The play of public and private helps to provide a possible solution to a central problem of Kofman’s text: how can she create a textual monument that, on the one hand, is is true to her personal experience and that, simultaneously, creates an intersubjective space in which a reader, that is, another person, can come to understand that experience. On the one hand, Kofman is leery of putting forward her experience as somehow emblematic of every wartime experience. As I mention above, she purposely omits contextual details that would ground her experience in larger historical trends. Yet to write a purely personal memoir would be an exercise in hermeticism. Kofman intended to, and did, publish her work for others to read. Kofman bookends her text with discussions of two monuments, one very public and the other very private. Neither of these monuments is sufficient to represent her experience to another. Between those two monuments stretches her response to this challenge: a text that upends the distinction between public and private in order to allow a third, intersubjective space to come into existence. This, she argues, is the domain of writing. The one traditional monument discussed at any length is mémé’s tomb, mentioned in the final sentence of the text. She writes, “je sais que le prêtre a rappelé sur sa tombe qu’elle avait sauvé une petite fille juive pendant la guerre.” [“I know that at her grave the priest recalled how she had saved a little Jewish girl during the war” ] The priest’s words are the final words on mémé and the final words in Kofman’s book. A tombstone is an ending, a conclusion. What is written on it, or said over it, is meant to be the durable legacy of a person’s life. Yet the words at mémé’s tomb fill the reader with mistrust: Kofman’s whole book seems to stand in opposition to the priest’s pat statement. mémé did not just “save,” Kofman, after all, she also saved her mother. Yet at the same time she separated Kofman from her mother irrevocably. And an argument could be made that the little girl who entered mémé’s apartment at the beginning of the war was not the one who left after the liberation: from her clothing to her diet, rolling hydro tables mémé transformed Kofman into a more French, Christian girl.

Placed at the end of a book that so unflinchingly looks at the torturous ambiguity of her feelings and experience, the priest’s words seem terribly inadequate as a summary of their relationship. How could a simple marker and a short speech really do justice to the relationship between Kofman and mémé? In this light, Rue Ordener, Rue Labat stands in opposition to the simplicity of the tomb as a complex monument to mémé. The tomb’s very substantialness, its promise that it is the final word on mémé, seems duplicitous or propagandistic in comparison. If there is a discourse of tombs, Kofman may be saying, beware of what they say. The permanence, solidity and public nature of the traditional monument is an inapt representation of the fluidity and fragility of memory and legacy, especially in the context of a book that takes as its subject the metaphorical insubstantiality of things that we tend to think of as solid: family, religion, identity, home and even language. Other monuments in Kofman’s text have a greater claim to truth in that they are fragile testaments not to presence , but testaments to absence. Most of these monuments, perhaps more accurately, mementos, relate to her father. The book opens with a description of such a monument, Kofman’s father’s pen. Placed in implicit opposition to the traditional tomb described at the end of the book, we are encouraged to compare the fragile pen with mémé’s tomb, and to consider the pen as a monument. “De lui, il ne me reste que le stylo,” [“Of him all I have left is the fountain pen” ] writes Kofman . This pen, which “m’a ‘lachée’ avant que je puis me décider de l’abandonner” [“‘failed’ me before I could bring myself to give it up” ] no longer functions, even though it has been “rafistolé avec du scotch” 6. [“patched up with Scotch tape” ] This pen, a standin for her father, a way, when it functioned, to experience a tenuous physical connection with him, is now a stand-in for the absence of her father. Yet it is that absence that “me contraindre à écrire, écrire” [“makes me write, write” ], positioning Kofman’s writing as another expression of absence . It is in the absence of the functioning pen and in the absence of her father that her writing takes shape.

Her father’s last letter to her family, written from the Drancy prison camp, similarly serves as more of a monument to absence than to former presence. Kofman writes, “Nous ne revîmes, en effet, jamais mon père. Aucune nouvelle non plus, sauf une carte envoyé de Drancy, écrite à l’encre violette, avec un timbre sur le dessus représentant le maréchal Pétain. Elle était écrite en français de la main d’un autre” . [“As it turned out, we never did see my father again. Or get any news of him, either, except a card sent from Drancy, written in purple ink, with a stamp on it bearing Marshal Pétain’s picture. It was written in French by someone else’s hand” ] While any letter represents the absence of the writer, this card is doubly a symbol of absence, since it is written by another in a language her father does not speak. Kofman’s father is already becoming a ghost, disembodied. His handwriting—a link, after all, to the body that produced the text—is no longer accessible. All the while Pétain hovers above his words, reminding the recipient that they and the sender no longer have the privilege of private communication. Yet despite these pressures, his words and personality come through: he is asking for cigarettes, his great pleasure . [In this last sign of life we had from him, where he told us he was being deported, he asked that in two kilogram packages we were legally authorized to send we be sure to include cigarettes ] And despite the letter’s weakness as a proxy for her father, when Kofman cannot find the letter after her mother’s death, she writes, “c’était comme si j’avais perdu mon père une seconde fois” [“it was as if I had lost my father a second time” ]. Like the broken pen, Kofman writes about the absence of the letter, itself a testament to the absence of her father’s own writing, which, in turn, signifies the absence of the man who wrote. In this chain of absence, Kofman’s father announces his presence in that last letter by a request for cigarettes. The smoke from cigarettes is a fitting symbol for an absent man. “‘Envoie-moi surtout des cigarettes, des gauloises bleus or vertes,’” [“‘Most of all, send cigarettes, blue or green Gauloises’” ] he writes in that final letter. Kofman’s memory of this request provides a link to another, earlier memory, of the end of the Sabbath as the moment when her father was able to smoke again. Cigarette smoke, like Kofman’s father and the objects that represent him, is a play of presence and absence, a symbol of the functioning of memory. The smoke is the present evidence of the absent smoker and the burned cigarette, with a tenuous connection to the person who exhaled it. The smoke chains together Kofman’s separate memories of her father, connecting his final letter to his family with a memory of him lighting a cigarette after the sabbath. Yet smoke also disappears gradually into the air, vertical horticulture like a fading memory. A trace of its scent can linger on for longer until it, too, fades. These private mementos of her father, the pen and the letter, appropriately capture the evanescence of memory and the feeling of absence that is, in a sense, the essence of Kofman’s memory of her father.

These mementos fall short of being true monuments, however. Alone, the pen and the letter only have evocative power for Kofman herself. They stand as private monuments, but not public ones. Mémé’s tomb, upon which a simplified narrative is carved in stone, cannot provide truth. More honest are the mementos of Kofman’s father, since in their very fragility and absence they allow the holder to reexperience the loss of a loved one. Yet if a discussion of monuments and memory in Rue Ordener, Rue Labat were to end here, an enormous part of the book would be neglected. Much of the text involves the streets and spaces of Paris. How can we reconcile this memoir, a book about memory, with the preeminence of space of the city? Between the bookends of the private, absent monuments to Kofman’s father’s absence and mémé’s public, solid, untrustworthy tomb stretch the Paris streets, which function both as a stone monument like mémé’s tomb and a testament to the absence of those who lived on these streets and the complexities of their lives.The Vichy statement seems to contend that Clermont-Tonnerre’s requirement that the Jews “soient individuellement citoyens” has not been followed, though the Vichy government will still extend some of the respect that Clermont-Tonnerre saidshould belong to the individual Jew, excepting certain positions of power7. As with Clermont-Tonnerre, the Vichy government sees Jews as “corruptive and finally decaying” because of their “individualistic tendency” of keeping to themselves as a group. Such an argument implies an either/or logic: either you are a member of the Jewish community, or the French one. The idea of the Jewish neighborhood might have seemed to reinforce these ideas: a space occupied by a community with a particular ethnic belonging looked, to the Vichy mind, a lot like the “nation within a nation” that Clermont-Tonnerre finds unacceptable. Yet, as Caron argues, a “nation within a nation” would be more like a ghetto and less like a neighborhood. A ghetto has tight, clear borders that inhabitants may not be allowed to breach while the borders of a neighborhood are fluid and traversable. Caron shows that Mayol’s description of a neighborhood as both public and private gives the lie to the idea that public and private zones can ever be completely separated, and thus also to the idea that one could lead a life in public without any kind of reference to the life lived in private. The relationship of the individual to his or her neighborhood is therefore, as Mayol says, “existential.” Caron shows how this existential relationship carries over to how inhabitants of a city understand themselves as individuals and part of the collective, not just how they understand the relationship between their homes, the neighborhood and the rest of the world. In Kofman’s text, as in Mayol and Caron’s writing, the problem of public and private, of their relationship and how they might be negotiated by the individual, is more complex than simply imagining the private home and the public street. As with the bombed out building, these distinctions are not easily negotiated. At issue in Kofman’s text is the question of who exactly she is, and how she might write an autobiographical text that is the story of her confusion about her identity. Is she the Jewish girl who grew up speaking Yiddish and admiring her father, the rabbi? How could she be? She forgot her Yiddish and purposefully distanced herself from Judaism. Is she “Suzanne,” mémé’s daughter? No again, since she also has a relationship, albeit fractious, with her biological mother. During the occupation, Kofman has a public, Christian mother, and a private, Jewish, mother. This situation mimics her experience on the streets of occupied Paris: publicly, she can only appear as mémé’s Christian daughter, while her private Jewish identity languishes in a back room of mémé’s apartment. Kofman’s walk from her mother’s apartment on rue Ordener to mémé’s on rue Labat is a central moment in the text, symbolizing Kofman’s negotiation of her identity in the not-quite-private and not-quite-public environment of the neighborhood. It is significant that Kofman vomits on rue Marcadet, the path from rue Ordener to rue Labat. Vomiting is an act of rejection, but it is also a way of making public what is private.

Growers can currently charge roughly double the price per kg for dry farm-quality compared to irrigated tomatoes; therefore, short of doubling yields, current dry farmers may be reluctant to shift management to maximize yield over quality. However, these high yields do open the possibility that dry farm management could expand to industrial-scale markets that do not rely on consumer trust in high quality produce, competing instead with irrigated production if larger scale farmers adopt dry farm practices while choosing to intentionally manage for yields over quality.Only soil nutrients at 30-60cm depth showed correlations with BER, while marketable yields and fruit percent dry weight were only influenced by nutrients below 60cm. Specifically, ammonium concentrations were associated with increased fruit quality but decreased yields and incidence of blossom end rot, while nitrate was associated with increased yields. Because soils dry down quickly in dry farm fields–available water content on average decreased by 65% in the top 30cm from transplant to midseason, while decreasing by only 16% below 60cm –plants likely devote rooting efforts to exploring deeper soils that are not too dry for efficient nutrient acquisition. Farmers also make an effort to plant transplants as deeply as possible, quickly delivering roots to depths below 30 cm. Though tomatoes root adventitiously from their stems and can therefore send out roots at shallower depths, rapidly drying surface soils likely limit nutrient uptake by adventitious roots, directing resources instead towards deeper rooting. The importance of soil nutrients at transplant at 30-60cm in predicting BER incidence, grow trays 4×4 as compared to 60-100cm for yields/quality, suggests that calcium uptake occurs at an earlier stage of plant development when a higher proportion of roots were likely present at 30-60cm . Roots likely concentrated more heavily in deeper soils during fruit set and development, causing only nutrients below 60cm to show a relationship with fruit yields and PDW.

Our results also show a surprising relationship between transplant ammonium levels and fruit yields/quality. Though ammonium levels are quite low below 30cm , their negative association with yields suggests that either these low ammonium concentrations were still able to inhibit calcium/water uptake and further stress plants, as seen in studies with higher ammonium concentrations61,62, or that higher transplant ammonium levels were indicative of other soil circumstances that negatively impacted yields. One possibility is that wetter transplant soils led to higher rates of nitrification, causing decreased ammonium levels and also higher yields due to increased water availability. While GWC was included in our models and was not significant, ammonium concentrations could in some ways be a better indicator of water availability than GWC if they more fully reflect the conditions that lead to nitrification. It is possible that, within the range of textures seen in this study, plots with higher clay content at depth inhibited plants’ ability to root deeply or led to decreased plant available water. This possibility is supported by the water x texture interaction that links plots with low clay and high GWC to increased yields. We note that the plots with the highest ammonium levels were all from one field , which exerted a strong influence on results; however, excluding Field 5 from analyses does not change the direction of nutrient coefficients, or the depth at which nutrients show a significant relationship with these outcomes. Additional research is needed to understand the unexpected relationship between ammonium concentration and harvest outcomes found here. Because nitrate levels correlate positively with yields and do not show a statistically clear relationship with BER or fruit quality, it may be tempting to conclude that farmers should increase nitrate availability in dry farm soils. However, risk of nitrate leaching must be taken into account, especially in this agricultural region that suffers from severe nitrate pollution of groundwater.

