Hemp Growing

The contents of the stomach—which not even the possessor usually sees—is put on public display. The common reaction to vomiting, both producing it and witnessing it, is disgust. Julia Kristeva specifically mentions vomit as a material producing the situation of abjection. To abject literally means to “throw away.” For Kristeva, this concept helps us understand how we create ourselves in opposition to other things we deem not-us and radically unacceptable. Food and vomit occupy a middle space between me and not-me. As Kristeva writes, when I vomit, “I spit myself out.” The act of vomiting, as well as seeing vomit, reminds the vomiter or viewer of her own implication in the not-me, even as vomiting represents an attempt to draw a bright line between self and other. In this sense, abjection is both an action and a relationship, tying the self to the not-self. Vomiting, then, like the place of the neighborhood in the city, marks a zone in between private and public. Following Kristeva, that zone is paradoxical: it is both undeniably me and undeniably not-me. In Rue Ordener, rue Labat, the space of the neighborhood is both the symbol and the stage for the enactment of this paradox. Kofman’s family apartment on rue Ordener also performs an act of abjection. When Kofman and her mother return to their apartment on rue Ordener, they find that the Gestapo, “dans leur fureur d’être venus pour rien, . . . avaient, nous dit le concierge, jeté les meubles par la fenêtre. Les fauteuils et le divan de la chambre de mon père, tout avait été cassé, brisé. Ils avaient fait le vide.” [“In their anger at coming up empty-handed, . . . had thrown the furniture out the window, the concierge told us. The armchairs and the sofa from my father’s room—everything had been broken, smashed. They’d emptied it out” ] Like Kofman herself when she vomits on the street, hydroponic rack system her apartment is made to disgorge its contents so that the private furnishings of the house are made public. With Mayol’s help, we can see how destructive the “abjection” of Kofman’s apartment on rue Ordener is.

The apartment is a bounded space that allows the dweller to lead a life: within one’s four walls, one can know one’s personal space and organize one’s furnishings. The bounded space of the neighborhood allows the inhabitant to negotiate between the perfectly private and the perfectly public outside world. When the apartment on rue Ordener is made to vomit its contents onto the street, that “interdependent” relationship between home and neighborhood is destroyed, making, as Mayol writes, daily life impossible. The “device” of the neighborhood negotiates between home and not-home so that you can understand your place in society. If you do not have a home, if your home is suddenly public and lying in the street, such a negotiation is impossible. If your home is not private, or is gone entirely, how do you know where your neighborhood is? After the violent abjection of her family’s belongings and the loss of her home, Kofman stands as open to the depersonalizing environment as that bombed out building. The prominence of the public/private environment of the neighborhood underscores the importance of space, particularly the space of the city, in Kofman’s text. In fact, the neighborhood is not the only intermediary space Kofman addresses in Rue Ordener, Rue Labat. After describing how her father would light a cigarette as soon as the sabbath was over, and how she used to love to purchase Zig-Zag rolling papers for him, she writes, “Plus tard, dans un rêve, je me représentai mon père sous la figure d’un ivrogne qui traversait la rue en zigzaguant” [“Later, in a dream, my father appeared to me as a drunk zig-zagging across the street” ]. This dream inscribes her father, and the smoke that symbolizes the fragility of her memory of him, onto the street, creating a path in space from her memory of her father. The zig-zag shape, usually two parallel lines with a slanted line connecting them, is a visual rendering of intermediate space; the slanted line mediates between one parallel line and the other.

Kofman’s narrative about her father, and about her own life, cannot move straight forward in textual space; it must zig zag.Zig-zagging occurs at several levels of Kofman’s narrative: at the level of chapter organization, narrative structure and diction. All of these levels create an image of the mental space that Kofman asks the reader to experience. In addition to the image of zig zagging, I will use the concept of the crossroads and Kofman’s term, voie de traverse, to consider the way space functions at these three levels of the text. Kofman employs zig-zagging to “spatialize” her text, creating a 2-dimensional plane instead of a straight narrative. Her story does not move straight from the beginning to the end. Instead, hers is of a dual identity, of a woman who is simultaneously the daughter of her mother and the daughter of mémé. Because a linear, chronological text makes such simultaneity difficult to convey, spatialization allows the reader to experience more than one moment in time concurrently. Freud begins by discussing children’s tendency to fantasize about having a different set of parents, one that is higher in social class . The “romances” Freud describes— perhaps of having been switched at birth, or of being secretly adopted—allow a child to envision him or herself as separate from his or her parents. Because, according to Freud, these fantasies occur before a child knows about the mechanics of sexual reproduction, the child does not necessarily see a reason why he or she could not be the child of another couple, or the product of a secret liaison between their mother and another, superior, man. In fantasy, then, the child is able to leave the family and create an identity of his or her own, a step towards maturity and independence. Rue Ordener, rue Labat is a very literal “family romance.” Kofman’s childhood experience of persecution is the peacetime child’s experience writ large. Freud mentions that, as “intellectual growth increases, the child cannot help discovering by degrees the category to which his parents belong” .

Kofman becomes aware of her “category”—Jewish, a child of immigrants—not by degrees, but rather all at once. And unlike the child in Freud’s essay, Kofman does not need to fantasize that she has another mother—she actually has one. mémé, like the fantasy parents in “Family Romances,” is “of higher social standing” insofar as she is a full French citizen and a Christian. In order to discover the “category” to which his or her parents belong, the child must develop the ability to look at his or her parents from the perspective of an outsider, a step towards imagining him or herself as separate from the parents. For Kofman, her survival depends upon imagining herself from another’s perspective well enough to actually inhabit a different, Christian, identity. In Freud’s formulation, the child escapes from the suffocating triad with the parents by way of fantasies in narrative, employing the texts of others to build a new story. In a sense, they write their own Bildungsroman, their own story of their origins and maturation. Children’s fantasies of being adopted, he says, are “usually a result of something they have read” . These narratives offer escape routes by which the child may strike out on his or her own, independent from the family. In using others’ texts to create a genealogy, the child builds a bridge to the biologically unrelated fantasy parents, a crucial step towards becoming an independent person. Separating oneself from the private realm of the family, even in fantasy, means entering the public outside world as an individual. Like the child in Freud’s essay, Kofman’s also uses others’ texts to escape from her family and operate as an individual in the public world, rolling tables grow charting a movement from private to public via a coming of age story involving multiple parentage. Kofman has a deep relationship to these texts, and description of a new living situation seldom comes without a note about what books she read there. While the people who gave her the books disappear from her life, she carries the books they gave her from place to place.Kofman’s intertextual escape routes are not confined to passing textual references. At two points in Rue Ordener Rue Labat, Kofman breaks the narrative thread of her story to dwell on the work of others that seems, at least on a very superficial level, to be tangential to the story at hand. Chapters 18 and 19 point outside of Kofman’s text, allowing both Kofman and the reader to escape her story. Chapter 18, “Les deux mères de Léonard”, discusses Leonardo’s “carton de Londres” and Freud’s analysis of it. Chapter 19 remains separate from the narrative thread, this time allowing Kofman to discuss “un de [s]es films préférés” [“one of [her] favorite [films]” ], Hitchcock’s The Lady Vanishes. These two intertextual chapters come in the last third of the text, directly after the chapter recounting the “Liberation” of Paris and her mother’s liberation from the house of mémé. A story only about Kofman’s experiences during the war would end there, implying her relationship with mémé ended with the war and perhaps implying a slightly happy ending as she becomes again her mother’s daughter and is reunited with her siblings. Chapters 18 and 19 offer the reader a chance to pause before diving into the unhappy ending that awaits, as well as a chance to reflect on the resonances Kofman’s story has with these three works that mean so much to her.

While these chapters allow Kofman to meditate on her own story through resonances with these other texts, they also accomplish a meta-textualization, in which not only Kofman-the-subject of the text, but also Kofman-the-writer of the text seeks to escape the story. The intertextual chapters, insofar as they create “escape routes” from Kofman’s story, puncture the text, unsealing it, making it less hermetic. Through these intertextual references, which, on the one hand, import others’ texts to create a path out of the text and, on the other hand, enlist the reader for the creation of the text. The reader must do his or her own work when faced with intertext, using previous knowledge to unpack the significance of the reference. One way to envision the particular relationship of Kofman’s text to space is to imagine a Möbius strip, with Rue Marcadet, or the intertextual moments, as the twist. A Möbius strip is merely a loop with a twist in it, but tracing a path on one side will bring you to the other. If you begin at the interior of the strip and follow the loop through the twist, you get to the exterior side. Similarly, Kofman begins in the interior, in her family home, and takes rue Marcadet to a life on the exterior in which she can be seen on the street as the Christian daughter of mémé. Rue Marcadet, in this formulation, is an intermediary space between the public and private realms. Like the two sides of the Möbius strip, rue Ordener and Rue Labat do not intersect without the voie de traverse of rue Marcadet. Thus Kofman’s public and private lives, represented by the two streets, take place simultaneously but do not intersect. The intertextual moments in the text make it clear, however, that the public and private realms do not merely share an ambiguous liminal space. The intertextual sections of the text, which make reference to shared culture beyond that text and are thus in a sense public and outward-facing, paradoxically lead the reader more deeply into Kofman’s private domain. When Kofman seems to move from private to public, as when she addresses a work of art or a film, she is always also moving in the opposite direction, towards intimate mental consequences of her story that she does not address directly. Chapter 18 works in this way, enabling Kofman to examine facets of her mother’s experience that she cannot or does not discuss in the course of the memoir. She begins the chapter by mentioning that she chose the “carton de Londres” as the cover image for her first book , L’Enfance de l’Art. After introducing the guest speaker , the rest of the chapter is given over to Freud’s analysis of Leonardo’s drawing. Freud hypothesizes that the Virgin and St.

In order to experiment with potential dry farm rotations, as well as cover crops that can best scavenge excess nitrates and soil management regimes that can increase soil fertility at depth, farmers must be given both research support and a safety net for their own on-farm experimentation. Funding to mitigate the inherent risk in farmers’ management explorations will be key in further developing high-functioning dry farm management systems. Expanding land access to farmers who are committed to exploring dry farm management can additionally benefit these explorations.Dry farm tomato systems on the Central Coast point to key management principles that can both help current growers flourish and provide guidance for how irrigation can be dramatically decreased in a variety of contexts without harming farmer livelihoods. In these systems, managing nutrients at depth–at least below 30cm and ideally below 60cm–is necessary to influence outcomes in fields where surface soils dry down quickly after transplant. Fostering locally-adapted soil microbial communities that are primed for water scarcity can improve fruit quality. Farmers can otherwise manage nutrients to maximize either yields or quality, giving latitude to match local field conditions to desired markets. As water scarcity intensifies in California agriculture and around the globe, dry farm management systems are positioned to play an important role in water conservation. Understanding and implementing dry farm best management practices will not only benefit fields under strict dry farm management, plant grow trays but will provide an increasingly robust and adaptable example for how farms can continue to function and thrive while drastically reducing water inputs.These works all employ textual space and the space of the city to create monuments to ambiguous and divided histories.

All three of these texts lay a particular importance upon space, both the physical space in which the texts take place and the textual space the author creates. At its most basic, a monument is the combination of memory and space, a concretization of memory that is both the product of a collective desire to commemorate and the locus of subsequent commemorative activities, such as pilgrimages or ceremonies. Thus the texts I have chosen to investigate here, and texts like them, have a claim to monumentality in that they have a pronounced spatial aspect and are specifically concerned with a past moment that has both personal and collective importance. By thinking of these texts as monuments, one can move towards answering an otherwise puzzling set of questions: why do so many memoirs and novels of historical violence lay such a heavy emphasis on space, especially the space of the city, and why are so many formally innovative? Considering a text as a monument also emphasizes the spatial aspects of these texts, providing a framework for understanding them not simply as texts among texts but also as monuments among monuments. Each of these texts is in dialogue with traditional, physical monuments; in each there are moments when the protagonist must consider the ways in which a physical monument memorializes, and the ways in which it may also fail to do so. The texts consider how physical monuments seek to shape memory and whose agenda they serve. Implicit in these encounters with monuments is the alternative of the text itself, the ways in which the textual monument can add a voice, complement or enter into dialogue with the physical monument. Language, from which these monuments are built, may be a powerful way to influence the “collective frameworks” for memory of which Halbwachs speaks.

