distributors.

The reach of farmers’ markets and restaurants contrasts those of the CSA and institutional sales, which are roughly half the distance and oriented more toward surrounding suburbs. The most proximate networks are those for byproduct, educational visits and the county food bank, showing that these networks may rely more on proximity of resources and social contacts. Farm byproducts, such as compost and spent grain generally move away from urban areas toward rural land. Similarly, the gardening and gleaning programs organized by Chester County Food Bank are proximate in space.Social network mapping of Chester County farm networks by the ten network-type codes indicates the degree to which various farm relationships are intertwined . Based on the network connections, the food bank plays an important role in linking volunteer groups to educational farm visits. Many of the farms involved in the food bank’s gleaning program are centrally located in the social network, and are connected to numerous other networking typologies. For example, farms that participate in the food bank’s gleaning programs are also likely to host educational visits from the same institutions that participate in the gardening program for the food bank. The Force Atlas layout of the social network draws apart disparate nodes based on their network coding. From this layout, we see that many farms specialize in one network type, be it CSA sales, sales to wholesale distributors, or participating in multiple different farmers’ markets.

One can also see threads that run centrally to the social network, such as farm-to-farm and farm-to restaurant sales. This view also allows us to see overlap in networks. Every CSA node has a link to a farmers’ market, but the opposite is not true. Likewise, many farms that specialize in wholesale markets also sell through farmers’ markets.Interviews with key agricultural and food policy experts helped to verify the social network findings and provide explanations. Many interviewees emphasized how networks evolve over time and in relation to one another, adding a time-component to this analysis. Chester County has had a long history of direct-to consumer sales. Interviewees agreed that the proximity of suburbs, particularly wealthy suburbs, aided in the establishment of farm-to-market networks throughout the region.These statements indicate that the geographic distance-decay function of social networks built around food marketing. Indeed, Anthony’s assertion that farms involved with CSAs “bring people to the farm” is visualized with the social network map where numerous farms involved in CSAs also host educational visits that tend to be geographically proximate in nature . The central role of the food bank in purveying directly from farms and coordinating on-farm volunteer efforts may also help explain the breadth of Chester County farming networks. Larry Welsch, director of the Chester County Food Bank, notes that the food bank currently has a fleet of over 3000 volunteers, which “flock” to volunteer opportunities on farms after school and on the weekends. The size and willingness of this volunteer base speaks to Chester County’s wealth but also the draw of agritourism. Through the volunteer participation in the gleaning program, the farms generate goodwill and donate excess food to the food bank. Larry Welsch, asserts that the gleaning allows farms to showcase the good work they do to volunteers and further build their market potential for agritourism activities beyond volunteer days. As a result, farms involved in the gleaning program get practice and market exposure, helping them to later operate on-farm agritourism events, CSAs, and farmers’ market stands to further their market base and generate more profit per pound of product sold. Indeed, plastic planter pot the social network mapping indicates that participating food bank farms use multiple networks that are all highly localized geographically . Welsch noted that the majority of the forty farms that participate in the gleaning program are incapable of contiguous expansion and surrounded on all sides by urban and suburban land-uses. The network analysis in this research captures only 11 farms currently involved in the food bank gleaning program . Welsch also noted that many of the participating farms are located in southwest Chester County, the headquarters of the Food Bank before it moved to its more central location in 2010. Though the northwestern portion of Chester County has large, contiguous blocks of farmland, few of these farms participate in food bank programs. The food bank readily leverages geographically and socially proximate networks. Welsch attributes the success of gleaning program with spawning the more recent “raised-bed” program, in which local churches, businesses, schools or residents grow produce for the Food Bank. The Food Bank now has 546 gardens at 129 sites, including 49 schools, up from a total of 25 in 2009. From this rapid success, the Food Bank launched a greenhouse initiative, providing schools with high tunnels so that students can grow food year-round for their cafeterias. The school presence spurred the development of curriculums for healthy eating, farming and nutrition in elementary and middle schools with high tunnels.

Staff have pioneered cooking classes and lunch-time tastings of fresh food, such as frozen squash popsicles, in order to introduce children to vegetables that they grow and try to persuade school catering companies to source locally and provide more fresh food. All of these programs make use of the same networks to facilitate food donations through gardening and gleaning along with farm visits for educational purposes with the aim of promoting healthy eating for low-income Chester County residents. Chester County interviewees agreed that the limits to farm networks were not based on farmer will or consumer demand, but land-use regulation. As Marilyn Anthony stated, “The barriers to entry-it’s policy, regulation. Many of those things are controlled by small groups-whether that’s county commissioners or land conservation groups. They can change the language in their easements, but that doesn’t happen easily.” Moreover, zoning regulations “can be counter-intuitive, irrational, arbitrary. A lot of it is really outdated. It’s based on false assumptions of agriculture.” These sentiments are supported in recent studies, such as the Green Space Alliance Commission’s report on “Transforming Open Space,” which highlights zoning language as an obstacle for the transformation of vacant land . Zoning restrictions apply not only to the farm parcel, but to traffic regulation. As Anthony explains, “you may be farming in an area that is zoned agricultural, but it may not be able to have any retail or commerce on that site, so you would have ag zoning but not commercial. And you may not be able to conduct retail or have a farm store. There may be ordinance restrictions on traffic, so you may not be able to have parking for 20 cars- or it’s a two-lane road and they don’t want that level of traffic on it.” Such land-use regulations would limit the ability for farms to host any network which brings users to the farm, such as: education tours, gleaning volunteers, CSA pick-up locations, or roadside stands. Restrictive land-use regulations may force farms into a long-distance network typology characterized primarily by wholesale marketing. Interviewees noted that farms struggle not only with land-use regulations at the farm, but also variations in state and county level land-use regulations encountered en route to the market. Matthew Wiess works for Farm-to-City, a Philadelphia-based nonprofit which helps farmers navigate urban market regulations while also helping communities who would like to open a farmers’ market in their neighborhood. Farm-to-City manages over 20 farmers markets in Philadelphia, but does not work with New Jersey farms or farmers’ markets due to the numerous differing county and state health regulations. Wiess notes that the chief concern for farmers’ market managers is the cost of street closure permits and various approval processes for new farmers market citation. Philadelphia has an ordinance allowing farmers’ markets, but to put a new site on the ordinance, the city council member in the proposed district has to introduce and pass new legislation. Weiss notes that the demand for farm-city connections is as much as urbanite driven as farmer-driven. At the time of the interview, Farm-toCity had a waiting list of 40 farms for farmers’ markets and over 20 applications to open new farmers’ markets throughout the city. The waitlist speaks to both an abundance of supply and demand, but forming the connection for each farm network is difficult due to land-use regulations and public service limitations in access to restrooms, parking and water. Moreover, Farm-to-City likes to see desire by neighbors for the market in the form of resident petitions. Some residents may not want the traffic, noise or commercial activity that a farmers’ market brings. Bryan Snyder, one of the original founders of Buy Fresh Buy Local, a national local food marketing campaign that started out of Pennsylvania, goes further in asserting that more local networks could be had if there were higher quality public receiving points in urban areas. The farm-to-city network requires infrastructure; ironically, an infrastructure that most cities had until shortly after the 1950s when many central covered farmers markets were removed for public health reasons . As recently as 1918, a majority of cities in the United States with populations over 30,000 had a municipal food market where local and fresh produce was hocked to urbanites .As Hinrichs supposed, CSAs and farmers’ markets appear to connect over differing geographies as represented by the generalized reach diagram . Namely, CSA markets are more proximal . Yet, this research shows that CSAs and farmers’ market networks cluster socially ; and both marketing typologies are not well interwoven in other food system networks. This finding begs the question: are direct markets embedded socially at the local level? The social network analysis reveals the important role that the food bank plays in convening many of the farms involved at this nexus of networks . Interviews and review of the comprehensive plan corroborate the social embeddedness of the food bank in land-use policy and food planning in Chester County.

