Cannabis Grow

Over this time the deportees’ own understanding of their situation also changed. While at first most considered their experience a personal one, beginning in the 70s there was an increasing recognition among Jews of the collective aspect of the genocide. In her text, Kofman struggles with the ambiguities of public and private identity, and with the seeming paradox of being both Jewish and French.The Spanish Civil War, fought between Republicans and Nationalist rebels from 1936 to 1939, left Spain exhausted, broke and bitter. While the two sides had neither the energy nor the resources to keep fighting, the war continued in some senses many years after its official end. Years after Franco’s Nationalist victory, there were still pockets of resistance in the form of the maquis, guerrilla fighters still living in hiding . Leftist leaders and intellectuals were in exile in Europe and Latin America, especially Mexico. After peace was declared, the victors continued to punish the losers with forced labor camps, imprisonment and executions. Exhumations of Nationalist soldiers and frequent memorial rallies held by the fascists continued to spark violence against the erstwhile Republicans, while the famine years created by Franco’s policy of economic independence also affected the former Republicans more acutely . In fact, Spain’s economic situation was actually worse in the years after the war than during. Rodoreda’s protagonist in El carrer de les Camèlies goes hungry in the postwar years, reflecting a reality for a large portion of Spain’s population. She also becomes a prostitute after her first lover, with whom she lived, is put in prison and then killed after the authorities learn, or claim to learn, that “durant la guerra havia estat d’un comité [republicà]” . [“he’d been on a [Republican] committee during the war” ] Prostitution was a common occupation for single or widowed women, especially Republican war widows, indoor grow trays who could not claim a pension . The Spanish population during the Franco years was ruled by fear. “The continually reinforced message,” writes Antonio Sánchez, “was that Franco and his regime meant peace, while liberal democracy meant chaos and death.

It followed therefore that for Spaniards to enjoy peace, they had to give up freedom, to which they were unsuited” . “Franco’s peace” meant that the population exchanged certain freedoms for the promise that the Civil War would not be repeated. During this time, there was a general feeling in the population that no good could come of talking about politics, or even thinking about it. Sometimes, in the text, the narrator is unable to give an explanation of why something might have happened, giving the impression of a universe in which things happen for no apparent reason. In real life during this time, people could not or chose not to talk about the real reasons for things like the famine, giving a similar impression of a universe in which cause and effect have an uneasy relationship. Memorialization of the Civil War began while Franco was still in power. In 1959 Franco finished construction on the Valle de los Caídos , a monument ostensibly constructed to heal the rift between Republicans and Nationalists after the civil war. Located near the Escorial palace outside of Madrid, the Valle de los Caídos includes a basilica topped with the largest cross in the world. In the surrounding valley, 40,000 mostly Nationalist soldiers are buried. Inside the Basilica are the tombs of Franco and Primo de Rivera, the President of the Falange party executed by the Republicans in 1936. Yet, built partially with forced labor by Republican political prisoners and dominated by massive religious icons, the Valle de los Caídos appears more as a monument to fascist victory than to national healing.2 Today, Spain is deliberating what to do with the Valle de los Caídos. Some wish to incorporate a museum that explains the Franco years and the history of the monument. Artist Leo Bassi has satirically suggested converting the site to “Francolandia” following the model of Disneyland .

Still others, notably those in the conservative Partido popular, wish to leave the site as it is. Rodoreda published El Carrer de las Camèlies in 1966, seven years after work was finished on the Valle de los Caídos. Franco was still in power and would be for another 9 years. In many ways, the war still had not ended. While the country was faring better economically, this success, combined with Franco’s anticommunist stance, made his regime more palatable to powers like the US and Britain, and Spain was attracting tourists . It seemed as though the world had decided to overlook Franco’s oppression if the country were safe, anticommunist and economically and politically stable. Reflecting this time of hopeless stasis, Rodoreda’s protagonist is continually looking backwards, unable to move forward in her life. By the 1960s, however, economic liberalization and the cultural changes brought by Spanish migration to other European countries did begin to loosen Franco’s grip on Spanish culture. Yet even after his death in 1975 it was a number of years before Spain could start a society-wide conversation about the civil war. A “pacto de silencio” [pact of silence] between political parties after Franco died, intended to help the country avoid another civil war, made a national conversation about the past impossible, while also leaving statues and other images of the Francoist regime intact. Tellingly, the first history of the civil war was written by an Englishman, Hugh Thomas3. Spain is still taking first steps toward memorializing the civil war and postwar periods, as well as toward thinking through how and what to memorialize. In 2007 Spain passed the Law of Historical Memory, a law even whose name is problematic that, on the one hand, offers support to those who want to identify those buried in mass graves and, on the other, also calls for the removal of Francoist symbols from public buildings . Memory, it seems, also involves a measure of forgetting. The country is currently in the throes of a “memory boom”—a national hunger for information, testimony and memorials—that France experienced in the early 1970s and, at the same time, is grappling with how to treat the tangible monuments of Francoist rule.

One of the difficulties of creating a monument to a historical moment like the occupation of Paris is how to account for the diverse experiences of those who lived through it, while at the same time creating something that expresses collective experience. The monument must both acknowledge the uniqueness of individual experience and invite or allow a visitor to access those experiences to the extent that he or she can. In Rue Ordener, Rue Labat, Sarah Kofman struggles with the same dilemma. On the one hand, she seeks to avoid making her experience representative of collective experience, since to do so would fail to acknowledge the specificity of others’ individual experiences. On the other hand, a purely personal text would be inaccessible to the reader with whom she would like to share her memories. In expressing this second concern, I am already using a spatial metaphor: Kofman’s concern is with creating a textual space that allows the reader to enter, but that is, at the same time, her private space. Kofman does not write directly about the purpose of her narrative or her vision of it. Her structural and thematic emphasis on space and its permeability, however, vertical grow racks for sale make an edifice of her text. If a monument, in the modern sense of the word, is the combination of memory and space, then we may say that Kofman has written a monument in text. A major, if not the major, theme of Rue Ordener, Rue Labat is the unstable distinction between the public and private realms. On every level of the text, from her depiction of her life on Rue Ordener and Rue Labat to the chapter organization, Kofman closely examines how identity—hers and the reader’s—is not simply a question of how a private “me” relates to the outside world. The structure of the chapters, Kofman’s use of intertextuality and the content of her memoir all work together to create an intersubjective space, a “home” with a door open to the reader. Rue Ordener Rue Labat chronicles Kofman’s experiences as a child during the Nazi occupation of Paris. Rue Ordener and Rue Labat are two streets in the 18th arrondissement of Paris, separated by Rue Marcadet. The 18th had a large population of Eastern European Jews at least since the first pogroms there in 1880. . According to a special census ordered by the German authorities of Jews in the occupied zone, by 1940 the 18th arrondissement had the second largest Jewish population in Paris. and Jarrassé. In Rue Ordener, Rue Labat, Kofman mentions none of this historical context. In this text, Rue Ordener and Rue Labat define spatial and temporal zones specific to Kofman’s experience. Rue Ordener is the spatial representation of her life with her family before the occupation, and, after her father’s arrest and deportation, a symbol of her mother’s influence. It is the zone of Judaism, family and private life. Before his arrest, Kofman’s life on Rue Ordener was conducted in Yiddish and revolved around the Jewish rituals led by her father, a rabbi. Rue Labat, where she and her mother flee to escape the Gestapo and French authorities who had already deported her father, defines the zone of “mémé”, a beautiful gentile woman who hides Kofman and her mother for the duration of the war4. There, life is no longer ordered by her father and the Jewish rituals he conducted. Instead, mémé’s Christian, French way of life structures Kofman’s experience. Rue Labat is the zone of Christianity and life in public: while her mother must hide in a back bedroom, Kofman can leave the apartment on Rue Labat as the supposed Christian daughter of mémé. Over the course of the war, mémé gains a larger and larger place in Kofman’s mind and heart. She showers her with love and attention, even sewing her new clothes. Mémé also introduces Kofman to Jewish thinkers like Freud and Bergson, and, more generally, to an academic and intellectual milieu she did not previously have access to. Despite all of the horrors of the war and occupation, Kofman remembers it partially as an idyll that allows mémé and her to enjoy one another’s company without interference . [Now I even dreaded the end of the war! ] After the liberation of Paris, Kofman returns to life with her mother and siblings and pines for mémé. Life with her mother during this time is difficult. Her mother beats her, withholds food and cuts the electricity at night to prevent her from studying, yet Kofman manages to go off to the university. The text ends with her reflections on mémé’s death. I mentioned above that we may conceive of Rue Ordener, Rue Labat as a meditation on the uneasy relationship between public and private. The sections of bombed-out buildings that appear in the text are emblematic of Kofman’s experience of the dissolution of the distinction between these realms. The next day we went out to see the damage. Almost all the nearby apartment buildings had been destroyed, and the sight of the ruins—only a few sections of wall still standing—made a great impression on me”] A single section of wall shows not just the exterior of the building, but also the interior dwelling. Without the roof and other walls, the private life of an individual or a family is on display, visible from the street. For Kofman in occupied Paris, many of the things that were previously private become public. Religion, for instance, is traditionally a private affair in France. After the revolution, French people became citoyens or citoyennes, shorn of religion and ethnicity in the eyes of the state. During the occupation, this normally private identity was literally brought into the public sphere, symbolized by the yellow stars sewn on Jews’ overcoats for appearance in public. The opposite also occurs in Kofman’s story: what was formerly public becomes private. Eating is a public act, and keeping kosher is an act of community with other Jews. In Kofman’s story, dietary rules become secret, private information. In order to survive, Kofman must not reveal her unwillingness to disobey those rules.

Potentially leachable soil nitrate levels were calculated for each field using nitrate concentrations from the top 15cm at the harvest sampling event, which occurred within the first three weeks of harvest. Though the plants continued to grow for the duration of the harvest, it is unlikely that nitrate from the top 15cm were used due to the soil’s low water content, and no precipitation or irrigation occurred for the duration of harvest. Bulk density in the top 15cm was assumed to be 1.2 g soil/cm3 as experimental bulk density was measured with 1m of soil and likely overestimated the bulk density at the surface of the soil.Soil sub-samples taken from 0-15cm and 30-60cm at midseason were set aside for DNA analysis. In addition to the experimental plots, samples were also taken from both depths at the nearest irrigated crop production areas and non-cultivated soils, such as hedgerows, field sides, etc. . Gloves were worn while taking these samples and the auger was cleaned thoroughly with a wire brush between each sample. Roots were also collected from one plant per plot and were dug out using a trowel from the top 15 cm of soil. These samples were stored on-site in an ice-filled cooler and transferred to a -80 degree C freezer immediately upon returning to the lab . Roots were later washed in PBS Buffer/Tween20 and ground using liquid N.Root DNA was extracted using a NucleoSpin Plant II kit . Soil DNA was extracted using a DNeasy PowerSoil Pro Kit . Two technical replicates were extracted for each sample for a total of 0.5g of soil and 0.2g of roots. All samples were sent to the University of Minnesota Genomics Center for sequencing using ITS2 primers.The ITS2 rRNA region was selected for amplification and fungal community analysis. This region has been successfully utilized in recent AMF community studies.