Three of the seven fields in our study had nitrate levels at harvest—in just the top 15cm—above the threshold considered likely to cause groundwater contamination if that nitrate were to fully leach out of the rooting zone when it mobilizes in the first large rain event of the fall/winter wet season. These levels would likely be further accentuated by the Birch effect as soils are rewetted65. Because this first rain event typically occurs after plants are terminated, or is the terminating event itself, these systems may be particularly prone to nitrate loss when living roots are not present in the soil to recapture it. Though careful cover crop management, which is practiced by all of the farms in this study, can likely attenuate leaching, decisions to fertilize should be made with caution. Taken together, these results highlight two core challenges for dry farmers. First, there is a tension between fruit quality and yields, with conditions that lead to high yields decreasing fruit quality and vice versa. Second, it is difficult to manage soil fertility deep in the soil profile, especially when nutrients are prone to leaching.While a commercial AMF inoculant applied at tomato transplant changed AMF community composition in roots, it did not provide any benefit to yield outcomes, if anything lowering fruit quality. Diversified farm management likely made AMF communities in these soils more diverse with higher spore counts than would be seen in more industrialized systems. Altering the AMF community through inoculation may have disrupted or simply not altered functions that the endogenous community was as well or better-equipped to provide. This result has been seen repeatedly in field research, where commercial inoculants often fail to impact agriculturally relevant outcomes, or local AMF communities outperform exogenous ones. It is also possible that, while the inoculum established enough to shift the AMF community and lower fruit quality, inocula generally will not have a large influence on dry farm tomatoes given that they are applied to surface soils while plants focus on deeper rooting, or that the specific species in the inoculant we used were not well-suited to this system. From a conceptual standpoint, there has been considerable debate in recent decades over how to best maintain agricultural productivity while also achieving systems that can maintain long-term productivity through resilience to environmental stress.

These conversations often pivot around the idea of replacing industrial input-intensive agricultural practices with ecologically-based, knowledge-intensive systems. These ecologically-based systems are typically depicted as relying on on-farm biological diversity as a mechanism for increasing crops’ resilience to environmental conditions, whereas industrial systems are maintained with off-farm inputs. Even as biological diversification enters the agricultural ethos, there continues to be a pull towards achieving these biological outcomes through off-farm inputs. We typically think of chemicals and energy as the off-farm additions to conventional systems; however, products that mimic the biological effects of diversification practices can similarly be introduced from external sources rather than fostered on the farm. AMF inoculation is a prime example of how biological outcomes might be realized via external inputs. While AMF inoculation has indeed shown some benefit in more industrially managed systems, in the present study we observe that in a more diversified system, augmenting a field’s endogenous AMF community does not improve plant outcomes. Rather than replacing one external input with another , horticulture products we find that farmers who already practice diversified management will likely have better luck pairing local climatic conditions with locally-adapted microbial communities.More broadly, the full fungal community in dry farm, irrigated, and non-cultivated soils were distinct, indicating different selective pressures in each soil condition. Irrigation seems to be a filter on agricultural soils, resulting in a smaller community that overlaps substantially with dry farm soils. Given that in this study only tomatoes were present in dry farm soils, while crops on irrigated soils varied from field to field, we likely overestimate the diversity of irrigated soils relative to dry farm, making this community shrinkage in irrigated soils even more pronounced. While fungal community responses to drought vary widely in the literature, there is precedent for deficit irrigation shifting bacterial communities in processing tomato fields, and natural experiments with drought conditions have led to increased fungal diversity in cotton rotations. This lower fungal diversity in irrigated systems may be driven by lower soil temperatures that are less conducive to fungal growth, or directly linked to changes in fungal competition induced by water stress that enhance diversity in dry farm systems. On the other hand, agricultural soils and non-cultivated soils seem to be distinct communities with roughly equal magnitudes of taxa numbers despite high levels of disturbance that might act as a narrowing selective pressure. Dry farm fungal diversity may be caused by external inputs that introduce non-endogenous taxa to cultivated soils. Dry farm soils were not only distinct from the other soil locations, but consistently enriched in taxa in the class Sordariomycetes. These indicator taxa formed a dry farm “signature” that was not only present in dry farm soils, but increased in magnitude in soils that had gone multiple years without external water inputs.

This signature showed positive associations with fruit quality outcomes, which is of particular importance to farmers in this quality-driven system. Sordariomycetes were also associated with an increased likelihood that a plot would not have any marketable tomatoes on a given harvest day; however, as this was a rare occurrence that happened almost exclusively in the first/last weeks of harvest when yields were low for all plots, we do not expect that farmers will see an association between Sordariomycetes and yield declines. If anything, farmers may notice a slight truncation of harvest season duration in fields that have been dry farmed for several years. Sordariomycetes themselves may not be causing these outcomes, but rather point to the fact that soil microbial communities–possibly including bacteria and other microorganisms in addition to fungi–are consistently adapting to dry farm management. Sordariomycetes enrichment may indicate other community shifts that are ultimately the cause for enhanced fruit quality. It is also possible that Sordariomycetes themselves are improving dry farm outcomes. Endophytes in the Hypocreales class, which was enriched in dry farm fields, are known to increase drought resistance and decrease pest pressure in their hosts, though none of the specific species known to exhibit this behavior were enriched in dry farm soils. On the other hand, Nectriaceae, the family that contains the Fusarium genus, was found to be enriched, though similarly no known pathogenic species were enriched in dry farm soils.Our study explored dry farm management practices and their influence on soil nutrient and fungal community dynamics in 7 fields throughout the Central Coast region of California, allowing us to explore patterns across a wide range of management styles, soil types, and climatic conditions. Though we were able to sample from a large swath of contexts in which tomatoes are dry farmed, we are also aware that conditions will vary year to year, especially as climates change and farmers can no longer rely on “typical” weather conditions in the region. While we are confident in the patterns we observed and the recommendations below, we also encourage further study across multiple years to better understand the full scope of the decision space in which dry farm growers are acting.Given the scope of our current findings, we outline several management and policy implications for dry farmers and dry farming. Though we aim these implications towards the context of dry farm tomatoes in coastal California, we expect that they are likely to generalize to other dry farm crops grown in other regions with Mediterranean climates. First, given the expense and possibility that it is detrimental to fruit quality, we do not advise AMF inoculation for dry farm tomato growers. Second, we note the importance of nutrients below 60cm and the complexities of subsurface fertility management, and we recommend experimentation with organic amendments and deeply rooted cover crops that may be able to deliver nutrient sources that persist at depth, as well as planning several seasons in advance to build nutrients deeper in the soil profile. Finally, given our finding that dry farm soils develop a fungal signature that increases over time and its association with improved fruit quality, we encourage farmers to experiment with rotations that include only dry farm crops and even consider setting aside a field to be dry farmed in perpetuity. However, fully dry farmed rotations currently do not exist, likely due to a lack of commercially viable options for crops to include in a dry farm rotation.

Hannah’s mentorship has been invaluable at inflection points in my PhD process, and I can’t overstate how lucky her new grad students will be to have her as an advisor. I feel incredibly privileged to have the community support of more people than I can thank individually without making my acknowledgements longer than my dissertation. Communities that have given me particular encouragement, joy, and solace include the 2018 ESPM cohort, Friendship Village, the Sunset/Pomona/floating/CCST crew, my Park Palace queens, my sweet childhood friends, and every last Sheline and Socolar. You all make me feel connected to something I want to be accountable to. Within these communities, a few people stand out as being particularly instrumental in helping me thrive throughout this PhD. The folks at Rat Village–Abby, Alli, Brendan, and Charley–made a beautiful house into a beautiful home. You taught me how organization and communication can create abundance, and gave new meaning to what it can mean to live communally. Everything from fridge leftovers to card nights to casual kitchen encounters carried me through this experience, and I hope you will see my use of the term “Rat Village” in my dissertation as indicative of the lengths I am willing to go to to express my gratitude. Two dear friends, Erin Curtis Nacev and Claire Woodard, have been cornerstones of my PhD experience. They were both my gateway to the Bay Area–I would never even have arrived here if Berkeley hadn’t felt like the homecoming that you created. Through med school, residency, and raising a child, Erin found time for visits and calls, and is my–and perhaps the entire world’s–best model for what a can-do attitude can be. She is generous, loyal, principled, a source of such joy, and capable of everything. Plus she and Zach made Evie, which is really the highest praise you can give a person.

Of the narratives I have watched unfold over the course of my PhD, indoor grow rack few have made me happier than watching Claire transform from the best of friends to the best of collaborators. It was her overwhelming loyalty as a friend and endless capacity for hard work that brought her to my first tomato field, and my own incredible luck that has kept her farming ever since. I marvel that the person I’m most likely to call crying on the phone is the same person I’m most likely to call about transplanting techniques. Claire’s accompaniment through this entire experience has been so thorough that it’s alarming to remember there was a time before Claire was a farmer, and to imagine what my field seasons would have looked like without her there. I have also been lucky to have the deep support of many family members on this journey. That my brother, sister-in-law, and sister-cousin all had PhDs when I arrived at Berkeley meant that my PhD did not have to be demystified, but rather was never mystified in the first place. Jacob, Bethanne, and Annelle’s guidance, encouragement, and commiseration have been the sweetest set of bumper rails as I ricocheted through this experience. Jacob in particular has fielded enough “hi how are you, but actually can we talk about statistics?” phone calls from me that you might think “random effect” is a family member we desperately need to gossip about. Luckily my niece, Isabelle, has been the most brilliant distraction when things get too heady–my heart remembers to refocus when I see her shining eyes. Though none of my grandparents are here to read this dissertation, I can see the way their faces would beam if I could show it to them. Their influences are almost comically obvious in my career choices–Grandpa Ray’s determination and proclivity for natural sciences, Grandma Yvonne’s steadfast commitment to social justice, Grandpa Milt’s philosophy and politics, and Grandma Molly’s effortless ability to connect to everyone she met.

From antiracism to interviews, DNA work to policy ideas, they have created a foundation that I want to build on, and their obvious pride in me has given me the confidence to start building. For my mom and dad, I reach the limits of what I know how to do with words. To say that your love and support for me was unwavering suggests the possibility that it might have wavered, and the knowledge that that is not possible is baked into the bedrock of my existence. You are the people I want to consult with every conundrum that comes my way, and the people who most celebrate my every success. Dad, you know it’s not possible to fill the space Mom left in our lives, and you fill every space around that. My luck at having Varun, my partner, in my life can be measured in the mornings I wake up happy, my growing ability to process out loud , the days my grump melts into grins, the times I go backpacking, the plants in our living room, the edited drafts of each chapter below, the width of our couch, and the number of dissertation-fueling treats in our cupboard. He is patient, joyful, loving, smart as all get-out, and an inspiration to me. His curiosity has brought a new perspective to the work I do, and I can navigate my decisions more clearly in the paths he reflects back to me. Varun, you extend yourself to nurture my growth, and you can see that growth written in these pages. I want to be with you everywhere. These soils continue to inspire, feed, and live through millennia of care, and I am indebted to those who built relationship with these places. I want to acknowledge and pay my respect to the Awaswas speaking Uypi Tribe and Chochenyo-speaking Ohlone people, whose unceded territory encompasses the field sites and laboratories where this work took place.