Hypothetically, an area is at higher risk of disease transmission if it is more likely to experience interactions between feral pig and domestic pigs raised outdoors, as these outdoor based pigs can serve as a conduit for disease spread from wildlife to humans. Locations at greatest risk for increased contact between both swine populations are those regions that contain feral pig suitable habitat located near outdoor-raised domestic pig premises, especially those OPO with relatively low levels of bio-security., , Contact between feral pigs and outdoor-raised pigs in California has been documented, as feral pigs are attracted to agricultural regions for food, water and mates., , There is enormous value in identifying agricultural regions with a higher probability of feral pig contact, because these areas could benefit from targeted cost-effective disease surveillance and risk-mitigation strategies to prevent disease transmission. Predicting suitable habitat for feral pigs in combination with spatially characterizing the distribution of OPO can provide an important tool to ascertain possible high-risk areas of contact at the feral-domestic pig interface and identify future disease spillover areas.

Species distribution modeling methods have been widely used in ecological studies and are becoming popular for use in epidemiological investigations of disease transmission between wildlife and livestock. Maximum Entropy , which is one type of SDM, plastic planters bulk allows usage of presence-only data for the species of interest . In combination with biologically-appropriate covariate factors, MaxEnt is able to spatially predict the probability of suitable habitat for a species for a chosen spatial unit . These two parallel trends of expanding feral pig populations and a resurgence of raising domestic swine outside has important implications for disease transmission, which could negatively impact both public health and California’s agricultural industry. To the best of our knowledge, there are no maps characterizing where suitable feral pig habitat overlaps with domestic pigs raised outdoors at the farm-level in California. The overall objective of this study entailed spatially identifying potential high-risk areas of disease transmission between these two swine populations. This objective was achieved by a three-step process: 1) predicting suitable feral pig habitat in California using MaxEnt; 2) mapping the spatial distribution of OPO in California; and 3) identifying high-risk regions where there is spatial overlap between feral pig suitable habitat and OPOs, as potential disease transmission areas.

MaxEnt is an established SDM method that produces an output prediction map containing estimates of the relative probability of suitable habitat areas for the species of interest within each pixel, using presence-only points and predictor rasters . For feral pig presence data, we obtained feral pig hunting tags from 2012-19 that were cleaned and recorded with GPS coordinates by the California Department of Fish and Wildlife . Hunters in California are voluntarily asked to report feral pig harvest locations by submitting hunting tags to CDFW. Using hunting records for presence-points of feral pigs or wild boars has been used in previous studies. CDFW 2012-19 feral pig hunting tags totaled 5,148 after removing duplicates. Due to the large amount of data points, hunting tags were also manually filtered by year as a way to decrease the abundance of points before running models to reduce sampling bias and increase model stability, as suggested by previous analyses of MaxEnt. Publicly available predictor layers online, including biotic and abiotic , were included in variable selection steps, see Table 2.1. These predictors were chosen based on known feral pig behaviors, habitat and food preferences.For instance, AVGMODIS was the annual maximum green vegetation fraction combined with 12 years of normalized difference vegetation index data and relates to food and shrub cover for feral pigs.

Other variables included elevation, as feral pigs may prefer specific altitudes, and nineteen environmental variables from the WorldClim set of 30 year trend climatic factors. Examples of environmental variables used from the WorldClim site included BIO6, which is the minimum temperature of the coldest month, BIO13 which represents precipitation of the wettest month and BIO15 which is the coefficient of variation for seasonal precipitation.MaxEnt models were built in R Statistical Software version 0.98.110253 ©. The following R packages were used to run MaxEnt: dismo, sp, and raster. MaxEnt settings were chosen based on previously published literature and included using 25 random test points, 15 replicates, 5000 maximum iterations and the 10-percentile training for the threshold rule. , , A regularization multiplier of 1 through 5 was assessed to avoid overfitting and the default 1 was determined to be the optimal setting for the final model. Logistic values for output was used as well as cross validation, which separates presence points into 80% training and 20% testing data , using k-fold sub-sampling to fit a model. The relative contribution of each variable in a MaxEnt model was assessed comparing both percent contribution and permutation of importance, averaged over the number of iterations run and ascertained by jackknife tests. Predictors for the final model were assessed using a backward variable selection approach: variables remained at each step if their percent contribution or permutation importance was approximately 10% or more. The response curves generated within MaxEnt showed the predicted probability of suitable feral pig habitat for each individual variable, changing per each level of the predictor. MaxEnt model performance was assessed using the area under the curve of the receiver operator characteristic , averaged over the number of chosen replicate runs. AUC reflects a model’s prediction ability, on a scale of 0 to 1.00, with 0.50 representing random chance. While AUC is a standard diagnostic method to evaluate MaxEnt models, some authors suggest calibrating the AUC , which removes spatial sorting bias  by using point-wise distance sampling. A ssb close to 1 indicates no spatial sorting bias, whereas a ssb close to 0 suggests a large spatialbias, and the need to use AUCc. The final model was chosen based on the highest AUCc, collection pot relative to other models. The feral pig-domestic pig risk map was built by overlapping California OPO locations with the final MaxEnt feral pig suitable habitat raster. Between 2014-2019, a list of California OPOs was compiled through various sources advisors, web-based searches . GPS coordinates for all OPO were identified using Google Earth Pro v7.3.3. Additionally, an online survey that contained an interactive map component was built with Survey 123 v3.6. The survey contained 29 questions that consisted mainly of multiple choice questions, with a few open ended questions about the number of animals raised . The survey included questions regarding biosecurity practices, swine health and feral pig presence. This online survey was announced electronically to swine related groups and organizations or conducted in-person at events, such as agricultural fairs. The survey instrument and protocols were reviewed and exempted by the Institutional Review Board of the University of California-Davis . To build a risk map for California, the final MaxEnt model predicting suitable habitat for feral pigs was overlapped with the location of OPOs to categorize areas at greatest risk for disease transmission, due to contact between these two swine populations, and characterize risk at the farm-level. The underlying assumption presumed that direct or indirect contact between feral pigs and domestic pigs raised outdoors is a risk for disease transmission. The probability of suitable habitat for feral pigs was extracted from the final MaxEnt model for each OPO location, using the Sample Raster Value tool in QGIS and added to the OPO shape file. Then the Kernel Density tool in QGIS was used to make the risk map, matching the 270m x 270m resolution of the MaxEnt model and using the MaxEnt model probabilities as weights.

