Using the ggplot and tidyverse packages , we displayed the difference in values between Field A and Field B for each indicator for soil fertility sampled at each farm using bar plots. We also included error bars to show the range of uncertainty in these indicators for soil fertility. Lastly, we further compared Field A and Field B for each farm using radar plots. To generate the radar plots, we first scaled each soil indicator from 0 to 1. Using Jenks natural breaks optimization, we then grouped each farm based on low, medium, and high N-based fertilizer application, as this soil management metric was the strongest coefficient loading from the first principal component . Using the fmsb package in R , we used an averaging approach for each level of N-based fertilizer application to create three radar plots that each compared Field A and Field B across the eight indicators for soil fertility. Farmers provided an overview of their farm operation, including farm size , the total number of crops each farm planted per growing season at the whole farm level, the types of crops planted in their field during the initial field visit , the type and amount of nitrogen-based fertilizer they applied on farm, and key aspects of soil health in their own words . Farm sizes ranged from 15 to 800 acres, with about one third of farms in the 15 – 50-acre range, another third in the 100 – 450-acre range, and roughly a final third in the 500 – 800 acre-range. Farmers grew primarily summer crops, including tomato, a variety of cucurbits, strawberry, herbs, nightshades, root vegetables, and sunflower/safflower for oil. Farmers reported applying a range of external N-based organic fertilizers, including fish emulsion, Wiserg , pelleted chicken manure, and seabird guano, at varying rates . On the low end, farmers applied <1 kg-N/acre, and on the high end, farmers applied 90 – 180 kg-N/acre per season. About a third of farmers applied 2 – 25 kg-N/acre of N-based fertilizer.

Farmer responses for describing key aspects of soil health were relatively similar and overlapped considerably in content and language . Specifically, farmers usually emphasized the importance of maintaining soil life and/or soil biology, promoting diversity, greenhouse rolling benches limiting soil compaction and minimizing disturbance to soil, and maintaining good soil structure and moisture. Several farmers also touched on the importance of using crops as indicators for monitoring soil health and the importance of limiting pests and disease. Discussion of the importance of promoting soil life, soil biology, and microbial and fungal activity had the highest count among farmers with ten mentions across the 13 farmers interviewed. Next to this topic, minimizing tillage and soil disturbance was the second most discussed with six of 13 farmers highlighting this key aspect of soil health. In addition to discussing soil health more broadly, farmers also provided in-depth responses to a series of questions related to soil fertility—such as key nutrients of interest on their farm, details about their fertility program, and the usefulness of soil tests in their farm operation— summarized in Table 2. When asked to elaborate on the extent to which they considered key nutrients, a handful of farmers readily listed several nutrients, including nitrogen, phosphorous, potassium , and other general macronutrients as well as one micronutrient . Among these farmers that responded with a list of key nutrients, some talked about having their nutrients “lined up” as part of their fertility program. This approach involved keeping nutrients “in balance,” such as for example, monitoring pH to ensure magnesium levels did not impact calcium availability to plants. These farmers also emphasized that though nitrogen represented a key nutrient and was important to consider in their farm operation, tracking soil nitrogen levels was less important than other aspects of soil management, such as promoting soil biological processes, maintaining adequate soil moisture and aeration, or planting cover crops regularly.