Though AMF-specific primers exist , we chose the more general ITS2 fungal primers for several key reasons. First, in the field, SSU primers detect more taxa in nonGlomeraceae families but give lower resolution in the Glomeraceae family. Because the four species in our inoculant are in the Glomeraceae family and this family is dominant in agricultural systems and clay soils, cannabis vertical farming we prioritized species resolution in Glomeraceae over other families. More broadly, the higher variability in the ITS2 region can lead to more unassigned taxa, but does not run as much of a risk that distinct taxa will be lumped together. Third, and of particular importance in our root samples, these primers are better able to select for fungal over plant material than other ITS primer options. Finally, ITS2 allowed us to also examine the broader fungal community in our samples, whereas SSU and LSU options are AMFspecific and cannot be used to characterize other fungi.Qiime2 was used for all bioinformatics. Reads without a primer were discarded, and primer/adapter sequences were trimmed off reads using cutadapt. Samples were denoised with DADA2, and taxonomy was assigned using the UNITE version 9 dynamic classifier for all eukaryotes. Taxa outside of the fungal kingdom were removed from all samples and SRS normalization was used to reduce each sample to 7190 reads. 7190 was chosen as a cutoff due to a natural break where no samples fell between 4000 and 7190 reads. Because depths below 4000 retained less than 90% of sample richness, 7190 was chosen, retaining over 95% of richness. The 22 samples out of 301 samples that fell below this cutoff were discarded. These samples included all 5 blanks, 3 samples from field 1A , 4 samples from field 1B , 2 samples from field 2 , 4 samples from field 3 , and 4 samples from field 4 .In addition to the variables of interest, each model had a random effect of field and block within field.

Yields were modeled using the total marketable fruit weight harvested from each plot at each harvest point, while BER was modeled using the proportion of fruits that were classified as non-marketable due to BER from each plot at each harvest point. Yield models and BER models treated weekly harvests as repeated measures, adding random effects of plot within block and harvest number. For hurdle models, random effects were treated as correlated between the conditional and hurdle portions of the model. Because PDW was measured at three time points, the initial PDW model treated the time points as a repeated measure and added a random effect of plot within block. However, given the nonlinear relationship between PDW and fruit quality described by farmers, further models used only PDW at the 6th harvest when fruit quality was at its peak and therefore did not include any repeated measures.The initial model for each outcome variable included plant spacing and PC1 for soil texture , along with PC1 for GWC and PCs 1 and 2 for nutrients at all four depths , as well as the interaction between texture and GWC. In this initial model, only one depth showed a statistically clear relationship with each outcome variable . To improve model interpretability, we then replaced the two PC’s from the depth of interest with the scaled transplant values of nitrate, ammonium and phosphate at that depth, also adding the ratio of nitrate to ammonium and an ammonium-squared term to allow for non-linearities in outcome response to nitrogen levels. Because all nutrient variables had variance inflation factors over 5 in this model , we dropped nutrient PC’s for each depth that was not of interest, leaving only the transplant nutrient values at the depth of interest in the model.

All nutrient VIF values were below 5 in the resulting model. Reported models were run using unscaled nutrient values for ease of interpretation. Transplant nutrient levels were used rather than midseason/harvest both because they are the most relevant to farmer management and because their interpretation is more clear than later time points, when low levels can either indicate lower initial nutrient levels, or that plants have more thoroughly depleted those nutrients.Two fungal community descriptors were calculated for each soil depth and root fungal community: the Shannon index and the count of OTUs in the class Sordariomycetes, which was identified as an indicator of dry farm soils . Counts were scaled, and both community descriptors were added to the final model described in the “Variable selection” section to determine the impact of fungal community structure while controlling for water, nutrients, and texture. Because the metrics between roots and the two depths of soil fungal communities were highly correlated, three separate models were run: one with both fungal community metrics from 0-15cm, one with metrics from 30-60 cm, and one with root community metrics.After preliminary modeling with principal components , cannabis drying rack we determined that nutrients at 60-100cm had a statistically meaningful influence on yields and PDW, while nutrients at 30-60cm showed an influence on BER. We then regressed inoculation and nutrient levels from these depths of interest against each harvest outcome variable–yields, proportion BER and percent dry weight–while controlling for other soil and field characteristics , as well as random effects; see Table 6 and “model structure” above. We also added two fungal metrics to each model . Sordariomycetes counts at 30-60cm, a signature of dry farmed soils, showed a clear relationship with fruit quality, after controlling for all variables in Table 6 . Full results for each model can be found in the supplement. Where indicated, significant and positive coefficients in the hurdle portions of models signify that the outcome is more likely to be zero. Specifically, BER was less likely to occur in plots with higher ammonium levels , and Sordariomycetes counts were associated with plots where no marketable tomatoes were harvested on a given day .Of the AMF taxa that were identified to the species level in soils and roots, none was a species present in the inoculum. After removing samples that did not contain any AMF taxa, PERMANOVAs using Bray distances showed a statistically clear difference between community composition in inoculation vs. control roots but not bulk soils when stratifying by field and controlling for water, nutrients, and texture. No AMF taxa were significantly enriched in the inoculation or control condition. Taken together, these AMF community results suggest that the inoculum shifted the root fungal community at transplant and did not persist in bulk soils for the 9 weeks before DNA samples were taken.A PERMANOVA using Bray distances showed statistically clear differences in fungal community composition in irrigated, dry farm, and non-cultivated bulk soils as well as communities at 0-15cm and 30-60cm when stratifying by field and controlling for water, texture and their interaction, which also significantly differentiated between communities . Though dry farm, non-cultivated and irrigated soils each had more unique taxa than taxa shared with another location, dry farm and non-cultivated soils each had nearly twice as many unique taxa as taxa shared with a single other location, while irrigated soils had more taxa shared with dry farm soils than unique taxa . Abundance analysis showed that there were 466 taxa that significantly discriminated between the three soil locations. We then set the LDA threshold to 3.75 to highlight only the most stark differences, resulting in 13 discriminative taxa . All of the taxa identified as being enriched in dry farm soils were sub-taxa of Sordariomycetes, a fungal class that is highly variable in terms of morphology and function. We therefore identified Sordariomycetes as a dry farm indicator taxa, or a sort of dry farm “signature”.

We included the Sordariomycetes count in models as an indication of how much the soil had shifted towards a dry farm-influenced community . AMF taxa were notably absent as discriminative taxa and PERMANOVA did not show a difference in AMF community composition between the two depths, suggesting that AMF are not limited in their dispersal down to 60cm59.After identifying Sordariomycetes as an indicator taxa for dry farming, we further explored whether multiple years of dry farming enhance soils’ dry farm signature by comparing fields that had not received external water inputs for multiple years and those which had received regular external water inputs the summer prior to the study. The extent to which Sordariomycetes were enhanced was measured by the difference between counts in dry farm and irrigated soils in the study year . We found that fields that had not received regular external water inputs the previous year showed a significantly higher difference in Sordariomycetes counts between dry farm and irrigated soils , indicating that multiple years without irrigation enhance a soil’s dry farm signature.On-farm research across seven commercially managed dry farm fields allowed us to observe tomato, nutrient and soil fungal community dynamics in situ, opening a window into how dry farm systems function on working farms. Given the long-term specialized management that farmers have tailored to their dry farm practice and fields, this on-farm approach facilitated results that reflect this management paradigm across the region and are therefore broadly applicable to dry farm management choices and outcomes on the Central Coast of California.Marketable yields per plot surprisingly did not correlate with plant spacing, which runs counter to current common wisdom in extension publications. Because spacing ranged from 15-48 inches between plants , relatively consistent yields on a per-plant basis contributed to a wide range in yields on a per-area basis . As there are very few irrigated tomatoes in the Central Coast region due to its cool, moist climate, it is difficult to compare dry farm yields to what might be found in an irrigated system in the same region. However, in 2015 , the statewide average fresh market tomato harvest was 39 T/ha, a number that is surprisingly on par with the average dry farm yield in this study . Because there is a clear trade off between yield and fruit quality–the highest yielding fields also had the lowest fruit quality, and increasing ammonium concentrations improve fruit quality while lowering yields–it may be difficult to increase yields above the state average while still charging consumers a premium for dry farm quality.

Amidst an increasingly industrialized food system, farmers and activists the world over have advocated and struggled to move agricultural production towards diversified farming systems. Agroecology–a form of agriculture based in small-scale, thought-intensive, diversified farming systems and the socio-political movements necessary to defend them and advocate for their wider adoption–has emerged as a combination of science, practice, and movement that can lead farming systems towards ecological, economic, and social sustainability. As climate, economic, and political injustices accelerate in the food system, transitions towards agroecology are increasingly urgent; however, these transitions have been slow to gain traction in dominant political and economic regimes. The current era of climate change is creating shocks that open windows for food systems transition, forcing farmers, researchers, and policy makers to consider new approaches to farming and food production. My own work has focused on water scarcity, which is perhaps the most salient climate shock in California where my home institution is located, and a key agricultural concern across the nation and globe. In California, the 2020–2022 drought caused the estimated loss of 15,000 jobs and $3 billion in agricultural output, vertical racking system and followed a similarly devastating drought in 2011- 2016, calling attention to an urgent need to address future water scarcity in the state. Meanwhile, 60% of US farms experienced drought in 2012, with extreme drought in the Midwestern US causing price spikes and yield declines, followed by extensive flooding in 2019.

In response, local, state, and national advocacy groups and policymakers have begun to call for and implement policy with the intention of making farm systems more resilient to water shortages. For example, the Sustainable Groundwater Management Act in California now calls for groundwater basin water budgets to be balanced by 2042; however, there is considerable debate surrounding how to achieve such a goal. Given the complexities of the systems in which these policies operate, implementation can be difficult, and even the best-intended policies can act to either create or curtail opportunities for transitions towards agroecology. In my own work, I have seen climate-motivated policies in the US–in this case the bio-fuel mandate–lead farmers in the Midwest towards degradative soil practices, while farmers in California respond to water scarcity by growing the tastiest tomatoes chefs have ever encountered. As farmers navigate a complex web of physical, biological, political, and economic environments, they arrive at a wide array of outcomes that reflect both a unique local context and influences that act on entire regions and nations. Yet current economic and political structures have overwhelmingly led US farmers to make choices that have moved agricultural towards the input intensive, large-scale production that now defines the country’s dominant agriculture. First, what are the farming practices that actually improve farms’ capacity to adapt to water scarcity without jeopardizing farmer livelihoods?