Language is a product of the collective and creates the social space in which a community exists. On the other hand, because words may have many layers of meaning and association, they may bridge the gap between the purely personal and the purely collective, allowing individual memories to influence and change the “collective framework.” It may be worthwhile to consider what we mean by the term monument, and how a text may also lay claim to the word. A monument maybe triumphant, as in Roman victory columns and the Arc de Triomphe, or it may stand as a reminder of a painful past experience, like Henri Pingusson’s Mémorial des Martyrs de la Déportation, on Ile de la Cité. In many cases, a monument develops layers of meaning over time, creating multiple dialogues between different moments. Even when layers of meaning are not added purposely, the meaning of any monument changes over time as newer historical events cast the subject of the monument in a different light. The Arc de Triomphe, for instance, is a monument that has acquired state-sanctioned layers of meaning and has also, because of its extraordinary symbolic importance, attracted others who would use and change the symbol. First built by Napoleon to celebrate his victories, after World War I the tomb of the Unknown Soldier was placed underneath it, complicating the meaning of the arch. When, in 1940, the occupying Germans marched around the arch, they were both capitalizing on the symbolic importance it already had and adding yet another layer of meaning. Such physical public monuments are political instruments. A monument constructed or approved by the state represents a purposeful shaping of public memory, whether tacit or explicit. These monuments express the explicit agenda of those in power and also may reveal an agenda unexpressed by them elsewhere.

A monument may express the unity of the nation that created it, since to erect a monument at all implies a certain agreement among the various constituencies about what to memorialize and how to do it, but it can also show the anxious efforts of those in power to smooth over differences between factions. Tombs represent a kind of nexus of institutional and personal monumentalizing. On the one hand, an individual tomb has less of a claim to expressing collective memory than a monument created and funded by a large group. On the other hand, they are public monuments to the memory of a person. Whereas a small, highly personal memento is inaccessible to others and not meant to resist the damaging effects of time, a tomb is built of weather-resistant stone and can be visited by any member of the public. The grave, positioned as it is between collective monument and private memento, may seem a good solution to the problems of memorializing I mention above. It is personal enough to express individual complexity, but public enough to avoid hermeticism. Yet, for the authors I discuss, the grave can still be problematic. For Kofman, the words said over it cannot capture the complexity of the person underneath, and their relationships with others. For Perec, the grave cannot fulfill its promise to serve as a boundary; it cannot provide a space for the dead so that they can be truly “at rest” for the mourner. In Rodoreda, the fixedness of a tombstone, its immobility and the way it yokes a single name to a single body, overvalues the past and a specific place, denying the mourner the ability to grow and change, to move forward in time. The word monument, whether referring to large public monuments, to personal graves or to texts, derives from the latin “monere,” to remind or warn . A “monument” was not always a physical edifice. At first, the Latin word monumentum referred to “a commemorative statue or building, tomb, reminder, cutsom grow room written record, literary work” . At this early moment in its life, the word intriguingly connects writing and literature with commemoration and also with death. A dress is still an object that encloses space. In the texts I analyze, Perec, Kofman and Rodoreda similarly critique monumentality, suggesting smaller memorial objects that, especially in Perec and Rodoreda, are be pieced together to form a shape. Yet textual monuments are not just monuments on the metaphorical level; texts can function as monuments quite literally. After the destruction of the Second Temple, the Torah became a temple in writing, a substitute for and representation of the missing physical temple. In their introduction to From a Ruined Garden, a collection of translations of yizker-bikher, memorial books written in Yiddish to document and commemorate Jewish communities in Eastern Europe that were liquidated during the Second World War, Jack Kugelmass and Jonathan Boyarin touch on how the need for historical memory in the absence of the physical temple provided a template for textual memorial forms that emerged much later . When survivors of the war, as well as those who remembered the towns and cities even though they had emigrated earlier, wished to memorialize a place and its occupants, they were imagining the text they created as a literal monument to those who had died. “It is worthwhile noting,” write Kugelmass and Boyarin, “that when landslayt from the town of Zwolen set out to produce a memorial book, their expressed intention was ‘to create a monument’” . Many memorial books have “a table of contents . . . and title page [with] drawings of gravestones as their backgrounds,” making very clear the book’s function as a textual monument . The original yoking of literary works to space, memory and death that can be uncovered in the meanings of the original Latin word monumentum thus continues to cling to the word monument. We can trace it from Horace through Du Bellay, as they describe literary monuments as simultaneously living and dead, all the way to the memorial books translated by Kugelmass and Boyarin.Like a building, a city is a city because of its boundaries. The boundedness of the city took on particular significance during the Occupation and the Spanish Civil War .

Rodoreda’s protagonist never leaves Barcelona, and Perec and Kofman give particular weight to the moments when they enter or leave Paris. Their texts are not just buildings or structures: on another level, they are also city-texts. Kofman and Rodoreda even name their texts after city streets, cementing the relationship between the book and the city. Following Benjamin, we can see that the city is the space of history. In his article on this topic, “Archiving,” Michael Sheringham elaborates on this point. The city is a “memory machine” . Palimpsestic, it is the place of history while in the countryside “myths hold sway” . A city, in this formulation, is thus the proper home of monuments. If the city is a nation’s microcosm, then the monuments placed there speak for the whole. And if the city is a psyche writ large, monuments are concretized memories, a culture’s memory cues. In the texts I analyze, the city itself is also a kind of monument, a giant memory palace whose spaces and buildings can function like a spatial monument. The narrators move through the space of the text seeking to construct their identities through the city and its monuments. Their experiences evacuate the traditional monuments of meaning, however, and they find themselves adrift in a city without landmarks. The text itself becomes the missing monument. A monument is a piece of a system for perpetuating memory, working together with rituals of memory and the visitors themselves. Whether textual or physical, monuments are thus containers for memory, or locations where memory may take place. Monuments, by providing a literal space for memory, help to preserve it for, and transmit it to, those who come later. They therefore always function in relation to time, creating a dialogue between the past and the present. A monument might be understood as an expression of time in space, existing in the present but acting as a pointer to a moment in the past and thus stretching back in time. Created as a permanent reminder of an event, monuments have a claim to a certain timelessness. Physical monuments are traditionally made of durable materials like stone or metal, designed to withstand the effects of time. Yet in their apparent timelessness, they nonetheless evoke the timefulness of the relationship between the visitor and the past event. A visitor senses his or her own mortality in the face of a more permanent structure like a monument, and is also conscious of the distance between the present and the memorialized past event.

To test for a relationship between RCI and predictive factors, all variables were centered and RCI was regressed against a set of covariate data in a linear mixed model including US state as a random effect to account for regional differences . We included interactions for which we had a priori hypotheses . The model was estimated using the R package `lme4`64. Two model assumptions are violated in the above model, requiring updated estimates of the parameters’ standard errors. First, because RCI is a derived statistic with an unusual domain, the index is not distributed according to a known distribution family and violates the assumption of normality in the residuals. Second, residuals showed high spatial autocorrelation at multiple scales and with an unknown structure, necessitating a non-parametric approach. Both violations are likely to shrink standard errors of the estimated parameters, leading to overconfident estimates; to illustrate, in the case of spatial autocorrelation, if the explanatory variables are randomly located in relation to crop rotation, spatial autocorrelation in crop rotation would falsely inflate significance. An algorithm for sparsely distributed spatial data, derived by Lahiri 2018, was implemented in R . Spatial block bootstrapping involves iteratively resampling data in spatial blocks to mimic the generation of autocorrelated data. Choice of block size is nontrivial, and choosing the optimal block is an open question, but blocks should be larger than the scale at which autocorrelation operates. Using the R package `gstat`to compute a variogram of the residuals generated by the naive LMM, hemp drying racks we determined that range was 400815m. We used this as the dimension of each spatial block . We repeated this bootstrap with a range of possible spatial block sizes and found that this inference on parameters was robust to the choice of block size .

RCI scores have statistically clear correlations with land capability, mean rainfall, distance to the nearest bio-fuel plant, and field size, as well as with several interactions between these variables . Standard errors from the spatially blocked bootstrap were much larger than uncorrected naive confidence intervals, reflecting that accounting for spatial non-independence is necessary to estimate uncertainty of parameter estimates. Rotational complexity decreased with NCCPI, a proxy for land capability. We find that land of higher inherent capability is more likely to be used for lower complexity rotations. Rotational complexity decreased with average rainfall during the growing season. Fields with ample precipitation during the growing season are more likely to have simplified rotations. Though the relationship between the proximity of the nearest grain elevator and a field’s rotational complexity is not statistically clear , RCI showed a clear increase with distance to the nearest bio-fuel plant. Fields that are closer to bio-fuel plants are therefore more likely to have simplified rotations. Rotational complexity decreased with field size, with larger fields being more likely to have simplified rotations. Two of the interactions included in the model show statistically clear relationships. There is a positive interaction between land capability and field size, with higher quality land associated with decreasing RCI on small fields and slightly increasing RCI on large fields . The interaction between land capability and rainfall variance show a negative effect on RCI, with highly variable rainfall accentuating land capability’s impact on RCI . Interpretations of the relationship that each variable has with rotational complexity are shown in Table 4. Though each change is associated with a small shift in average RCI across the region, these can represent massive shifts in regional land management.

As crop rotations continue to simplify in the Midwestern US despite robust evidence demonstrating yield and soil benefits from diversified rotations, our ability to explain and understand these trends will come in part from observing the biophysical and policy influences on farmers’ crop choices at one key scale of management: the field. By developing a novel metric, RCI, that can classify rotational complexity over large areas at the field scale, we open the door to regional analyses that can address the unique landscape conditions that impact farmers’ field-level management choices and their subsequent influence on rotational simplification. We find that as farmers are pushed towards simplification by broad federal policies , physical manifestations of these policies like bio-fuel plants are correlated with intensified simplification pressures. Similarly, we see that the pressure to build soils and boost crop yields through diversified rotations intensifies in fields with lower land capability, while conversely the negative effects of cropping system simplifications are accentuated on the region’s best soils.RCI uses the sequence of cash crops on a given field as a proxy for crop rotation, and sorts these sequences into scores based on the sequence’s complexity and potential for agro-ecosystem health. Because this metric has not been used in previous analyses, we verified RCI’s validity through comparisons to previous estimates of rotational prevalence in the region. For example, two separate surveys of farmers in the Midwestern US showed that between 24% and 46% report growing “diversified rotations”which we consider to be an RCI of greater than 2.24 . In the present study, 34% of fields had an RCI greater than 2.24. This and further comparisons of RCI to previous work show that RCI is capable of capturing previously-noted trends in the region.

The ability to analyze rotations at the field scale across the entire Midwestern US allows us to ask how farmers optimize their rotations in complex economic and biophysical landscapes that include pressures to both simplify and diversify. Several biophysical and policy variables show statistically clear relationships with rotational complexity: high land capability, high rainfall during the growing season, and proximity to bio-fuel plants are all associated with rotational simplification. Given policy incentives, farmers often find that “corn on corn on dark dirt usually pencil out to be the way to go,” with farmers growing corn year after year when high quality soil is available. However, when that proverbial “dark dirt” is not available, calculations are not so simple. If growing conditions are sufficiently poor , these intensive corn systems may not be profitable, and farmers will have to rely more heavily on non-corn crops to maintain crop health and profitability in their fields. We see this dynamic at play with land capability in the present analysis. Despite—or rather because of— the fact that more diverse rotations improve soils, the most degrading cropping systems counter intuitively tend to occur on the highest quality land. Highly capable lands can be farmed intensively without dipping into a production “danger zone” in years with weather that is historically typical for the region, creating a pattern of land use that is likely to degrade these high quality lands in the long term and potentially jeopardize future yields, particularly in the face of climate change. Recent analyses show that enhanced drought tolerance and resilience for crops is one of the key benefits of diverse crop rotations. In the present analysis, mean rainfall during the growing season correlates positively with rotational simplification. Farmers may therefore be employing crop rotation in areas of low rainfall to achieve production levels that will keep a farm solvent, industrial rolling racks as was seen with rotational complexity increases in Nebraska during a drought period from 1999 to 200773. This trend is further accentuated by the negative interaction between land capability and rainfall variance in our analysis, where higher rainfall variability leads to even more diverse rotations on marginal lands. Proximity to bio-fuel plants, the main policy indicator in our model, showed a statistically clear trend towards rotational simplification, likely due to increased economic profits. Local corn prices increase by $0.06 – $0.12/bushel in the vicinity of a bio-fuel plant, amplifying incentives to grow corn more frequently. Wang and Ortiz Bobea were surprised not to find an impact of bio-fuel plant proximity on county-level frequencies of corn cropping in their own analysis, and the present analysis—done at a field rather than county scale—shows exactly this expected effect: corn-based rotations are simplified when in closer proximity to a bio-fuel plant. In the current economic and policy landscape, farmers are pushed to simplify rotations through more frequent corn cropping, especially in proximity to bio-fuel plants, while marginal soils and low rainfall pull fields towards more diverse rotations.