As one farmer put it, “if you add nutrients to the soil, and the biology is not right, the plants will not be able to absorb it.” Or, as another farmer emphasized, “It’s not about adding more [nitrogen]… I try to cover crop more too.” A third farmer emphasized, that “I don’t use any fertilizers because I honestly don’t believe in adding retroactively to fix a plant from the top down.” This same farmer relied on planting a cover crop once per year in each field, and discing that cover crop into the ground to ensure his crops were provided with adequate nitrogen for the following two seasons. While most farmers readily listed key nutrients, several farmers shifted conversation away from focusing on nutrients. These farmers generally found that this interview question missed the mark with regards to soil fertility. One farmer responded, “I’m not really a nutrient guy.” This same farmer added that he considered [soil fertility] a soil biology issue as much as a chemistry issue.” The general sentiment among these farmers emphasized that soil fertility was not about measuring and “lining up” nutrients, but about taking a more holistic approach. This approach focused on facilitating conditions in the soil and on-farm that promoted a soil-plant-microbe environment ideal for crop health and vigor. For example, the same farmer quoted above mentioned the importance of establishing and maintaining crop root systems, emphasizing that “if the root systems of a crop are not well established, that’s not something I can overcome just by dumping more nitrogen on the plants.” Another farmer similarly emphasized that they simply created the conditions for plants to “thrive,” and “have pretty much just stepped back and let our system do what it does; specifically, we feed our chickens whey-soaked wheat berries and then we rotate our chickens on the field prior to planting. And we cover crop.” A third farmer also maintained that their base fertility program—a combination of planting a cover crop two seasons per year, an ICLS chicken rotation program, minimal liquid N-based fertilizer addition, and occasionally compost application—all worked together to “synergize with biology in the soil.” This synergy in the soil created by management practices—rather than focusing on nutrient levels—guided this farmer’s approach to building and assessing soil fertility on-farm. Another farmer called this approach “place-based” farming. This particular farmer elaborated on this concept, saying “I think the best style of farming is one where you come up with a routine [meaning like a fertility program] that uses resources you have: cover crops, waste materials beneficial to crops, animals” in order to build organic matter, which “seems to buffer some of the problems” that this farmer encountered on their farm. Similar to other farmers, greenhouse bench top this farmer asserted that adding more nitrogen-based fertilizer did not lead to better soil fertility or increase yields, in their direct experience. Regardless of whether farmers listed key nutrients, a majority of farmers voiced that nitrogen was not a big concern for them on their farm. This sentiment was shared among most farmers in part because they felt the amount of nitrogen additions from fertilizers they added were insignificant compared to nitrogen additions by conventional farms. Farmers also emphasized that the amount of nitrogen they were adding was not enough to cause environmental harm; relatedly, a few farmers noted the absurdity and added economic burden of the recent nitrogen management plan requirements—specifically among organic farms with very low N-based fertilizer application. The majority of farmers also expressed that their use of cover crops and the small amount of N-based fertilizer additions as part of their soil fertility program ensured on-farm nitrogen demands were met for their crops. Across all farmers interviewed, cover cropping served as the baseline and heart of each fertility program, and was considered more effective than additional N-based fertilizers at maintaining and building soil fertility. Farmers used a range of cover crop species and often applied a mix of cover crops, including vetches and other legumes like red clover and cowpea , grains and cereals like oats .