Additionally, we used a radius of 5 km at each OPO location, which was an extrapolated average estimate from US based studies that measured home range of feral pigs, understanding that home ranges vary depending on age and gender of animal, as well as resource availability. The Kernel Density map was overlaid with the final MaxEnt model. The final MaxEnt model was chosen based on the highest AUCc of 89.7, relative to other models . Probability values that predict suitable habitat were divided into five equal interval categories: minimal ; low ; moderate ; high ; and extremely high , with 0.87 being the highest predicted probability in the final MaxEnt model. Areas with the highest likelihood of suitable feral pig habitat in California included the north coast from Mendocino County all the way south along the coast to Santa Barbara County, and counties that border these coastal counties . Additionally, suitable habitat areas included the foothills of the Sierra mountains, from Shasta County south to Tulare County. Least likely suitable habitat included the Central Valley and eastern counties of California, from the most northern county of Modoc all the way to Imperial County in the south.Five variables were identified as significant in predicting suitable feral pig habitat in the final model based on 2017 hunting tags . The five significant variables were AVGMODIS, Elevation, BIO6, BIO13 and BIO15. All five variables provided approximately 10% or more percent contribution and permutation importance to the final model. . The jackknife test results provided more information regarding the importance of each variable in the final model . For example, BIO15 was the variable with the highest gain when used alone and elevation had the most information that was not available in the other variables. The response curves for the significant five variables indicated the predicted suitability range of each variable for feral pigs . For instance, feral pigs are predicted to prefer vegetative cover of at least 60% or more. The risk map reflects areas at greatest risk for contact between feral swine and domestic pigs raised outdoor and subsequent potential disease transmission . Areas with the most risk for contact between these two swine populations are denoted in orange or red, with sharper colors representing denser clustering of OPO. The counties with the highest likelihood of suitable feral pig habitat and densest clustering of OPO included: Sonoma, Marin, Napa, Yolo, Nevada, Mendocino and Lake counties. Areas at lowest risk include the full eastern edge of California, which includes the Cascadian and Sierra Nevada Mountain ranges as well as deserts in the south. Table 2 categorizes the distribution of OPO at each level of probable suitable feral pig habitat using the final MaxEnt model levels. The results indicated that 49.18% of OPO are located near extremely high or highly suitable feral pig habitat.In this study, we built a feral pig suitable habitat prediction model for California using MaxEnt at a fine scale of 270m x270m. Significant predictors of suitable feral habitat included precipitation, minimum temperature, elevation, and percentage of vegetation. Additionally, this study overlapped MaxEnt predicted suitable feral pig habitat and outdoor-raised pig operations to create a risk map for potential disease transmission in California at the feral pig-domestic pig interface. To the best of our knowledge, this is one of the first studies that identified areas at risk for feral and domestic pig contact in California. Although previous studies discussed the possibility of feral pig populations spreading disease to outdoor-raised pigs at the county level, to our knowledge, this is the first study to predict risk at the farm-level in California. Since the exact location of most feral pig populations is unknown, species distribution predictive methods like MaxEnt are important to understand where feral pigs could potentially interface with domestic swine raised outside, either currently or in the future. Our final MaxEnt prediction model provides a more informative picture of suitable habitat for feral pigs than previous studies, which only showed single presence points or reported feral pigs at the county level, even if only one feral pig was identified in that county. For instance, although previous county-level maps indicated that all California counties except for Imperial County harbored feral pigs, our MaxEnt model shows almost no suitable habitat in an additional five counties: Modoc, Mono, Alpine, Lassen and Inyo. This result may indicate that few feral pigs have been seen in those counties. Additionally, the final MaxEnt model was based on a fine spatial scale and indicated heterogenous suitable habitat, not a uniform distribution, for each county, which is compatible with the fact that feral pigs need shrub cover and food to survive, which would not be found in cities or deserts.

The grapem1355 candidate maps on chromosome 3, exactly on thefirst exon of its target , in a region where another two isoforms of the same gene are located . The last target of this miRNA candidate, codes a cinnamyl alcohol dehydrogenase known to be involved in the lignin biosynthesis . Other novel vvi-miRNA candidates seem to be involved in cell proliferation and in chloroplasts-related functions . Furthermore, for the new vvi-miRC482b candidate, besides the already known involvement of this miRNA family with disease resistance also predicted here, one predicted target encodes an anthocyanin 5-aromatic acyltransferase-like protein known to be involved in the biosynthesis of anthocyanin in different species . As for the conserved known vvi-miRNAs, most of the well-established examples of miR-targets, such as miR156-SPB, miR166-HD-ZIP, miR171-GRAS, miR172-AP2, confirmed in several plant species and already predicted in grapevine, blueberry containers were also predicted here. We studied miRNA profile of accumulation in the different samples.

Using their normalized abundance , i.e., their relative cloning frequency, we set an empirical cut off value equal to at least 10 TP4M in both biological replicates to consider a miRNA as expressed in a given library. Also, a miRNA was considered specific when it was expressed in one or more libraries of a unique cultivar, unique environment or unique developmental stage. According to our established cut off, 175 miRNAs were classified as expressed in at least one of our libraries . The libraries constructed from Sangiovese berries at bunch closure collected in Bolgheri showed only 24 expressed miRNAs . For all the other libraries, expressed miRNAs ranged from 76 to 148 . We found very few miRNAs specific to a given condition. The number of specific miRNAs for each cultivar, developmental stage and environment is reported in Figures 8A–C, respectively. Thirty-nine vvi-miRNAs were highly expressed in almost all libraries [21 ubiquitous plus 18 expressed in all libraries except in Bol_SG_bc ], whereas other miRNAs had different accumulation patterns. The normalized expression values of miRNAs were subjected to hierarchical clustering and represented in a heat map . To examine the relatedness among cultivars, environments and developmental stages, we generated a correlation dendrogram . The dendrogram shows, as already suggested by the heatmaps, that a fundamental dichotomy emerges between ripened and green berries. The most evident pattern of expression is observed when comparing different developmental stages, and confirm previous observation of miRNA modulation during fruit ripening .

For example, some members of the vvi-miRNA156 family were highly expressed in all ripened berries, but weakly or not expressed in green berries. Differently, vvi-miR396a-3p and vvi-miR396b-3p showed the opposite profile. Similarly, vvi-miR172d, vvi-miR166b-5p, vvi-miR166f-5p, and vvi-miR396d-5p were highly expressed in green berries but weakly expressed in ripened berries and the members of the vvi-miR319 family showed a gradient of decreasing abundance from pea size to harvest.To gain statistical evidence of miRNA differential expression driven by the environment and/or genotype, we made pairwise comparisons, keeping constant the developmental stage, and evaluating the miRNA modulation among vineyards or between cultivars . The analyses reveal that some miRNAs are differentially expressed between the two genotypes grown in the same environment, but also that a number of miRNAs are modulated by the environment. In particular the number of differentially expressed miRNAs is higher in ripened berries , while no miRNAs are differentially expressed at bunch closure stage . In details, 14 reads are differentially expressed at pea size stage, in at least one comparison, corresponding to 6 distinct miRNA families; 27 reads are modulated at 19 ◦Brix stage, corresponding to 12 miRNA families and 35 reads are differentially expressed in berries at harvest, corresponding to 12 miRNA families. It is worth noting that 4 of the 6 families modulated in the berries at pea size, are still present among the miRNAs differentially expressed in the berries sampled at 19 ◦Brix and at harvest , even though not always in the same comparisons. Some of the modulated miRNAs, both novel and known are intriguingly connected to berry development and secondary metabolism, even though most of the modulated families are still uncharacterized, or with targets not clearly involved in berry ripening and development, and deserve further studies to fully understand their biological roles. Using high throughput sequencing coupled with robust bio-informatics pipelines we analyzed small RNAs derived from the berries of Cabernet Sauvignon and Sangiovese, grown sideby-side in three vineyards, representative of different grapevine cultivation areas in Italy . We obtained nearly 750 MB reads comprising a significant proportion of small RNAs.