And second, can policies support an agroecological transition towards these practices that does not allow their cooptation towards an industrial agriculture–and conversely, what policies are leading our country towards input-intensive industrialized systems even in the face of changing climates? These questions play out in many ways across different agricultural landscapes, and I do not begin to tackle them in their entirety. Instead this dissertation explores both of these questions in two distinct systems: large-scale corn-based rotations in the US Midwest, and tomato dry farming in small-scale, diversified operations on the northern edge of California’s Central Coast region. In my attempts to answer these questions, I have tried to use the tools at my disposal to center farmers and their experience, wisdom, and intimate knowledge of the lands they work. From participatory research, to farmer interviews, to simply trying to understand farmers as complex actors in complex systems, my work has led me to see farmers as adept scientists, and I hope to honor and complement their skills with a few of my own. Given farmers’ limited access to time and resources, I have used mapping, lab analyses, field data collection, and statistics to help farmers answer the questions they find most pressing and garner the policy support needed to let diversified farming systems thrive. I begin in my first chapter, Biophysical and policy factors predict simplified crop rotations in the US Midwest, by asking what policy and environmental factors push farmers towards diversifying vs. simplifying their crop rotations in the US Midwest. After the 2012 drought, there is more reason than ever to shift this historically homogenized, highly input intensive agricultural region towards more complex rotations, which promote soil health and stabilize yields in times of environmental stress including drought. However, while soil health benefits give farmers every reason to explore complex rotations, there has been a continued trend towards rotation simplification in the region over the past century.

I therefore explored how policy was reshaping this system, asking how top-down policy pressures combine with biophysical conditions to create fine-scale simplification patterns that threaten the quality and long-term productivity of the United States’ most fertile soils. Given the availability of public, spatially explicit data, I developed a novel indicator of crop rotational complexity and applied it to 1.5 million fields across the US Midwest, using bootstrapped linear mixed models to regress field-level rotational complexity against biophysical and policy-driven factors. The second and third chapters explore water resiliency in California, using tomato dry farming in the Central Coast region as a case study. Dry farming–a management system that relies on diversified farming practices to build soil water holding capacity and fertility–allows farmers to grow crops with little to no irrigation and has quickly garnered interest from farmers and policymakers as an alternative to the irrigation-intensive farming that is nearly ubiquitous in the rest of the state. While dry farming is an ancient practice with rich histories in many regions, perhaps most notably the Hopi people in Northeast Arizona, vegetable dry farming emerged more recently in California, with growers first marketing dry farm tomatoes as such in the Central Coast region in the early 1980’s. In a lineage that likely traces back to Italian and Spanish growers, dry farming on the Central Coast relies on winter rains to store water in soils that plants can then access throughout California’s rainfree summers, indoor grow facility allowing farmers to grow produce with little to no external water inputs. While this system holds great interest and promise for farmers in California, no peer-reviewed research has been published to date on vegetable dry farming in the state. In my second chapter, Deep nutrients and fungal communities support tomato fruit yield and quality in dry farm management systems, I collaborated with farmers to identify and answer key management questions in the dry farm community. This participatory-based process allowed me to build relationships with farmers and begin to coalesce a community of practice that farmers were excited to connect to. As advocacy groups begin to shine a light on dry farming as a potential key to California’s water resilient future, it felt crucial to engage with the farmers who champion this system to collectively come to a deeper understanding of how dry farming functions and the farming practices that can best support its success. Growers were primarily concerned with fruit yield and quality, with fruit quality being of particular interest due to the quality-based price premiums that farmers rely on when growing in a region with some of the highest agricultural land values in the nation.

Managing soils to promote quality and yields presents a unique challenge in dry farm systems, as the surface soils that farmers typically target for fertility management in irrigated systems dry down quickly to a point where roots will likely have difficulty accessing nutrients and water. As deficit irrigation and drought change microbial community composition in agricultural and natural systems, farmers were also interested in how dry farm management might shift fungal communities, and if that in turn would improve tomato harvest outcomes. Beyond general shifts in fungal communities, farmers were specifically curious about arbuscular mycorrhizal fungi inoculants, which are increasingly available from commercial sellers. Recent research has shown that AMF can help plants tolerate water stress, and that inoculation can improve harvest outcomes in some agricultural systems. Farmers therefore wanted to test commercial AMF inoculants’ potential benefits in the dry farm context.It is difficult to imagine what this dissertation would have looked like without the collaboration, mentorship, and friendship of my advisor, Timothy Bowles. Working with Tim has been one of the greatest joys, privileges, and teachers of my career, and his influence can be seen in every corner of the ideas and approaches in these pages. Tim’s example is one I want to follow wherever I go, whether it be his drive to include justice and equity in conversations of science, his thoughtful and generous approach to any collaboration, or his commitment to honoring family, friends, art, and his own wellbeing alongside the demands of an academic lifestyle. My thanks also go to Todd Dawson and Eoin Brodie, who generously served on my committee, leant me all sorts of fun field and lab equipment, invited me to lab meetings, and provided valuable gut checks all along the research process. Todd’s enthusiasm for understanding plant-AMF symbioses has been contagious, and I so appreciate our conversations and the excitement they breathed back into me when I was mired in research logistics. Eoin continues to surprise me with his ability to glance at my results and understand them better than I do, and my work is certainly better for it. Little of this research would have been possible without Jim Leap. As far as I’m aware, Jim knows every dry farmer in the state of California, and he connected me to nearly every farmer I worked with. I’m honored to consider him a friend and a mentor, and delighted every time I get to visit his farm. Jim is limitless in his capacity to teach and learn about diversified farm management, and also in his ability to guide me towards joy in this work. Of course literally none of the dry farm work in this dissertation would have been possible without the brilliant farmers I was able to collaborate with. Though of course I won’t out them all here for privacy reasons, I hope they know that they are both the reason I do this work, and the reason I can do this work. Of all the farms I have gotten to connect to over the course of my dissertation, I want to give Brisa Ranch an extra dose of gratitude. Verónica Mazariegos-Anastassiou, Cole MazariegosAnastassiou, and Claire Woodard have taught me what agroecology can look like, and their farm has been the inspiration for much of the research I have done in this PhD. It was always such a gift to stop by after a long field day and remember what this work is all about. The undergraduates I worked with in the lab and field were also a source of inspiration. Rose Curley, Alex Dhond, Melanie Rodríguez, Javier Matta, and Bethany Andoko were at my side for the work that has built the foundation of my research. Amidst sample collection and analysis that at times seemed interminable, you kept me afloat with your careful diligence and enthusiasm, and allowed me to grow with you as we explored our way through the research process. My gratitude also goes to the many other undergraduates whose work made this research possible: Karly Ortega, Grace Santos, Yordi Gil-Santos, Amiri Taylor, Moe Sumino, Gisel De La Cerda, and Joey Mann. Also at my side throughout this work were the members of the Berkeley Agroecology Lab: Cole Rainey, Kenzo Esquivel, Miguel Ochoa, Paige Stanley, Aidee Guzman, Ansel Klein, Hannah Waterhouse, Janina Dierks, Franz Bender, Maria Mooshammer, Khondoker Dastogeer, Jennifer Thompson, Kait Libbey, and Kangogo Sogomo have created a community that I could rely on, learn from, and grow with. From before day one, Cole has shown up for me as a friend, sounding board, teacher, and mood-lifter, and I can say beyond a shadow of a doubt that the trajectory of my career is better for their influence. Kenzo is a joy to work, cook, organize, and make music with, and his friendship has buoyed me along this ride. Ben Goldstein, though not technically part of the lab, holds a similar place in my heart, and has become an invaluable colleague as well as friend.

Overall, higher wages and employer-provided health care would not only lower state and federal public assistance costs, and allow all levels of government to better target how their tax dollars are used, but it would also rightfully hold corporations accountable to their employees and thus challenge the status quo of federal and state subsidization of corporate profit. Beginning in the late 1990s and early 2000s, as part of the larger shift toward privatizing public assistance systems and putting SNAP benefits on ATM-style Electronic Benefit Transfer cards, large banks themselves have also benefitted from SNAP and other safety net programs. They have done so, in part, by way of the contracts they hold with states to help administer benefits. Specifically, regardless of the actual effectiveness of EBT-based benefits, J.P. Morgan Chase and other banks cover none of the operating and equipment costs, which are instead covered by and split evenly between states and the federal government, while reaping the benefits of large contracts, interest collected on federal reserve money held for government programs, and user penalties including EBT card loss, out-of-network-use, and balance inquiries. According to the Government Accountability Institute, for example, J.P. Morgan Chase made more than $500 million between 2004 and 2012 from the transaction fees of government benefits to US citizens. In New York alone, J.P. Morgan Electronic Financial Services has a nine-year EBT services contract with the State Office of Temporary and Disability Services worth $177 million. Furthermore, according to a 2012 study entitled “Food Stamps: Follow the Money,” the characteristics of such contracts provide other key indices of banking power and profit. The study found that J.P. Morgan Chase held contracts for EBT in 21 states, Guam, and the Virgin Islands, clone rack signaling significant market power and a relative lack of competition.

Contract terms varied widely among states, thus indicating a lack of efficiency and standards as well. Collectively, and perhaps most significantly, banks profits from government programs during both bad and good economic times: during times of economic hardship because more people enroll in assistance programs, and during times of economic strength because rising interest rates mean more profit on the money they hold to distribute to beneficiaries. Furthermore, corporate and banking control and windfall profits—enhanced and secured by neoliberal restructuring—have affected the socio-economic well-being, and thus food security, of low-income communities and communities of color beyond the struggle over wages. The trend toward bio-fuels in particular—shown in Part I to be predominantly a corporate-controlled affair— has had a direct impact on the cost of food. A 2011 Food and Agriculture study concluded that the expansion of bio-fuels production, particularly in the United States with corn-based ethanol, and in the EU with biodiesel, is at fault for the demand shock for cereals since 2000. Such control of demand has had a large impact on tight commodity markets, such as corn. US ethanol, for example, consumes 40% of the country’s corn, and 15% of global corn production. While estimates vary on the impacts, the National Academy of Sciences concluded that 20 to 40% of the global food price increases in 2008 and the growth widespread hunger were due to bio-fuels expansion. Furthermore, other studies have found that each billion-gallon increase in ethanol production yields a 2 to 3% increase in corn prices. Finally, although the Farm Bill originally intended to stave off food insecurity and support the economy, the result has been detrimental to public health. Specifically, the continued subsidization of commodity crops, and re-entrenchment of this system of supports under neoliberal political and economic restructuring, has helped produce the obesity epidemic in the United States As of 2012, for example, 96% of US cropland was dominated by grain and oilseed commodity crops.

Between 1995 and 2010, $16.9 billion in federal subsidies went to companies and organizations that produced and distributed corn syrup, high fructose corn syrup , cornstarch and soy oils. In this light, as of 2012, the United States has the highest global per-capita consumption of HFCS at a rate of 55 pounds per year. Furthermore, as of 2013, 54% of the oil consumed by Americans is soy oil primarily in the form of cooking oil, baked good, and frying fats. As can be expected from mass consumption of these products, the rates of diabetes and obesity in the US have reached alarming levels: more than a quarter of the US population, or approximately 90 million people are obese, and 21 million have diabetes. Moreover, these food-related health challenges disproportionately impact communities of color as follows: Black adults have 47.8% obesity, Latinos/as have 42.5% obesity, and Asian Americans have 10.8% obesity. Significantly, the combination of ease of access, low cost, and negative health impacts of such foods, further harms low-income communities well-being while corporations themselves continue to profit. Conservative politicians and news pundits have maintained an assault on federal anti-poverty and safety net programs, and on SNAP in particular. The attacks on federal anti-poverty and safety net programs have consistently targeted the use of SNAP by such communities by relying upon anti-poor and racist “culture of poverty” stereotypes that readily blame marginalized communities for their social and economic conditions. Leading up to the passage of the 2014 Farm Bill, for example, House and Senate Republicans—both House Republicans inside and outside the House Agriculture Committee— aimed to impose new work requirements on SNAP recipients, under the assumption that those that receive public assistance have no incentive to work; to allow states to require drug testing for SNAP beneficiaries, under the assumption that low-income people and people of color are likely to use that money to purchase drugs, or that their substance abuse is the primary cause of their hardship, not vice-versa; and to ban ex-felons from ever receiving nutrition assistance, under the belief that ex-felons no longer deserve the support of society.