RCI’s ability to classify rotational complexity across large regions at the field scale and with low computational cost opens doors to future analyses that explore the interplay between localized landscape conditions, management choices, and agricultural, environmental, and economic outcomes. We see a strong potential to employ this metric not only in new regions, but in analyses that address how results from field experiments with crop rotation may scale up to regional levels. We also note that the metric should be used with caution. For example, because RCI cannot recognize functional groups in crop sequences , it cannot capture the added benefits that diverse functional groups often add to a rotation. In addition, though RCI includes a perennial correction that avoids penalizing multiple consecutive years of perennials the metric likely still underestimates the benefits of perennials in rotations. RCI is neutral to the soil benefits of annuals vs. perennials, while in practice the year-round cover and crop species mixes that often accompany perennials may boost soil benefits beyond those of annuals. Consecutive years of perennials are uncommon in our study area , and we encourage caution before applying the metric to regions with a more substantial perennial presence. We therefore recommend using RCI in studies that explore a wide range of cropping sequences where large differences in RCI are very likely to be meaningful, rather than as a tool to rank sequences that give similar scores. It is also important to note that, though the index can be applied to data of any sequence length, RCI values from different sequence lengths cannot be compared to each other; a rotation that results in a 2.2 from examining a six-year sequence will not be a 2.2 when examining a five or seven-year sequence. We also note that in using crop sequence as a proxy for crop rotation, RCI cannot fully capture the cyclical nature of true crop rotations. Because RCI examines a fixed number of years, it may “split up” identical rotations in ways that give slightly different scores or ABBAAB in a six-year sequence. As these discrepancies will decrease when longer sequences are considered, we recommend applying RCI to sequences that are as long or longer than the longest expected rotation in the study region.We hope to see RCI used in future analyses that extend beyond the Midwest; however, regional and historic patterns of crop production likely influence farmers’ rotational decisions and may render RCI scores calculated from disparate geographical regions difficult to interpret when called into direct comparison. We therefore see great promise in RCI as a rotational metric, and caution against applications that are overly narrow and overly broad .The time period chosen in this study, 2012 – 2017, coincides with the introduction of the Renewable Fuel Standard, or “bio-fuel mandate,” which took full effect in 2012. This policy mandates that 7.5 billion gallons of bio-fuel be blended with gasoline annually, and caused bio-fuel plants to open and local corn prices to soar across the Midwestern US. Now in 2021, there is significant political pressure both to maintain the bio-fuel mandate in its current state and to relax the standards, and new exemptions to the mandate have already caused several bio-fuel plants to close in the region. Given the link between bio-fuel plant proximity and rotational complexity, our analysis suggests that these closures, if continued, would likely be associated with an increase in mean RCI in the Midwestern US. Using our current model, simulations of randomly closing 20 of the 198 bio-fuel plants in the region lead to an increase of 0.003 in average RCI in the region, driven by greater distance to the nearest bio-fuel plant. In turn, increasing average RCI by 0.003 represents, for instance, the equivalent of 41,000 ha of cropland switching from corn-soy rotations to the most diverse rotation possible . Rotational simplification near bio-fuel plants is a pertinent example of the influence that policy can have on farm management decisions and its landscape repercussions.

It was not until early 2012, however, that federal regulations were made consistent with legislative changes. Because of the historic discrimination against farmers of color, and other structural barriers to land ownership for people of color, the population of agricultural producers is already heavily skewed toward white men. Thus, such measures to guarantee FSA committees are representative of agricultural producers in any particular region fall short in their attempts to address the acutely historical causes and outcomes of structural racialization that have upheld white land ownership in particular. The second major channel among the Farm Bill and other federal food and agricultural policies that have played a historic and ongoing role in structural racialization is the Farm Bill’s commodity programs, which have undergirded white farmland ownership at the expense of farmland ownership by people of color. While the FSA lending programs have upheld white farmland ownership amidst increasing consolidation and specialization, the Farm Bill commodity programs uphold white farmland ownership by way of increasing consolidation and The second major channel among the Farm Bill and other federal food and agricultural policies that have played a historic and ongoing role in structural racialization is the Farm Bill’s commodity programs, which have undergirded white farmland ownership at the expense of farmland ownership by people of color. While the FSA lending programs have upheld white farmland ownership amidst increasing consolidation and specialization, the Farm Bill commodity programs uphold white farmland ownership by way of increasing consolidation and The second major channel among the Farm Bill and other federal food and agricultural policies that have played a historic and ongoing role in structural racialization is the Farm Bill’s commodity programs, grow racks with lights which have undergirded white farmland ownership at the expense of farmland ownership by people of color.

While the FSA lending programs have upheld white farmland ownership amidst increasing consolidation and specialization, the Farm Bill commodity programs uphold white farmland ownership by way of increasing consolidation and The third major channel within the Farm Bill and other federal food and agricultural policies that has played a historic and ongoing role in structural racialization is the Farm Bill’s Rural Development programs, which are intended to help strengthen small communities by investing in water systems, housing, new businesses, infrastructure, and similar projects. Because many farms owned by people of color are in counties with little wealth and limited opportunities for non-farm employment, and because many rural and small town communities of color are faced with persistent poverty, Rural Development programs have the potential to promote socio-economic well-being for people of color and other historically marginalized communities. As of 2012, there is a larger percentage of whites in rural communities than in urban communities . Yet, within rural communities, people of color face higher rates of poverty: while only 14% of rural whites live in poverty, 34% of rural Blacks live in poverty. Additionally, as of 2010, Latinos/as, Blacks, and Pacific Islanders have the lowest home ownership rates compared to home ownership rates for whites . Thus, it is unsurprising that, according to a 2013 Tuskegee University study, farmers and rural communities of color have had The third major channel within the Farm Bill and other federal food and agricultural policies that has played a historic and ongoing role in structural racialization is the Farm Bill’s Rural Development programs, which are intended to help strengthen small communities by investing in water systems, housing, new businesses, infrastructure, and similar projects. Because many farms owned by people of color are in counties with little wealth and limited opportunities for non-farm employment, and because many rural and small town communities of color are faced with persistent poverty, Rural Development programs have the potential to promote socio-economic well-being for people of color and other historically marginalized communities.

As of 2012, there is a larger percentage of whites in rural communities than in urban communities . Yet, within rural communities, people of color face higher rates of poverty: while only 14% of rural whites live in poverty, 34% of rural Blacks live in poverty. Additionally, as of 2010, Latinos/as, Blacks, and Pacific Islanders have the lowest home ownership rates compared to home ownership rates for whites. Thus, it is unsurprising that, according to a 2013 Tuskegee University study, farmers and rural communities of color have had PART III OUTLINED HOW LENDING, commodity, and rural development programs have historically undergirded white farmland ownership at the expense of people of color farmland ownership, and how long term changes in the structure of US farmland—the consolidation and specialization of agricultural production, in particular—have exacerbated such trends. Part IV continues this line of argumentation regarding the structure of US farmland and examines how programs geared toward supporting supposedly environmentally sustainable management practices also shape the socio-economic well-being of and farming and rural communities of color relative to white farming and rural communities. First, this part does so by providing a snapshot of the racialized distribution of costs and benefits regarding programs under the conservation title of the Farm Bill . It then outlines the significance of the historical continuity between environmentally-oriented programs and commodity support programs. Finally, it outlines the significance of four federal rural and agricultural support programs in particular—the Conservation Reserve Program , Environmental Quality Incentives Program , organic agriculture programs, and outreach and assistance programs—as well as recent corporate-backed trends in increased bio-fuel production. Part IV argues that, because of their inseparability from commodity crop production, and the consolidation and specialization of agricultural production, and despite the countless environmental benefits they produce, Farm Bill programs under the conservation title also undergird white farmland ownership at the expense of farmland ownership by people of color. Ultimately, they do so by funneling benefits primarily to white large-scale landowners on high quality land and keeping even low quality white-owned farmland profitable—an inadvertent result of the history of farmland ownership in the United States that cannot be seen as separate from the history of racial discrimination.

This part argues, furthermore, that this is the case not only with commodity crop and acreage-based conservation programs , but that management practice-based conservation programs have similar effects. Furthermore, a fourth program, the Outreach and Assistance for Socially Disadvantaged Farmers and Ranchers and Veteran Farmers and Ranchers Program, contributes to the social and economic inequities that characterize commodity and conservation programs alike, yet holds great potential as a strategic rallying point against structural racialization. Finally, Part IV then addresses the relationship between structural racialization, industrial agriculture, environmental degradation, and climate change, and argues that farmers of color and communities of color bear the brunt of such environmental change.PART III OUTLINED HOW LENDING, commodity, and rural development programs have historically undergirded white farmland ownership at the expense of people of color farmland ownership, and how long term changes in the structure of US farmland—the consolidation and specialization of agricultural production, rolling benches for growing in particular—have exacerbated such trends. Part IV continues this line of argumentation regarding the structure of US farmland and examines how programs geared toward supporting supposedly environmentally sustainable management practices also shape the socio-economic well-being of and farming and rural communities of color relative to white farming and rural communities. First, this part does so by providing a snapshot of the racialized distribution of costs and benefits regarding programs under the conservation title of the Farm Bill . It then outlines the significance of the historical continuity between environmentally-oriented programs and commodity support programs. Finally, it outlines the significance of four federal rural and agricultural support programs in particular—the Conservation Reserve Program , Environmental Quality Incentives Program , organic agriculture programs, and outreach and assistance programs—as well as recent corporate-backed trends in increased bio-fuel production. Part IV argues that, because of their inseparability from commodity crop production, and the consolidation and specialization of agricultural production, and despite the countless environmental benefits they produce, Farm Bill programs under the conservation title also undergird white farmland ownership at the expense of farmland ownership by people of color. Ultimately, they do so by funneling benefits primarily to white large-scale landowners on high quality land and keeping even low quality white-owned farmland profitable—an inadvertent result of the history of farmland ownership in the United States that cannot be seen as separate from the history of racial discrimination. This part argues, furthermore, that this is the case not only with commodity crop and acreage-based conservation programs , but that management practice-based conservation programs have similar effects. Furthermore, a fourth program, the Outreach and Assistance for Socially Disadvantaged Farmers and Ranchers and Veteran Farmers and Ranchers Program, contributes to the social and economic inequities that characterize commodity and conservation programs alike, yet holds great potential as a strategic rallying point against structural racialization.