The size distribution profiles of our libraries were in general consistent with previous reports in berry grapevine, where the 21-nt class was more abundant than the 24-nt class . Our analysis revealed dynamic features of the regulatory network mediated by miRNAs and other small RNAs, at the basis of genotype-environment interactions. Plants evolved a series of pathways that generate small RNAs of different sizes with dedicated functions . Although the various small RNA classes have been intensively studied, we are still far from understanding how many small RNA pathways exist, and how they are connected . Additionally, new classes of small non-coding RNAs continue to be discovered and many studies demonstrate a substantial redundancy and cross-talk between known small RNA pathways . Estimating the exact percentage of the plant genome covered by small RNA-generating loci still remains a challenge. By applying static cluster analysis, we investigated small RNA abundances across the genome, identifying 4408 small RNAs producing hotspots. We analyzed their expression in different cultivars, environments and developmental stages, highlighting that the majority of the considered small RNA producing regions was modulated in different conditions. This suggests a strong influence of small RNAs in the response to environment in grapevine berries. Only 462 small RNA-generating loci, corresponding to about 10% of the total, were expressed in all the analyzed libraries, possibly involved in essential biological pathways. Comparing the two cultivars, we observed, with few exceptions, that Cabernet Sauvignon berries have a higher number of expressed sRNA-generating loci than Sangiovese berries when collected in the same conditions . Considering the fact that small RNAs are implicated in the regulation of gene expression in several processes , the higher number of small RNAs expressed in Cabernet Sauvignon compared to Sangiovese berries may reflect a buffering effect of small RNAs influencing grapevine response to diverse growing environments. We believe that these characteristics may have contributed to the wide diffusion of Cabernet Sauvignon, allowing its wide cultivation in almost all wine producing countries. This is not the case for Sangiovese whose cultivation is more restricted. It is worth noting that Sangiovese is considered a very unsettled grapevine cultivar , showing a wide range of variability in response to year, clone and bunch exposure . Differently, Cabernet Sauvignon is a cultivars showing less inter-annual differences in terms, for example, best indoor plant pots of concentration of secondary metabolites . To better evaluate varietal differences in response to the environment, we calculated the CS/SG ratio for the small RNA producing hotspots in the three vineyards. A region on chromosome 4 showed a 390-fold change in the small RNA abundance, when comparing Cabernet vs. Sangiovese . Most of the reads produced in this region are 21 nt long and are also phased in intervals of 21 nt from both strands, typical of a phased locus . The gene in this locus, also known as VvRD22g, encodes a BURP domain containing protein, involved in an ABA-mediated abiotic stress response, which persists still after long periods of stress . The small RNAs profile suggests that the locus is regulated by phased siRNAs similarly to the mechanisms already described for PPR, NB-LRR, and MYB gene families . This is a clear example of GxE interactions since the BURP domain gene modulates phased siRNAs production in the two cultivars only when grown in Riccione. When removing the threshold of minimum cluster abundance set to 5 HNA, in the CS/SG ratio, a high number of clusters with fold change greater than 50 was found, where one of the libraries has 0 HNA and the other any number greater than 30 HNA. This fact suggests a very strong modulation of the expression of small RNAs between the two cultivars, which is more or less pronounced depending on the vineyard where the berries were cultivated. A similar situation was observed comparing the expression level of small RNAs between reciprocal hybrids of Solanum lycopersicum and S. pimpinellifolium .

The ripening process of grapevine berries is highly affected by the environment and we observed the impact of the environment on the ripening process in the expression of small RNAs. The most relevant observation is that Riccione is very peculiar in relation to the activation of sRNA hotspots, as indicated by the high number of Riccione specific clusters and by the extreme modification it induces in the CS/SG ratio : in Riccione in fact this ratio decreases in green berries and increases in ripened berries, and this is not observed in any other vineyard; in addition to this the already discussed example of BURP domain gene, is observed in Riccione, as well. Riccione is the most diverse environment when compared to Montalcino and Bolgheri. Riccione is located at the Adriatic coast and has a temperate sub-littoral climate, while Montalcino and Bolgheri are both located in Tuscany with typically Mediterranean climate. Moreover, both cultivars show a peculiar profile of small RNA loci during berries ripening, in Riccione. The expression of small RNA loci in Cabernet Sauvignon berries drastically changed during development, especially when collected in Riccione , not only in the number of active loci but also in the different genic or intergenic disposition: ripened berries have a 2.6-fold increase in small RNA loci active in genic regions. Differently, when Sangiovese is grown in Riccione, there is a very high number of small RNA loci active in green berries, mainly associated to transposable elements that remains almost stable during development although the proportion of intergenic loci is reduced. Sangiovese berries collected in Montalcino show a 2.5- fold increase of small RNA producing loci during development. Differences during berry development between the cultivars may explain their different behavior in different environments, and the characteristics of each vineyard may favor one or other variety according to their demands. For example, Sangiovese needs a long growing season with sufficient warmth to fully ripen . Consequently, cooler environments will require a reprograming of Sangiovese gene expression in order to achieve ripening. Other factors such as composition of soil, level of humidity, photo period and density of cultivation may be exerting the same influence on the ripening of the berries triggering the activation of different small RNA loci.Applying a conservative pipeline to the analysis of our 48 small RNA libraries, we recognized 89 known and annotated grapevine miRNAs. In addition, when compared to previous reports in grapevine we identified 7 completely novel miRNAs plus 26 homologous to other plant species, but novel to grapevine. This is a remarkable number considering the stringency of our pipeline and that our study is based only on four developmental stages of berries. The outline of miRNA accumulation across samples is different from that of sRNA-producing loci. While the expression of sRNA-generating regions allows distinguishing very well between ripened and green berries and also between cultivars , the accumulation of miRNAs shows a clear distinction only between ripened and green berries, and when the berries were green, we observe a further dichotomy separating the two cultivars and the two green developmental stages. The same pattern of miRNA accumulation among green and ripened berries of grapevine was observed when we described the miRNA expression atlas of Vitis vinifera . Comparing the distribution of miRNAs expressed throughout our samples, we found a set of 39 miRNAs ubiquitous or nearly ubiquitous to all the libraries, and very few miRNAs specific of a cultivar, vineyard or developmental stage. All these 39 miRNAs belong to known vvi-miRNA families. With few exceptions, the same set of miRNAs was also found expressed in all the small RNA libraries constructed with different tissues of the grapevine cv. Corvina , where the population of expressed miRNAs appears highly variable apart from a well-defined group of miRNAs, probably related to the basal metabolism. These findings are also consistent with previous report in grapevine where a small number of known tissue-specific miRNAs was described .