My work has benefited from the occupation of this land, and thus, with this land acknowledgement, I affirm Indigenous sovereignty.Biological simplification has accompanied agricultural intensification across the world, resulting in vast agricultural landscapes dominated by just one or two crop species. The Midwestern US is a prime example1, where corn currently dominates at unprecedented spatial and temporal scales. An area the size of Norway is planted in corn in the Midwest in any given year with little variation in crop sequence; over half of Midwestern cropland is dedicated to corn-soy rotations and corn monoculture3. Directly and indirectly, this agricultural homogeneity causes environmental degradation that harms ecosystem health while also contributing to climate change8 and increasing vulnerability to climate shocks. Agricultural diversification in space and time reverses this trend towards homogeneity with practices like crop rotations that vary which harvested crops are grown in a field from year to year. Crop rotations are a traditional agricultural practice with ample evidence that complex rotations— ones that include more species that turn over frequently—benefit farmers, crops, and ecosystems. As one of the principles underlying agricultural soil management, diverse croprotations promote soil properties that provide multiple ecosystem services including boosting soil microbial diversity, enhancing soil fertility, improving soil structure and reducing pest pressur. These soil benefits combine to increase crop yields and stabilize them in times of environmental stress. Crop rotations’ environmental and economic benefits typically increase with the complexity of the rotation, while conversely, biophysical aspects like soil structure and microbial populations are degraded as rotations are simplified12,20,30,31. Despite its benefits, crop rotational complexity continues its century-long decline in the Midwestern US. Corn-soy rotations increasingly dominate over historical crop sequences that included small grains and perennials, with corn monocultures also on the rise1. This increasing simplification is in part the result of a set of interlocking, indoor farming equipment long-standing federal policies aimed at maximizing production of a handful of commodity crops that distort farmers’ economic incentives. Regional rotation simplification is clear from analyses of crop frequency, county-level data, and farmer interviews. However, fine-grained patterns that more completely reflect farmers’ rotational choices across the region, and how those choices relate to influences from policy and biophysical factors that play out across agricultural landscapes, remain largely unstudied. This knowledge is essential for understanding how national agricultural policy manifests locally and interacts with biophysical phenomena to erode—or bolster—soil and environmental health, agricultural resilience, and farmers’ livelihoods. Bio-fuel mandates and concerted efforts to craft industrial livestock systems as end-users of these corn production systems make corn lucrative above other commodities, while federal crop insurance programs push farmers to limit the number of crops grown on their farms. These policies, along with the current corporate food regime, drive pervasive economic incentives to grow corn, and farmers must increasingly choose between growing corn as often as possible to provide a source of government guaranteed income, and maximizing soil benefits and annual yields through diversified rotations. These policies both alter agricultural economics at a national level by boosting corn prices and manifest locally in grain elevators and bio-fuel plants that create pockets of high corn prices with rising demand closer to each facility.

Biophysical factors like precipitation and land capability that are highly localized and spatially heterogeneous can catalyze or impede this simplification trend. For example, increasing rotational complexity is one strategy that farmers may employ to manage marginal soils or greater probability of drought, while ideal soil and climate conditions allow for rotation simplification to be profitable, at least in the short run5. As these top-down and bottom-up forces combine, we ask: how do farmers optimize crop rotational diversity in complex social-ecological landscapes, with top-down policy pressures to simplify intertwined with bottom-up biophysical incentives to diversify? Because biophysical factors and even policy influences vary greatly at the field scale at which management decisions occur, an approach is needed to assess patterns of crop rotation that can capture simplification and diversification at this scale. Though remotely sensed data on crop types can now show fine-scale crop sequences, previous approaches to quantifying rotational complexity have relied on classifying rotations based on how often a certain crop appears in a region over a given time period, aggregating over large areas, or examining short sequences. To date, methods to capture rotational complexity have therefore been unable to address management decisions at the field scale , and/or lose valuable information about the number of crops present in a sequence and the complexity of their order . At the other end of the spectrum, farmer surveys have impressively detailed the economic and biophysical considerations that go into farmers’ rotation decisions35, yet are limited by the number of farmers they can reach and who chooses to respond. Here, we explore how aspects of farm landscapes influence field-scale patterns of crop rotational complexity across the Midwestern US. We developed the first field-scale dataset of rotational complexity in corn-based rotations, covering 1.5 million fields in eight states across the Midwest and ranking crop sequences based on their capacity to benefit soils. We examined rotations from 2012-2017 to coincide with the introduction of the Renewable Fuel Standard, or “bio-fuel mandate,” which took full effect in 2012. We then correlated fields’ rotational complexity with biophysical and policy outcomes factors, using bootstrapped linear mixed models to account for spatial autocorrelation in the data. By identifying spatially explicit predictors of rotational complexity, we illuminate 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.We focused our analysis on the eight Midwestern states with the highest corn acreage 2. We considered the six-year period from 2012 to 2017, which coincides with the introduction of the Renewable Fuel Standard in 2012. After deriving a novel field-scale rotational complexity index , we used spatially blocked bootstrapped regression to assess how key landscape factors associated with this indicator. These statistical methods account for overly confident parameter estimates that arise in naive models due to spatial autocorrelation in the data. All analyses were conducted in R47.We compiled a dataset that shows the crop sequence on each field in the study area and used these sequences as a proxy for crop rotation to derive a novel indicator of rotational complexity that could be applied at the field scale. To date, no metric exists that can supply both the flexibility of quantifying different length rotations that occur in the same time period, and the specificity of operating at the field level.

A 2010 Census Bureau report found that the recession not only grew the wealth gap between rich and poor; it also exacerbated the gap between different racial/ethnic groups. Between 2007 and 2009, the wealth gap between whites and Blacks nearly doubled, with whites having 22 times as much household wealth as Blacks and 15 times as much as Latinos/as. By 2010, the median household net worth for whites was $110,729 while for Blacks it was $4,995 and for Latinos/as it was of $7,424. Between 2005 and 2010, furthermore, median household net worth for Blacks, Latinos/as, and Asian Americans fell by roughly 60%, while the median net worth for white households fell by only 23%. Many people of color were pushed into bad mortgages by the nation’s biggest banks, while the loss of 600,000 public sector jobs during the recession also had a significant impact on communities of color, as Black and Latino/a workers are more likely to hold government jobs than their white counterparts. Although the current slow economic recovery is not unusual, the cumulative and sustained impacts of unemployment, income loss, and housing loss disproportionately experienced by low-income communities and communities of color signal the value of a safety net that protects such marginalized communities from sustained poverty and food insecurity. Two major parts of the recessionary safety net are the USDA’s Supplemental Nutrition Assistance Program and the Unemployment Insurance program of the US Department of Labor, which provides financial support to workers who become unemployed through no fault of their own. As with SNAP, horticulture solutions expenditures for UI generally expand during economic downturns and shrink during times of economic growth, primarily because economic downturns result in wider eligibility and participation. 

Significantly, households that participate jointly in both SNAP and UI can improve their ability to sustain food expenditures, nutrition, and overall standard of living during times of economic challenge and are an indicator of the strength of the recessionary safety net itself. Toward this end, a 2010 USDA study found that the recession not only increased the number of SNAP households but also increased the extent of joint SNAP or UI households: an estimated 14.4% of SNAP households also received UI at some point in 2009—nearly double that of 7.8% in 2005. Moreover, an estimated 13.4% of UI households also received SNAP at some point in 2009, an increase of about one-fifth over the estimate of 11.1% from 2005. Significantly, people of color, hardest hit during the economic downturn, benefitted the most from the safety net. In 2009, the estimated joint SNAP and UI use for Blacks and for Latinos/as exceeded joint use by whites by about 16.6 and 9.8%, respectively. Together, SNAP and UI help sustain aggregate household spending and national production in economic downturns, making the impact of such downturns less severe than they would be in the absence of the programs. Such benefits are particularly pronounced for communities of color who not only experience relatively greater degrees of poverty, but also are hardest hit during economic downturns. In April 2012, the Congressional Budget Office estimated that temporarily higher benefit amounts enacted in the American Recovery and Reinvestment Act of 2009 accounted In April 2012, the Congressional Budget Office estimated that temporarily higher benefit amounts enacted in the American Recovery and Reinvestment Act of 2009 accounted THE STRUCTURE OF US AGRICULTURE determines and reflects the challenges faced by US farmers and rural communities. This includes farm size, type, cropping patterns, and ownership. Moreover, the federal food and agricultural policies, including the Farm Bill, affect the structure of US farmland through multiple forces and drivers, including taxes, lending programs, environmental and safety regulation, rural development programs, research and development funding, and commodity programs. 

In this light, Part III examines how such programs have shaped the structure of US farmland and, in turn, how they have affected the socio-economic well-being of low-income farmers and communities, as well as farmers and communities of color. It does so, first, by providing a snapshot of the structure of US farmland, including the outcomes of structural racialization with regard to farmland ownership and government payments . It then outlines the historical significance of change in the structure of US agriculture over the 20th century, and examines three federal rural and agricultural support programs in particular: Farm Service Agency lending programs, Farm Bill commodity programs, and Farm Bill Rural Development programs. Ultimately, Part III argues that such programs have historically undergirded white farmland ownership at the expense of farmland ownership by people of color. Significantly, these programs also highlight how white agricultural land ownership was held up amidst, and by way of, increasing consolidation and specialization, with farmers of color on the losing side of such shifts in the structure of US farmland. In the push for the dismantlement of corporate control and structural racialization, such trends thus require greater attention with regard to their role in intensifying marginality that low-income communities and communities of color face in terms of wealth, access to program benefits, and land access. One of the most significant changes in the US economy since the beginning of the 20th century is the national abandonment of farming as a household livelihood strategy. This “agricultural transition” is marked by a number of characteristics: the move away from farming by most Americans and the challenging conditions that remaining farmers experience; the decline in the number of farms and farm population; the growth of larger farms vis-à-vis acreage, sales, and real estate capitalization; and the gradual replacement of family with hired labor. The post-World War II period ushered in perhaps the most rapid transformation, particularly by way of New Deal interventions, and their reformulation and erosion over the next few decades. Between 1940 and 1980, for example, the farm population declined ten-fold, the farm numbers declined by more than half, acreage more than doubled, and real average sales increased six-fold.

Farmers also experienced periodic crises during key moments within such long term structural change, such as those that took place during the 1980s and in the mid-1990s. Such shifts were linked to the polarization of production. For example, between 1939 and 1987, the market share of sales by the largest 5% of producers increased from 38.3% to 54.5%. Agricultural firms have expanded not just through vertical and horizontal consolidation, as outlined in Part I, they have also done so through production contracts, wherein a farmer raises or grows an agricultural product, including livestock, for such firms. While only about 8.9% of farms operated under production contract in 2012—up from 3% only a decade earlier—they produced 96% of all poultry, 43% of all hogs, and around 25% of all cattle. Furthermore, most farms cannot fully employ or sustain families. To survive in farming, families have taken off-farm jobs. As of 2013, for example, 87% of farmers’ median household income came from non-farm sources. The median farm income for operations that specialize in grains, rice, tobacco, cotton, or peanuts, 23% of income came from on-farm sources. Conversely, livestock operations, apart from dairy, have generally not had a positive income from farming. That is, without income garnered by way of off-farm sources, such operations would go negative. As outlined below, the complete lack of profitability of such operations, and the relatively great profitability of grain and other commodity crop operations, cannot be understood as separate from the racialized distribution of operation types, with white producers generally running more profitable grain and other commodity crop operations, and producers of color running less profitable livestock operations. Shifts in agricultural production were tied not only to the polarization of production but also to racial, gender, and economic polarization. For example, although Blacks were able to establish a foothold in southern agriculture post-Emancipation, grow benches rural Blacks were virtually uprooted from farming over the next several decades. In 1920, 14% of all US farmers were Black , and they owned over 16 million acres. By 1997, however, fewer than 20,000 were Black, and they owned only about 2 million acres. While white farmers were losing their farms during these decades as well, the rate that Black farmers lost their land has been estimated at two and a half to five times the rate of white-owned farm loss. Furthermore, although between 1920 and 2002, the number of US farms shrank—from 6.5 million to 2.1 million, or by 67%—the decline was especially steep among Black farmers. Specifically, between 1920 and 1997, the loss of US farms operated by Blacks dropped 98%, while the loss of US farms operated by whites dropped 65.8%. As outlined above, such shifts have been attributed to the general decline of small farms, land erosion, boll weevil infestations of cotton, New Deal farm programs geared toward white landowners, postwar cotton mechanization, repressive racial and ethnic relations, and the lure of jobs and relative safety in the North. Remaining Black farmers were not only older and poorer than others, they also continued to disproportionately face structural discrimination with regard to land ownership and access to federal support, whether because of ineffectiveness, discrimination in implementation, poor design, lack of funding, or unintended shortcomings. The following section focuses on three sets of Farm Bill programs in particular and elaborates upon the history of each as they relate to racial and economic inequity, particularly in terms of income and wealth, access to program benefits, land access, access to positions of power, and degree of democratic influence.Discrimination by the USDA and FSA Loan Distribution Program is among the most significant causes of limited access to, and loss of, farmland by people of color. Specifically, lending program discrimination has undermined the economic capacity of farmers of color to anticipate and respond to rapid consolidation and specialization, such as limited capacity to adopt scientific and technological innovations in agricultural production, and greater vulnerability to price volatility. 