Finally, Part IV then addresses the relationship between structural racialization, industrial agriculture, environmental degradation, and climate change, and argues that farmers of color and communities of color bear the brunt of such environmental change.outlined below, however, maintain the structural benefits historically afforded to whites while keeping people of color at a structural disadvantage.One major Farm Bill conservation program that has undergirded white farmland ownership at the expense of farmland ownership by people of color is the Conservation Reserve Program . The CRP is the largest federal, private-land retirement program in the United States, with 27.5 million acres covered at a cost of $20 billion over the next 10 years. It provides financial compensation for landowners to voluntarily remove land from agricultural production for 10 to 15 years in order to improve soil and water quality and create wildlife habitat. Acres enrolled in CRP have indeed shown a number of environmental gains, including reduced soil erosion, water quality improvements, and wildlife population improvement. However, a number of factors shape the purpose the CRP serves and for whom: first, enrollment is considered to be undesirable by some land owners, primarily because of the cost of compliance and the potential loss of farm income due to the prevention of the use of such land for agricultural production. Thus, as with the 1956 Soil Bank Program from which the CRP grew, it is the least productive land and lowest income households that are often enrolled and kept profitable. Second, studies have shown that conservation compliance does not present a strong economic deterrent for landowners who want to crop former CRP acreage after the CRP term is over, thus highlighting the potentially temporary nature of such economic relief. Third, and perhaps most importantly, only lands planted with commodity crops, especially, corn and wheat are eligible for CRP and not fruits or vegetables, or lands used for livestock. Because white farmers have historically owned large-scale grain and oil seed farmland while farmers of color have been relegated to smaller, non-commodity crop farmland, the Conservation Reserve Program potentially undergirds white farmland ownership, both during times of economic hardship and on marginal land. A 2005 Texas A&M University survey study, for example, found that white landowners were more likely to have land qualified for reserve programs—as well as programs such as the Stewardship Incentives Program and the Forestry Incentives Program . Such landowners not only received more favorable program outreach and assistance, as will be addressed below, they also had more incentives to participate due to the economies of scale and tax savings. Toward this end, the study found that white landowners, on average, were enrolled in the CRP longer and signed up more acres than landowners of color . One major Farm Bill conservation program that has undergirded white farmland ownership at the expense of farmland ownership by people of color is the Conservation Reserve Program . The CRP is the largest federal, private-land retirement program in the United States, with 27.5 million acres covered at a cost of $20 billion over the next 10 years. It provides financial compensation for landowners to voluntarily remove land from agricultural production for 10 to 15 years in order to improve soil and water quality and create wildlife habitat. Acres enrolled in CRP have indeed shown a number of environmental gains, including reduced soil erosion, water quality improvements, and wildlife population improvement. However, a number of factors shape the purpose the CRP serves and for whom: first, enrollment is considered to be undesirable by some land owners, primarily because of the cost of compliance and the potential loss of farm income due to the prevention of the use of such land for agricultural production. 

Toward this end, employers go to great lengths to unlawfully exclude qualified US workers in favor of H-2A workers, many of whom have themselves migrated to the United States during prior seasons. For example, employers schedule interviews at inconvenient times or locations; hire too early in the season, lead workers to arrive for work when there is none; limit their hours in order to discourage them from continuing to work; use employment contracts that demand that workers forfeit their right to sue a grower for lost wages and/or other illegalities; and impose productivity quotas and other unrealistic work demands on employees. These practices greatly discourage US workers from applying to these jobs, which then allows employers to “legally” hire H-2A workers. Additionally, the profits reaped by large agricultural employers and by corporations at all levels of the food system not only come at the expense of the food system worker’s livelihoods and US job loss, but are also subsidized by taxpayers themselves. For example, Walmart, which sells 25% of all the groceries in the United States and is the largest employer in the US and world, has among the lowest wages across the retail industry. Walmart workers cost US taxpayers an estimated $6.2 billion in public assistance that would counteract the consequences of their low wages, including SNAP, Medicaid and subsidized housing. Because 58% of food system workers surveyed reported having no health care coverage, more than one-third of workers surveyed have used the emergency room for primary care, which taxpayers help cover. Finally, corporations like Walmart are able to determine wages and benefits for workers throughout their entire supply chain, given their massive procurement power and ability to dictate purchasing prices to its suppliers. This pressure and influence forces suppliers to lower their worker’s wages, drain trays for plants multiplying the number of workers robbed of fair and livable wages and taxpayer subsidization of corporate profits.

In short, when food system workers require public assistance, the onus rests on taxpayers and the federal government, rather than on those that are responsible for creating these unhealthy outcomes—corporations. After over thirty years of liberal trade policies beginning in the late 1970s and early 1980s, many developing countries have been left with a great dependence on the global market for basic food and grains. Developing countries had yearly agricultural trade surpluses of $1 billion in the early 1970s. Yet by 2000, the food deficit in such countries had grown to $11 billion per year. At the height of the 2007–2008 global food price crisis, Low-Income Food Deficit Countries import bills reached over $38 billion for basic cereal grains. Such systemic vulnerability is, in part, a result of international finance institutions, structural adjustment, free trade agreements, and a broader divestment of the state from agricultural development. Furthermore, not only are overproduction and US food aid to blame, but also corporate actors use such international crises as oppor-tunities to make additional calls for emergency aid coupled with further trade liberalization and increased investment in agricultural productivity. The Farm Bill in particular has been instrumental in establishing and maintaining such systemic vulnerability. For example, although the 2014 Farm Bill authorizes $80 million annually for the Local and Regional Procurement Program, which encourages greater use of food that is locally or regionally grown for food aid, it pales in comparison to the $1.75 billion Food for Peace Title II through which United States Agency for International Development provides food assistance. Furthermore, foreign economies are undermined not only by such efforts that directly shuttle surplus and heavily subsidized commodities—produced for the benefit of corporate entities—to developing countries, but also by production support programs themselves, such as commodity payments or crop insurance.

For example, a 2012 International Centre for Trade and Sustainable Development report found that the shift from direct payments to crop insurance support for farmers is likely to have far reaching effects on global trade and prices because of the anticipated change to cropping patterns. Specifically, the likelihood that the new programs will influence planting decisions is greatly enhanced because payments in all the new programs are calculated using actual planted acreage. Ultimately, if planting decisions are influenced enough, then program-induced changes in US crop acreage will be reflected in trade flows that have the potential to harm farmers in developing countries and cause fluctuations in global food prices. One major way corporations profit and exert their control with regard to education, research, and development is their influence over academic research and development. Agricultural research in the United States is carried out primarily by three entities: the federal government, largely through the US Department of Agriculture; academia, primarily through land-grant universities; and the private sector. Over the past several decades, corporate interests have co-opted publicly-oriented agricultural research and land-grant university research efforts in particular. The federal government created land-grant universities in 1862 by deeding tracts of land to every state to pursue agricultural research to support agricultural production in the United States. Although public investments have maintained agricultural research since the creation of these universities, over recent decades public funding has stalled, prompting land-grant universities to appeal to agribusiness to remedy such financial shortcomings. Significantly, the landmark 1980 Bayh-Dole Act pushed universities to take this particularly entrepreneurial role, generating revenue through producing patents from which the private sector could profit. The Bayh-Dole Act, as part of the neoliberalization of science and academic research itself, prompted greater industry influence over land-grant research, as university research agendas became oriented toward the needs of corporate partners.

Major agribusiness donors to land-grant universities across the United States, including Syngenta, Monsanto, PepsiCo, Nestle, Dow Agroscience, Chevron, DuPont and others, now push research carried out by faculty and students toward developments in bio-fuels, commodity crops research, genetically engineered foods, and other areas of interest. Land-grant universities today not only carry out corporate-directed research but also depend on agribusinesses to underwrite research grants, endow faculty chairs, sponsor departments, and finance the construction of new buildings. Even USDA research and USDA-funded research itself reflects corporate interests. The USDA spends roughly $2 billion per year on agricultural research, which goes toward funding USDA researchers and researchers at land-grant universities. This money, however, is largely directed toward a corporate-friendly industrial agriculture research agenda: the National Academy of Sciences found that USDA research prioritizes commodity crops, industrialized livestock production, technologies geared toward large-scale operations, and capital-intensive practices. The Farm Bill does not prioritize funding for more sustainable farming programs, with programs such as the Organic Agriculture Research and Education Initiative and Specialty Crop Research Initiative accounting for only 2% of the USDA’s research budget. Most research funding is directed toward commodity crops research. In 2010, for example, the USDA funded $204 million to research all varieties of fruits and vegetables, and spent $212 million to research just four commodity crops: corn, soybeans, wheat, and cotton. Another major way private industry continues to profit and exert their influence vis-à-vis relations of education, research, and development, is seed research and patents. Since the early 1980s, the global seed industry has grown substantially and is now worth an estimated $44 billion and is expected to grow to an estimated $85 billion by 2018. The cumulative effect of seed legislation has facilitated the massive consolidation of corporate power, thus securing corporate control of one of the most crucial agricultural inputs. This history of seed legislation began shortly before the New Deal, beginning with the US Plant Patent Act of 1930 and continued with the 1970 Plant Variety Protection Act. Significantly, seed legislation did not move into the judicial system until the 1980 Supreme Court decision Diamond v. Chakrabarty, which laid the legal groundwork for the privatization and commodification of the genetics of seeds. In 1985, Ex Parte Hibberd, an administration decision by the US Patent and Trademarks Office, extended property rights to the individual components of organisms, 4 x 8 grow tray including genetic information, thus anticipating some of today’s contentious Genetically Modified Organism debates. Ten years later, Asgrow Seed v. Winterboer denied the rights of farmers to save and resell patented seed products, marking the continuation of a series of legislation that progressively placed power in corporate hands. In 2001, J.E.M. AG Supply v. Pioneer Hi-Bred International, a legal dispute between a large seed company and small seed supply center, affirmed that newly developed plant breeds are covered by expansive utility patents. In 2013, furthermore, Bowman v. Monsanto held that patent “exhaustion doctrine” does not cover farmers’ reproduction of patented seeds through planting and harvesting without the patent owner’s permission, further reflecting and securing corporate profit and influence. Although inadequate disclosure laws make it difficult to determine the exact amount expended on the Farm Bill and on other pieces of legislation, during the two years preceding the passage of the Farm Bill on February 7, 2014, at least 600 companies spent over $500 million in lobbying. The largest spenders ranged from Fortune 500 leaders in banking, trade, transportation and energy to non-profit organizations. A joint investigation by Harvest Public Media and the Midwest Center for Investigative Reporting found that the top 18 corporations and groups spent at least $5 million each in total lobbying from 2012 to the First Quarter of 2014.

These corporations and groups include: the US Chamber of Commerce, Exxon Mobil, Du Pont, the American Bankers Association, Pharmaceutical Research and Manufacturers of America, Grocery Manufacturers Association, Wells Fargo, AARP, Monsanto, Independent Community Bankers of America, Coca-Cola, Association of American Railroads, Nestle, Nextera Energy, BNSF Railway Company, PMI Global Services Inc., Bayer Corporation, and American Forest & Paper Association. The commodities support programs outlined above make up one major set of Farm Bill issues influenced by such lobbying efforts. These direct payments have long received the attention of growers groups and other interest groups that are beholden to corporate interests. Specifically, alongside the Farm Bureau, the Farmers Union, and other general farm organizations, all major agricultural commodities are represented by a lobbying organization that aims to keep the Farm Bill’s commodity programs intact as per the supposed interest of the producers of such commodities. These organizations include: the National Cotton Council, the Sugar Association, and the National Corn Growers, among others. While indeed all industries are represented by lobbying organizations, the relative political and economic strength of actors within the US food system that are already oriented toward large-scale production, processing, distribution, and service—such as those above—highlights their significance, particularly concerning contemporary campaign finance reform efforts. With the change to crop insurance as the safety net centerpiece, banks and insurance companies spent at least $52.6 million in lobbying the 2014 Farm Bill and other issues in the two years prior to its passage. For example, Wells Fargo, the fourth-largest US bank, spent approximately $11.3 million in lobbying efforts, signaling the potential gain to be had by the company’s Rural Community Insurance Services, the largest crop insurance provider in the country. The American Bankers Association, another group that will benefit most from the boost to crop insurance, reported spending $14 million on lobbying, including advocacy for crop insurance and other rural lending plans. Other lobbyists for crop insurance included Independent Community Bankers of America, ACE INA Holdings and Zurich , the National Association of Professional Insurance Agents, and Deere & Co., the large equipment manufacturer that also has a crop insurance arm. Private sector spending on agricultural research has risen steeply since the 1970s and 1980s, exceeding public sector spending on agricultural research. From 1970 to 2006, private agricultural research expenditures—both in-house research and donations to land-grant universities—rose from $2.8 billion to over $8 billion, in inflation-adjusted 2014 dollars. Yet total public funding—directed toward land-grant universities and the USDA—rose from $3.1 billion to $6.1 billion in that same period. Federal funding of land-grant universities in particular reflect such trends: by the early 1990s, industry funding had already surpassed USDA funding of agricultural research at land-grant universities and by 2009, private sector funding had soared to $822 million, compared to $645 million from the USDA. Significantly, the economic recession substantially restricted research funding. Yet USDA land-grant university funding dropped twice as fast as private funding between 2009 and 2010, from 39.3% and 20.5%, respectively, reflecting the increasing dependence of university research on corporate funds, particularly during economic downturns. 