The Berry phases of bosonic and fermionic coherent sates and the special cases with a 1D Hilbert space are summarized in the Appendix. The classical approach to the central limit theorem and the accuracy of approximations for independent random variables rely heavily on Fourier transform methods. However, the use of Fourier methods is highly limited without an independence structure, which makes it far less possible to capture the explicit bounds for the accuracy of approximations. In 1972, Charles Stein introduced a novel technique, now known as Stein’s method, for normal approximation. The method works for both independent and dependent random variables. The method also provides bounds of approximation accuracy. Extensive applications of Stein’s method to obtain uniform and non-uniform Berry–Esseen-type bounds for independent and dependent random variables can be found in, for example, Diaconis , Baldi et al. , Barbour , Dembo and Rinott , Goldstein and Reinert , Chen and Shao , Chatterjee , Nourdin and Peccati and Chen and Fang . In addition to the traditional study of Berry–Esseen bounds, container growing raspberries new developments to Stein’s method have triggered a series of research on Cramér-type moderate deviations, which address the relative error of two tail probabilities. See, for example, Raič , Chen et al. and Shao and Zhou , among others.

Various extensions of Stein’s idea have been applied to many other probability approximations, most notably to Poisson, Poisson process, compound Poisson, binomial approximations and more recently to multivariate, combinatorial and discretized normal approximations. Stein’s method has also found diverse applications in a wide range of fields, see for example,Arratia et al. , Barbour et al. and Chen . Expositions of Stein’s method and its applications in normal and other distributional approximations can be found in Diaconis and Holmes , Barbour and Chen . We also refer to Chen et al. a thorough coverage of the method’s fundamentals and recent developments in both theory and applications. The paper is organized as follows. In the next section, we give a brief review on recent developments on Stein’s method. In Section 3, we present the main results in this paper, the Berry–Esseen bounds and Cramér type moderate deviations for Studentized nonlinear statistics. Applications to Studentized U-statistics and L-statistics are discussed in Section 4. The proofs of the main results are in Section 5, while other technical proofs are postponed to Appendix. Vitis vinifera grapevines originated approximately 65 million years ago from Eurasia and have been cultivated for at least the last 8000 years for its fruits that are crushed to make wine. Grapevines are now grown throughout the world in many kinds of environments. Grape berry development is a complex process involving three developmental phases and multiple hormones.

It is in the latter ripening phase that many compounds involved in flavor and aromas are synthesized, conjugated or catabolized. Most of these compounds reside in the skin of the berry and seem to develop in the very last stages of berry development. Aroma and flavor are important sensory components of wine. They are derived from multiple classes of compounds in grapes including important volatile compounds from the grape and from yeast metabolism during grape fermentation. Each grape cultivar produces a unique set of volatile and flavor compounds at varying concentration that represents its wine typicity or typical cultivar characteristics. Esters and terpenes are volatile compound chemical classes largely responsible for the fruity and floral aromas in wines. Esters are largely produced during yeast fermentation from grape-derived products such as aliphatic alcohols and aldehydes. Grape lipoxygenases are thought to provide the six carbon precursors from fatty acids for the synthesis of the fruity aroma, hexyl acetate, in yeast during wine fermentation. Terpenes mostly originate from the grapes and are found in both the free and bound forms. Both plant fatty acid and terpenoid metabolism pathways are very sensitive to the environment. Climate has large effects on berry development and composition.

Besides grape genetics other factors may influence metabolite composition including the local grape berry microbiome, the soil type and the rootstock. While there is evidence that rootstock can affect fruit composition and transcript abundance, this effect appears to be minor relative to other environmental factors. Many cultural practices used by the grape grower may directly or indirectly affect the environment sensed by the grapevine . Temperature and light are major contributors to “terroir”. Terroir refers to the environmental effects on grapes and how it contributes distinctive characteristics to the typicity of a wine . The terroir term includes biotic and abiotic factors, soil environments as well as the viticultural practices. In the present work, we will use the term “place” to address all of the above except for the viticultural practices. Recently, a transcriptomic approach was used to elucidate the common gene subnetworks of the late stages of berry development when grapes are normally harvested at their peak maturity. One of the major sub-networks associated with ripening involved autophagy, catabolism, RNA splicing, proteolysis, chromosome organization and the circadian clock. An integrated model was constructed to link light sensing with the circadian clock highlighting the importance of the light environment on berry development. In this report, in order to get a better understanding of how much of the gene expression in Cabernet Sauvignon berry skin could be attributed to environmental influences, we tested the hypothesis that there would be significant differences in gene expression during the late stages of Cabernet Sauvignon berry ripening between two widely different locations: one in Reno, NV, USA and the other in Bordeaux, France . The analysis revealed a core set of genes that did not depend on location, climate, vineyard management, grafting and soil properties. Also, the analysis revealed key genes that are differentially expressed between the two locations. Some of these differences were linked to the effects of temperature and other environmental factors known to affect aromatic and other quality-trait-associated pathways. Many gene families were differentially expressed and may provide useful levers for the vine grower to adjust berry composition. Among others, these families encompassed genes involved in amino acid and phenylpropanoid metabolism, as well as aroma and flavor synthesis.To test the hypothesis that the transcript abundance of grape berries during the late stages of ripening differed in two locations with widely different environmental conditions, we compared the transcript abundance of grape berry skins in BOD and RNO. The vineyards were originally planted in RNO in 2004 and in BOD in 2009. The RNO vines were grown on their own roots, whereas the BOD vines were grafted on to SO4 rootstock. A vertical shoot positioning trellis design was used in both locations. There were a number environmental variables that differed between the two locations. BOD is located at a slightly more northern latitude than RNO. This resulted in slightly longer day lengths in BOD at the beginning of harvest and slightly shorter at the end of harvest . On the final harvest dates, blueberries in pots the day length differed between RNO and BOD by about 30 min. RNO had warmer average monthly maximum temperatures than that in BOD, but minimum September temperatures were cooler in RNO . Thus, RNO had a larger average daily day/night temperature differential of 20 °C, whereas BOD had a smaller average daily day/ night temperature differential of 10 °C during the harvest periods. RNO had warmer day temperatures by about6 °C and cooler night temperatures by about 4 °C than that of BOD. The RNO vineyard location was much drier than the BOD vineyard location . The monthly precipitation totals for RNO in September were 2.03 mm whereas it was 65.5 mm in BOD; the average relative humidities were 34 and 74% for RNO and BOD, respectively.