Although the underlying set of beliefs remains deeply embedded within society, many of these provisions were ultimately stripped from the bill and none of those measures were included in the 2014 Farm Bill. The most pervasive myth is that people on SNAP are “not in a hurry to get off,” primarily because of the supposed lack of incentive to work and the ease of profiting off federal support. On the contrary, most SNAP recipients remain in the program for a short period of time until they become financially stable and are able to transition to self-sufficiency, with half of all new participants leaving SNAP within nine months and many others leaving the program once their immediate need has passed. Moreover, as of 2011, many SNAP beneficiaries are already working: nearly 10.3 million working families receive assistance, comprising 36% of the total program enrollment, with more than three times as many SNAP households working as those that rely solely on public assistance for their income. Moreover, 4×8 tray grow according to a 2012 Congressional Budget Office report, SNAP usage is expected to decline between 2012 and 2022, reflecting a potentially improved economic situation and declining unemployment rate. Finally, despite sustained claims of fraud that accompany efforts to cut SNAP benefits, SNAP continues to have one of the lowest fraud rates among Federal programs. According to a 2013 USDA Food and Nutrition Service report, the rate of SNAP fraud has declined from 4% of benefits down to about 1% over the last 15 years. SNAP is among the most widely used anti-poverty programs in the United States and, according to the Center on Budget and Policy Priorities, the second most responsive federal program during economic downturns, only behind Unemployment Insurance . The percentage of the population with income below 130% of the federal poverty line—the income limit for SNAP eligibility—increased substantially during the period of the Great Recession, from 54 million in 2007 to 60 million in 2009, and 64 million in 2011. During this period, the rate of SNAP participation rose among eligible households from 65% in 2007 to 75% in 2010, up to 83% in 2012, with the program expanding at a record pace of 20,000 people per day. By the end of 2014, more than 46 million people, over 14% of all Americans, were using SNAP. SNAP eligibility and use, however, varies significantly by race/ethnicity, with communities of color experiencing the highest rates of eligibility for, and use of, SNAP, particularly during economic downturns. For example, by end of 2009, SNAP was used by 12% of the US population , 28% of all Blacks and 15% of Latinos/as nationwide were using SNAP. On the other hand, only 8% of whites were using SNAP, substantially below the national average. Such trends follow racial/ethnic and economic geographies as well, with SNAP use greatest where poverty and racial/ethnic stratification runs deep. Across the ten core counties of the Mississippi Delta, for example, 45% of Black residents receive SNAP support, while in larger cities such as St. Louis, with a population of 353,064, the percentage of Black residents receiving SNAP support rises to 60%. Even in the largest cities, those with over 500,000 people, such trends remain: white SNAP use peaks at 16% in the Bronx, New York for example, while Black SNAP use peaks at 54% in Kent, Michigan.

Significantly, there are 20 counties across the United States where Blacks are at least 10 times as likely as whites to be SNAP beneficiaries, and 26 counties in the United States where over 80% of Blacks were SNAP recipients. Conversely, there are only 5 counties with more than 39% of white receiving SNAP benefits. The growth of SNAP use amidst the Great Recession has been especially rapid in locations worst hit by the housing bubble burst, and particularly in suburbs across the United States where SNAP use has grown by half or more in dozens of counties. Furthermore, this is the first recession in which a majority of low-income communities and communities of color in metropolitan areas live in the suburbs, giving SNAP and other federal aid new prominence there. The increase in SNAP eligibility and use thus mirrors the impacts of the crisis in housing and employment, and the racialized distribution of impacts of such crises. Specifically, SNAP use was found to have increased by the greatest amount in places characterized by increased poverty, increased unemployment, more home foreclosures, and increased Latino/a populations. A 2012 Congressional Budget Office report confirmed such findings and estimated that although 20% of the growth in SNAP spending was caused by policy changes, including the temporarily higher benefit amounts enacted in the American Recovery and Reinvestment Act of 2009 , the housing crisis and weak economy were responsible for about 65% of the growth in spending on benefits between 2007 and 2011, with the remainder caused by other factors, including higher food prices and lower incomes among beneficiaries. Such has been the case historically: when unemployment rose, SNAP use always did too, signaling how SNAP use has long played a role in alleviating periods of economic distress. As such, SNAP is heavily focused on the poor. According to a 2015 Center on Budget and Policy Priorities report, about 92% of SNAP benefits go to households with incomes below the poverty line, and 57% go to households below half of the poverty line . Because families with the greatest need receive the largest benefits, and because households in the lowest income bracket use twice the proportion of their total expenditures on food than do those households in the highest income bracket, SNAP is a powerful anti-poverty tool. SNAP, when measured as income, kept 4.8 million people out of poverty in 2013, including 2.1 million children, and lifted 1.3 million children above half of the poverty line in 2013. Furthermore, SNAP is also effective in reducing extreme poverty. A 2011 National Poverty Center study found that SNAP, when measured as income, nearly halved the number of extremely poor families with children in 2011 by 48% and cut the number of children in extreme poverty by more than half . That the increase in SNAP eligibility and use during the start of the Great Recession mirrored larger trends in the economy—and was patterned after long-standing racial and economic inequality—signals the need to again assert that the experience of food insecurity is one part of a larger structure that continues to affect the most historically marginalized populations.

The primary purpose of the federal crop insurance program is to offer subsidized crop insurance to producers who purchase a policy to protect against losses in yield, as well as crop revenue and whole farm revenue. Significantly, more than 100 crops are insurable. The 2014 Farm Bill increased funding for crop insurance, primarily for two new insurance products: the Stacked Income Protection for cotton and the Supplemental Coverage Option for other crops. Ultimately, with the decline in projected spending for Title I , and the increase for Title XI , the 2014 Farm Bill underwent a decline of $8.59 billion in spending on the farm “safety net.” Under the Farm Bill, the miscellaneous title includes various provisions affecting research, jobs training, and socially disadvantaged and limited resource producers, as well as livestock production and oil heat efficiency, among other provisions. The 2014 Farm Bill extended authority for outreach and technical assistance programs for socially disadvantaged farmers and ranchers, expanded support for military veteran farmers and ranchers, and created a research center to develop policy recommendations for socially disadvantaged farmers and ranchers. Finally, it reauthorized funding for the USDA Office of Advocacy and Outreach for socially disadvantaged and veteran farmers and ranchers, and mandated receipts for service or denial of service in order to increase transparency.CORPORATE POWER HAS LONG PLAYED A ROLE in the institutions, processes, practices, plants racks and infrastructure that make up the US food system: how food is produced, processed, distributed, and consumed.

Part I provides a snapshot of the state of corporate consolidation and control in the US food system then addresses the history of the US food system with regard to the relationship between the federal government, corporate consolidation and control, and structural racialization: first, from the 1930s to the 1950s with the Great Depression and New Deal farm programs; and second, from the 1950s to the late 1970s with the erosion of such programs. It then addresses the emergence of neoliberal economic and political restructuring in the late 1970s and early 1980s—characterized by privatization, free trade, deregulation, and cuts in government spending in favor of the private sector—and the emergence of the neoliberal corporate-controlled food system. Part I then elaborates upon two major domains within which corporate influence under neoliberalism remains particularly salient. The first domain is that of food production, processing, distribution, and service—with such influence exerted by way of commodity support and crop insurance programs, labor regimes, and international food aid. The second domain is that of education, research, and development—with such influence exerted by way of lobbying efforts, private funding, strategic mergers, and the “revolving door” between corporate employees and government officials. Significantly, corporations continue to exert such influence via lobbying efforts, private funding, strategic mergers, and the “revolving door” between corporate employees and government officials. Ultimately, Part I argues that the Farm Bill, from the first Farm Bill in 1933 to the Farm Bills of the 1980s onward, is defined by the long term shift from the subsidization of production and consumption to the subsidization of agribusiness, and that low-income communities and communities of color have been structurally positioned on the losing side of such shifts. It is important to note that corporate consolidation and corporate control are two related, yet different, phenomena.

Corporate consolidation can take the form of horizontal consolidation, which refers to the consolidation of ownership and control within one part of the food system, such as production, processing, or distribution; or vertical consolidation, which refers to the consolidation of firms at more than one part of the food chain, such as upstream suppliers or downstream buyers. The term “agribusiness” is often deployed in reference to corporations that exhibit one or both sets of processes within the food system. Corporate control, however, refers to the control of political and economic systems by corporations in order to influence trade regulations, tax rates, wealth distribution, among other measures, and to produce favorable environments for further corporate growth. It should be noted that corporate consolidation is a prerequisite to corporate control. In other words, it can be looked at as a two-part process: once corporate consolidation has been achieved, corporations are much better suited to assert their control over political and economic systems as they have little competition in their respective sectors and industries. Thus, as Susan George states: “It is not just their size, their enormous wealth and assets that make the [corporations] dangerous to democracy. It is also their concentration, their capacity to influence, and often infiltrate governments, and their ability to act as a genuine international social class in order to defend their commercial interests against the common good.” A New and Changing Farm Bill: Toward Low Prices and Big Buyers The period of agricultural policy between the 1930s and the 1950s was greatly informed by the Great Depression—a consequence of the stock market crash of 1929. The crash marked the disruption of capital accumulation in every sector of the economy, including agricultural production. During the 1930s, the massive drought and soil erosion that characterized the Dust Bowl intensified the impact of the Depression upon agricultural production and had far-reaching social, economic, and environmental consequences.