Toward this end, allegations of unlawful discrimination against farmers of color in the management and local administration of USDA lending programs—and the USDA’s limited response to such allegations—have been long-standing and well-documented. For example, in 1965, the US Commission on Civil Rights found evidence of discrimination in the USDA’s treatment of employees of color and in its program delivery. Furthermore, in the early 1970s, the USDA was found intentionally forcing farmers of color off their land through its loan practices. In 1982, the US Civil Rights Commission again found evidence of continued discrimination actively contributing to the decline in minority farm ownership. Despite such findings, in 1983, only one year later, President Reagan pushed for budget cuts that ultimately eliminated the USDA Office of Civil Rights, the primary body for addressing such claims of discrimination. Even after the USDA Office of Civil Rights was restored in 1996 during the Clinton Administration, discrimination in the lending programs continued for years. Although the USDA officially prohibits discrimination, the structure for the election of FSA county, area, and local committees that decide who receives loans and under what terms facilitates continued racial discrimination.Toward this end, a 1997 USDA Office of Civil Rights report observed that FSA county committees operate as closed networks and are disproportionately comprised of white men, noting that, in 1994, 94% of the county farm loan committees included no women or people of color. As of 2007, such trends continue, with just 90 Black committee members among a total 7,882 committee members around the country, slightly over 1%. Decades of discrimination and lack of access to such crucial positions have sparked several class-action lawsuits by women farmers and by various groups of farmers of color. Only recently has the Farm Bill attempted to address a major cause of racially discriminatory FSA lending program outcomes by targeting the lack of people of color within FSA committees. Specifically, it was not until a provision, Section 10708, in the 2002 Farm Bill that the composition of FSA county, area, and local committees were required to be “representative of the agricultural producers within the area covered by the county, area, or local committee,” and to accept nominations from organizations representing the interests of socio-economically marginalized communities. Furthermore, a provision, Section 1615, of the 2008 Farm Bill required county or area committees that are themselves undergoing rapid consolidation to develop procedures to maintain representation of farmers of color on such committees. 

Such trends culminated in the 1996 Farm Bill—the “Freedom to Farm” bill. This Farm Bill eliminated the structural safety nets that had long protected producers during lean years. Corporate buyers and groups such as the National Grain and Feed Association, composed of firms in the grain and feed industry, pushed the 1996 Farm Bill to completely eliminate price floors, the requirement to keep some land idle, and the grain reserves that were meant to stabilize supplies and therefore stabilize prices, while simultaneously encouraging farmers to plant as much as possible. The 1996 Farm Bill thus marked the culmination of the shift from the federal government subsidizing production and consumption to diminishing price supports and the subsidization of agribusiness itself. The dismantling of such price controls drove prices down and allowed corporate buyers to profit off heavily subsidized commodities while securing their power over producers. Specifically, deregulation left farmers increasingly vulnerable to market fluctuations caused by speculation, price volatility, and the profit-motives of corporate buyers. The shifts under the 1996 Farm Bill were deemed a failure by both farmers and legislators, and by 1997, rapidly falling farm prices resulted in direct government emergency payments to farmers, despite the fact that the legislation was designed to completely phase out farm program payments. Between 1996 and 1998, expenditures for farm programs rose dramatically, from $7.3 billion to $12.4 billion. They then soared to $21.5 billion in 1999 to over $22 billion in 2001. From 1996 to 2001, US net farm income dropped by 16.5% despite these payments. Rather than address the underlying cause of the price drop—overproduction—Congress voted to make these “emergency” payments permanent in the 2002 Farm Bill.

As outlined below, neoliberal corporate influence remains particularly salient within two domains: the first is food production, processing, distribution, and service, rolling grow tables and the second is education, research, and development.One major way corporations continue to profit and exert their influence on food production, distribution, and consumption is through commodity support programs. Once the safety nets of the New Deal farm programs were cut back during the 1980s and 1990s, and completely eliminated in the 1996 Farm Bill, farmers began to produce much more corn, soybeans, wheat, and other commodity crops. Specifically, the 1996 Farm Bill eliminated the requirement to keep some land idle, which encouraged farmers to plant far more than they had before. As a result, the higher supplies of these crops brought down their prices, which drastically hurt farmer incomes and greatly increased the profits corporate purchasers reaped from purchasing even cheaper commodities. These low prices undermined the economic viability of most crop farms in the late 1990s, and subsequently, Congress provided a series of emergency payments to farmers. Furthermore, because continued oversupply kept prices from recovering, Congress eventually made such payments permanent in the 2002 Farm Bill. The dismantling of direct payment support for farmers thus ushered in another form of federally subsidized cheap commodities for corporate buyers that still leaves farmers themselves relatively vulnerable: disaster assistance programs and other emergency aid. The 2014 Farm Bill in particular cut funding allocated to direct payments by about $19 billion over 10 years—the most drastic policy change in this Farm Bill—with much of this money going into other types of farm aid, including disaster assistance for livestock producers, subsidized loans for farmers, and, most significantly, the crop insurance program. As fundamental as direct payments and emergency payments have been for subsidizing agribusiness profits, under neoliberal political and economic restructuring, crop insurance has surpassed them as the most egregious and expensive subsidy for agribusiness.

For decades, farmers have been able to buy federally subsidized crop insurance in order to protect against crop failure or a decline in commodity prices. However, private insurance corporations and banks that administer the program, such as Wells Fargo, benefit the most from crop insurance subsidies. In 2011, these corporations received $1.3 billion for administrative expenses with $10 billion in profits over the past decade. In order to help cushion the blow from the reduction of direct payments, under the 2014 Farm Bill, $90 billion over 10 years will go toward crop insurance, which is $7 billion more than the previous farm bill. However, much of this money will go to private insurance corporations and banks instead of farmers. On the production side, the increase in government support will be directed toward the deductibles that farmers have to pay before insurance benefits begin. In other words, unlike non-farm insurance policies , crop insurance insures not only the crops, but also the expected revenue from selling those crops. Thus, Agricultural Risk Coverage and Price Loss Coverage only pays out when prices drop below a certain threshold. As of early 2015, corn crops have already reached this threshold. There exists a risk that this insurance program could cost far more than expected depending on how crop prices continue to shift: therefore, this is one of the more contentious aspects of the 2014 Farm Bill. Another contentious part is the uneven distribution of benefits. A 2014 report by the Environmental Working Group estimates that 10,000 policyholders receive over $100,000 a year in subsidies, with some receiving over $1 million, while the bottom 80% of farmers collect only $5,000 annually. In short, under the guise of cutting subsidies by repealing unpopular direct payments to farmers, the 2014 Farm Bill instead increases more costly crop insurance subsidies.The pressure for corporate profit and the history of corporate consolidation with regard to the food system, both vertical and horizontal, has driven corporations to continue to lower wages for millions of food system workers and accumulate more wealth. A 2011 national survey of over 630 food system workers conducted by the Food Chain Workers Alliance found that the median hourly wage was $9.65 per hour. More than 86% of food system workers were paid poverty wages while 23% of food system workers were paid less than the minimum wage.

Despite their significant role in every part of the food system—from production to processing to distribution and service—food system workers experience a greater degree of food insecurity than the rest of the US workforce. For example, according to the Food Chain Workers Alliance report, food system workers use SNAP at more than one and a half times the rate of the remainder of the US workforce. Additionally, as of 2014, twice as many restaurant workers were food insecure compared to the overall US population; as of 2011, in Fresno County, the country’s most productive agricultural county, 45% of farmworkers are food insecure. The situation is even worse in other parts of the country: in 2011, 63% of migrant farmworkers in Georgia were food insecure. Women and people of color disproportionately feel the economic pressure experienced by food system workers as a result of corporate consolidation. A comprehensive 2011 study of food workers and economic disparity found that people of color typically make less than whites working in the food chain. It found that half of white food workers earn $25,024 a year while workers of color earn $19,349. The study found that women of color in particular suffer the most, earning almost half of what white male workers earn. Furthermore, workers of color experience wage theft more frequently than white workers. More than 20% of all workers of color reported experiencing wage theft, while only 13.2% of all white workers reported having their wages misappropriated. Significantly, the study found that such discrepancies exist in all four sectors of the food system: production, processing, distribution, and service. Furthermore, such trends hold across the overall workforce. As of 2012, 11.8% of executive and senior level officials and managers, and 21% of all first- and mid-level officials and managers were people of color, growing rack despite people of color comprising over 25% of the US population. Agricultural workers in particular experience ongoing and widespread violations of the limited protections afforded to them by federal law. This is oftentimes the result of competing producers aiming to drive down their costs by not complying with employment laws. Between 2010 and 2013, for example, among agricultural employers, the Department of Labor found 1,901 violations of the Fair Labor Standards Act , which sets the federal minimum wage, overtime pay, child labor rules, and payroll record keeping requirements. A 2009 survey of approximately 200 farmworkers paid by “piece-rate” in Marion County, Oregon, found that workers experienced extensive violations of the state’s minimum wage law. Almost 90% of workers surveyed reported that their “piece-rate” earnings frequently amounted to less than minimum wage, averaging less than $5.30 per hour—37% below hourly minimum wage Furthermore, a 2013 survey of farmworkers in New Mexico found extremely low wages and high levels of wage theft: 67% of field workers surveyed were victim to wage theft within the year prior to the survey; 43% stated that they never received the minimum wage, and 95% said they were have never been paid for the time spent waiting each day in the field to begin working. 

The combination of employers’ exploitation of the immigration system, and workers’ low income, limited formal education, limited command of the English language, and undocumented status, greatly hinders farmworkers from seeking any retribution or recognition of their rights. For example, as of 2009, the National Agricultural Workers Survey found that 78% of all farmworkers were foreign born, with 75% born in Mexico; 42% of farmworkers surveyed were migrants, with 35% of migrants having traveled between the United States and another country, primarily Mexico. Furthermore, 44% said they couldn’t speak English “at all” and 26% said they could speak English only “a little”; and the median level of completed education was sixth grade, with a large group of farmworkers completing fourth to seventh grades. With limited legal aid, many agricultural workers fear that challenging the illegal and unfair practices of their employers will result in further abuses, jobs losses, and, ultimately, deportation. Worse yet, few attorneys are available to help poor agricultural workers, and federal legal aid programs are prohibited from representing undocumented immigrants. The exploitation of migrant agricultural workers begins long before they reach the United States, and this migration has largely been driven by US trade and foreign policy in Central and Latin America. Specifically, most agricultural workers are in the United States as part of the H-2A Temporary Agricultural Workers program, which allows US employers to bring foreign nationals to the United States to fill temporary or seasonal agricultural jobs. However, nearly all such employers rely on private recruiters to find available workers in their home countries and arrange their visas and transportation to the fields. US agricultural employers thrive and rely upon an immigration system and recruitment network that provides “cheap” labor , and, as such, this recruitment network outside US borders remains unregulated and highly exploitative. Among the most grievous of such practices, for example, is the collection of fees from workers as a prerequisite to being hired. Many growers are willfully ignorant of recruiters’ activities, despite recently revised regulations that require growers to promise that they have not received any such fees. With many potential workers striving to escape poor conditions in their respective homelands, there is much incentive for recruiters to charge “recruiting fees” for personal profit, leaving H-2A workers with a great deal of debt upon their arrival to the United States. While some have paid upwards of $11,000 for such opportunities to work, others have given the deed to their house or their car to recruiters as collateral so as to ensure “compliance” with the terms of their contract. Many farmworkers been deceived about their wages and working conditions , and, to make matters worse, many workers are tied to one employer and therefore have no choice but to work regardless of the low pay and abysmal working conditions of their employers. Ultimately, the H-2A program and US labor market creates conditions ripe for debt-peonage. Furthermore, although H-2A program regulations require employers to give job preference to qualified US workers, in practice the H-2A program ultimately puts US workers out of work given the seeming cost benefits of employing H-2A workers.