The soil at the RNO vineyard was a deep sandy loam with a pH of 6.7; the BOD vineyard was a gravelly soil with a pH of 6.2. No pathogens, nutrient deficiencies or toxicity symptoms were observed on or in the vines at either site.The analysis of transcript profiles of Cabernet Sauvignon grapes harvested in RNO in September of 2012 was previously described. Individual berry skins were separated immediately from the whole berry and the individual total soluble solids level of the berry, which is mostly composed of sugars, was determined. The Cabernet Sauvignon berry skins from BOD were harvested in a similar manner as the RNO berry skins. The berry skins in BOD were harvested from midway in September, 2013 until the first week of October . The berry skins were separated and the °Brix analyzed in the same manner as that in RNO. Grapes were harvested at a lower °Brix range in BOD than in RNO because fruit maturity for making wine is typically reached in the BOD region at a lower sugar level. Transcript abundance of the RNA-Seq reads from both RNO and BOD was estimated using Salmon software with the assembly and gene model annotation of Cabernet Sauvignon. The TPM were computed for each gene from each experimental replicate from berry skins at different sugar levels ranging from 19 to 26°Brix . Principal component analysis of the transcriptomic data showed clear grouping of experimental replicates with the largest separation by location = 51% variance and then °Brix = 22% variance of the berry skin samples . To get different perspectives of the data, three approaches were used to further analyze the transcriptomic data. One focused on expression at one similar sugar level in both locations. Another identified a common set of genes whose transcript abundance changed in both locations. And the third one was a more comprehensive network analysis using all of the sugar levels and the two locations. We chose two very similar sugar levels to determine the differential gene expression between the two locations, since sugar levels were not exactly the same at harvest. We identified 5528 differentially expressed genes between the two locations in approach 1 at the sugar level closest to the 22°Brix level using DESeq2. DEGs will refer to this set of differentially expressed genes throughout this manuscript. Gene set enrichment analysis using topGO determined the top gene ontology categories for biological processes for these 5528 genes . Based on the number of genes identified, the top GO categories were cellular metabolic process , biosynthetic process , and response to stimulus . Other important and highly significant categories were response to stress and developmental process . The relationship between the top 25 GO categories can be seen in Additional file 4. We use the term “significantly” throughout this text to mean statistically significant at or below a padj-value of 0.05. Amongst the top stimulus subcategories with the largest number of genes were response to abiotic stimulus , response to endogenous stimulus , response to external stimulus , and biotic stimulus . Some other significant environmental stimuli GO categories included response to light stimulus , response to osmotic stress , and response to temperature stimulus . In approach 2, we examined which gene expression was changing with °Brix level in both locations to identify a common set of genes differentially expressed during berry development with very different environmental conditions. The significant differences in transcript abundance in each location was determined with DESeq2 using the lowest °Brix sampling as the control. For example, the control sample in RNO was the lowest sugar sampling at 20 °Brix; the transcript abundance of the three higher °Brix samplings were compared to the transcript abundance of the control. The genes that had significantly different transcript abundance relative to control in at least one of the comparisons were identified in RNO and BOD. These gene lists were compared and the common gene set consisting of 1985 genes for both locations was determined . Comparing this common gene list to the DEGs from approach 1 identified 907 genes that were common to both sets, indicating that this subset was differentially expressed between the locations at 22°Brix. The other 1078 genes did not differ significantly between locations. This 1078 gene subset list can be found in Additional file 5 . The GO categories most enriched in this gene set included response to inorganic substance, response to abiotic stimulus and drug metabolic process. In approach 3, using a more powerful approach to finely distinguish the expression data for all sugar levels, Weighted Gene Coexpression Network Analysis identified gene sets common to and different gene expression profiles between BOD and RNO. All expressed genes for all °Brix levels were used in this analysis.

The first intrinsic two dimensional ferromagnets were discovered in 2017, so I think it’s safe to say that our field hasn’t yet come particularly close to identifying all possible two dimensional magnets. It’s hard to do an accurate accounting of all of the so-far discovered two dimensional magnets, and it is certainly the case that many of these are are not Chern magnets. But of the two dimensional magnets we have found, a surprisingly large fraction are intrinsic Chern magnets. We know of eight intrinsic Chern magnets stable in the absence of an applied magnetic field in the published literature so far. These are presented, along with a few of their basic properties, in Table 8.1. We have discussed several of these materials in this thesis, but we have also skipped a few,including the only currently known intrinsic Chern magnet in an atomic crystal, i.e., not on a moir´e superlattice: MnBi2Te4. These other materials all also represent areas of active research. Of the Chern magnets we know about, 2/8 have C < 0 with B > 0, so that property might be quite common. Indeed, there’s no particular reason to expect the B > 0 ground state to have one sign of the Chern number over the other as far as I know. It’s worth mentioning that if we ever find one, a room temperature Chern magnet with C < 0 for B > 0 would also have extremely large ∆m, large pots plastic and will therefore likely be switchable, since ∆m increases linearly with EGap.

It is also the case that two of these materials have been observed to be switchable with pulses of electric current, although it is not yet clear if the tBLG/hBN and ABMoTe2/WSe2 Chern magnets share a common current-switching mechanism, or if their respective mechanisms would generalize well to large gap Chern magnets. I think it’s clear that we are in the early days of the study of this class of material systems, and without discovering more Chern magnets there is little we can say with much generality. All of this is to say that I don’t think it’s crazy to expect to discover Chern magnets at much higher energy scales than we have already encountered, and that should we ever find such a system, there are a variety of intriguing technological applications for which this class of material systems could be exploited. I have put some effort into making this thesis a self-contained explanation of the background, details, and impact of the instrumentation and research work I participated in during my PhD. More can always be said, of course, and there exist a few articles targeted at a general physics audience discussing these discoveries in the context of the field written by other authors. They are referenced at the end of the Curriculum Vitae at the beginning of this thesis, and they are worth reading if you are interested in more of the details of these experiments and their implications for the field. Chern magnets were predicted to exist in the 1980s and realized for the first time in the form of doped topological insulators in 2013.

The first intrinsic Chern magnets were discovered in 2018. I hope I’ve convinced the reader that there are reasons to study this class of materials beyond a simple desire to catalogue all possible phases of matter. The phenomenology of intrinsic Chern magnets turned out to be very rich and may one day add something to a wide variety of electronic technologies, including low dissipation, electronically switchable magnetic memories and electronic metrology. Over the course of my PhD, four nanoSQUID microscopes were proposed, and construction began in some form on all of them. By the time I left we had finished three of these microscopes. The first nanoSQUID microscope we completed was inserted into a bath of liquid helium and could operate at 4 K. The CrI3 magnetic imaging campaign was performed in this system. The second nanoSQUID microscope had a pumped He-4 evaporative cooling pot, and could reach temperatures of 1.5 K. The tBLG/hBN Chern magnet transport measurements, the tBLG/hBN Chern magnet imaging measurements, and the AB-MoTe2/WeSe2 Chern magnet imaging measurements were all performed in this system. The third nanoSQUID microscope had a closed cycle He-3 sorption pump cooling system, and could reach 300 mK. The fourth and final microscope remains under construction, and is designed to operate inside of a dilution refrigerator. Pictures of several of these microscopes are shown in Fig. 8.6. Acoustic isolation chambers and the 300 mK system are not shown. All nanoSQUIDs have liquid He-4 baths for primary stage cooling, and all are mounted on several thousand pound vibration isolation tables floating on air legs to protect the nanoSQUID sensors from mechanical and acoustic shocks close to the surface.

