Future research projects could also benefit from a larger sample size to analyze farm-level differences and whole genome sequencing of positive isolates to ascertain any genetic relatedness between livestock species or between farms, as well as presence of antimicrobial resistance genes. In Chapter 2, the risk map determined regions at highest risk for contact between feral pigs and outdoor-raised pigs, and these areas will be important to target surveillance and outreach, in the case of future disease transmission between these two swine groups. This project can be expanded nationwide to create awareness of high-risk contact and potential disease transmission areas, to protect both public health and agriculture in the US. For example, this risk map could be used to plan surveillance programs to prevent transmission of imported diseases to the US, such as African Swine Fever that was recently detected in the Dominican Republic, or prevent the spread of reemerging diseases like pseudorabies, which was detected in feral pig populations in Mendocino County, CA in 2015. A 2015 report by the European Food Safety Authority concluded that surveillance programs will be key in preventing the introduction and spread of ASF in North America and other naïve countries. Additionally, if feral or outdoor-based domestic swine disease location data were readily accessible in the US, additional risk maps could predict the spread of specific pathogens. Disease risk maps are useful to support decision making for agencies focused on wildlife management and conservation as well as animal and public health.
Currently, USDA collects data through surveys for swine operations only in the top 16 producing pork states in the US, and California is not included, raspberry grow in pots which reduces available data for disease risk models. However, the swine NAHMS is due to be conducted again in 2021 with a larger focus on small-scale swine operators. Another avenue for future research entails using covariate rasters from specific years or decades to predict the distribution of a feral pigs. Few SDM or MaxEnt comparison projects have compared temporality of covariate rasters for model prediction accuracy. Shifts in weather patterns, as well as the dynamics of large fires, will most likely be exacerbated by climate change and will affect wildlife movements and locations in the future, which may indicate the importance of choosing temporally-specific variables for model building. Additionally, if climate change and wildfires accelerate in California, current static rasters may become inaccurate in predicting future suitable habitat. California’s annual precipitation levels fluctuate between drought and excessive precipitation associated with El Niño and La Niña events. Climatologists predict volatility of rainfall patterns and temperature for California, which may affect suitable habitat for wild mammals such as feral pigs, and emphasizes the need for dynamic climate rasters. Both the MaxEnt model and risk map in Chapter 2 are limited because they are static maps that used fixed layers as their foundation; consequently, they do not incorporate dynamic events. Future species distribution models could incorporate temporal environmental patterns into models, due to dynamic changes over time, especially in regions like California where climate change and large wildfires can affect the distribution of certain species.
For instance, Snow et al used temporally dynamic prediction models and examined three decades from 1982 to 2012 to report evidence of feral pig expansion due to climate change. Also, feral pigs in Canada build “pigloos” to be able to survive the harsh Canadian winters, expanding their habitat northward. A 2015 study on climate change affecting wild boars in Europe also reported that milder winters allow for expanded abundance of these mammals.. Additionally, feral pigs may migrate seasonally due to food and water availability in California, therefore future projects could incorporate migration patterns and develop risk maps for specific time periods as Lee et al conducted with waterfowl species, although these data are not available yet for most predictors in California. Building real time dynamic risk maps that incorporate remote sensing data, such as satellite information, could be the next step in predicting high-risk disease transmission areas, as built previously for avian influenza by the California Waterfowl tracker. However, tracking birds may be easier than collaring feral pigs. Chapter 3 combined aspects of Chapters 1 and 2, by determining the prevalence of STEC in feral pigs that reside near domestic swine raised outdoors and predicted possible areas of contact between these two swine populations. Both multilevel logistic models in Chapters 1 and 3 identified outdoor-raised livestock with access to wild areas, such as wetlands or forests, as a significant risk factor for the presence of STEC in samples. One possible pathway for shared pathogens in wild areas may be wildlife contaminating food or water, which are then consumed by livestock. Additionally, Chapter 3 results could be improved upon by using WGS to analyze relatedness between feral pig and domestic pig samples. Although the pathway for pathogen spillover can be bi-directional and temporality may be unclear, identifying clusters of shared indicator pathogens is an important next step in analyzing disease risks from feral pigs in California and nationwide. Chapters 2 and 3 analyzed feral pig populations and their risk to farms that raised domestic swine outdoors.