Table 5.2 shows the share of commercial dairies with at least one female operator by state and year. This has very interesting results with all commercial dairies reporting at least one female operator in 2017. The actual share of female operators compared to the share of operators gives us a better representation of demographic changes. The share of female core operators increased from 2002 to 2017 in every state but New Mexico, for which the share of female core operators decreased in 2007 and 2012 but was the same in 2002 as in 2017 . California and New York both increased the number of across each Census year. California had a 27% increase in female core operators from 2002 to 2017 and the share of female core operators in New York increased by 33%. Idaho, Texas, and Wisconsin all had a slight decrease in female core operators in 2007 and 2012, but an increase in 2017 relative to all previous years. Interestingly, when we look at the share of female operators it follows a similar pattern. California and New York both increases in the share of female operators across each Census. Wisconsin, Idaho, and Texas all had slight decreases in 2007 and 2012 relative to the 2002 share, but the share of female operators in 2017 was larger than in 2002 . However, the share of female operators in New Mexico had a small decrease from 2002 to 2017. This suggests that despite the addition of a fourth core operator in the 2017 COA the pattern is not substantially different from the trend in operators and that the trend was not only facilitated by capturing previously unmeasured management activities by women. From here characterizing the trend could be thought of in two ways: 1) this describes an actual increase in women operators playing a more prominent role and/or 2) an increase in their male associates being more likely to recognize and report female operators.

Disentangling exactly what characterizes these trends is impossible, trimming cannabis but it seems likely that the addition of a fourth core operator and the ability for more than one principal operator may have signaled a conversation about representation on the COA for many commercial dairies. Next, it is important to characterize the management characteristics of commercial dairy operators. These results are only characteristic of core operators as this data was not collected for all operators. The COA asked core operators whether their principal occupation was off farm. Overall, a larger share of female core operators had a principal off-farm occupation than male core operators . In California, less than 10% of the male core operators had an off-farm principal occupation, but about 30% of female core operators had an off-farm principal occupation with little variation over time. In other states, like Idaho and Texas, the share of core operators with off-farm principal occupation followed a similar pattern to California by gender. However, there was an 86.6% increase in male core operators with an off-farm principal occupation and an 18% decrease in female core operators in New Mexico. Along a similar thread, a very small portion of female core operators was labeled as principal operators. Now, the definition of a principal operator did change for the 2017 COA, but even with the 2017 addition of more than one core operator being labeled as a principal operator the share of female core operators that are labeled as a principal operator is relatively small. In California, 5% of female core operators are principal operators from 2002to 2012 with a jump in 2017 to 17% with the addition of the fourth core operator .

Idaho, New York, and Wisconsin follow a similar pattern as California with little to no change from 2002 to 2012 and a large jump in 2017. New Mexico and Texas, however, had a decrease from 2002 to 2012 and then a large jump in 2017. In 2017, most states had about 16- 20% of female core operators listed as a principal operator, but New Mexico only had 11%. This research would be incomplete without a description of the presence of spousalrun dairy farms in the U.S. A spousal-run dairy refers to a dairy that is managed by two operators that are married to one another. There is a historic assumption that many dairy farms are run by spouses, however, this research finds that trends in spousal commercial dairy operations does differ greatly by state . For some states, like Wisconsin, New York, and Idaho, a significantly large share of commercial dairy farms was being run by spouses, with over 40% of commercial dairy farms in each state being spousal run. In California, 31% of commercial dairy farms are run by spouses, but New Mexico had relatively few commercial dairies run by spouses and a decrease from 15% to 13% from 2012 to 2017. A large share of female core operators of commercial dairies was married to a principal operator in 2012 and 2017 . In 2017 Texas had the largest share with 80% of female core operators married to a principal operator and then Idaho and Wisconsin both had more than 75%. New Mexico had the smallest share of female core operators married to a principal operator with 48%, but that remains a significant share. Next, age of commercial dairy operators has been a point of discussion for because of the increasing age of dairy farm operators. Table 5.9 presents the share of operators by gender and age group for the Census year and state. Across all state the largest share of female operators was in the less than 50 years old age group with all states following a similar trend of a decreasing share of younger operators and increase in the share of older operators. For male operators the largest share was the less than 50 age group also had the largest share.

There was a significant share of male operators in the larger age group categories across all states with every state, but Wisconsin, have at least 10% of operators being male and over the age of 66. Finally, previous literature suggested that women may be more likely to adopt sustainable-minded practices. Regarding organic production, this seems to be true. In 2017, most organic commercial dairies have at least one female core operator, except in New Mexico for which only 17% of organic commercial dairies have at least one female core operator . The share of organic commercial dairies with at least one female operator is larger than the overall share of commercial dairies with at least one female operator. There was an increase in the share of female core operators that operated an organic commercial from 2007 to 2017 , but this was also with the addition of the fourth operator. There has been a slight increase in the share of organic commercial dairies across all states, but in 2017 all states had less than 15% of commercial dairies with organic production . Organic dairies do tend to have smaller herd sizes, in general and more milk sales revenue per cow. Organic commercial dairies have a larger share of female core operators than commercial dairies overall for all states, except New Mexico. In 2017, organic commercial dairies report at least a 30% or more share of female core operators, except New Mexico which only had an 8% share of female core operators . In every state, except New York, there was an increase in the share of female core operators that manage organic commercial dairy. The share of female core operators that manage an organic dairy decreased by 28% in New York but increased by 66% in Idaho. Next, I turn to explore the relationship between the farm size and gender demographics of farm operators and spousal-run operation. COA is panel data, gardening rack meaning that it is both times series and cross sectional in nature. For my analysis, I utilize a log-linear model with fixed effects in order account for cross-state and cross-time differences. The farm size variables, of the individual farm at time , are the logged dependent variables including Cowsit number of milk cows , TMDit total sales revenue from dairy or milk, and TVPit total value of production. I utilize farm-level operator characteristics variables including a binary variable for the presence of a female core operator , the share female operator on the individual farm , and a binary variable that indicates a spousal run farm variable .

Furthermore, I included a variable to control for a relationship between the age demographics of operators on farm size. MaxAgeit describes the maximum age listed by any given core operator on an individual commercial dairy. Table 5.13 shows the list of variables use in regressions and their corresponding definition. In addition, αi and λt represent the state fixed effect and the time fixed effect, respectively, and uit is an error term. Xit represents a vector of farm operator characteristics and farm management characteristics. logFarmSizeit represents a vector of the logged farm size variables listed above. Equations 1 is the regression equation used to show the relationship between the presence of a female operator and farm size, accounting for age, state, and year influences on farm size. Table 5.14 shows the relative coefficients and standard errors of each regression. Concerning the number of milk cows, the presence of at least one female core operator relates to a decrease in the herd size by about 12.9%, when holding constant for age, state, and year influences on farm size. With herd size, when accounting for the presence of a female operator, the max age corresponds to an increase in the herd size by 0.5%. The presence of at least one female core operator suggests a decrease of the total value of production by 31% and all milk or dairy sales by about 13.4% as well. So, across all farm size measures, there are relatively similar results. A one-year increase of the maximum age of any core operator relates to an increase in the total value of production by about 0.7%. Since 2002, there has been an increase in the share of female core operators and commercial dairies with at least one female operator. The trends in the share of the core operator and the share of operators suggest that these increases are not due to the increase in the number of core operators’ data collected by the COA, but, in fact, an actual increase in female commercial dairy farm management. Furthermore, both the presence of female operators and the share of female operators had significant negative relationship with farm size of commercial dairies across states and time. Furthermore, the presence of spouses running the commercial dairy also shows a significant decrease on the farm size. Due to the significant share of female core operators that are married to principal operators, it seems likely that this trend could be due to change in management and risk incentives of the operators resulting from both spouses’ income being likely determined by the success of the dairy. A garden may be understood as a place where the ‘geography of the mind meets that of the earth’, making it entirely apropos that the vertical garden finds its origins in the monumentally horizontal prairies and farmlands of the American Middle West, where the topography of Professor Stanley Hart White’s creative intellect meets a seemingly endless geography of flatness. White patented the first known green wall in 1938, prototyping the technology in the backyard of his Urbana residence, yet the concept emerges in his writings and drawings as far back as 1931 as a response to the problem of modern garden design. The significance of this invention has ‘still unrealized provocations’3 on the history of gardens and designed landscapes, having been conceived during a trajectory towards modernism in the same geographic region as the Prairie School and American Skyscraper. Although the provenance of this new technological garden is topographically uncanny, the invention itself is pure genius, synthesizing ideas from modern landscape and architectural theory, building sciences, horticulture, and industrial arts alike. White’s vertical garden finds its legal origins in 1937–38, albeit the technological and material precursors to the invention extend back to early horticultural experiments and industrialization of modern building materials.

New Mexico saw an increase in the number of farms with milk and/or dairy sales between 2002 to 2007, but then subsequent decreases in 2012 and 2017 . Overall, there was a decrease in farms with herd sizes between 200-999 milk cows. Figure 3.8 show the relative decrease in farms with milk and/or milk sales in New York and the decrease in the share of dairies with 1-199 milk cow herd size. There was an increase in the number of farms with a herd size greater than 1,000 milk cows. Figure 3.9 shows a decrease in the number of dairies with milk and/or dairy sales with significant decrease in the share of farms with a 1-199 milk cow herd size between 2002 to 2017 in Texas. There was also an increase in the number of farms with herd sizes greater 1,000 milk cows. Figure 3.10 shows that between 2002 and 2017 Texas farms with a herd size greater than 1,000 milk cows saw a significant increase in the share of milk or dairy sales revenue, from about 40% in 2002 to almost 90% in 2017. The majority of Wisconsin farms have a small herd size, although there has been a decrease from 2002 to 2017. There has been an increase in the number of farms with larger milk cow herd sizes . Figure 3.12 shows that the majority of milk and/or dairy revenue in Wisconsin used to come from farms with smaller milk cow herd size but has shifted overtime towards farms with larger milk cow herd sizes. The size distribution of farms in the U.S. has been a topic of economic research and discussion for decades. Changes in farm size along with reductions in farm numbers have raised concerns based on it the possible impact on rural communities, particularly movement out of certain regions leading to a possible decrease of employment opportunities in that region. Moreover, accurate and descriptive analysis of farm size is often used to inform agricultural policy and discussion, particularly in the dairy industry. In both industry discussion and policy-based decision-making, surrounding farm size, cannabis dryer the trend of consolidation is central to the discussion on the future of the dairy industry. Some suggest that the trend of farm size is characterized by consolidation with an increase in large farms, and fewer small farms remaining.

One assumption is centered around the idea of the disappearing middle, mid-sized farms, in agriculture with some arguing that the farm size distribution can be considered bimodal. This language can be vague and detailed analysis by state is needed for a clear characterization of farm size. Wolf and Sumner find that the argument of U.S. farms being bimodal is not the case for the dairy industry in 1989 and 1993. This thesis research aims to expand on this finding by discussing correlations related to farm size changes, kernel density plots of herd size and using parametric statistical density functions to characterize the herd size by state, utilizing recent Census of Agriculture data. The COA is a representative sample of all farms in the United States. This is individual farm level data across six states and four years which is a unique sample for research studies. This research looks at individual farm-level characteristics including farm size and operator characteristics and discuss the shifts across time and states. The trend of dairy consolidation in the United States has been characterized by a decrease in the number of dairies with the number of milk cows remaining relatively stable . Using the COA data, the number of milk cows on a commercial dairy has remained relatively stable with most states seeing slight increases in the number of milk cows, except New York . Whereas the number of commercial dairies has decreased significantly across all six states, except New Mexico which only decreased slightly . California and Idaho both had about a 36-37% decrease in the number of commercial dairies, while in New York, Texas, and Wisconsin the number of commercial dairies decrease by about 50%. Farm size distribution remains a prevalent agricultural policy issue, as characterization of the dairy industry’s farm size is used to inform legislation and often characterizes colloquial discussion about the state of the industry. This is in part due to firm size growth’s correlation with innovation and technology, as well as the firm’s ability to capture economies of scale. Although dairy farm size can be characterized for the U.S. overall, there are important distinctions by state, as the dairy farm size distributions differ greatly by state.