The nanoSQUID sensor circuit is fairly simple, with only one important non-standard circuit element in it, other than the nanoSQUID itself of course. This is the series SQUID array amplifier. Current is forced into the nanoSQUID sensor in parallel with a shunt resistor of comparable resistance to the nanoSQUID sensor in the voltage state, which is generally a few Ohms. Current through the nanoSQUID side of the circuit is inductively coupled to a series of identical SQUIDs. These SQUIDs in series generate a large voltage, which is detected at room temperature. Current is forced through a feedback coil to maintain constant flux through the SQUIDs in series. This allows the circuit to maintain sensitivity over a wide range of currents . This current amplification circuit has good current sensitivity and enormous dynamics range, easily able to accommodate the several hundred µA necessary to reach the critical current of the nanoSQUID sensor. There are a lot of things that make scanning probe microscopy tough relative to other techniques for performing microscopy. One particularly challenging issue is navigation of the sensor to the sample. Those experienced with optical imaging might be spoiled by a contrast mechanism that is sensitive to a ton of different phenomena- the nanoSQUID can only see local gradients in magnetic field and temperature, and those are rare unless you have intentionally built structures and devices that generate them for use in navigation. In particular, large thermal gradients and variations in local magnetic field aren’t general properties of surfaces, square planter pots so it’s very easy to blunder a nanoSQUID sensor into a surface without ever seeing it coming! Experiments are thus much safer and more expedient if we can provide the nanoSQUID sensor with topographic feedback- i.e., some way of detecting surfaces without crashing into them and destroying the sensor. We did this using shear force microscopy, which is a form of atomic force microscopy, or AFM. There is nothing particularly atomic about this contrast mechanism in the nanoSQUID microscope- we don’t have nearly that much resolution- but it is incredibly useful for navigation because it allows us to safely and reliably detect surfaces without destroying the SQUID. Researchers and companies building scanning tunneling microscopes will often accomplish this by gluing their sensor, which is a microscopic metallic wire, onto a piezoelectric tuning fork and then exciting the tuning fork at its resonant frequency. This is a good strategy, but it must be modified for use with the nanoSQUID sensor, because the nanoSQUID sensor is considerably more massive thanscanning tunneling microscope wires, so it cannot be glued onto the tuning fork without destroying its quality factor. We preserve the tuning fork’s quality factor by instead pressing a piezoelectric tuning fork against the side of the nanoSQUID sensor and performing shear force microscopy instead of tapping mode microscopy. The glass micropipettes serving as substrates for the nanoSQUID sensors are so thin that they bend easily when pressed agains the tuning fork, and this keeps them in mechanical contact with the fork. An optical microscope image of a nanoSQUID sensor pressed against a tuning fork is shown in Fig. 8.8A, and the resonant frequency of the piezoelectrically driven tuning fork is shown in Fig. 8.8B, with a fit to a Butterworth Van-Dyke model. A phase-locked loop and PID feedback system together allow us to approach the surface with the nanoSQUID sensor, detect it without crashing into it and destroying the tip, and maintain feedback while scanning.

Schematics of this assembly are shown in Fig. 8.9. A calibration of the scan range and height of the nanoSQUID AFM is shown in Fig. 8.10, with a comparison to a Bruker Icon AFM displayed as well. An image of these assemblies mounted on the microscope and ready to scan is provided in Fig. 8.11. By far the most common experimental campaign for the nanoSQUID microscope during my time in Andrea’s lab involved being handed a sample fabricated primarily for transport or capacitance measurements, with little consideration afforded to the viability or ease of a scanning probe microscopy campaign on the sample. I think this is fairly common in scanning probe microscopy, and it often means that we need to get sensors to samples without much in the way of navigation infrastructure. For this reason the vast majority of nanoSQUID microscopy campaigns start with thermal navigation. Before cooling down the nanoSQUID microscope, an attempt is made to align the nanoSQUID sensor with the heterostructure under an optical microscope, but the nanoSQUID sensor often still starts several hundred microns away from the sample. Once the system is cold, we generally proceed by injecting a few mBar of helium gas into the sample chamber. This facilitates thermal transport between the nanoSQUID sensor and the sample. We then run an AC current through the sample, heating it and generating an AC temperature distribution. The nanoSQUID sensors are excellent thermometers as well as magnetometers, so we can use this thermal gradient to navigate to the sample. An image of the resulting distribution of temperature over the device is shown in Fig. 8.13A. Some of the details are described in a later section, but in summary this technique works surprisingly well- we can usually find samples even several millimeters away from the nanoSQUID sensor using this technique. Once the nanoSQUID is reasonably close to the sample, it is usually necessary to pump out the heat exchange gas before attempting magnetic imaging, since thermal contrast can produce large backgrounds. After the heat exchange gas is removed, further navigation must proceed by imaging the magnetic fields produced by applied current through the Biot-Savart effect, as illustrated in Fig. 8.13B. Thermal navigation does not work for all systems. In the simplest case in which other techniques are necessary, current cannot be driven through magnetic insulators, so if you want to find them with the nanoSQUID you must arrange for some navigation technique other than flowing current through the sample. There are a variety of solutions to this problem, and perhaps the simplest is fabricating an additional device adjacent to the one you’d like to investigate and running current through that instead. There are reasons you might want to avoid this- some samples are so unstable in air and moisture that it makes sense to avoid photolithography on heterostructures entirely- and for these situations, I’m going to discuss ferromagnetic navigation. We start by generating a photolithography mask containing a large array of microscopic QR codes, as illustrated in Fig. 8.14A. These QR codes and the associated sample area with contact wires is shown in Fig. 8.14, and a chip with this pattern deposited onto it is shown in Fig. 8.14C, D. The GDSII patterns for these QR codes were generated procedurally using the GDSPy python package, and all of the associated software is available on Github, including a few different QR code designs, here: https://github.com/afylab/QR-Code-Generator. These patterns and wires are composed of 2 nm of Cr , 10-60 nm of permalloy, which is a nickel/iron alloy, and50 nm of Au, to prevent extensive oxidation of the permalloy and to facilitate electronic transport through the wires and easy wirebonding. NanoSQUID images of the magnetic field distributions above these patterns are shown in Fig. 8.14E, with line-by-line subtraction illustrating the visability of the QR code in Fig. 8.14F. Navigation of the nanoSQUID sensor to the chromium iodide flake was performed using these patterns, and an optical image of the scan region for that device is shown in Fig. 8.14G,H.

For this reason, orbital magnetism does not need spin-orbit coupling to support hysteresis, and it can couple to a much wider variety of physical phenomena than spin magnetism can- indeed, anything that affects the electronic band structure or real space wave function is fair game. For this reason we can expect to encounter many of the phenomena we normally associate with spin-orbit coupling in orbital magnets that do not possess it. I would also like to talk briefly about magnetic moments. It has already been said that magnetic moments in orbital magnets come from center-of-mass angular momentum of electrons, which makes them in some ways simpler and less mysterious than magnetic moments derived from electron spin. However, I didn’t tell you how to compute the angular momentum of an electronic band, only that it can be done. It is a somewhat more involved process to do at any level of generality than I’m willing to attempt here- it is described briefly in a later chapter- but suffice to say that it depends on details of band structure and interaction effects, which themselves depend on electron density and, drainage gutter in two dimensional materials, ambient conditions like displacement field. For this reason we can expect the magnitude of the magnetic moment of the valley degree of freedom to be much more sensitive to variables we can control than the magnetic moment of the electron spin, which is almost always close to 1 µB.

In particular, the magnetization of an orbital magnet can be vanishingly small, or it can increase far above the maximum possible magnetization of a spin ferromagnet of 1 µB per electron. Under a very limited and specific set of conditions we can precisely calculate the contribution of the orbital magnetic moment to the magnetization, and that will be discussed in detail later as well. Finally, I want to talk briefly about coercive fields. The more perceptive readers may have already noticed that we have broken the argument we used to understand magnetic inversion in spin magnets. The valley degree of freedom is a pair of electronic bands, and is thus bound to the two dimensional crystalline lattice- there is no sense in which we can continuously cant it into the plane while performing magnetic inversion. But of course, we have to expect that it is possible to apply a large magnetic field, couple to the magnetic moment of the valley µ, and eventually reach an energy µ · BC = EI at which magnetic inversion occurs. But what can we use for the Ising anisotropy energy EI ? It turns out that this model survives in the sense that we can make up a constant for EI and use it to understand some basic features of the coercive fields of orbital magnets, but where EI comes from in these systems remains somewhat mysterious. It is likely that it represents the difference in energy between the valley polarized ground state and some minimal-energy path through the spin and valley degenerate subspace, involving hybridized or intervalley coherent states in the intermediate regime. But we don’t need to understand this aspect of the model to draw some useful insights from it, as we will see later.