In Chapter 3, 45.45% of survey respondents observed feral pig presence on their farm, with 36.36% stating that feral pigs had direct contact with their domestic pigs in pastures, pens or barns. These results match the risk map from Chapter 2, which overlapped predicted suitable habitat for feral pigs and OPO locations and showed that 49.18% of the 305 OPO identified in California overlapped with suitable feral pig habitat, indicating that spillover of an emerging or transboundary disease is possible, given the correct drivers. We know human or livestock encroaching into tropical forests are drivers for zoonotic diseases such as COVID-19 or Nipah Virus, usually with an intermediate host such as bats. Although emerging zoonotic diseases in many cases originate at the interface of wildlife-livestock-humans, the US is not considered a hot spot for zoonotic disease outbreaks according to Daszak and the EcoHealth Alliance , yet zoonotic pathogen outbreaks can still occur. Possible drivers of disease spillover in the US between feral and domestic pigs raised outdoor include density of animals, shared natural areas between domestic and wildlife and increasing contact between these two growing swine populations. As the number of DSSF farms continues to grow, continued evaluation of risk factors and agricultural management practices that are unique to these small operations will identify additional risk mitigation strategies and develop extension outreach materials to keep food safe from farm to fork and protect California’s agricultural economy. Additionally, as the two parallel trends nationwide of expanding feral pig populations and outdoor-based domestic swine continues, disease surveillance of feral pigs located near outdoor-raised domestic swine is key in preventing transmission of emerging or reemerging pathogens in the future.Encouraged by consumer preference for local foods and willingness to pay more than double the price for local products , both large and small-scale farming is increasingly turning to direct markets through you-pick operations, farm stands, farmers’ markets, 30 planter pot and Community Supported Agriculture . Currently, nearly 7% of U.S. farms are involved with direct marketing with an 8% increase in sales since 2007 . The federal government began tracking the number of farmers markets in 1994 and CSAs in 2007. The number of farmers’ markets has more than doubled in the past decade, rising to 8284 in 2014 from 3706 in 2004 . Local food is also increasingly promoted through food hubs and sales to restaurants and grocery stores . Numerous practitioners of planning, land-use management, policy and economic development encourage local food programming . ‘Buy Local’ campaigns have been codified in every state with branding and are buoyed through formal and informal economic development support in comprehensive planning documents. With its growing popularity, the local food movement is expected to change both consumers and farmers. The movement often emphasizes ‘weak social ties’ created through food as bringing together novel constituents for political persuasion which combines purchasing power with the ‘soft power’ of a social movement. Where markets should emphasize the highest financial returns, economic sociologists have noted their non-economic logic , terming them ‘embedded’ in both geographies and social value systems . Hinrichs states that part of what direct marketing producers sell is “social connection. Local embeddedness itself then becomes some of the value added in the farmers’ market experience” .
Embeddedness describes the noneconomic logic of how markets yoke together two separate geographies through shared economies and social values . This research asks: what is the extent and orientation of embeddedness in the local food system? First, a literature review demonstrates the current understanding in the field and the need for new methodologies to help test theories of embeddedness within local food systems. Namely, the local food movement is expected to transmit values through proximate economic and social networks. But which communities are connected, and across which local marketing strategies? In response to this question, I pilot a method for mapping the local food system socially and spatially. Document review and program director interviews help to verify and explain the findings as well as their consequences for food systems planning and economic development.Local food activists have reconceptualized food supply chains as a means of spatially distributing social values by leveraging economic capital. The values encompassed by the food system are exemplified by the over 300 different labelling schemes which promote fair labor, sustainable land-use, and animal welfare practices to name a few . Yet, only a few global corporations control distribution, connecting consumers to producers . This bottleneck in supply chains reveals an important lever for altering geographies and financing shared value systems. Renting et al. asserts that shortening the supply chain by decreasing the number of intermediaries involved in production, distribution, processing and purchasing should clarify the values and geographies involved. In sum, geographically explicit, personal relationships between producers and consumers are expected to raise awareness about social, economic, and environmental effects of food consumption by tightening feedback loops which concentrate economic and social capital toward values-based goals . Hinrichs cautions that even the shortest supply chains, such as direct marketing from farms to consumers, can have varied power structures. Namely, farmers often travel to cities for farmers’ markets, while consumers travel to farms in which they own a share of the commodities produced in the CSA model. Hinrichs asserts that while both supply chain typologies emphasize direct, local consumer relationships with farmers, the resulting geo-social embeddedness of the network and the values it promotes will fundamentally differ. In addition, local values-based supply chains are not limited to direct-marketing. Nearly 50,000 farms in 2012 sold some or all of their products directly to retail outlets such as restaurants, grocery stores, schools, hospitals, or other businesses that in turn sold to consumers . Intermediaries between farms and consumers can also play important roles in food system-based social change. For example, chefs, like Alice Waters of Chez Panisse in California, are often seen as the forefront of the local food movement where they change consumer demand for certain types of local food. In the process, their search for ingredients resulted in direct contracts with farmers to grow specific products using agroecological methods . Similarly, farm-to-school programming is conceptualized as a means of encouraging healthy eating, transferring farming education to the next generation, and preserving local farming land-uses . Sonnino finds that school food reform in the UK gave small producers access to new income streams while offering students food that is more nutritious. Similar rationales underpin the motivations behind promoting regional food hubs . Planning practitioners have also noted that public procurement anti-hunger efforts that champion local food have had a successful track record of protecting farmland, spurring rural economic development and increasing urban food security in Canada and Belo Horizonte, Brazil . Most importantly, the geo-social embeddedness of food systems may not be driven solely by food purchases. In addition to supplying food, farms serve numerous socio-ecologic functions for urban users and nearby communities . In 2012, over 33,000 farms listed income from agritourism and recreational services such as farm tours, hayrides, school visits, and other activities . A review of the mission and vision statements from 130 nationally accredited farmland preservation agencies notes that ecosystem, social and cultural services are among the top reasons for preserving farmland, ranking far above food supply .