Macdonald et al. detail that larger dairy farms are able to capture economies of scale, more so than smaller dairies, resulting in a lower average milk production cost. However, the article does go on to specify that the distribution of dairy farm size differs greatly by state based on the specific financial and economic environment of the dairy industry in that state. Alternatively, some dairy farms lower the average milk production costs by capturing the economies of scope, i.e., diversification of sales. This could be characterized as raising and selling replacement dairy heifers, or other agricultural products such as grain to maintain economic viability. Finally, I consider the relationship that farm operator characteristics may have with farm size and the decision of a farm to exit. In Chapter Five, I detail a specific line of analysis related to the influence of female farm operators on farm size, but in this chapter, I will discuss the influence that the age of the farm operator may have on the farm size. Dairy farm size changes in response to these and other factors is important in considering future trends in farm size and their impact on milk production in the U.S. and the future structure of the dairy industry. This chapter aims to characterize the herd size distributions of the U.S. dairy industry, present evidence on the characteristics of the farm size distributions, and then finally discuss the correlation between farm level characteristics and farm size. This chapter will be structured as follows: a brief overview of previous literature on firm and farm size, a discussion about farm size distribution estimation, and then the results and discussion. Economic research and discussion have produced several theories on firm size and firm growth to characterize industries and the economy. This section will briefly review important studies related to firm size more generally and then will move on to research specific to the study of farm size and the economics of dairy farm size and size distributions. The study of firm size by economists can be best discussed chronologically, as much of the research builds off one another or finds results inconsistent with previously held theories. In 1931, Gibrat postulated what has come to be known as Gibrat’s Law that a firm’s growth rate is independent of its size.

This would mean that the growth rate of an individual firm over a particular time period should not be influenced by its original size. Ijiri et al. , using the foundation built by Gibrat’s Law, finds that firms that grew over 10% in the subsequent period are more likely to see above industry average growth, due to continued benefits of innovation that occurred in the subsequent periods. Viner theorizes that firm size distribution is based on the industry environment and that individual firms have a U-shaped average cost curve and will function at the minimum of this curve. He goes on to specify that firm entries and exits are determined by the quantity demanded by the market. Lucas used these previous works to build a new theory about the size distribution of firms in an industry that looks at size distribution as a solution for output maximization with a given set of production factors and managers with varied human capital levels. This model predicts the size distribution of firms based on the managerial ability of laborers and then subsequent resource allocation. Jovanovic finds that smaller firms will tend to have higher growth rates than larger firms, but that these smaller firms are more likely to exit the industry than the larger firms. Evans discusses growth relative to a firms age, finding that a firm’s growth can be tied to the age of the firm itself and that older firms have a slower growth rate. This theory is hypothesized to remain true for dairy farms. Stemming from foundation of Gibrat’s law, which claims that the firm size distribution follows a lognormal distribution, drying weed there has been significant literature on the size distribution of firms that looks at fitting parametric distributions to actual firm size data. Kondo, Lewis, and Stella evaluate recent non-farm panel data from the U.S. Census Bureau and find that the current U.S. firm size data best fits with a lognormal distribution, but there are differences in goodness of fit by industry. Akhundjanov and Toda use the original data, in Gibrat’s original paper, find that a Pareto distribution better characterizes the empirical size distributions. The distribution of firm size remains a fundamental part of research firm growth patterns and the literature on firm size has been directly applied to research on the growth rate of farms and farm size changes in different agricultural industries. Two common parametric distribution used in farm size distribution analysis are lognormal and exponential. Allanson evaluates farm size trends in England and Wales finding that the lognormal distribution fits farm size measures relatively well across time. Whereas Boxely uses an exponential distribution to evaluate farm size data from the Agricultural Census and finds that from 1935-1964 farm size shifted to the right, but that at the state level farm size does tend to follow the exponential distribution with some regularity. Before going any further in the analysis, it is important to outline the concept of farm size for this analysis.

Farm size measures across the whole agricultural industry tend to leave out key details that give better and more accurate accounts of the size of the farm for the commodity/industry. For example, when looking at the size of U.S. farms overall measuring the size of the farm based on acreage will lead to inaccurate or confusing results. The acreage needed to generate the same revenue for corn versus dairy milk or strawberries is substantially different. However, looking at the dairy industry specifically, many different characteristics shape a dairy’s economic footprint on the market, and therefore, defining how to characterize dairy farm size is fundamental to discussing changes in the dairy market. One can characterize the size of a dairy by the number of milk cows, or herd size, as one measure of dairy firm size. However, other characteristics such as the quantity of milk produced, the value of production, and value-added on the farm could also be considered as farm size measures . Different farm size measures allow us to answer different agricultural economic questions. While analyzing the dairy industry it is relevant to consider herd size, the milk and/or dairy sale revenue of the firm, and the total value of production, as we have already discussed in Chapter 2. Previous research on dairy farm size documents strong trends toward consolidation in the U.S. with a decrease of about 50% of all registered U.S. dairies from 2002 to 2019 . These trends in consolidation have differed by location with historically dairy producing regions seeing a large share of exits, these states were historically made up of smaller and mid-size dairies. MacDonald et al. detail the cost differences between larger and smaller dairies with cost advantages for larger dairies that drive the investment decision to increase herd size. This research suggested that there would continue to be a steady decline in the number of smaller and mid-size dairies and that the trend of consolidation would likely continue. This trend has raised research questions about what factors influence the distribution of farm size and the decisions of some farms to exit the industry. A common, albeit incorrect, assumption about the size distribution of the U.S. dairy industry is that it is bimodal. This assumption comes from news reporting and political commentary that there is a “declining” middle of farms in the U.S. and that there is this dichotomy between small, sometimes organic, farms and larger farms.

All farmers interviewed mentioned direct experience as being one of the most important modes for understanding their landscape, their farming system, and management practices essential to their farm operation. The farmers described this accumulation of experience as “learning by doing,” being “self-taught,” or learning by “trial and error” . These farmers added that in learning by experience, they made “a lot of mistakes” and/or faced “many failures” but also learned from these mistakes and failures – and importantly, that this cycle was crucial to their chosen learning process. More than half of the farmers interviewed maintained that no guidebook or manual for farming exists; while reading books was viewed as valuable and worked to enhance learning for individual farmers, to farm required knowledge that could only be gained through experience. Moreover, nearly all the farmers also explicitly commented on the fact that they have never stopped learning to farm . Overall, farmers in this study learned primarily through personal experience and over time, making connections and larger conclusions from these experiences. On-farm experimentation was a critical component of knowledge building as well. Experimentation consisted of methodical trials that farmers implemented at small scales on their farms, and most often directly on a small portion of their fields. Experimentation was often incited by observation , a desire to learn or to increase alignment with their own values, or a need to pivot in order to adapt to external changes. The farmers experimented to test the feasibility of implementing specific incremental changes to their current farming practices before applying these changes across their entire farm. For example, cannabis indoor greenhouse one farmer relied exclusively on trucking in urban green waste compost as part of the farm’s fertility program when she first started farming.

However, one year, she decided to allow chickens to roam in a few of the fields; within a few years, those fields were outproducing any other field on her farm in terms of crop yield. She quickly transitioned the entire farm away from importing green waste compost to rotating chickens on a systematic schedule throughout all fields on her farm. This form of experimentation allowed this farmer to move from relying on external inputs for fertility to cycling existing resources within the farm and creating an internally regulated farming system . For this farmer, this small experiment was monumental and shifted her entire farm toward a management system that was more in alignment with her personal farming values. As she described, “When you look at everything on the farm from a communal perspective and apply that concept of community to everything on the farm . . . it literally applies to every aspect of your life too.”Though this farmer had initially used direct observation to implement raised beds on his farm, as he learned the purpose of raised beds through his own direct experience, he slowly realized – over the course of decades – that raised beds served no purpose for his application. One year, he decided not to shape some of his beds. At the end of the season, he evaluated no real impact on his ability to cultivate or irrigate the row crops on flat ground, and no impact on yield or crop health. In fact, he observed less soil compaction and more aeration due to fewer passes with heavy machinery; and, he saved time and fuel. The transition to farm on flat ground took several seasons for this farmer, but over time, his entire farm operation no longer used raised beds to grow row crops. This breakthrough in farming for this particular farmer was informed by personal experience and guided by careful experimentation.Second to experience, observation also influenced the farmer learning process.

Whereas direct experience is usually immersive, and embedded within a larger social context, observation is a detached, mechanical form of knowledge production, where a farmer registers what they perceive to transpire . For example, farmers cited observing other farmers in a multitude of ways: “By watching other farmers, I really mean I’d just drive around and look. I’d see what tools they were using;” or “If I saw someone working in the field, I would stop my car on the side of the road to see what people are doing;” or “I really would just observe my father farm,” as well as making observations about the status of their land . Several of the farmers summed up their cycle of learning as a cycle of observation, trial, feedback, observation, trial, feedback, etc . However, several of the farmers clarified that this type of learning did not necessarily involve talking to fellow farmers. One farmer shared that he learned certain farming practices from a neighbor farmer through distant observation and then borrowed ideas he subsequently applied on his farm; to achieve this, he admitted that he had never really talked to the other farmer directly. Another farmer noted that he would “go back at night if they [another farmer] left their equipment in the field and just study how it was set up, so I [he] could see what was going on.” Based on interviews with other farmers, farmer-to-farmer knowledge exchange often consisted of detached observation rather than personal conversation or direct contact with another farmer.During the initial field visit, the farmers shared their definitions of soil health. Across all farmers interviewed , responses appeared mechanical and resembled language disseminated by government entities such as the Natural Resources Conservation Service . As such, most responses emphasized building soil organic matter, promoting biological activity, maximizing diversity, and minimizing soil disturbance. During the in-depth interview, farmers shared specific indicators used to evaluate soil health on their farms. These responses were varied compared to definitions of soil health and were generally based on observation and personal experience.

Generally speaking, the farmers relied heavily on their crops and on the health of their crops to inform them about the basic health of their soil. In fact, the farmers cited using their crop as their foremost indicator for gauging optimum soil health. One farmer shared, “Mostly, I’m looking at the plants, if the color of green on a particular leaf goes from shiny to matte, or slightly gray undertone to it. These subtle cues, I pick up from just looking at my crops.” The growth habit of weeds within and around fields was also cited as an indicator of soil health. For example, one farmer explained, “I’m looking at how the weeds are growing at the edges of the field; in the middle of the field. Is there a difference between what’s happening around the edges and what’s happening in the field?” Some farmers also frequently relied on cover crops as indicators for determining soil health and soil behavior. When acquiring new fields, for example, the farmers tended to first grow cover crops to establish a baseline for soil health and also understand soil behavior and/or soil type. The farmers also used cover crop growth habits to gauge the status of soil health and soil fertility for a particular field before planting the next iteration of crops. As one farmer elaborated, “I’m judging a field based on how a cover crop grows. It’s one thing if you’re planting a nutrient-intensive crop in a field, but if you have a cover crop in the field and there’s a swath that’s this tall and another swatch that’s only this short, then you know there’s something seriously different about that section of field and the soil there.” The organic farmers in Yolo County that were interviewed for this study demonstrated wide and deep knowledge of their soil and farming systems. Results show that white, first- and second-generation farmers that farm alternatively accumulate substantive local knowledge of their farming systems – even within a decade or two of farming. These particular organic farmers demonstrated a complex understanding of their physical environments, soil ecosystems, and local contexts that expands and complements other knowledge bases that inform farming systems. While the content and application of farmer knowledge may be locally specific , below we consider aspects of this case study that may be more broadly applicable. First, we discuss emergent mechanisms for farmer knowledge formation using existing frameworks in the social-ecological systems literature, and also summarize key features of farmer knowledge that coalesced from the results of this study.To further examine how farmers in this study acquire and incorporate their knowledge within their farm operation, cannabis growing equipment we first explore emergent mechanisms that underpin farmer knowledge formation. Because farmer knowledge encompasses knowledge of both social and ecological systems – and the interactions thereof – it is useful to draw upon existing frameworks from the social-ecological systems literature in order to trace the process of farmer knowledge formation among farmers in our case study. Briefly, social-ecological systems recognize the importance of linking social and ecological processes to capture interactions between humans and the environment; importantly, existing literature within SES studies also emphasizes the interactive and adaptive feedback among social and ecological processes that link social and ecological system dynamics . Boons offers a conceptual guide for identifying social-ecological mechanisms, which adapted to our case study provides a starting point for tracing aspects of farmer knowledge formation. Here, social-ecological mechanisms for farmer knowledge formation refer to – on the one hand, social and cultural phenomena that influence farmer knowledge and their personal values – on the other, farmers’ observations of and experiences with environmental conditions and ecological processes on their farms that influence their knowledge and their values – and the interactions thereof . Drawing upon Bar-Tal , we further define farmer values as a farmer’s worldview on farming – a set of social values or belief system that a farmer aspires to institute on their farm .