The Dust Bowl affected over 100 million acres and prompted the largest migration in US history within a short period of time. Approximately 3.5 million people moved out of the Great Plains states in search of work between 1930 and 1940. Pressured by the need to support remaining farmers and thwart massive farm loss, Congress passed the New Deal-era 1933 Agricultural Adjustment Act, which aimed to raise the value of crops and reduce crop production and surplus. The 1933 Farm Bill reduced agricultural production by paying farmers subsidies not to plant on part of their land and to kill off excess livestock. However, the goal of agricultural policy did not remain tied to the support of production. Rather, by the end of the 1940s, “doctrine of parity” set standards for commodity prices and undergirded the 1941 Steagall Amendment, the Agricultural Acts of 1948 and 1949, and the permanent funding of the Commodity Credit Corporation . For the next few decades, particularly between the 1950s and 1970s, agricultural production was characterized by high-yielding varieties of a few cereals , the heavy use of subsidized fertilizers, pesticides, irrigation and machinery, and their global proliferation under the “Green Revolution.” Furthermore, from 1952 onward, the “parity” farm programs of the New Deal era were eroded, as price floors were lowered and supply management was reduced. Beginning in 1973, policy changes during the Nixon Administration precipitated the drastic deregulation of the corn market in particular by dismantling New Deal era supply management policies, selling off federal grain storage reserves, and implementing “fencerow to fencerow” planting, plant growing trays ultimately promoting overproduction and the consolidation of farm operations. Simultaneously, the system of loans and land idling schemes that supported farmers was replaced with a system of direct subsidies that supported low prices for corporate purchasers by encouraging farmers to sell crops at any price and ensuring that direct payments from the government would make up the difference. Ultimately, these changes not only reflected and upheld corporate consolidation and control, they also resulted in massive farm loss: the number of farms decreased from 7 million in 1935 to 1.9 million in 1997, with the greatest drop occurring from 1935 to 1974. The changes from both the 1930s to the 1950s, and the 1950s to the 1970s, were tied to corporate power, as reflected by several key moments in the history of the Farm Bill. First, the money for production subsidies under the 1933 Farm Bill was originally generated by way of an exclusive tax on corporations that processed farm products. Yet, according to the 1938 Supreme Court case, United States v. Butler, the act’s tax provision unfairly targeted corporations and was thus deemed unconstitutional. Subsequently, under the 1938 Farm Bill, the federal government, and not a processor’s tax, would finance such subsidies, thus relieving corporations of any responsibility to maintain high commodity prices or profitable farms. Significantly, this funding structure was held in place during the shift in agricultural policy from the support of production to the support of prices by way of the doctrine of parity. The ongoing erosion of the doctrine of parity from 1952 onward, which included the lowering of price floors and reduction of supply management practices, sent farm prices crashing and ushered in a period of agricultural policy driven by agribusiness. Specifically, corporations such as Archer Daniels Midland and Cargill were instrumental in helping replace New Deal-era loan programs and land-idling arrangements with direct subsidies that supported low prices for corporate purchasers themselves.

Anticipating the 1973 Farm Bill, for example, and alongside Secretary of Agriculture, Earl Butz, Cargill and the Farm Bureau argued that crashing farm prices would be a plus. They argued that not only would greater exports and new uses such as ethanol and sweeteners remedy the drop in price, but also that farms would remain profitable with the support of government subsidies. The winners and losers were clear under such policies: corporate buyers could acquire commodity crops for record low prices that were subsidized by the federal government while farmers continued to lose their lands and their income. Such policies, furthermore, constituted part of the larger trend in corporate growth, not limited solely to agribusiness. For example, according to a 2013 Bureau of Economic Analysis, corporate profit as a percentage of GDP more than doubled between 1980 and 2013, rising from less than 5% to over 10%; before tax, corporate profit, as a percent of GDP, rose from less than 8% to over 12.5% between 1980 and 2013. Both periods, from the Great Depression and New Deal farm programs, to their erosion over the following decades, were characterized by structural racialization. Although New Deal-era legislation was geared toward pulling Americans out of poverty, it was itself a project of racial exclusion, with Black communities and other communities of color systematically barred from such supports. Southern committee members in Congress, for example, blocked efforts to include agricultural workers and domestic workers in the Social Security Act—the New Deal’s centerpiece legislation—largely because of the high concentration of black workers within those lines of work. In the 1930s, 60% of Black workers held domestic or agricultural jobs nationally while, in the southern United States, domestic and agricultural occupations employed almost 75% of Black workers, and 85% of Black women. Furthermore, although the National Recovery Administration set wages within the cotton industry at $12 a week, many Black workers had jobs that were not covered by the law and thus had their wages reduced by employers so that white workers could be paid more. Finally, Black agricultural workers were also left out of New Dealera agricultural union programs—namely the National Labor Relations Act, enacted and signed into law on July 5, 1935—while Black landowners in particular were excluded from federal farm support under the Agricultural Adjustment Administration. Significantly, the distribution of federal support during this period resulted in the dramatic decrease in the number of Black farms, from about 900,000 in 1930 to 682,000 in 1939. Although these programs were slowly eroded over the next few decades, farmers of color continued to face great hardship relative to white farmers. The period of agricultural mechanization and industrialization after World War II, marked by the widespread adoption of scientific and technological innovations is usually credited with weeding out supposedly “non-productive, inefficient” farmers. Yet farmers of color and particularly Black farmers, in the context of the uneven application of New Deal era supports and years of discriminatory practices, were at a great disadvantage during this period because they were prevented from attaining the requisite access to capital and thus economic stability for such a transition. From the late 1970s and early 1980s until today, corporations have taken on a new and more deeply entrenched set of relationships within the food system. In short, this period is defined by neoliberal capitalist expansion and corporate control that began with the global economic shocks of the 1970s and 1980s. During the 1980s, and working for the interests of multinational corporations in securing markets abroad for agricultural commodities produced domestically, Structural Adjustment Programs broke down foreign tariffs, dismantled national marketing boards, and eliminated price guarantees in the Global South. Alongside this destructive guarantee of foreign markets, the 1950s-onward trend of dismantling domestic safety net programs for farmers, guaranteeing low prices for commodity purchasers , and making up the potential loss for farmers with government direct payments continued.

Listing a female farm operator among all the farm operators may be at least correlated with a willingness to adopt new technology, diversify sales, or increase vertical integration on the dairy farm. This is a feasible hypothesis because the presence of a female operator may indicate that the farm is more open to change than many peers in the industry. Part-time farming is common in crop and beef cow-calf operations, whereas commercial dairy farm operators tend to be full-time operators. Also, in the dairy industry, a female operator of dairy farms is likely to be married to a principal operator. Having both spouses as farm operators likely implies less off-farm income and, therefore, higher financial reliance on the dairy farm’s success than for families with more diversified income sources. Moreover, dairy farms tend to have more concentrated farm incomes with crop and dairy enterprises vertically integrated rather than the diversification common among crop farms. This changes the incentives of the spousal operators to remain economically viable because it likely increases risk aversion leading to diversification of sales and mitigation of feed price volatility risk by increasing economies of scope. The COA finding of an increase in the share of women dairy operators and farms with women operators reflects three things: an actual increase in women operators playing a more prominent role, their male associates being more likely to recognize and report female operators, weed drying room and changes in COA questions that better collect previously unmeasured management activity by women.

The increase in the share of female dairy farms must be considered against the broader pattern of dairy farm consolidation, changes in dairy farm size distribution, farm characteristics, and geographic shifts . This research seeks to provide statistical evidence of differences in farm size of dairies operated by dairies with at least one female operator relative to all male operators, the share of female operators, and those operated by spouses. By considering farms with at least one female operator and/or married operators as a “treatment” group, I compare the herd size, milk or dairy sales, and total value of production, between the two treatment groups, while holding location and year constant. This chapter is structured as follows: a brief overview of previous literature on the intersection of women and agriculture, a description of COA data related to women and farm operators, a discussion of statistics, empirical method, and results, and then a brief conclusion. Research on the intersection of women and agriculture has tended to be limited in scope and by academic discipline. Previous research on the topic from an agricultural economic perspective has focused on the intersection of women and agriculture in developing countries or limited its analysis to some demographic statistics on female farm operators without much commodity distinction within the agricultural industry. Industry distinction is important because of generally held assumptions about particular commodity farms, including that dairy farms are run by spouses. Moreover, although there have been many anthropology and sociology research studies that have been done on the intersection of women and agriculture in both developing and developed countries, these have tended to be on a case study basis that are limited in geographic scope. I found little empirical agricultural economics research on the patterns over time and across states of female farmers, and I found no prior research on the economics of patterns of female operators in the dairy industry, specifically.

A recent article by Schmidt et al. summarizes the current literature on the intersection of women and agriculture, specifying that most economic literature on this subject focuses on developing nations. The article calls for further research on this topic to further characterize the change in gender demographics and collect information on influences in the economy that may have impacted or continue to impact the number of female farm operators in agriculture. Schmidt et al. outline three possible influences on the share of female farmers, including push-pull factors, characteristics of local agriculture, and the type of farming practiced. Push-pull factors refer to the influence of off-farm employment wages that may influence an individual’s decision to be an entrepreneur or push them to seek off farm employment. For this analysis, this influence could be considered on an individual basis or at a spousal level. The change incentives when both spouses’ incomes come from farming could change and push or pull one or both spouses into off-farm employment or to stay on the farm. Characteristics of local agriculture describe the general state of the region’s agricultural economy. This is accounted for by holding constant location and presenting statistics by state. Finally, Schmidt et al. suggest the influence that the type of farming might have, or farming characteristics may influence, as their results find that farms run by women tended to be smaller. There is some association of dairy farms being family-run, or spousal run, this claim is one that we provide evidence on for the dairy industry, specifically. The characterization of such influences provides insight into the possible impacts of female representation on farms across different industries. Again, the agricultural economic literature on the intersection of gender and agriculture has tended to be limited to developing countries. However, in a recent article by USDA Economic Research Service , ERS released statistics about the characteristics of U.S. female-run farms and female operators based on the 1978 to 2007 COA . Their results focus mostly on statistics of characteristics of overall U.S. female-run farms and female farm operators.

They find that 58% of all female operators have no reported off-farm labor, and that female operators of dairy farms tend to be younger than the U.S. female operators’ average age. Griffin et al. utilize the COA data over five Census rounds and discuss the impact of farm operators’ demographics on farm exit rates. They find that larger farms are less likely to exit, and those female operators are more likely to exit than male operators. However, their study includes all farms with no industry limitations. Furthermore, research on female operators’ impact and representation within the dairy industry is a point of interest because, historically, it was not uncommon for dairy farms to be run by spouses and because off-farm employment is less likely on a dairy farm than it is on other farms. Sander finds that women working on dairy farms tend to have less off-farm employment than other farm types. He outlines the role of income variability on farms run by spouses’ decision to be both spouses’ main income with off-farm work as a possible risk mitigation strategy for farms run by spouses when farm revenue is highly variable. Schultz detailed some economic theories related to women focusing mainly on developing nations. Specifically, drying rack for weed the role of family dynamics in economic choices on farms and female influence on such outcomes. Rather than taking a theoretical approach, Zeuli and King provide detailed statistics of the characteristics of farmers and their commercial farms in 13 states. They find that in 1991 the average age of females relative to males is insignificant, but that the women in their sample tended to have a higher level of schooling. Interestingly, they found contradicting results, at least based on acreage, to other studies stating that women tend to manage smaller farms, with women operating more acreage on average, but this could be heavily influenced by what they grow and location. Sociology and anthropology research on female farm labor and agriculture tends to report findings based on case studies of specific regions and industries . These papers tend to discuss social incentives, norms, or barriers that influence the gender demographics of the industries of interest and, therefore, influence female representation and the impact of management decisions on the farm. Brasier et al. discuss the history of how women identify their labor on farms. Historically, female participation in farming communities was accessed through family or marriage. Typically, women involved in agriculture were either born into a family that farmed or married a farmer. In the past women often viewed their role on the farm as farm homemakers or farm helpers, following gender norms of the times, and often because they had off-farm income or only participated in farm labor seasonally . This way of thinking about farm labor could have influenced the representation of female operators of farms. Other sociology research has documented trends in farm management through case studies on regions. Trauger finds that women are more likely to adopt sustainable agriculture. Trauger limits its scope to a few farms in Pennsylvania, finding that there may be a trend of female-operated farms to adopt socially minded practices, i.e., community education. This research helps build evidence that supports our claim that the presence of female operators can be considered a proxy variable for being adaptable to change.