Stanley Hart White, Professor of Landscape Architecture at the University of Illinois Urbana-Champaign from 1922 to 1959, was granted US Patent 2,113,523 on 5th April 1938 for the Vegetation-Bearing Architectonic Structure and System in which he describes the method for creating an ‘architectonic structure of any buildable size, shape or height, whose visible or exposed surfaces may present a permanently growing covering of vegetation’. In six beautifully illustrated pages, Professor White reveals the new art of growing plants within/on a vertical, architectonic, substrate-holding frame, and in the process describes a new vertical garden type not fully realized till after his death in 1979. All that remains of White’s invention are his careful diaries, a series of patents, and his brother E. B. White’s correspondences about Stan’s new invention. Stanley Hart White is best known as an educator who modernized landscape pedagogy at the University of Illinois, influencing the work of Hideo Sasaki, Peter Walker, Richard Haag, and others, through his innovative teaching style and creativity. With the discovery of his patent for the first known green wall, or Botanical Bricks, he may also be credited as an inventor and technological innovator, conceptualizing the vertical garden and pioneering green modernism . White’s thoughts on vegetation-bearing architecture crystallize in his patent of 1938, 4×4 grow tray yet notions of a green wall emerge as early as 1931 in his lectures and writings on the modern garden.

Although the intended audiences for White’s early writings on vertical greenery are not yet apparent, the idea of a vegetation bearing garden enclosure preoccupies him for several years as documented in his personal journals, or Commonplace Books, in the University of Illinois Library Archives. Technical aspects of White’s green wall find their clearest articulation in US Patent 2,113,523, filed on 18th August 1937, yet the theoretical dimensions developed as a treatise on modernism and garden design, in which the vertical surfaces of the garden create a backdrop for modern living. In an essay titled ‘What is Modern’, White discusses the green wall as a design solution for the modern garden, allowing for the preservation of a free plan and composition of a garden in the vertical dimension. His references to Walter Gropius, Le Corbusier, Frank Lloyd Wright, Louis Sullivan, Walt Whitman, Charlie Chaplin, Norman Bel Geddes, Adolph Appia, Sheldon Cheney, Walt Disney, and others, situates the work among a group of ‘moderns’ concerned with changing lives through art and architecture.6 The Vegetation-Bearing Architectonic Structure and System evolved as a response to the problem of modernism in garden design, and is a unique contribution of landscape architecture to this effort, representing a clear translation of garden theory into garden form and legalese. The prescience of this work is astounding, predicting not only the emergence of the vertical garden in the contemporary built environment, but a method of scholarship in patent development not widely accepted by US universities until the 1970s. White’s inchoate drawings and description of a green wall in 1931–32 mature until his application for the Vegetation-Bearing Architectonic Structure and System on 18th August 1937, where he artfully translates garden theory into United States Patent and Trademark Office legalese with the help of his attorney, Elmer Hovenden Gates of Arlington, Virginia.

The new art of vegetation bearing architecture was entirely novel at the time of application, and no citations of prior art are associated with White’s invention. Currently, thirty four international patents cite US Patent 2,113,523 as prior art, encoding an array of inventions from grass cube chairs, to vegetation-bearing gabion walls. Interestingly, White’s lawyer, Elmer Hovenden Gates, and proposed business partner, William M. McPherson, patented related vegetation-bearing technologies within weeks of his submission. More than 50 patents cite the Vegetation Bearing Cellular Structure and System, Vegetation-Bearing Display Surface, and the Vegetation-Bearing Architectonic Structure and System, collectively encoding a diverse ensemble of environmental technologies. The legalese defining this new field offers valuable insights into the founding principles of vegetation-bearing architecture as a chimera of architectonic structure and vegetated system. According to White, architectonics relates to ‘the art of landscaping structure as well as to buildings, but distinguished from the art of plant culture’. Within this architectonic structure, plant growth is supported through a layering of horticultural substrates and reticular materials. In this configuration, the ‘vegetation in its final positions has its roots within the compost while the tops of the vegetation would extend through the reticular surfaces of the units or compounds into the open air where their normal development occurs’. The patents legalese describes not only the technical specifications of White’s new invention, but also the proposed scope of vegetation-bearing architecture as a new art. This scope is of particular interest with the emergence of the vertical garden and green wall in the contemporary built environment, as the language that defines the new art also encodes innovations in related technologies today.

Specifically, corporate control refers to control of political and economic systems by corporations in order to influence trade regulations, tax rates, and wealth distribution, among other measures, and to produce favorable environments for further corporate growth. Structural racialization refers to the set of practices, cultural norms, and institutional arrangements that are reflective of, and help to create and maintain, racialized outcomes in society, with communities of color faring worse than others in most situations. In this light, the production of racial/ethnic, gender, and economic inequity in the United States is more so a product of cumulative and structural forces than of individual actions or malicious intent on behalf of private or public actors. In order to challenge and eliminate corporate control and structural racialization in the United States, therefore, it is necessary to analyze the ways that public and private institutions are structured. It is also necessary to analyze how government programs are administered and operate in ways that reproduce outcomes that marginalize low-income communities, women, and communities of color in terms of health, wealth, land access, power, and degree of democratic influence. Additionally, as this report aims to do, it is crucial to analyze the genesis and formation of critical institutions and structures themselves. Therefore, the US Farm Bill—the flagship piece of food and agricultural legislation since its inception in 1933, which informs the heart of public and private policies that make up much of the US food system—is the subject of this report. This report is of particular importance now for two reasons. First, the Farm Bill will be under consideration again in 2019, yet there is no comprehensive critique of the Farm Bill that addresses its underlying contradictions, particularly with regard to racial/ethnic, gender, and economic inequity. Second, it is imperative that campaigns by grassroots, community, and advocacy organizations—generally most active during the period of Farm Bill negotiations in Congress—have enough time to gather adequate information and conduct in-depth analysis for targeted yet comprehensive policy change. As such, the timing of this report is also imperative for coalition-building efforts and the growth of an effective broad-based food sovereignty movement.Corporate consolidation and control have become central features of the US food system, and of the Farm Bill in particular. As of 2014, large-scale family-owned and non-family-owned operations account for 49.7% of the total value of production despite making up only 4.7% of all US farms. As of 2013, only 12 companies now account for almost 53% of ethanol production capacity and own 38% of all ethanol production plants. As of 2007, four corporations own 85% of the soybean processing industry, 82% of the beef packing industry, 63% of the pork packing industry, and manufacture about 50% of the milk. Only four corporations control 53% of US grocery retail, and roughly 500 companies control 70% of food choice globally. At every level of the food chain, from food production to food service, workers of color typically earn less than white workers. For example, a majority of farm workers who receive “piecerate” earnings , and many of whom are migrants from Mexico, frequently earn far less than minimum wage—an exploitative practice deeply tied to immigration policy, as elaborated upon below. On average, white food workers earn $25,024 a year while workers of color make $19,349 a year, greenhouse racking with women of color, in particular, suffering the most. Furthermore, few people of color hold management positions in the food system, while white people hold almost three out of every four managerial positions. One result of this racial disparity in food system labor is that non-white workers experience a far greater degree of food insecurity than their white counterparts.Food insecurity in the US disproportionately affects low-income communities and communities of color, and these communities are over represented in the lowest-paying sectors of the labor market.

For example, as of 2013, 14.3% of US households—17.5 million households, roughly 50 million persons—were food insecure. The report also found that the rates of food insecurity were substantially higher than the national average among Black and Latino/a households, households with incomes near or below the federal poverty line, and single parent households. Racial/ethnic inequity with regard to land access is a defining feature not only of the corporate-controlled food system, but also of the US government itself, which, even years after emancipation, has made it nearly impossible for Blacks and other communities of color to acquire and keep land in substantial numbers. For example, in 1920, 926,000 US farmers were Black and they owned over 16 million acres of land, and by 1997, fewer than 20,000 US farmers were Black and owned approximately 2 million acres of land. While white farmers were losing their farms during these decades as well, the rate that Black farmers lost their land has been estimated at more than twice the rate of white-owned farm loss.Though the Farm Bill itself does not deal directly with immigration, the impact of the Bill on farmworkers cannot go unnoticed. The combination of an immigration system easily exploited by employers, 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. 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. Given the fact that the Farm Bill supports many of those companies that employ farmworkers, connections must be drawn to highlight how the Farm Bill upholds and perpetuates structural injustice among farmworkers. In the US, exposures to environmental hazards have disproportionately impacted low-income communities and communities of color. As a major contributor to global climate change and the racialized distribution of its impacts, conventional agricultural production practices, in particular, have been instrumental in maintaining and upholding these disparities. Furthermore, low-income communities and communities of color in the United States bear the burden of the impacts caused by climate change. For example, these populations breathe more polluted air than other Americans, suffer more during extreme weather events, have fewer means to escape such extreme weather events, and disproportionately experience greater hardship due to rising energy, food, and water costs.This report found a number of structural barriers to addressing these racial/ethnic, gender, and economic inequities. First, the Farm Bill itself is increasingly imbricated in, and ultimately functions as a pillar of, neoliberalism. The long term shift from the subsidization of production and consumption to the subsidization of agribusiness has structurally positioned low-income communities and communities of color on the losing side of such shifts. This population has also been given fewer options for recourse, given the ways in which the Farm Bill has been designed to be insulated from democratic influence, particularly by way of countless layers of congressional committees. Second, under the current Farm Bill, supporting public nutrition assistance programs and fighting poverty and racial/ethnic inequality, are antithetical to one another, despite the evidence that suggests otherwise. Specifically, while such public assistance programs do provide support to some of the most marginalized communities, they ultimately maintain structural inequity, particularly in terms of wealth, by channeling profits to corporations such as Walmart and other large retailers, which benefit greatly from distributing benefits such as SNAP. Many of these corporations are then able to funnel profits back to their corporate headquarters outside their respective retail sites, while still paying workers low wages and granting few benefits at every level of the food system. Finally, this report found that supporting the inclusion of producers of color into current payment schemes, and fighting poverty and racial/ethnic [ii] Neoliberalism is a new period of capitalism, particularly since 1970s and 1980s, characterized by unparalleled global reach of economic liberalization, open markets, free trade, and deregulation.

Again, Wolf and Sumner find no evidence of a bimodal dairy industry using Farm Cost and Return Surveys of dairy farms for the years 1989 and 1993. In MacDonald et al. , they suggest that larger dairies tend to have lower costs per cow, which allows them to capture greater economies of scale. The cost-minimizing efforts of individual dairy farms will influence the specific farm management choices that the farm makes, as only the individual farm has a true sense of where it sits on its long-run average cost curve. Some of these management decisions include the dairy’s strategy to capture economies of scope, through sales diversification, or vertically integrate to minimize input and production costs. Sumner and Wolf find that vertical integration has little influence on the farm size and that the tendency for farms in the Pacific and South to have larger herd sizes remains true, even when accounting for the levels of vertical integration. The farm’s choice to incorporate different management strategies reflects the incentives and constraints that the farm faces, i.e., influences of geographic location and capital. Other influences on management choices by dairies are in part due to different environmental regulations in each state that impact the average cost of production for dairy farms. There has been a significant amount of agricultural economic research on dairy farm size with respect to their risk management and technical efficiency. Tauer finds that smaller dairies in New York do have a high average cost of production than dairies with larger herd sizes, grow trays but that these higher costs are due to inefficiencies and efficient small dairies are competitive with the larger dairies. Tauer and Mishra examine whether differences in technology or efficiency characterize the higher cost that smaller dairy farms face and find that using a frontier cost of production analysis show that inefficiencies in smaller dairies characterize the higher costs, not technological differences.