Real magnets are composed of constituent magnetic moments that can be modelled as infinitesimal circulating currents, or charges with finite angular momentum. It can be shown that the magnetic fields generated by the sum total of a uniform two dimensional distribution of these circulating currents- i.e., by a region of uniform magnetization- is precisely equivalent to the magnetic field generated by the current travelling around the edge of that two dimensional uniformly magnetized region through the Biot-Savart law. It turns out that this analogy is complete; it is also the case that a two dimensional region of uniform magnetization also experiences the same forces and torques in a magnetic field as an equivalent circulating current. The converse is also true- circulating currents can be modelled as two dimensional regions of uniform magnetization. The two pictures in fact are precisely equivalent. This is illustrated in Fig. 2.9. It is possible to prove this rigorously, but I will not do so here. One can say that in general, every phenomenon that produces a chiral current can be equivalently understood as a magnetization. All of the physical phenomena are preserved, although they need to be relabeled: Chiral edge currents are uniform magnetizations, and bulk gradients in magnetization are variations in bulk current current density. The details of this situation aren’t important; the lesson that is important is quite simple. In classical physics, we know how charged particles respond to local magnetic and electric fields. These rules are enough to completely explain the phenomena. This is apparently not the case in quantum mechanical systems. We can certainly attempt to describe systems this way, but in a wide variety of situations our models would be wrong, as in this one.

There is no point in this experiment at which an electron interacted with a magnetic field through the Lorentz force, and yet it turns out to be true that the magnetic field impacts the kinematics of electrons participating in the experiment. In a landmark result published in 1984, Michael Berry showed that our understanding of a variety of systems- including crystalline systems in condensed matter theory- suffered from a close analog of this misunderstanding. Researchers have since gone back to fix this oversight, plastic gutter and this led to the introduction of Berry curvature in condensed matter systems. Every crystal is defined by a periodic electric potential profile. In two dimensional crystals this is a scalar function of two dimensions over the lattice in real space. Let us switch our focus to momentum space. The periodic electric potential in real space produces a set of functions over momentum space E that define quantum states that electrons within the crystal can occupy. A correctly executed attempt to account for the effects of the Berry phase in crystalline systems produces a new vector-valued function over momentum space Ω that affects the kinematics of electrons in electronic bands. In two dimensional systems Ω is always oriented out-of-plane, but it can be positively or negatively oriented. We call this function the Berry curvature, and it must be accounted for to correctly explain a vast array of electronic phenomena, including electronic transport in metals, electronic transport in insulators, and angular momentum and magnetization in magnetic systems. In the same way that the Berry phase impacts the kinematics of free electrons moving through a two slit interferometer, Berry curvature impacts the kinematics of electrons moving through a crystal. You’ll often hear people describe Berry curvature as a ‘magnetic field in momentum space.’ You already know how electrons with finite velocity in an ambient magnetic field acquire momentum transverse to their current momentum vector. We call this the Lorentz force. Well, electronswith finite momentum in ‘ambient Berry curvature’ acquire momentum transverse to their current momentum vector. The difference is that magnetic fields vary in real space, and we like to look at maps of their real space distribution. Magnetic fields do not ‘vary in momentum space,’ at non-relativistic velocities they are strictly functions of position, not of momentum. Berry curvature does not vary in real space within a crystal. It does, however, vary in momentum space; it is strictly a function of momentum within a band. And of course Berry curvature impacts the kinematics of electrons in crystals. Condensed matter physicists love to say that particular phenomena are ‘quantum mechanical’ in nature. Of course this is a rather poorly-defined description of a phenomenon; all phenomena in condensed matter depend on quantum mechanics at some level. Sometimes this means that a phenomenon relies on the existence of a discrete spectrum of energy eigenstates. At other times it means that the phenomenon relies on the existence of the mysterious internal degree of freedom wave functions are known to have: the quantum phase. I hope it is clear that Berry curvature and all its associated phenomena are the latter kind of quantum mechanical effect. Berry curvature comes from the evolution of an electron’s quantum phase through the Brillouin zone of a crystal in momentum space. It impacts the kinematics of electrons for the same reason it impacts interferometry experiments on free electrons; the quantum phase has gauge freedom and is thus usually safely neglected, but relative quantum phase does not, so whenever coherent wave functions are being interfered with each other, scattered off each other, or made to match boundary conditions in a ‘standing wave,’ as in a crystal, we can expect the kinematics of electrons to be affected.

We will shortly encounter a variety of surprising and fascinating consequences of the presence of this new property of a crystal. Berry curvature is not present in every crystal- in some crystals there exist symmetries that prevent it from arising- but it is very common, and many materials with which the reader is likely familiar have substantial Berry curvature, including transition metal magnets, many III-V semiconductors, and many elemental heavy metals. It is a property of bands in every number of dimensions, although the consequences of finite Berry curvature vary dramatically for systems with different numbers of dimensions. A plot of the Berry curvature in face-centered cubic iron is presented in the following reference: We will not be discussing this material in any amount of detail, the only point I’d like you to take away from it is that Berry curvature is really quite common. For reasons that have already been extensively discussed, we will focus on Berry curvature in two dimensional systems. This equation is telling us that in systems with significant Berry curvature, applying an electric field will produce current density transverse to that electric field. It is illustrated in Fig. 3.1 for an isolated electron in a specific Bloch state. Berry curvature has a few general properties that are worth knowing. Kramers’ pairs- i.e., pairs of spin subbands related by time reversal symmetry- must have opposite Berry curvature. As a result, systems that don’t break time reversal symmetry cannot have any net current flow as a result of Eq. 3.3. The equation still applies, but each spin-polarized current density is precisely balanced by the other spin polarization’s contribution. This does not mean that Berry curvature has no consequences in such systems; in these systems, spin concentrates on opposite sides of the system, transverse to the applied electric field. This state of affairs is known as the spin Hall effect, and it is illustrated in Fig. 3.2A,B. In the presence of magnetism, electrons can occupy states with unbalanced Berry curvature, and as a result Eq. 3.3 produces a net current density. The resulting electron accumulation transverse to the applied electric field produces a transverse voltage called the Hall voltage. It is often useful to put contacts on the edges of devices in order to probe this voltage, as illustrated in Fig. 3.1. Of course, the sign of this voltage is a spontaneously broken symmetry, and it follows the magnetization of the magnetic order, as illustrated in Fig. 3.2C-F. It is possible for magnetic insulators to form in systems with bands that have finite net Berry curvature . This produces an extremely special situation, and the bulk of this thesis will be devoted to probing and understanding the properties of these kinds of systems. They are called Chern magnets.Several chapters of this thesis focus on the properties of a particular class of magnetic insulator that can exist in two dimensional crystals. These materials share many of the same properties with the magnetic insulators described in Chapter 2. They can have finite magnetization at zero field, and this property is often accompanied by magnetic hysteresis. The spectrum of quantum states available in the bulk of the crystal is gapped, and as a result they are bulk electrical and thermal insulators. They have magnetic domain walls that can move around in response to the application of an external magnetic field, or alternatively be pinned to structural disorder. And of course they emit magnetic fields which can be detected by magnetometers.