It seems like a basic assumption, but there was, and remains, a large share of women that participate in farm labor that were/are married to principal operators; this trend continues today. Therefore, the research on the relationship between gender and agriculture would not be complete without mentioning research done on agricultural spouses. A large share of female operators are the spouses of a farm operators. Barlett details the typical marriage models of agricultural spousal relationships, characterizing how farm labor related to agricultural spousal relationships is defined from a social perspective and may have influenced how women viewed their labor on the farm and subsequently the data representing farm labor, historically. The role of identity for female farmers and the professional connections can be a pivotal part of female farmer participation. This research provides evidence of the change in gender demographics based on farm size for the dairy industry. It adds to the literature detailed agricultural economic analysis on the intersection of women and agriculture for the dairy industry and discusses the change in data collection and availability by one of the most prevalent data sources for agricultural data, the COA. The survey questions asked of farmers and ranchers by the COA change slightly every Census round, although most remain the same across time. Below are descriptions of questions changes for relevant variables to the analysis. First, in 2002 and 2007, farms were asked for the total amount of dairy sales in that year, but in 2012 and 2017, this question was dropped and replaced with the total amount of milk sales. Furthermore, whether the dairy farm had any level of organic production was only asked 2007, 2012, and 2017. Second, operator characteristic questions have become more detailed over the years and allowed more operators’ data to be collected. In 2002, 2007, and 2012, the COA asked detailed operator characteristic questions about up to three operators, and only one operator was able to be identified as the principal operator. However, in 2017, the COA expanded its detailed operator questions to include up to four operators and now allows for up to four operators to be identified as a principal operator. Furthermore, in 2012, the COA started asking farmers and ranchers if the secondary operators were married to the principal operator. This question was then adapted in 2017 to reflect the increase in possible principal operators identified and asked if the operator was married to a principal operator. The Census collects two categories of operators. The first category is for which detailed operator characteristics and for which at most three operators are listed per farm in 2002-2012 and at most four operators per farm are listed in 2017. Going forward, the operators for which the number per farm is limited and detailed information is provided will be referred to the “core operators”. The second category has no limit to the number listed per farm and only gender of each operator and the number per farm is provided in the data. This section detail statistics and characteristics of female commercial dairy farm operators and their commercial dairies. The number of commercial dairies with at least one female core operator increased in every state, except New Mexico, which experienced no change from 2002 to 2017 . In 2017, every state, but New Mexico, has more than 40% of the commercial dairies reporting at least one female core operator. Although these states demonstrate significant increases in the representation of female core operators, the addition of a fourth core operator for the 2017 Census could distort these results.

Consolidation may have allowed dairies to capture improved productivity and efficiency on the farm. How dairy farm size changes in response to these and other factors are important in considering future trends in farm size and their impact on milk production in the United States. My research seeks to help explain recent patterns of farm size change in the dairy industry, considering trends in operator characteristics and management, while accounting for regional differences. The share of women dairy farmers has increased. Historically, farming has been a stereotypically male occupation. Despite contributing to farm production and farm management, surveys, and censuses, have been limited in their collection of data on the contributions of women as farm operators. I hypothesize that some of growth in female contribution to farm operation is due to changes in social and gender norms in reporting. One contribution of my research is to attempt to separate, to the extent possible, changes in management and operations on dairy farms from how such activities are reported. Demographic trends in farm operation and management are important because they help researchers and policy makers get a better sense of who runs the operations in an industry by age, gender, indoor grow cannabis and other characteristics. The dairy industry remains predominately male. However, since 2002, there has been a substantial increase in the share of women dairy farm operators and an increase in the absolute number of dairy farms with at least one female operator in many places.

The share of commercial dairies with at least one female core operator has increased across all states, except New Mexico. New York saw the largest increase in the share of commercial dairies with at least one female core operator from 36% to 55%. California saw a 40% increase in the share of commercial dairies with at least one female core operator. This trend, which has occurred while dairy farm consolidation has proceeded at a similar pace suggests that the participation of female dairy farm operators may positively affect dairy farm herd size and economic viability. As noted in the previous chapter, for the statistical estimation in the thesis I will utilize data for the USDA COA. Under “Census of Agriculture Act of 1997”, The COA is a federally mandated Census of all U.S. farms and ranches every five years, and it captures individual farm-level data on production costs, operators’ characteristics, land use, number of milk cows, revenue, etc. The data and statistics resulting from this Census are reported at the county or state level and research using the individual level data is restricted to USDA research or special request for non-USDA entities. I was given special permission to have access to individual farm-level data for census years of 2002, 2007, 2012, and 2017 from the following specified states: California, Idaho, New Mexico, New York, Texas, and Wisconsin. The National Agricultural Statistics Service , which conducts the Census, attempts to gather responses from every farm in the United States, where a farm is defined as, “is any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.” NASS uses a complex sampling procedure that starts with the Census Mail List .

The CML is a mailing list of all potential U.S. farms, as defined by UDSA, The CML is built and improved upon using outside sources, from government lists or different agricultural producer lists. When new names, of potential farms, are discovered they are then treated as a potential farm and added to the CML until the farm is found to not meet the USDA definition of a farm. From there Census data is collected by mail or Computer Assisted Self Interview on the Internet. The respondents submitted one of four different forms: general, short, Hawaii, or American Indian form. The COA data that I use can be described as unbalanced panel data with both attrition and replacement and with occasional errors in recognizing continuing cross-section units. Although the data used is at the individual farm level, no data presented in this thesis reveals any information concerning an individual farm or person. All present research has been subject to a disclosure review and all research using COA data follows the following guidelines, “In keeping with the provisions of Title 7 of the United States Code, no data are published that would disclose information about the operations of an individual farm or ranch. All tabulated data are subjected to an extensive disclosure review prior to publication. Any tabulated item that identifies data reported by a respondent or allows a respondent’s data to be accurately estimated or derived, was suppressed, and coded with a ‘D’. However, the number of farms reporting an item is not considered confidential information and is provided even though other information is withheld.” The survey questions asked of farmers and ranchers by the COA change slightly every Census round, although most questions remain the same across rounds. Below are descriptions of questions changes for relevant variables to the analysis. First, in 2002 and 2007, farms were asked for the total amount of milk or dairy sales in that year, but in 2012 and 2017, this question was dropped and replaced with the total amount of milk sales. Second, operator characteristic questions have become more detailed over the years and allowed more operators to be captured by the Census.

In 2002, 2007, and 2012, the COA asked detailed operator characteristic questions about up to three operators, and only one operator was able to be identified as the principal operator. The COA defined a principal operator as “… the person most responsible for making day-to-day decisions on the farm, during the data collection process.” Whereas an operator is defined as “A farm operator is a person who runs the farm, making day-to-day management decisions. An operator could be an owner, hired manager, cash tenant, share tenant, and/or a partner. If land is rented or worked on shares, the tenant or renter is an operator.” However, in 2017, the COA expanded its detailed operator questions to include up to four operators and now allows for up to four operators to be identified as a principal operator. The definition of principal operator is “Demographic data were collected for up to four producers per farm. Each producer was asked if they were a principal operator or senior partner. A principal operator is a producer who indicated they were a principal operator. There may be multiple principal producers on a farm. Each farm has at least one principal producer.” Whereas operators were defined as “A non-principal is a producer who did not indicate they were a principal operator. There may be no non-principal producers on a farm.” Furthermore, in 2012, curing cannabis the COA started asking farmers and ranchers if the secondary operators were married to the principal operator. This question was then adapted in 2017 to reflect the increase in possible principal operators identified and asked if the operator was married to a principal operator. For our further data analysis, I consider farms that have substantial dairy operations. I want to leave aside those operations that meet the USDA definition of a farm but have minimal connection to commercial dairy production and revenue. To be included as a commercial dairy operation in our sample requires meeting two minimum criteria. First, the farm must have dairy or milk sales revenue above the dollars of milk sale revenue that would have been generated by 30 milk cows. Second, at least 20 milk cows were on the farm as of December 31 in the Census year. This minimum was set to remove dairy farms that had “exited” and had already removed most of their cow from the farm but still had milk or dairy sales revenue for the year above our minimum criteria. For this research I choose to analyze dairies from six states, California, Idaho, New Mexico, New York, Texas, and Wisconsin. These six states were chosen for my research because they capture the major the U.S. commercial dairy industry and therefore are a useful and representative characterization ofU.S. dairy farming. The following Chapter details the reasoning behind the selection of the six states and characterizing the U.S. dairy industry on the whole. The six states used in this research were selected because they capture a significant share of the U.S. dairy industry and reflect the overall trends. Figure 3.1 shows that these select six states make up the majority share of the total number of milk cows in the United States.

These six states made up almost 55% of total U.S. number of milk cows in 2017 and demonstrated an increasing trend in share of U.S. number of milk cows since the 2002. Figure 3.2 shows that these six states also make up the majority share of milk sales revenue in the United States, with Texas and California making the largest shares in the group. The six states are the leading milk producers in the United States. Although, there are significant differences between each state that I will discuss below, including differences in herd size trends. They represent the majority of the dairy industry, by multiple measures, and they are and representative of national distribution. As discussed in Sumner and Wolf the Eastern states are characterized with many smaller dairies than the other states, including New York and Wisconsin. Whereas, Pacific and Southern states such as, California, Idaho, and New Mexico and Texas , tend to have dairies with larger herd sizes. From 2002 to 2017, California saw a 36% decrease in the number of commercial dairies and a slight larger percentage decrease for farms with milk and/or dairy sales and farms with milk cows. Idaho saw its largest decrease in the number of commercial dairies with farms with milk or dairy sales close behind. However, farms with milk cows only decreased by 20% in Idaho. New Mexico had a very slight 3% increase in the number of farms with milk cows, but a 21% decrease in the number of farms with milk and/or dairy sales and a 26% decrease in the number of commercial dairies. New Mexico saw the smallest percent decrease in commercial dairies from 2002 to 2017 of any of the six select states. New York had about a 50% decrease in the number of commercial dairies and about a 40% decrease in the number of farms with milk cows and farms with milk and/or dairy sales. Texas had the largest percent decrease of across all definitions of dairies. Texas saw a 56% decrease in the number of commercial dairies and 60% decrease in the number of farms with milk and/or dairy sales. Interestingly, there was a 78% decrease in the number of farms with milk cows in Texas which is a significant decrease. Across all three definitions of a dairy Wisconsin had very similar trends with about 46-49% decrease in the number of dairies. In California, there tends to be an increase in larger herd sizes. Figure 3.3 shows the number of farms with milk and/or dairy sales in California for the four Census years and the share of farms with milk or dairy sales by herd size. California saw significant decreases in the smaller herd sizes. Figure 3.4 shows the share of all milk or dairy sales and number of farms with milk or dairy sales by herd size for the state of California. From 2002 to 2017, the share of revenue generated by smaller herd sizes has decreased significantly. The majority of the share of milk or dairy sales revenue has come from dairies with 1,000 or more milk cows and this share has increased to over 80% in 2017. Idaho follows a similar trend as California, Figure 3.5 shows a significant decrease in the smaller herd sizes and growth in the larger herd size groups. Furthermore, between 2002 and 2017 there was significant increase in the share of milk and/or dairy sales from farms with herd sizes greater than 1,000 milk cows . The share of sales revenue from farms with herd size smaller than 499 milk cows fell from about 15% in 2002 to less than 10% in 2017.