In our study, examples of social-ecological mechanisms for farmer knowledge formation among these farmers included direct observation, personal experience, on-farm experimentation, and inherited wisdom from other local farmers. Similar to Boons’ conceptual guide, our results suggest that social-ecological mechanisms may play a central role in producing a farmer’s values and in integrating ecological knowledge into their farm operation. At the same time, results also highlight that social-ecological mechanisms may contribute to a farmer’s local ecological knowledge base, and importantly, place limits on the incorporation of social values in practice on farms. It is possible that social-ecological mechanisms may also provide the lens through which farmer values and ecological knowledge are reevaluated over time. Moreover, farmer values may also mutually inform ecological knowledge – and vice versa – in a dynamic, dialectical process as individual farmers apply their values or ecological knowledge in practice on their farm. Social-ecological mechanisms may also be key in translating abstract information into concrete knowledge among farmers interviewed. For example, experimentation may codify direct observations to generate farmer knowledge that is both concrete and transferable; or, to a lesser degree, personal experience may enhance farmer knowledge and may guide the process of experimentation. In general, we found that farmers interviewed tended to rely less on abstract, “basic” science and more on concrete, “applied” science that is based on their specific local contexts and environment . This finding underscores that for these farmers, their theory of farming is embedded in their practice of farming, and that these farmers tend to derive theoretical claims from their land.For example, the farmers who possessed a stewardship ethos viewed themselves as caretakers of their land; one farmer described his role as “a liaison between this piece of land and the human environment.” Farmers that self identified as stewards or caretakers of their land tended to rely most heavily on direct observation and personal experience to learn about their local ecosystems and develop their local ecological knowledge. This acquired ecological knowledge in turn directly informed how farmers approached management of their farms and the types of management practices and regimes they applied. That said, farmer values from this study did not always align with farming practices applied day-to-day due to both social and ecological limits of their environment. For example, one farmer, who considered himself a caretaker of his land expressed that cover crops were central to his management regime and that “we’ve underestimated how much benefit we can get from cover crops.” This same farmer admitted he had not been able to grow cover crops the last few seasons due to early rains, the heavy clay present in his soil, and the need to have crops ready for early summer markets.

The symptom cluster with substantial evidence of CEDS is present for migraine, fibromyalgia, and irritable bowel syndrome. The endocannabinoid system regulates gut function, the CNS, and has a communicative relationship with the microbiome. Therefore, many other disorders and diseases are linked to a deficiency and dysfunction of the endocannabinoid system. Dysregulation of the endocannabinoids and CB2 receptors lead to many disorders affecting the liver, kidneys, CNS,neuromuscular, GI, immune system, lungs, bone, and mental health. Deficiency of endocannabinoids disrupts homeostasis. This provides an opportunity for the additional assessment of the therapeutic potential of phytocannabinoids, naturally-occurring cannabinoids in the cannabis, or hemp plants. These phytocannabinoids interact with the endocannabinoid system in the same way as endocannabinoids. There was a reduction in expression of the tight junction proteins occludin and claudin-5 on brain microvascular endothelial cells in germ-free mice . Expression of these proteins and BBB integrity was restored after gut colonization with the butyrate producing species, Clostridium tyrobutyricum or by administration of butyrate. Probiotics improved gut integrity and enhanced endocannabinoid signaling. Zebrafish were treated with a probiotic formulation for 30 days. Compared to untreated animals, histological analysis of gut Thissue from treated animals showed an intact epithelial barrier with increases in enterocyte length, villus length, and crypt depth. There was a reduction in epithelial and mesenchymal apoptotic cells, microgreen grow rack confirming molecular level changes of the pro-apoptotic factors casp3 and BCL2 associated X , and an increase in antiapoptotic signals such as B cell lymphoma 2 . Probiotics also decreased the gene expression for fatty acid amide hydrolase and monoacylglycerol lipase , which are involved in the degradation of endocannabinoids AEA and 2-AG.

One must take into consideration these metabolism pathways. Thus, probiotic treatment improved gut integrity and enhanced endocannabinoid signaling. The influence of cannabis on host Thissues, particularly gut permeability and its subsequent indirect effects on the gut microbiome, suggests significant potential therapeutic applications in HIV. Cannabis has been used for its medicinal properties for thousands of years in ancient cultures. Being a novice to cannabis use can be an intimidating issue for providers making recommendations to their patients. There are hundreds of strains that have names that are not based on structured nomenclature that clinicians are normally familiar with. Only recently, in the 1960s have scienThists begun to explore the properties of cannabis and even more recently the medicinal application in conjunction with Western medicine. Understanding the general physiological mechanism of endocannabinoids will support the framework in forming strategies to strain selection for symptom management. Despite suppressive ART, PWH maintain a high symptom burden with GI disorders, HAND, depression/ anxiety, pain, and fatigue. In addition, CD4 + T cell depletion and gut microbiota dysbiosis promote dysfunction of the gut epithelial barrier, resulting in a positive feedback loop sustained by increased microbial translocation of pro-inflammatory antigens such as LPS and subsequent immune activation and chronic inflammation. Consequences of these events in PWH are associated with poor health outcomes, including organ damage, cognitive decline, and decreased quality of life. Phytocannabinoids may be a viable supplement to accommodate for deficiencies in the endocannabinoid system. Components of cannabis have an anti inflammatory and antioxidant effect addressing problems on a molecular and cellular level. Responsibly used, cannabis can be given as an antidepressant and for relief of post-traumatic stress disorder, sedative, and anticancer benefits and relief of obsessive behaviors. Benefits extend to symptomatic relief for symptoms like fatigue, poor appetite, depression,anxiety, insomnia, pain, nausea/vomiting, and cognitive changes.

Clinicians will find confidence in educating themselves on the effects of cannabis to support a conversation with patients during office visits. One caveat to widespread adoption by the medical community is that as of 2019, the Department of Justice Drug Enforcement Agency holds that cannabis is a controlled substance with no evidence of medical benefit and high potential of abuse, even with 33 of 50 states and the District of Columbia currently having state-legislated approval to dispense cannabis for medical purposes. Furthermore, the federal government enforces barriers and restrictions on studies investigating the benefits of cannabis due to federal restrictions.104 Regardless of the discordance of laws between federal and state governments, patients are in fact using or interested in using cannabis to manage aspects of their health. Providers should have a working understanding of cannabis and its various effects on the body, including benefits and potential risks. While effects of cannabis on gut barrier function have been studied in pre-clinical models, the translation to humans is uncertain. Evaluation of the gut microbiome in both PWH and HIV transgenic animals exposed to chronic cannabis is necessary to begin to test beneficial effects to correct gut permeability and dysbiosis. The additive effect of probiotics and cannabis may result in synergistic effects in terms of supporting healing of the gut and also the reduction of inflammation, immune activation, and neuropsychiatric disorders within the context of ART.Modern agriculture faces environmental concerns about the use of pesticides. Organic agriculture is an alternative production method that limits the use of synthetic pesticides and fertilizers. The literature has documented that organic crop production does has a lower environmental impact per unit of land than conventional agriculture . However, previous studies often concentrate on a small geographic or crop variety scoop. In essay 1, I use the California Pesticide Use Report database to examine the environmental impacts in conventional and organic crop production at a full scale. It includes all pesticide use in commercial production. I examine the period 1995 to 2015 and find that pesticides used in organic production had smaller negative environmental impacts on surface water, groundwater, soil, air, and pollinators than pesticides used in conventional production.

Over time, this difference has declined. I also investigate how farm size and farming experience are correlated with pesticide use. I find that farmers with more acreage use pesticides that have larger environmental impacts. Specifically, more experienced farmers use pesticides that have greater impact on surface water and groundwater, and less impact on soil, air, and pollinators. The environmental impacts of pesticide use in organic agriculture increased over my study period, which is an interesting observations that requires further investigation. In essay 2, I focus on organic crop production and try to quantify the change in pesticide use. I find that the pesticide portfolio has changed dramatically for organic crop growers, as illustrated by the decline in sulfur use and the increase in spinosad use. Pesticide use is correlated with farm size. The consolidation of organic cropland is another trend documented in essay 2. Historically, ebb and flow flood table consolidation in agriculture as a whole has manifested as an decrease in the number of farms while the total cropland remains stable . In the organic sector, in contrast, both the number of farms and acreage have grown significantly for the last two decades. Nonetheless, consolidation has occurred because the share of large farms in total acreage had increased. In 2015, 56% of organic cropland was operated by growers with at least 500 acres of organic cropland, up from 15% in 1995. At the other end of the spectrum, growers with 10-50 acres accounted for 18% of organic cropland in 1995, which dropped to 8% in 2015. The average organic farm size increased from 46 acres in 1995 to 103 acres in 2015. The median organic farm size increased from 15 to 17 from 1995 to 2015. Farms with larger organic acreage, holding other variables constant, applied sulfur and fixed copper pesticides more frequently than those with smaller acreage. As a result, they had greater impacts on surface water and smaller impacts on soil and air because those ingredients are more toxic to fish and algae, and less toxic to earthworms and have lower Volatile Organic Compound emissions than other ingredients used in organic fields. The composition of organic crop has changed in California with the acreage share of vegetables increasing from 30% in 1995 to 50% in 2015. However, pesticide use patterns and the correlation with farm size do not differ between vegetables and other crops. The consolidation of cropland has not been limited to the organic sector. MacDonald et al. documented that the consolidation of acreage and value of production into a smaller number of larger operations has characterized U.S. agriculture for decades. In essay 3, I adapt and extent the endogenous growth model introduced in Lucas to explain changes in the size distribution of farms and specialization over time. In the theoretical model, farmers have knowledge regarding the production of each crop, and this knowledge grows only through learning from other farmers. Increased knowledge increases the profitability and knowledge can be apply across crops to various degrees. In my modeling framework, the opportunity cost of producing crops that farmers know less about increases as specialized knowledge accumulates, which reduces the number of crops produced by each farmer. The evolution of the farm size distribution in equilibrium and simulation results are presented to demonstrate how model parameters including learning rate, budget share, and elasticity of substitution alter the distribution of farm size and specialization.