There has also been significant analysis in farm structure changes of the dairy industry. Zimmermann and Heckelei utilize a Markov Chain Model on dairies in the European Union to characterize farm size change and find that regional characteristics such as off-farm opportunities and unemployment rates are significant in relation to dairy farm size change. They also find that high milk prices slow down farm size change due to high milk prices correlation to uncertainty and price volatility leading to a decrease in investment. Wolf details how dairy farms in Michigan have increased their use of risk management tools from 1999 to 2011 and find that the use of such risk management tools was positively correlated with measures of dairy farm size. This research also discusses how age related to risk management adoption with younger dairy farmers being less likely to utilize the risk management tools. Wolf outlines characteristics of dairy farm size change across time Beyond management decisions influencing or being correlated with the farm size and farms’ decision to exit, previous economic literature has hypothesized about the possible influences of operator characteristics, like human capital , the number of female operators, the age of operators, or other farm operator characteristics on farm size. Sumner and Leiby find that human capital positively influences the size of the farm, and this is hypothesized to be due to increasing opportunity costs for dairy farmers with high levels of human capital. Dairy farmers that have the possibility of making more money elsewhere will do so, therefore it seems likely that dairy farms with sufficient returns, which tend to be found on larger dairy farms, will attract high human capital management. Another aspect of the previous research related to farm size and the dairy industry is farm exits. There have been several studies of individual farm movement across farm size groups and characterization of exits.

Most of this literature, however, has been limited to regions or states. Macdonald et al. finds that in 2016 about 40 percent of dairy farms with at least 2,000 milk cows did not have positive net returns and that the share of dairies that did not have positive net returns increased as herd size decreased. However, they do note that negative returns in the dairy industry are seen as temporary lows by dairy operators, so they do not serve as a direct indication of an expected exit from the industry. Other reasons for exits from agriculture, or dairy specifically, include increased suburbanization of previously agricultural land, driving land prices up, and strong local economies, opening off-farm employment opportunities for farm operators. As outlined in Sumner and Leiby and Sumner , the human capital element remains prevalent through economic explanations of farm exit. Of course, age of the farm operators plays key role. Macdonald et al. discuss the role of the advanced age of many dairy farmers and the fact that many dairy farms are family-run, suggesting that there will be an increase in exits as more farmers choose to retire. Furthermore, the study relates the probability of exit to farm size, finding that not only does the age of the operator increase the likelihood of exit, but the smaller the farm size also increases the probability of exit. This section discusses the sample used in this analysis and details changes in the COA questions that are relevant to this analysis. The research utilizes COA data from 2002, 2007, 2012, and 2017 for six select states: California, Idaho, New Mexico, New York, Texas, and Wisconsin. The results presented have gone through a disclosure review process and no data on individual/farm-specific is specific to individual farms and instead characterizes them more generally. Although the COA is federally mandated, it does not collect data on every U.S. farm and as such weights responses to create the most accurate sample that reflects the true U.S. farm sample. As discussed in Chapter 2, I use a specific definition of a commercial dairy in order to capture dairies with significant engagement with the dairy industry.

A commercial dairy for the purposes of this analysis is defined as a farm with at least 20 milk cows on the farm as of December 31 of the Census year and the farm must have dairy or milk sales revenue above the dollars of milk sale revenue that would have been generated by 30 milk cows. The survey questions asked of farmers and ranchers by the COA change slightly every Census round, although most remain the same across time. Below are descriptions of question changes for relevant variables to the analysis. First, in 2002 and 2007, farms were asked for the total amount of dairy sales in that year, but in 2012 and 2017, this question was dropped and replaced with the total amount of milk sales. Furthermore, whether the dairy farm had any level of organic production was only asked in 2007, 2012, and 2017. Second, operator characteristic questions have become more detailed over the years and allowed more information about operators to be collected. In 2002, 2007, and 2012, the COA asked detailed operator characteristic questions about up to three operators, but only one operator was identified as the principal operator. In 2017, the COA expanded its detailed operator questions to include up to four operators and allowed for up to four operators to be identified as principal operators. In this Chapter, pruning cannabis the operators for which the number per farm is limited and detailed information is provided will be referred to as the “core operators.” There is other no limit to the number other operators listed per farm and only the gender of each such operator and the total number per farm are provided in the Census. The COA has three potentially relevant farm size variables for dairy farms, the number of milk cows, the value of farm production, and the value of milk or dairy sales. I utilize all three in this chapter. However, I focus particular attention on the number of milk cows for the kernel density graphs. I characterize the distributions of number of milk cows per commercial dairy farm using two approaches. One approach is to fit a non-parametric distribution by year, and by state for each year to the data on milk cow herd size per farm. The other approach is to fit two commonly used parametric distributions to characterize dairy farm size distributions for the national and individual states over census years. One aim of my thesis is to characterize the farm size distribution of dairy farms and fitting parametric density functions serves as a starting point for characterizing and analyzing dairy size distribution. As explained above, there is previous literature that utilizes parametric distributions to characterize farm size and this research provides evidence that commonly used distributions do not fit well with the U.S. commercial dairy industry. It is common in farm size analysis to fit parametric density functions to characterize farm size distribution . I create kernel density plots for the herd size distribution by state across the years and then find and fit two common parametric density functions to the distribution.

This section will be structures as follows: a brief overview of the mathematics used in fitting parametric density functions. There are three steps to fitting the parametric density function to the farm size variables. First, I hypothesize based on the kernel density plots what distributions seem reasonable. For this analysis I use the lognormal and the exponential function, as those are two common distributions used in farm size literature and are likely shapes for most farm size distributions. Lognormal is the typical selection, as it is referenced in Gibrat’s Law. The exponential distribution was selected because it can account for the same skewed shape but has more flexibility. Second, I estimate the parameters of interest needed to form that distribution in order to create an estimated distribution of random numbers that follow the specific distribution. For this analysis, the measures of farm size, the number of milk cows for each farm, are random variables x1, x2, x3, …, xn, where n is the sample size of farms, for which the joint distribution depends on distribution parameters. For example, using the lognormal the parameters are the mean and variance, and there are two related parameters for the exponential distribution. From there, we can calculate the estimates of these parameters to create a different distribution with those same parameters and compare them to the actual distribution of the number of milk cows. Some estimated parametric distributions appear to have slight irregularities, this is due to the number of observations and the impose parameters. This section will summarize the resulting farm size graphs and detail the trends across time and states. Overall, when looking at the six select states together commercial dairy farm distributions have shifted towards larger dairies. In 2002, there was a clear peak in the number of farms with less than 200 milk cows, but the peak falls significantly from 2002 to 2017 . Whereas farm size distribution shows a clear increase in the farms with larger herd sizes in 2017. Although this graph gives interesting detail about the trends in herd size for the U.S. overall it is mostly characterized by Wisconsin and New York which have a significantly larger share of the number of commercial dairies and tend to have smaller herd sizes relative to other states. This graph clearly shows that there remains a large share of dairies that have a herd size of less than 200 milk cows, despite the relative shift in herd size. Moving to state-specific trends, overall California dairies have had larger herd sizes than other states, such as New York or Wisconsin across all years . California had a peak in the share of dairies with less than 1,000 milk cows from 2002 to 2017, but the peak fell significantly between 2007 and 2012. There was a clear shift in 2012 with an increase in the 1,000 to 2,000 milk cow herd size in 2012 and then another shift in 2017 in the 2,000 to 3,000 milk cow herd size. This documents a clear movement of California dairies towards larger herd sizes and a decrease in smaller herd sizes. Idaho had a large peak in commercial dairies with less than 500 milk cows in 2002 and then a significant drop in that peak in 2007 with smaller subsequent decreases in 2012 and 2017 .

In another example, several of the farmers learned about variations in their soil type by directly observing how soil “behaved” using cover crop growth patterns. These farmers discussed that they learned about patchy locations in their fields, including issues with drainage, prior management history, soil type, and other field characteristics, through observation of cover crop growth in their fields. Repeated observations over space and time helped to transform disparate observations into formalized knowledge. As observations accumulated over space and time, they informed knowledge formation across scales, from specific features of farmers’ fields to larger ecological patterns and phenomena. More broadly, using cover crop growth patterns to assess soil health and productivity allowed several farmers to make key decisions that influenced the long-term resilience of their farm operation . This specific adaptive management technique was developed independently by several farmers over the course of a decade of farming through longterm observation and experimentation – and, at the time, was not codified in mainstream farming guidebooks, policy recommendations, or the scientific literature . For these farmers, growing a cover crop on new land or land with challenging soils is now formally part of their farm management program and central to their soil management. While some of the farmers considered this process “trial and error,” in actuality, all farmers in this study engaged in a structured, iterative process of robust decision-making in the face of constant uncertainty, similar to the process of adaptative management in the natural resource literature . This critical link between farmer knowledge formation and adaptive mangement is important to consider in the broader context of resilience thinking, cannabis drying trays wherein adaptive management is a tool in the face of shifting climate and changing landscape regimes .

The underlying social and ecological mechanisms for farmer knowledge formation discussed here may have a role in informing adaptive management and pathways toward more resilient agriculture . In this sense, farmer knowledge represents an overlooked source for informing innovation in farming alternatively. Farmer knowledge provides an extension to scientific and policy knowledge bases, in that farmers develop new dimensions of knowledge and alternative ways of thinking about aspects of farming previously unexplored in the scientific literature. Farmers offer a key source of and process for making abstract knowledge more concrete and better grounded in practice, which is at the heart of agriculture that is resilient to increased planetary uncertainties .Most of the farmers considered themselves separate from scientific knowledge production and though scientific knowledge did at times inform their own knowledge production, they still ultimately relied on their own direct observation and personal experiences to inform their knowledge base and make decisions. This finding underscores the importance of embedding theory in practice in alternative agriculture. Without grounding theoretical scientific findings or policy recommendations in practice, whether that be day-to-day practices or long-term management applied, farmers cannot readily incorporate such “outsider” knowledge into their farm operations. Farmers in alternative agriculture thus may provide an important node in the research and policymaking process, whereby they assess if scientific findings or policy recommendations may or may not apply to their specific farming context – through direct observation, personal experience, and experimentation.Similar to Sūmane et al. , we found that the process for farmer knowledge formation, or precisely how farmers learn, is systematic and iterative in approach. In this study, farmer ecological knowledge was developed over time based on continuous systematic observation, personal experiences, and/or experimentation.

This systematic approach that relies on iterative feedback to learning applied among these organic farmers is akin in approach to examples of adaptive management in agriculture . As highlighted in the results, it is possible for a farmer to acquire expert knowledge even as a first- or second-generation farmer. Documenting this farmer knowledge within the scientific literature – specifically farmer knowledge in the context of relatively new alternative farmers in the US – represents a key way forward for widening agricultural knowledge both in theory and in practice . This study provides one example for documenting this farmer knowledge in a particularly unique site for alternative agriculture. Future studies may expand on this approach in order to document other sites with recent but practical agricultural knowledge on alternative farms.Farmers in this study tended to think holistically about their farm management. For example, when the farmers were asked to talk about soil management specifically, several of the farmers struggled with this format of question, because they expressed that they do not necessarily think about soil management specifically but tend to manage for multiple aspects of their farm ecosystem simultaneously. This result aligns with similar findings from Sūmane et al. across a case study of 10 different farming contexts in Europe, and suggests that farmers tend to have a bird’s eye view of their farming systems. Such an approach allows farmers to make connections across diverse and disparate elements of their farm operation and integrate these connections to both widen and deepen their ecological knowledge base.For most farmers in this study, maintaining ideal soil structure was the foundation for healthy soil. The farmers emphasized that ideal soil structure was delicately maintained by only working ground at appropriate windows of soil moistures. Determining this window of ideal soil moisture represented a learned skill that each individual farmer developed through an iterative learning process.