Following the farm financial crisis of the 1980s, land prices in the County sharply dropped ; this economic window provided an opportunity for a new generation of farmers to insert a more ecologically-minded approach to farming. Many of these farmers arrived to Yolo County relatively new to farming – often young, educated white urbanites with a desire to farm alternatively to the industrial agribusinesses that had historically dominated the landscape of Yolo County since the early 1900s . Informed by the organic movement, these so-called “back-to-the-land” farmers established innovative, high-value, diversified farms that still exist today. As diversified fruit and vegetable farmers, their approach to farming necessitated learning about and working with their local landscape and ecology . Upon arrival, many were particularly interested in soil fertility and made a conscious effort to avoid “mining the soil” and address ongoing issues with soil degradation in agriculture . While initially these back-to-the-landers lacked historically- and ecologically-specific knowledge of the lands they cultivated , over the last three decades or more, it is highly probable that they have individually amassed a wealth of local, place-based knowledge of their specific management contexts and soil landscapes . To our knowledge, farmer knowledge of local soil landscapes and related soil management practices remains entirely undocumented in Yolo County. Yet, the unique historical and ecological context makes farmer knowledge of soil health and soil management in this region especially important to document; this knowledge is potentially foundational as organic farmers adapt their farming approaches and management in the face of increasing social, economic, vertical grow shelf and environmental uncertainties.

Though many organic farmers in Yolo County are informed by principles of alternative agriculture when managing their soils, it is less clear how these particular farmers have translated their local knowledge of soil into practice and the substance of the soil management practices applied. To address this gap, we used an exploratory approach and examined local farmer knowledge of soil and practical knowledge of soil management in this region. Our objectives were to: 1) Understand how farmers acquire local knowledge of their soils; 2) Document what organic farmers know about their soils; and 3) Determine how these farmers translate this local knowledge into specific management practices related to soil health and on-farm resilience.This research is informed by a Farmer First approach, which recognizes farmers as experts and crucial partners in researching and innovating solutions for resilient, alternative agriculture . The Farmer First approach recognizes multiple knowledge forms and challenges the standard “information transfer” pipeline model that is often applied in research and extension contexts . We used an open-ended, qualitative approach that relies on in-depth and in person interviews to study farmer knowledge. Such methods are complementary to surveys that use quantitative methods for capturing a large sample of responses . Because they are more open-ended, qualitative approaches allow for more unanticipated directions ; however, as Scoones and Thompson point out, removing local knowledge from its local context and attempting to fit it into the constrictive framework of Western scientific rationality is likely to lead to significant errors in interpretation, assimilation, and application.

While interviews are not able to capture the quantity of farmers that surveys do, in-depth interviews allow researchers to access a deeper knowledge base that has inherent value – despite limitations in scalability and/or transferability – as participants respond in their own words, using their own categorization, and perceived associations . Such in-depth interviews are therefore essential to research on farmer knowledge and local knowledge .This research was part of a larger project examining soil health on working organic farms in the region. To identify potential participants for this study, we first consulted the USDA Organic Integrity database and assembled a comprehensive list of all organic farms in the county . Next, with input from the local University of California Cooperative Extension Small and Organic Farms Advisor for Yolo County, we narrowed the list of potential farms by applying several criteria for this study: grow fruit, vegetables, and other diversified crops ; located in Yolo County; at least 10 years of experience in organic farming; and, at least five years of farming on the same land. We chose to focus on diversified fruit and vegetable farmers specifically because of the high demands on soil health and management necessary to farm fruits and vegetables alternatively . We placed minimum limits on farmer experience and time on a particular piece of land in order to ensure farmer knowledge had time to develop; based on an informal survey of farmers throughout California, 10 years was the consensus among these farmers as the amount of time needed to experience aspects of farming “at least once.” This significantly reduced the pool of potential participants; in total, sixteen farms were identified to fit the criteria of this study .Working with a local UCCE advisor helped establish trust with farmers identified. These 16 farmers were contacted with a letter containing information about the study and its scope. Thirteen farmers responded and agreed to participate in the entirety of the study .

These 13 organic farmers represent a majority of the organic farms in the region that grow a diversified array of vegetable and fruit crops and that sell to a variety of consumer markets, including farmers’ markets, wholesale markets, and restaurants. These farmers interviewed also represented 13 individuals who oversaw management and operations on their farms. These individuals were most often the primary owner and operator of the farm, and made key management decisions on their farm.In-person interviews were conducted in the winter, between December 2019 – February 2020; three interviews were conducted in December 2020. We used a two-tiered interview process, where we scheduled an initial field visit and then returned for an in-depth, semi-structured interview. The purpose of the preliminary field visit was to help establish rapport and increase the amount and depth of knowledge farmers shared during the semi-structured interviews. The initial field visit typically lasted one hour and was completed with all 13 participants. Farmers were asked to walk through their farm and talk more generally about their fields, their management practices, and their understanding of the term “soil health.” The field interview also provided an opportunity for open dialogue with farmers regarding management practices and local knowledge . Because local knowledge is often tacit, the field component was beneficial to connect knowledge shared to specific fields and specific practices. After the initial field visits, vertical farming supplies all 13 farmers were contacted to participate in a follow up visit to their farm that consisted of a semi-structured interview followed by a brief survey. The semi-structured interview is the standard technique for gathering local knowledge . These in-depth interviews allowed us to ask the same questions of each farmer so that comparisons between interviews could be made. To develop interview questions for the semi-structured interviews , we established initial topics such as the farmer’s background, farm history, general farm management and soil management approaches. We consulted with two organic farmers to develop final interview questions. The final format of the semi-structured interviews was designed to encourage deep knowledge sharing. For example, the interview questions were structured such that questions revisited topics to allow interviewees to expand on and deepen their answer with each subsequent version of the question. Certain questions attempted to understand farmer perspectives from multiple angles and avoided scientific jargon or frameworks whenever possible. Most questions promoted open ended responses to elicit the full range of possible responses from farmers. In the interviews, we posed questions about the history and background of the participant and their farm operation, how participants learned to farm, and to describe this process of learning in their own words, as well as details about their general management approaches. Farmers were encouraged to share specific stories and observations that related to specific questions. Next, we asked a detailed set of questions about their soil management practices, including specific questions about soil quality and soil fertility on their farm. In this context, soil quality focused on ecological aspects of their soil’s ability to perform key functions for their farm operation ; while soil fertility centered on agronomic aspects of their soils’ ability to sustain nutrients necessary for production agriculture . A brief in-person survey that asked a few demographic questions was administered at the end of the semi-structured interviews. Interviews were conducted in-person, on farms to ensure consistency and to help put farmers at ease.

The interviews typically lasted two hours and were recorded with permission from the interviewee.Interviews were transcribed, reviewed for accuracy, and uploaded to NVivo 12, a software tool used to categorize and organize themes systematically based on research questions . Coding is a commonly used qualitative analysis technique that allows researchers to explore, understand, and compare interviews by tracking specific themes . Through structured analysis of the interview transcripts, we identified key themes and constructed a descriptive codebook to delineate categories of knowledge. Once initial coding was complete, we reviewed quotations related to each code to assess whether the code was accurate. First, we tallied both the number of coded passages regarding different themes or topics, and the number of farmers who addressed each theme. Second, we examined the content of the individual coded entries to understand the nature of farmer knowledge and consensus or divergence among farmer responses for each theme.The following results represent a small window into the collective pool of knowledge from the organic farmers involved in this study, based on their responses to interview questions. Consequently, these results only identify and characterize general types of knowledge that these 13 farmers shared during interviews, but does not fully encompass all types of knowledge that these particular farmers possess. Most importantly, these farmers are not necessarily representative of all organic farmers within their region, or beyond. Where we reference “the farmers” in the sections below, they refer specifically to the farmers in this study, not all farmers. Below, we introduce farmer demographics of this study, situate these farmers’ knowledge in terms of their connection to the land, and also provide insight on how farmers in this study accumulate knowledge; finally, we synthesize key themes that emerged from farmer interviews with regards to soil health and soil management.We interviewed 13 organic farmers, which represents about 80% of certified organic vegetable growers in Yolo County, California who focus on growing diversified crops and have been farming for at least 10 years. Farmer participants were majority white and all either first or second generation settlers in Yolo County, CA. This interview pool included 10 men and 3 women between the ages of 45 to 70. Nearly all farmers had post secondary education. In addition, each farmer interviewed was actively managing their farm at the time of the interview and represented the primary decision maker on the farm. Most of the farmers either grew up on a farm and/or had worked on a farm prior to assuming farm management at their current farm operation. Only three farmers were second generation farmers, and the remainder were first generation farmers. All the farmers had been farming for at least a decade, and most of the farmers had been farming for at least three decades, typically on the same lands. Nearly half of the farmers expressed they were at a big turning point in their personal lives when they decided to farm full time. For example, these farmers had either moved across the country to an unfamiliar place, had quit their office job, and/or had lost an important family member or their childhood home.Farmers interviewed possess embedded knowledge, which is knowledge that comes from living on the land and observing natural processes . To situate this type of knowledge in this particular place , the farmers described their relationship to the land they farmed. Not surprisingly, many of the farmers initially responded with personifications of their land . Initial responses also spoke to farmer perception of their role within the land as well as an expression of romanticism for their land . Among farmers who owned most of the land that they farmed , there was a distinct lack of reference to land ownership; these farmers described their relationship both as a responsibility and as part of a larger human inheritance.

The GBA encompasses bidirectional communication between the central and the enteric nervous systems , linking emotional and cognitive centers of the brain with intestinal functions . The autonomic nervous system, the ENS, and the hypothalamic pituitary adrenal axis mediate the communication of the GBA. Enteroendocrine signaling through enteroendocrine hormones activates neuronal pathways, including extrinsic afferent neurons, sending messages to the CNS. These pathways mediate not only behaviors associated with food intake but also cognition and mood. Cannabis has been associated with significant increases in ghrelin and leptin, and decreases in PYY, consistent with the modulation of appetite hormones mediated through endocannabinoid receptors.61The endocannabinoid system encompasses a key interface between the gut microbiota, the immune system, and homeostasis of the human host. Two main endogenous cannabinoids, or endocannabinoids, are the brain-derived arachidonoyl ethanolamide, known as anandamide , and 2-arachidonoylglycerol derived from lipid precursors, such as arachidonic acid, that are synthesized on demand. 2-AG has been isolated from both gut and brain Thissue. Endocannabinoids in the postsynaptic neuron are released into the synaptic cleft and travel retrograde to the presynaptic neuron, where they inhibit neurotransmitter release; then they are rapidly metabolized by lipoxygenase, cyclooxygenase, and epoxygenases, enzymes involved in eicosanoid metabolism. AEA and 2-AG can also be degraded by hydrolysis into arachidonic acid and glycerol/ethanolamine by serine esterases. The cannabinoid receptors type-1 and -2 are located throughout the periphery and are concentrated in the GI tract.