The food system has faced concerns about its use of pesticides since even before Rachel Carson published Silent Spring . Today, concerns about environmental impacts from pesticide applications continue to grow . In this context, organic agriculture is proposed as an alternative farming system as it prohibits the use of most synthetic substances . With strict modeling assumptions, Muller et al. presents sim- ulation results that support organic agriculture as an alternative production system capable of providing food for the world population by 2050. Consumers’ perception that organic agriculture is more environmentally friendly has facilitated its growth . According to the Organic Trade Association, U.S. organic food commodity sales reached $39 billion in 2015 in real terms, up from $4 billion in 1997, the base year. The share of organic food sales in total food commodity sales increased from less than 1% to 5% during the same time period . In 2002, the National Organic Program was launched. It established national standards for organic certification and took enforcement actions if there were violations of the standards. Organic growers are prohibited from using certain production practices that have significant negative environmental impacts. However, the regulation of organic agriculture is process-based, not outcome-based, and the regulatory agency does not monitor or enforce standards on environmental outcomes such as biodiversity and soil fertility . Another source of concern comes from the way organic farming practices may change as the sector grows. As pointed out by Läpple and Van Rensburg , late adopters of organic agriculture are more likely to be profit driven and care less about the environment than early adopters. And, the prices of organic products remained at least 20% higher than their conventional counterparts in 2010 , which could encourage additional entry. Therefore, unintended consequences might emerge and organic agriculture could be less environmentally friendly than commonly perceived. There is some evidence of this in the scientific literature. Organic agriculture has been reported to have higher nitrogen leaching and larger nitrous oxide emissions per unit of output than conventional agriculture . Certain pesticide active ingredients used in organic agriculture have been found to be more toxic than conventional AIs in laboratory environments and field experiments . For example, Racke reviewed the discovery and development of spinosad, a natural substance used to control a wide variety of pests, and observed that spinosad was approved based on its low mammalian toxicity. However, Biondi et al. found that spinosad is more harmful to natural predators than pesticides used commonly in conventional agriculture. As the case of spinosad demonstrates, pesticide use in organic agriculture could impose more environmental impact than conventional agriculture in one or more dimensions. Therefore more evidence is needed to evaluate the environmental impact of organic farming practices and its determinants. In this essay, I provide novel evidence regarding the impact of pesticide use in organic and conventional agriculture on different dimensions of environmental quality, and quantify the difference between the environmental impacts of pesticide use in the two production systems in California. In addition, I examine the relationships between farmers’ pesticide-use decisions and their experience and farm size. California is the leading state for organic agriculture in the U.S., accounting for 12% of certified organic cropland and 51% of certified organic crop value nationally in 2016 .

To test irritability-like behavior after WIN exposure, we used the bottle-brush test, based on the experimental method that was designed previously for mice and slightly modified to better monitor rat behavior. Currently, this model is increasingly used by both our laboratory and others as a measure of negative emotional states in animal models of addiction. This method has advantages over other behavioral paradigms that measure aggressive/ defensive behaviors, such as the social dominance/subordination paradigms and resident/intruder confrontation paradigm, because the experimenter has greater control over the mechanical stimulus and thus better precision in ensuring uniform provocation. Furthermore, the “social” factor in eliciting agonistic behavior and the risk of physical injury during an agonistic encounter are both circumvented in the bottle-brush test. The mechanical stimulus of the moving bottle-brush has also been found to be more effective in provoking these behaviors compared with either deceased or stuffed animals. In the present study, the animals were randomized, and three trained observers scored the rats’ behaviors in real-time as described below. Te observers were blinded to treatment of the animals. Testing consisted of ten 10-s trials with 10-s intertrial intervals in plastic cages with clean bedding. A bottle-brush was rotated rapidly toward the rat’s whiskers. Both aggressive responses and defensive responses were recorded. The behavioral responses were chosen based on Riittinen et al. and Lagerspetz and Portin. Total aggressive and defensive scores were calculated for each animal based on the average score of the observers. Both aggressive and defensive behaviors were summed to calculate the total irritability score. Irritability-like behavior reflects a composite measure of aggressive vs. defensive responses. Irritability-like behavior was assessed 6 days afer the last injection of WIN/vehicle in adolescence and again in adulthood 18h into withdrawal afer the escalation of cocaine self-administration .

The rats were anesthetized by isofurane inhalation, plant growing stand and intravenous catheters were aseptically inserted in the right jugular vein using a modified version of a procedure that was described previously. The right jugular vein was punctured with a 22-gauge needle, and the tubing was inserted and secured inside the vein by tying the vein with suture thread. The catheter assembly consisted of an 18 cm length of MicroRenathane tubing that was attached to a guide cannula . The guide cannula was bent at a near right angle, embedded in dental acrylic, and anchored with a mesh . The catheter exited through a small incision on the back, and the base was sealed with a small plastic cap and metal cover cap. The catheters were flushed daily with heparinized saline in 0.9% bacteriostatic sodium chloride that contained 20mg/0.2ml of the antibiotic Cefazolin .Self-administration in adulthood was performed in operant conditioning chambers that were enclosed in lit, sound-attenuating, ventilated environmental cubicles. The front door and back wall of the chambers were constructed of transparent plastic, and the other walls were opaque metal. Each chamber was equipped with two retractable levers that were located on the front panel. Cocaine was delivered through plastic catheter tubing that was connected to an infusion pump, which was activated by responses on the right lever. Responses on the left lever were recorded but did not have any scheduled consequences. Activation of the pump resulted in the delivery of 0.1 ml of cocaine . A computer controlled fluid delivery and behavioral data recording. The rats were first trained to self-administer cocaine under a fixed-ratio 1 schedule of reinforcement in daily 1-h sessions. Each active lever press resulted in the delivery of one cocaine dose. A 20-s timeout period followed each cocaine infusion. During the TO period, responses on the active lever did not have scheduled consequences. This TO period occurred concurrently with illumination of a cue light that was located above the active lever to signal delivery of the positive reinforcement. The rats were trained to self-administer cocaine in 14 sessions until a stable baseline of reinforcement was achieved .

The criterion for the acquisition of cocaine self-administration was defined as the intake of at least 2.5 mg/kg cocaine in the 1-h self-administration session, requiring at least five lever presses. This criterion was adapted from previous publications. After the 14-session acquisition period, the rats were subjected to fourteen 6-h cocaine self-administration sessions to allow them to escalate their cocaine intake. To study the motivation to seek cocaine, a progressive-ratio schedule of reinforcement was used, in which the response requirement began at one lever press/infusion and increased exponentially according to the following equation: lever presses/infusion=[5×e] − 5. The session duration was limited to 6h or ended when a rat failed to achieve the response requirement within 1h. The PR sessions were conducted after the training/acquisition phase and again after a stable level of escalation was achieved. In the course of the experiment, which lasted for more than 3 months, some rats were excluded at different stages of the experiment. Two rats were excluded during the acquisition phase because of the failure of catheter patency. In the course of the escalation phase, three rats were excluded from the study because of the failure of catheter patency at the end of the study, and one rat in the vehicle group died unexpectedly during the day of from cocaine self-administration before the study was completed, thus leaving n=6 rats/group for the final analysis. The present study found that adolescent WIN exposure increased irritability-like behavior in adolescence, which persisted into adulthood, induced cross-sensitization to the locomotor-stimulating effect of cocaine in adolescence, which did not persist into adulthood, decreased the speed of acquisition but not the rate of cocaine self-administration in adulthood, and had no effect on the escalation of cocaine self-administration in adulthood.

Overall, these results demonstrate that although cannabinoid exposure in adolescence induces irritability-like behavior and cross-sensitization to the psychostimulant effect of cocaine during adolescence, it does not promote cocaine self-administration once the animals reach adulthood. However, the effect of adolescent WIN exposure on cocaine self-administration in adolescence was not investigated in the present study because the animals reached adulthood by the time they had recovered from the surgeries that were required for self-administration. Reductions of both body weight and food intake were observed during WIN treatment. Although the activation of cannabinoid receptors typically produces an increase in food intake in adulthood accumulating evidence suggests that adolescent exposure to THC or WIN in rats decreases food intake and body weight. The increase in water intake during WIN exposure in the present study confirms the role of cannabinoid receptors in homeostatic responses that regulate not only energy homeostasis but also fluid balance. Irritability, anxiety, and dysphoria are key negative emotional states that characterize the withdrawal syndrome in humans, which arises when access to the drug is prevented and contributes to drug relapse. Irritability has also been reported to be greater in adolescents at higher risk for substance use. Irritability-like behavior has also been shown to increase during withdrawal from alcohol and nicotine in rodents. However, to our knowledge, whether early exposure to cannabinoids affects irritability-like behavior has not been studied in animal models. In the present study, we found that WIN exposure induced irritability-like behavior in adolescence and adulthood, suggesting that cannabinoid exposure in adolescence induces long-lasting neurobehavioral adaptations that can persist months after WIN exposure. However, further studies are needed to investigate whether this finding has translational relevance. An alternative explanation is that, despite blind randomization of the subjects to the two groups, the increase in irritability-like behavior that was observed in WIN-treated rats may be attributable to preexisting differences in irritability-like behavior. Further studies are needed to investigate whether this fnding has translational relevance. Numerous human studies demonstrate that early cannabis use is associated with greater vulnerability to the later development of drug addiction and psychiatric illness. A recent study reported a pivotal role for cannabinoid receptors as molecular mediators of adolescent behavior and suggested that cannabinoid receptors may be important in adolescent-onset mental health disorders. Chronic adolescent exposure to WIN has also been shown to induce anxiety-like behavior in rats. However, plant grow table contradictory findings have also been published, with either no change or even a decrease in anxiety-like behavior after cannabinoid exposure in adolescence. Rats that were exposed to cannabis smoke were also reported to exhibit a decrease in anxiety-like behavior. Interestingly, a previous study also demonstrated that long-term cognitive and behavioral dysfunction that was induced by adolescent THC exposure could be prevented by concurrent cannabidiol treatment. Importantly, WIN acts as a full cannabinoid receptor agonist, in contrast to THC, which only acts as a partial agonist. Moreover, cannabis is known to consist of dozens of additional phytocannabinoids apart from THC.

Furthermore, different strains of cannabis differ in their THC content, and THC levels in cannabis have increased year after year because of consumer demand, thus making direct comparisons of human data across time and across studies difficult. Nevertheless, we chose this model of early cannabinoid exposure and followed it precisely because it has been shown to induce cocaine cross-sensitization, thus supporting the gateway hypothesis. Further studies are needed to investigate whether the long-term irritability-like behavior that was observed in the present study can be prevented by concurrent cannabidiol treatment or whether adolescent exposure to cannabis smoke induces long-lasting irritability-like behavior in rats. Epidemiological data consistently document that cannabis exposure precedes the use of other illicit drugs. However, epidemiological data cannot provide causal evidence of this sequence. Animal models are particularly useful for studying effects that are related to cross-sensitization because they allow sequential administrations of the studied drugs while controlling for confounding variables. Several studies have reported behavioral cross-sensitization between cannabinoids and stimulants in rodents. WIN treatment during adolescence in rats induces long-lasting cross-tolerance to morphine, cocaine, and amphetamine, potentiates amphetamine-induced psychomotor sensitization, and induces cocaine-induced psychomotor sensitization in adolescence. WIN exposure also leads to increases in methylenedioxymethamphetamine-induced and cocaine-induced conditioned place preference. In the present study, WIN exposure in adolescence induced cross-sensitization to the stimulatory effect of cocaine in adolescence. However, this effect was no longer present in adulthood when the rats had self-administered cocaine for several weeks, suggesting that cannabinoid exposure in adolescence may increase the psychomotor effects of cocaine during the first exposure to cocaine, but this effect is not necessarily long-lasting. Cannabinoid exposure increased irritability-like behavior and the psychomotor effects of cocaine, but it did not promote the acquisition or escalation of cocaine self-administration. Indeed, we observed the slower acquisition of cocaine self-administration with 1-h short-access to cocaine in male rats with prior exposure to WIN compared with controls. In contrast, a previous study reported a trend toward an increase in cocaine self-administration during the short acquisition phase in female rats with prior exposure to the cannabinoid receptor agonist CP55,940 but not in male rats. However, this study did not discriminate between inactive and active levers, and no diference in cocaine self-administration was observed during the 14-day maintenance phase in either sex. A recent study showed that adolescent WIN exposure caused impairments in an attentional set-shifing task, a measure of cognitive fexibility, in adulthood. An alternative hypothesis is that the slower acquisition of cocaine self-administration in adulthood that was observed in the present study may be attributable to cognitive impairment that slows the acquisition of operant responding. In humans, several studies have indicated that the adolescent use of cannabis can lead to long-term cognitive deficits, including problems with attention and memory. During escalation, no differences were observed between the rats that were exposed to vehicle in adolescence and the rats that were exposed to WIN in adolescence. This suggests that if cognitive impairments affected the initial acquisition of self-administration, then they did not produce long-term deficits. Te model of long-access to cocaine self-administration is one of the most validated animal models of cocaine use disorder and drug addiction in general. This model has been shown to result in all seven of the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition , and seven of the 11 DSM-5 criteria, including most of the criteria that are required for severe use disorder: tolerance, withdrawal, substance taken in larger amount than intended, unsuccessful efforts to quit, considerable time spent to obtain the drug, important social, work, or recreational activities given up because of use, and continued use despite adverse consequences.