This knowledge making process was informed by both social mechanisms gained through inherited wisdom and informal conversations and ecological mechanisms through direct observation, personal experiences, and experimentation .As these farmers developed their ecological knowledge of the appropriate windows of soil moisture, their values around soil management often shifted. In this way, over time , farmers in this study learned that no amount of nutrient addition, reduced tillage, cover cropping, or other inputs, could make up for damaged soil structure. Destroying soil structure was relatively easy but had lasting consequences and often took years, in some cases even a decade, to rebuild. This key soil health practice voiced by a majority of farmers interviewed was distinct from messaging about soil health vis-a-vis extension institutions, heavy duty propagation trays where soil health principles focus on keeping ground covered, minimizing soil disturbance, maximizing plant diversity, keeping live roots in the soil, and integrating livestock for holistic management . While these five key principles of soil health were mentioned by farmers and were deemed significant, for most farmers interviewed in this study, the foundation and starting point for good soil health was maintaining appropriate soil structure. The results of this study emphasize that the most successful entry point for engaging farmers around soil health is context specific, informed directly by local knowledge. Among farmers in Yolo County – a significant geographical node of the organic farming movement – soil structure is a prevalent concept; however, in another farming context, this entry point may significantly diverge for social, ecological, economic, or other reasons. Each farming context therefore necessitates careful inquiry and direct conversation with local farmers to determine this entry point for engagement on soil health. For this reason, in some cases it may be more relevant to tailor soil health outreach to the local context rather than applying a one-size-fits all model.The capacity to learn and pass on that learning are essential for farms that practice alternative agriculture to be able to adapt to ever changing social and ecological changes ahead . Across all farmers interviewed, including both first and second-generation farmers, farmers stressed the steep learning curves associated with learning to farm alternatively and/or organically. While these farmers represent a case study for building a successful, organic farm within one generations, the results of this study beg the question: What advancements in farm management and soil management could be possible with multiple generations of farmer knowledge transfer on the same land? Rather than re-learning the ins and outs of farming every generation or two, as new farmers arrive on new land, farmers could have the opportunity to build on existing knowledge from a direct line of farmers before them, and in this way, potentially contribute to breakthroughs in alternative farming. In this sense, moving forward, agriculture in the US has a lot to learn from agroecological farming approaches with a deep multi-generational history . To this end, in most interviews – particularly among older farmers – there was a deep concern over the future of their farm operation beyond their lifetime.

Many farmers lamented that no family or individual is slated to take over their farm operation and that all the knowledge they had accumulated would not pass on; there exists a need to fill this gap in knowledge transfer between shifting generations of farmers, safeguard farmer knowledge, and promote adaptations in alternative agriculture into the future. As Calo and others point out, technical knowledge dissemination alone will not resolve this ongoing challenge of farm succession, as larger structural barriers are also at play – most notably, related to land access, transfer, and tenure .Most studies often speak to the scalability of approach or generalizability of the information presented. While aspects of this study are generalizable particularly to similar farming systems, the farmer knowledge presented in this study may or may not be generalizable or scalable to other regions in the US. To access farmer knowledge, relationship building with individual farmers leading up to interviews as well as the in-depth interviews themselves required considerable time and effort. While surveys often provide a way to overcome time and budget constraints to learn about farmer knowledge, this study suggests that to achieve specificity and depth in analysis of farmer knowledge requires an interactive approach that includes – at a minimum – relationship building, multiple field visits, and in-depth, multi-hour interviews. Accessing farmer knowledge necessitates locally interactive research; this knowledge may or may not be immediately generalizable or scalable without further locally interactive assessment in other farming regions.One of the best ways to plan a garden is to make a map of the proposed area using grid paper and drawing it to scale. Look up the space requirements for the vegetables you’re interested in; consult your local UCCE Farm Advisor or Master Gardener and the publications listed in “For More Information” for advice on this subject. Draw the vegetables in appropriate places on the map. Also include planting dates—this will help you remember when and where to plant different crops. Vegetables need a steady supply of water during growth, so make certain there is an adequate and handy water source near the site. A level garden is necessary for uniform watering, but if the ground slopes, contour planting and drip irrigation allow water to be distributed evenly. Choose a site with rich, fertile soil that is free of weeds, rocks, and debris. Avoid shallow or compacted soils. If your soil is less than ideal, you may need to amend it or plant in raised beds .Full sunlight—a minimum of 6 to 8 hours per day—is necessary for some crops that produce “fruit,” such as tomatoes and corn. Full sun is ideal for all vegetables, but root and leafy crops can tolerate some shade. Look for shadows that may be cast over the planted area; note how much of the garden would be in shade and for how long each day. Keep in mind that shadow patterns change with the seasons. If possible, avoid planting under trees or on the north side of tall buildings. If tall and short plants are to be planted closely together, put the tall ones on the north side so the tall plants don’t cast shade on shorter plants next to them.Soil should be spaded or rototilled when it is moist but not wet. A good time to do this is in autumn before rain begins; it can be done again in spring if necessary. Work the soil about 6 to 10 inches deep, but avoid bringing subsoil to the surface. While working the soil, add preplant fertilizer . Rake the turned seedbed in several directions while it is still soft and full of moisture, so that any large clods or layered soils are broken up. The soil should have a uniform texture to a depth of 6 to 10 inches .

The number of certified operations and cropland acreage in California doubled between 2002 and 2016. State organic crop sales increased almost tenfold at the farm level, in real terms, during the same time period . This essay uses field-level pesticide application records and a fixed-effects model to analyze changes in the environmental impacts of pesticide use for both organic and conventional fields over 21 years. The database covers all registered agricultural pesticide applications in California, and contains over 48 million pesticide application records for over 64,000 growers and 781,000 fields from 1995 to 2015. In total, data from more than 55,000 organic fields and 11,000 growers who operated organic fields are analyzed in this essay. The Pesticide Use Risk Evaluation model is used to assess the environmental impacts of pesticide use . The results show that the environmental impact of pesticide use per acre is lower in organic fields across all of the environmental dimensions for which PURE indexes are defined: surface water, groundwater, soil, air, and pollinators. The difference in the impact on air is the smallest because natural pesticides are not systematically different from synthetic pesticides in terms of volatile organic compound emissions. The estimated impacts on all five environmental dimensions are positively correlated with farm acreage. The measure of farmer experience is positively correlated with estimated impacts per acre on surface water and groundwater, hydroponic drain table and negatively correlated with estimated impacts on soil, air, and pollinators but the difference associated with variation experience are smaller than the estimated effect of whether the field is organic or not by orders of magnitude. Environmental impacts and the difference between organic and conventional production vary by crop.

Four major California crops, lettuce, strawberries, processing tomatoes, and wine grapes, are examined in detail.The benefit from organic agriculture is partially paid by consumers through a price premium for organic products . Whether organic production is the most cost effective way to reduce the environmental impacts of agriculture is not the focus of this essay. However, readers can gain some insight into the performance of organic agriculture by comparing the cost of alternative tools and their effects on environmental quality. The contribution of this essay is threefold. First, it links the environmental impacts of organic crop production directly to pesticide applications. To the best of my knowledge, no other studies have examined this relationship. Previous literature provided abundant evidence on the environmental impact of organic agriculture as a system but failed to quantify the impact of specific farming practices . Here, AIs and their contributions to environmental impacts are identified individually, which enhances the understanding of the differences in pesticide use between organic and conventional agriculture and how they vary across crops. Second, this essay uses the PURE model to assess the environmental impacts of pesticide use . Compared to the risk quotient approach, which is another common method in the literature , the PURE model provides a more salient measure of environmental impacts by incorporating additional environmental information, such as the distance from the pesticide application to the nearest surface water. The PURE model calculates risk indices for five environmental dimensions: surface water, groundwater, soil, air, and pollinators. Third, by using the Pesticide Use Report database, this essay’s findings are based on the population of pesticide application data.

Prior works include meta-analyses that cover numerous field experiments and commercial operations examined for a crop or a small geographic area over a limited period of time. California’s agriculture is characterized by many crops and diverse climate and soil conditions. The comprehensive coverage of the PUR database eliminates any sample selection issue. The rest of the essay is organized as follows: section 2 introduces the PUR database and PURE model and presents summary statistics of historical pesticide use, section 3 provides the identification strategy to tackle grower heterogeneity, section 4 presents industry level and crop-specific estimation results, and section 5 concludes. To obtain the USDA organic certification, growers must meet requirements on several aspects of production: pesticide use, fertilizer use, and seed treatment. The requirement on pesticide use is burdensome because pesticides approved in organic agriculture are expensive and have less efficacy. Pesticide and fertilizer AIs used in organic agriculture undergo a sunset review by the National Organic Standards Board every five years and the main criterion is whether the ingredient is synthetic or not. In general, it is not reasonable for growers to use those pesticides exclusively but not apply for the organic certification, given higher price and lower efficacy of those pesticides. Therefore, growers who comply with the NOP’s requirement on pesticide use can be viewed as equivalent to certified organic growers for the data sorting purpose. In Wei et al. , authors located individual organic fields using this approach. Namely, any field without a prohibited pesticide applied for the past three years is considered organic. Their paper compared organic crop acreage from PUR to other data sources and showed that pesticide use records alone can be used to identify organic crop production. Environmental conditions for each field and toxicity values for each chemical are used to calculate the value of the PURE index developed by Zhan and Zhang .

The PURE index has been used in previous studies to represent environmental impacts of pesticide use . The PURE index indexes environmental impacts of pesticide use in five dimensions: surface water, groundwater, soil, air, and pollinators. For each dimension, the PURE index is calculated on a per acre basis and it varies from 0 to 100, where 0 indicates trivial impact and 100 rep- resents the maximum impact. Excluding air, the PURE index is the ratio of the predicted environmental concentration to toxicity to the end organisms. The PEC estimates the effect of the pesticide application on the concentration level for chemicals in the environmental sample. The toxicity values cover both acute measures, such as LD50, and long-term measures, such as No Observed Effect Concentration and acceptable daily intake for humans. End organisms are fish, algae, and water fleas for surface water, humans for groundwater, earthworms for soil, and honeybees for pollinators. The PURE index for air is calculated based on potential VOC emissions, which is a common measure of airborne pollutants emitted from agriculture production . The emission of VOCs is defined as the percentage of mass loss of the pesticide sample when heated. Unlike toxicity, VOC emissions do not have a strong link to whether the AIs are synthetic or natural. For example, the herbicide Roundup®, which contains glyphosate, has zero VOC emissions because there is no evaporation or sublimation. Meanwhile, sulfur products, which are widely used in organic agriculture, also have zero VOC emissions. Inert ingredients, which are not covered in this essay, are also found to have negative impacts on the environment and on pollinators in particular . Conventional and organic growers adopt different pest management practices. As specified by the NOP, organic growers shall use pesticides only when biological, cultural, and mechanical/physical practices are insufficient. Chemical options remain essential for organic pest management programs. Currently over 7,500 pesticide products are allowed for use in organic crop and livestock production, processing, and handling. In Figure 1.1, the acreage treated with different types of pesticides is shown on the left y-axis for both conventional and organic fields. Treated acreage is divided evenly among types for AIs that belong to multiple pesticide types, such as sulfur, which is both a fungicide and an insecticide. The average number of pesticide applications per acre, which is defined as the total treated acreage divided by the total planted acreage, is plotted against the right y-axis in both panels. This is a common measure of pesticide uses that controls for differences in application rate among pesticide products . If multiple AIs are used in a single application, rolling benches hydroponics the treated acreage is counted separately for each AI. Planted acreage remained stable for conventional agriculture over the study period, so changes in the average number of applications per acre were due to changes in treated acreage. Organic planted acreage grew dramatically, but treated acreage increased even more. The number of applications per organic acre rose from 2 to 7. Figure 1.1 provides a highly aggregated view of pesticide use as different pesticide products with different AIs and application rates are used in conventional and organic fields. Examining the Figure 1.1 , insecticide is the most used pesticide type, accounting for 36% and 44% of total treated acreage in conventional and organic agriculture respectively in 2015. Herbicide is the second most used type of pesticide in conventional fields. In contrast, organic growers’ use of herbicides is limited. Fungicide is another major pesticide type, and sulfur is the most used fungicide AI in both conventional and organic fields.