CB1 receptors are most abundant in the brain, grow tray stand where they function in neurotransmision. The CB1 receptors are predominantly located in the nociceptive areas of the CNS, the cerebellum, hippocampus, limbic system, and the basal ganglia. These receptors have a limited concentration in the substantia nigra and periaqueductal gray matter, but are not observed in the medullary respiratory centers. CB1 receptors can also be expressed on immune, cardiac, and testicular cells. In the GI tract, CB1 agonists are involved in feeding behavior, GI motility, satiety signaling, and energy balance. CB1 peripheral activity includes lipogenesis and inhibition of adiponectin found at elevated levels in obese and diabetic individuals. CB1 signaling has been linked to increased levels of free fatty acids, low HDL, high triglycerides, and insulin resistance. CB2 receptors are expressed mainly on membranes of immune and hematopoietic cells in the periphery. CB2 receptors are densely located in immune Thissue and organs, expressed by diverse cell types, including macrophages, splenocytes, microglial, monocytes, and T cells resident in the thymus, spleen, and bone marrow and tonsils. CB2 receptors located in the periphery have an immunomodulatory function playing an important role in pain reduction, inflammation, and physiological immune defense. CB2 activation mediates a regulatory or suppressive anti-inflammatory effect but in some cases stimulation of CB2 stimulates Thissue destruction and apoptosis, such as in cancer cells. Agonists of this receptor do not have psychoactive properties and are effective in mediating immunosuppression, preventing fibrosis or other organ scarring or in certain diseases triggering Thissue damage.

Antagonistic ligands for the endocannabinoid receptor signaling in the gut have an anti-inflammatory effect with an attenuation of inflammatory cytokines, an increase in Akkermansia muciniphila, and decreases in Lachnospiraceae and Erysipelotrichaceae diversity in the gut. CB1 signaling is TLR4 dependent and known to be influenced by at least A. muciniphila.  In one study, A. muciniphila administration increased the intestinal levels of endocannabinoids that control in- flammation, the gut barrier, and gut peptide secretion. CB1 activation is anti-inflammatory in the gut. The dysbiotic gut environment and GALT are set up for an augmented cycle of inflammation and increased permeability of the gut epithelial barrier. The gut microbiota sends signals to the brain while affecting many functions through several pathways that comprise the GBA, including emotion and cognition . HIV infection may, therefore, lead to the destabilization of the GBA through alterations of the immune system and gut, a possible consequence of ongoing gut epithelial barrier abnormalities and viral replication in the GALT persisting despite ART effectiveness in suppressing peripheral virus. HIV infection and associated gut inflammation disrupt the gut-endocannabinoid-brain interface, and these disturbances adversely affect brain function. While interactions between the gut microbiota and the endocannabinoid system are complex, there are likely to be opportunities to develop therapeutics targeting this axis. Binding of the plant-based cannabinoid THC to presynaptic cannabinoid receptors, in the CNS, principally CB1, mimics the biological properties of AEA, acting like a mood enhancer and stimulant of joy and happiness or euphoria. 

Cannabis administration is associated with significant increases in ghrelin and leptin and decreases in hormones that modulate appetite mediated through endogenous cannabinoid receptors. Medicinal and recreational cannabis comes in at least two species, Cannabis indica and Cannabis sativa, and in a variety of cultivars and formulations with different reported efficacies for treatment of various conditions. ‘‘Cultivar’’ is short for ‘‘cultivated variety’’ and represents not a taxonomic category, but a horticultural one, describing different plants that have been bred and selected by humans . Examples of cultivars include Acapulco Gold and Charlotte’s Web. Acapulco Gold is a golden-leafed C. sativa strain originally from the Acapulco area of southwest Mexico. Charlotte’s Web is a high-CBD, low-THC cannabis variety and extract marketed as a dietary supplement under federal law of the United States. More than 2,000 cultivars are available to consumers, but their chemical constituents and consistency have not been systematically characterized. This highlights the importance of providing consumers with consistent product information. CBD/THC extracts are reported to be more effective at treating pain compared to THC alone both in rodents and in human cancer-related pain. Species and cultivar differences in effects on the microbiome have not been studied. There is also the chemovar referring to the different chemical varieties rather than the strain. Each of the chemovars will have variations in the cannabinoids isolated from the strain in various concentrations of CBD, THC, cannabivarins, CBN, cannabigerol, and other chemicals such as terpenes. Each of them has specific functions and is able to elicit unique pharmacological actions and effects. Modes of administration are also important with respect to effect of cannabis and its therapeutic uses. Whether mode of administration differentially influences the gut microbiome is unknown. For example, one might expect orally administered cannabis to have more potent effects on the gut microbiome given high local concentrations than inhaled cannabis. But this has not been studied. Modes of administration will vary in time to onset of effects. Inhaled versions have a rapid onset of effect based on inhalations, whereas ingestion has a longer time to onset due to the transit to digestive processing and absorption. Finally, it is difficult to ascertain the dosing due to individual effects on physiology and tolerance. The low systemic bio-availability of orally administered cannabinoids has led to exploration of other routes, including intranasal, transdermal, and transmucosal. These are possible because of the highly lipophilic nature of cannabinoids. Additional approaches to formulation that may influence bioavailability include salt formation , co-solvency , micellization , –emulsification, complexation , hydroponic racks and encapsulation in lipid-based formulations and nanoparticles. The U.S. Federal Drug Administration has approved three synthetic cannabinoids and one plant-derived cannabinoid. Marinol is approved for anorexia and nausea related to HIV and chemotherapy; Cesamet is approved to treat chemotherapyinduced nausea and chronic pain; and Epidiolex is a plant-derived CBD indicated for the treatment of seizures associated with Lennox–Gastaut syndrome or Dravet syndrome in patients 2 years of age and older.Emerging findings suggest that cannabinoids can modulate the gut microbiota and inflammatory states by stabilizing blood–brain barrier function and reducing neuroinflammation. Neuroinflammation related to chronic immune activation, oxidative stress, and microbial translocation by a leaky gut barrier could affect the CNS through enteroendocrine signaling and the vagus nerve.

These routes may interact with the better understood link between LPS translocation and chronic in- flammation in the CNS due to microglial activation. Since inflammation and immune activation are believed to contribute to neurocognitive impairment in HIV,  the antioxidant and anti-inflammatory properties and possible effects on gut barrier integrity of cannabinoids may favorably impact neurocognitive function. When THC and other exogenous cannabinoids interact with the endocannabinoid system, they can relieve pain as a result of neuromodulatory actions on both afferent pain signals and brain processing of pain. Several other potential therapeutic interventions have not been rigorously studied in randomized, controlled clinical trials. There is substantial evidence that THC stimulates appetite and reduces nausea. CBD is not psychoactive, acting as a serotonin 5-HT1A receptor agonist, and also has antioxidant and anti-inflammatory properties. In addition, although CBD has a low binding affinity for CB1 and CB2 receptors, it modulates several noncannabinoid receptors and ion channels and delays the ‘‘reuptake’’ of endogenous neurotransmitters such as anandamide and adenosine, by altering the binding of ligands to certain G-protein coupled receptors.Reports suggest that CBD may be effective for managing multiple anxiety based disorders, such as panic attacks, post-traumatic stress disorder, generalized anxiety, obsessive–compulsive disorders, and some cancers. Endocannabinoid signaling is known to influence gut barrier integrity, providing a highly relevant context for the study of the effects of cannabis. The endocannabinoid system in the large intestine interacts with the gut microbiota to regulate epithelial barrier permeability. The endogenous cannabinoid AEA, acting through CB1 and CB2 receptors, plays a pivotal role in maintaining immunological homeostasis and health in the gut. AEA contributes to the process by which the gut immune system actively tolerates microbial antigens. The bioactive lipid agonists and antagonists of cannabinoid receptors are known to have a direct effect on gut barrier function. Some CB1 and CB2 ligands are considered ‘‘gate openers,’’ promoting inflammation due to increased permeability of food antigens and pathogen-associated molecular patterns . Other CB1 and CB2 ligands promote increased barrier function and reduce inflammation. Enteroendocrine L cells are innervated by enteric glial cells and afferent neurons. Enteroendocrine L cells express endocannabinoid receptors . SIV infection was associated with gut epithelial barrier disruption, markers of increased inflammation/ immune activation , disrupting the translational control of IRAK1, and facilitating persistent GI inflammation. Previously published animal model studies support our focus on gut barrier permeability in the context of HIV and cannabis. One study demonstrated that chronic THC was associated with anti-inflammatory Th2 cytokine expression and reduced apoptosis among animals infected with SIV with markers of increased in- flammation and immune activation in epithelial crypt cells. These THC-mediated gut alterations were associated with reduced neuroinflammation measured as lower levels of TNFa, IL1b, IL6, and MCP1 in the striatum of SIV-infected rhesus macaques. These results may possibly translate to PWH; however, there were notable sex-specific differences in THC outcomes in SIV infected macaques. While THC mediated clinical differences in male rhesus macaques, reducing morbidity and mortality, as well as attenuation of SIV disease progression, female macaques did not demonstrate those protective benefits at similar doses. Male rhesus macaques had a reduction in plasma viral levels, decreased expression of Thissue pro-inflammatory cytokines, and a decrease in intestinal apoptosis. Female macaques did not have protective benefits with alterations in SIV viral load and CD4 + /CD8 + ratio, with chronic daily THC administration. These contrasting effects may be due to endocrine hormonal differences, requiring more research to investigate the mechanisms for differences. Given these findings and the numerous studies reviewed and cohorts, the field of HIV-related gut dysbiosis is biased toward males and particularly MSM. Future studies should consider of purposively including adequate sampling of HIV-infected cis-females especially in examining the effects of phytocannabinoids. Recently, studies have determined that cannabis is associated with reduced markers of immune activation and inflammation in CSF. This reduction was based on previous research demonstrating that selective stimulation of CB2 receptor leads to neuroinflammation and microglial activation. Thirty-six PWH and 21 HIV negative participants underwent lumbar puncture and provided estimated days since their last cannabis use . More recent use of cannabis was associated with significantly lower CSF levels of IL-16 and C-reactive protein . These findings are consistent with the notion that CNS anti-inflammatory effects of cannabinoids may be mediated directly through the microglial CB2 receptors or indirectly, for example, through cannabis-mediated alterations in gut microbiota composition, improved gut barrier function, or reduced translocation of proinflammatory bacterial products .Over time, internal and external factors such as prolonged stress, environmental factors, poor nutrition, and overuse of cannabis may influence the ability to produce endocannabinoids. Clinical endocannabinoid defi- ciency syndrome has been linked to migraines, neuromuscular pain, and GI disorders. Specific symptoms and symptom clusters have been linked to a defi- ciency in the endocannabinoids, AEA and 2-AG.