Sulfur is an important plant nutrient, fungicide, and acaricide in agriculture. The pesticide group “others” primarily includes plant growth regulators and pheromones. Disaggregating insecticide use provides more detailed insight into the nature of the difference between conventional and organic production. Figure 1.2 plots the insecticide treated acreage by physiological functions affected . Only three groups of insecticides are available to organic growers, while six are available to conventional growers. In conventional agriculture, 67% of treated acreage in 2015 was treated with insecticides that targeted nerves or muscles, which include organophosphates, pyrethroids, and neonicotinoids. For organic growers, two AIs, spinosad and pyrethrins, are available to target those physiological functions. The “unknown” category, which is mostly sulfur, accounted for a significant portion of treated acreage in organic agriculture. Insecticides that target the midgut, which includes Bacillus thuringiensis and several granulosis viruses, are widely applied in organic fields. Conventional growers rarely use them due to the high cost. In 2015, acreage treated with midgut targeted insecticides was 1% of total treated acreage in conventional agriculture and 24% in organic agriculture. A detailed discussion of insecticide and fungicide use by mode of action in conventional and organic production is in the appendix. Insecticides and fungicides in the two pest management programs have different modes of action and pose different levels of environmental impact. Simply comparing treated acreage or the amount of pesticide products used does not identify the differences in environmental impacts. In this context, the PURE index serves as a consistent measure across farming systems.Figure 1.3 plots PURE indices for conventional and organic fields by year. Index values for air and soil are significantly higher than those for the other environmental dimensions in both farming systems, which means that pesticide use in general has greater impacts on air and soil quality than groundwater, pollinators, and surface water. Risk indices of conventional fields are relatively stable from 1995 to 2015, with no obvious overall changes for air or soil, despite the many changes that have occurred during this 20-year period in regulations and grower portfolios. While PURE indices decreased 16% for surface water, 26% for pollinators, and 7% for groundwater over the same time period, these three were much less impacted by pesticides in 1995, the beginning of the study period. Despite the numerous regulatory actions designed to reduce environmental impacts over this 20-year period, such as the methyl bromide phase-out, large-scale substitution of pyrethroids for organophosphates, and regulations to reduce VOC emissions from non-fumigant products, the overall environmental impacts of conventional pesticide use show only limited reductions when aggregated across all crops. PURE indices for organic fields are similar to conventional fields in that the air and soil have significantly higher index vales than the others. However, the aggregate risk indices in all five dimensions are much lower in organic fields. Compared to conventional agriculture, organic agriculture has dramatically lower PURE indices for surface water , groundwater , air , soil , and pollinators . The reduction for air varies greatly across major California crops. Large reductions in the PURE index for air are observed for table grapes , wine grapes , and processing tomatoes , while others had relatively small ones such as leaf lettuce and almonds . The reduction in the PURE index for soil varies across crops as well, ranging from leaf lettuce to carrots . For surface water, groundwater, and pollinators, the differences between the PURE index in organic and conventional fields are similar across crops.

The present study found no effect of adolescent cannabinoid exposure in the escalation model, suggesting that adolescent WIN exposure may not facilitate the acquisition, maintenance, or escalation of cocaine use in adulthood. An alternative hypothesis is that the effect of cannabinoid use may not be observed on cocaine intake per se; instead, cannabinoid exposure may produce an increase in the motivation for cocaine, leading to an increase in compulsive cocaine seeking. Indeed, prior exposure to another potential gateway drug, alcohol, was found to have no effect on subsequent cocaine self-administration per se but produced greater motivation and compulsive-like cocaine seeking under a PR schedule of reinforcement. However, we observed no differences between the WIN-exposed and control groups in adulthood when we used a PR schedule of reinforcement to examine whether rats with prior exposure to WIN express alterations of the motivation to self-administer cocaine.One limitation of long-term behavioral studies in adolescent rats, including the present study, is that puberty in rats is relatively short . Compared with adults, rats that are allowed to self-administer cocaine during adolescence have been shown to be more vulnerable to cocaine addiction. Unfortunately, in the model of cannabinoid exposure during adolescence , cocaine self-administration can only be studied starting in late adolescence and continuing into adulthood because rats exit puberty by PND60. Because of this limitation, one possibility is that cannabinoid exposure during adolescence may affect cocaine intake in adolescence. The present results demonstrate that chronic exposure to cannabinoids does not facilitate the acquisition of cocaine self-administration or compulsive-like cocaine intake in adulthood, hydroponic table measured by the escalation of cocaine self-administration and PR responding in a relevant model of cocaine use disorder.

These results suggest that cannabinoid exposure per se is unlikely to be causally responsible for the association between prior cannabis use and future cocaine use in adulthood as purported by the gateway hypothesis. However, we found that cannabinoid exposure produced long-lasting increases in irritability-like behavior, which may indirectly facilitate the emergence of social conflicts and other mental disorders that may contribute to the abuse of drugs other than cocaine. Additionally, the cross-sensitization between WIN and cocaine in adolescence—which was not observed in adulthood—may highlight a short-term increase in the vulnerability to cocaine-induced behaviors. In summary, the present results showed that cannabinoid exposure during adolescence in rats produced cross-sensitization to cocaine in adolescence and a long-lasting increase in irritability-like behavior in adulthood. However, it did not facilitate the acquisition or escalation of cocaine self-administration or compulsive-like responding for cocaine in adulthood.Marijuana is the most widely used controlled substance in the world . In 2016, 192.2 million people used marijuana . Regular marijuana use, particularly initiated in adolescence, is associated with a range of adverse consequences, including poor cognitive and educational outcomes, low self-reported life satisfaction , downward socioeconomic mobility , psychiatric illness , marijuana-involved injury , and substance use disorders . Perceived risk and perceived availability of marijuana have historically been important drivers of adolescent marijuana use, and often targets of interventions to prevent or reduce adolescents’ use . However, these relationships may be changing. Most extant research on the changing associations between adolescent perceived risk, availability, and use of marijuana has been conducted in the United States , where 28 states and the District of Columbia have legalized medical marijuana since 2000, and 10 states and DC have legalized recreational marijuana since 2012 .

In this context, more adolescents now perceive no/low risk of marijuana use, but the prevalence of marijuana use has not increased simultaneously . Research on changes in the individual-level association between no/low perceived risk and use has been mixed. Some have found that the association weakened in recent years , while others have reported that it strengthened or remained stable . Additionally, perceived easy availability of marijuana has largely declined among US adolescents . Evidence suggests that the association between perceived availability and use of marijuana has remained strong and stable over time . Understanding these relationships is particularly important in light of recent liberalization of marijuana access, as perceived risk and availability are two key mechanisms through which legalization could impact use. In this study, we focus on the Southern Cone context for two reasons. First the Southern Cone has recently experienced changes in marijuana regulation, which could impact perceived risk and availability. Second, trends in adolescent marijuana use and perceived availability are different from those in the US, which could suggest distinct relationships between perceived risk and availability and use of marijuana. In 2013, Uruguay enacted a law providing the government full regulation over the large scale production and sale of recreational marijuana. Adults in Uruguay can purchase marijuana at pharmacies, grow marijuana at home, or acquire it through a cannabis club . In Argentina, possession of marijuana for personal use continues to be illegal ; however, a 2009 court judgment marked the beginning of a paradigm shift in the criminalization of marijuana since it raised a contradiction between Law 23,737 and Article 19 of the Constitution, which protects individuals’ freedom from state regulation . In 2017, Argentina approved access to medical marijuana under specific circumstances . In Chile, marijuana is decriminalized, a limited set of cannabis-based pharmaceutical products are available for medical use, and a new bill allowing other sources of access and formulations is under debate .

Since the early 2000’s, past-month adolescent marijuana use has increased in Uruguay , Chile , and Argentina . These trends are distinct from the US where marijuana use has remained stable , and from other South American countries where past-year use is less than 5% . Although perceived risk of marijuana use has decreased in both the Southern Cone and the US, perceived availability has increased in the Southern Cone, but decreased in the US . We know of no study that has assessed the individual-level relationships between adolescent perceived risk, availability, and use of marijuana in the context of the Southern Cone. Such research may inform the priority and scope of context-specific public health interventions to prevent adolescent marijuana use and help identify the drivers of use during these historical shifts. As more regions debate or enact policies to decriminalize or legalize marijuana use, the impetus for cross-country comparisons increases. Individual-level data from adolescents enrolled in secondary education in Uruguay, Chile, and Argentina were obtained from the National Surveys on Drug Use Among Secondary School Students . These cross sectional surveys, carried out every 2–3 years, collect information on substance use and related risk factors. The sampling design and survey instruments are similar to the Monitoring the Future Surveys and were implemented comparably across countries. Surveys were self-report and administered confidentially in students’ classrooms. The sample included 8th, 10th, and 12th graders in schools classified as public, private, and other in mostly urban areas. Net secondary school enrollment was 80–90% in Chile and Argentina over the last decade and increased from 67.6–82.8% from 2007 to 2016 in Uruguay . The sample was selected via clustered, multistage random sampling from areas with 10,000+ and 30,000+ inhabitants in Uruguay and Chile, respectively, greenhouse tables and schools with at least 20 students in the grades under study in Argentina. In Uruguay and Argentina, strata were types of school within urban areas of geographical regions in each country; primary sampling units were schools followed by classrooms. In Chile, strata were school type by grade within mostly urban areas and primary sampling units were classrooms. Individual-level survey weights were used. Recent school cooperation rates ranged from 76–86%. This study was determined not human subjects research by the University of California, Davis Institutional Review Board.Consistent with prior studies from South America and the US, our results indicate that the less risk an individual attributes to marijuana use, the more likely he/she is to use marijuana . However, in the Southern Cone countries, the overall magnitude of this association weakened, although it strengthened again most recently in Argentina. This suggests that risk perceptions became a weaker correlate of adolescent marijuana use over time. There are several implications of these results. First, given the overall increase in the proportion of adolescents who perceive marijuana use to pose no/low risk of harm, marijuana use would have likely increased to a greater degree in the Southern Cone had the risk/use relationship not weakened. Second, factors other than risk perceptions, such as marijuana availability, may have played a greater role in the increase in adolescent marijuana use observed during our study period. This highlights the need to consider changes in multiple individual and environmental determinants of marijuana use. Third, there may be a cross-national weakening of the risk/use relationship. We found this trend in all Southern Cone countries, and some have identified the weakening of this relationship in the US as well . This would suggest that risk perceptions may be, at least in part, shaped by broader societal norms that extend beyond local or national context. Increases in global information sharing via internet use, social media, and international news coverage may contribute to this trend .Consistent with extant research in Europe and the US , we found that adolescents who perceive marijuana to be easily available are more likely to use marijuana. However, the stability of this association varied over time and between countries.

In Chile, the availability/use association weakened, and became increasingly similar to the risk/use association, both in magnitude and trajectory, when risk and availability were modeled together. In contrast, the relationship between availability and use strengthened in Argentina and Uruguay, becoming stronger at times than the relationship between perceived risk and use in both countries. However, because we were not able to model both variables together in Argentina and Uruguay due to finite sample limitations, it is unclear how the associations relate to one another. Variation in the relationship between perceived availability and use of marijuana over time and between countries may be explained by several factors. First, different trends in availability by country may explain differences in the contribution that perceived availability makes to marijuana use. In Chile, perceived availability generally declined from 2001 to the late 2000’s and then increased until 2016, though at lower rates than use. For example, marijuana availability may depend on where individuals buy, grow, or use marijuana, neighborhood police presence and enforcement of laws, or norms about diversion of marijuana to youth . Second, alternative contributing causes of marijuana use may have arisen in Chile to a greater extent than in Uruguay and Argentina, displacing the contribution that perceived availability make to marijuana use. Such factors may include changes in peer or family substance use , changes in the illegal drug market , or social and cultural changes toward marijuana, influenced by strong lobbying for drug policy reform–particularly for cannabis– in a context of massive social movements among students . While examination of such exposures was outside the scope of the current study, future research should examine whether the contribution of perceived availability and risk to marijuana use is moderated by other potential contributing causes in the local environment. In Uruguay, the increase in perceived availability is not surprising, as the 2013 law created a concrete path to marijuana access for adults. Therefore, greater perceived availability in Uruguay likely corresponds to greater actual availability. For example, it is possible that a surplus of self-cultivated marijuana has tipped over into the streets. Second, even though minors cannot buy it, marijuana may seem more accessible because it sold to adults in pharmacies. Third, there may be increased exposure to marijuana use and contact with peers or family members who use marijuana, which could predictably result in a growing sense of availability. Our findings provide several insights about the availability/use association. First, in this region, where marijuana regulation is becoming progressively liberal and where adolescent marijuana use is increasing, perceived availability may be an increasingly important driver of marijuana use trends . Second, given the strengthening of the availability/use relationship in Argentina and Uruguay, and the high prevalence of perceived easy availability in all three countries, public health professionals in the Southern Cone may consider devoting additional resources towards regulating and intervening on the pathways by which adolescents gain access to marijuana. Relatedly, our findings raise questions about how perceptions of availability relate to real access to marijuana, including how adolescents most often obtain marijuana and whether modes of acquisition have changed over time.