Farmers cited several reasons for the effectiveness of cover cropping, such as increased organic matter content, more established root systems, greater microbial activity, better aeration and crumble in their soils, greater number of earthworms and arthropods, improved drainage in their soils, and more bio-available N. Whereas farmers agreed that “more is not better” with regards to N-based fertilizers, farmers did agree that allocating more fields for planting cover crops over the course of the year was beneficial in terms of soil fertility. However, as one farmer pointed out, while cover crops provide the best basis for an effective soil fertility program, this approach is not always economically viable or physically possible. Several farmers expressed concern because they often must allocate more fields to cover crops than cash crops in any given season, which means that their farm operation requires more land to be able to produce the same amount of vegetables than if they had all their fields in cash crops. Farmers also shared that in some circumstances, such as in early spring, they are not able to realize the full potential of a winter cover crop if they are forced to mow the cover crop early to plant cash crops and ensure the harvest timeline of a high-value summer vegetable crop. The cover crop approach to soil fertility takes “persistence,” as one farmer emphasized; another farmer similarly pointed out that the benefits of cover cropping “are not always realized in the crop year. You’re in it [organic agriculture] for the long haul, there is no quick fix.” Indeed, farmers who choose to regularly plant cover crops to build soil fertility, rather than just add N-based fertilizers, reported that they came up against issues of land tenure and access to land, market pressures, and long-term economic sustainability. To build on conversations about soil fertility, farmers also provided responses to interview questions that asked them to elaborate on the usefulness of available soil tests to gauge soil fertility more broadly—and then more specifically, the usefulness of soil tests in informing their soil fertility program and/or management approaches on-farm. Overall, only three of 13 farmers reported regularly using and relying on soil tests to inform their soil fertility program or aspects of their farm operation. These farmers offered very short responses and did not elaborate. For example, one farmer shared that they “test twice a year in general,” and that they “rely on the results of the soil tests to tweak [their] fertility program.” Another farmer said briefly, “We use soil tests… we utilize them to decide what to do to try to improve the soil.” A third farmer admitted that though he “used to do a soil test every year, literally used to spend hundreds of dollars per year on soil tests,” he found that the results of soil tests did not change year-to-year and were, as he put it, very “stable.” This particular farmer no longer regularly uses or relies on soil testing for their farm operation. The remaining ten farmers confirmed that they had previously submitted a soil test, usually once and most often to a local commercial lab in the region. These farmers expressed a range of sentiments when asked about the usefulness of soil tests, including disappointment, distrust, or both, particularly in the capacity of soil tests to inform soil fertility on their farm. Some farmers said directly, “I just don’t trust soil tests,” or “frankly, I don’t believe a lot in soil testing because it’s too standardized,” while other farmers initially stated they had used “limited” or “infrequent” soil tests, and then later admitted that they did not use or rely on soil tests on their farm operation. These farmers tended to focus on the limitations of soil tests that they encountered for their particular farm application. Limitations of soil tests discussed by farmers varied. Farmers stated that soil tests often confirmed what they already knew about their soil and did not add new information. For this reason, some farmers used results from a soil test as a guide, while other farmers found results to be redundant and therefore less useful to their farm operation. Because issues with soil fertility were sometimes linked to inherent soil characteristics within a particular field, such as poor drainage or heavily sandy soil, farmers found that soil tests were not able to provide new insight to overcome these environmental limitations. “I’m not able to correct that environmental limitation [ie, poor drainage] by adding more nitrogen,” one farmer emphasized.

In order to identify farm typologies based on indicators for soil organic matter levels, we first used several clustering algorithms. First, a k-means cluster analysis based on four key soil indicators—soil organic matter , total soil nitrogen, and available nitrogen —was used to generate three clusters of farm groups using the facoextra and cluster packages in R . The cluster analysis results were divisive, nonhierarchical, and based on Euclidian distance, which calculates the straight-line distance between the soil indicator combinations of every farm site in Cartesian space , and created a matrix of these distances . To determine the appropriate number of clusters for the cluster analysis, a scree plot was used to signal the point at which the total within-cluster sum of squares decreased as a function of the increasing cluster size. The location of the kink in the curve of this scree plot delineated the optimal number of clusters, in this case three clusters . To further explore appropriate cluster size, we used a histogram to determine the structure and spread of data among clusters. A Euclidean-based dendrogram analysis was then used to further validate the results of the cluster analysis. In addition to confirming the results of the cluster analysis, the dendrogram plot showed relationships between sites and relatedness across all sites. To visual cluster analysis results, the final three clusters were plotted based on the axes produced by the cluster analysis. One drawback of cluster analyses is that there is no measure of whether the groups identified are the most effective combination to explain clusters produced by soil indicators, or whether they are statistically different from one another. To address this gap, vertical grow rack we used ANOSIM to evaluate and compare the differences between clusters identified with the cluster analysis above. We calculated the global similarity in addition to pairwise tests of each cluster.

To formally establish the three farm types and also make the functional link between organic matter and management explicit, we used the three clusters that emerged from the k-means cluster analysis based on soil organic matter indicators, and explored differences in management approaches among the clusters. We then created three farm types based on this exploratory analysis. Specifically, we first analyzed management practices among sites within each cluster to determine if similarities in management approaches emerged for each cluster. Based on this analysis, we used the three clusters from the cluster analysis to create three farm types categorized by soil organic matter levels and informed by management practices applied. Using the three farm types from above, we then analyzed whether our classification created strong differences along soil texture and management gradients using a linear discriminant analysis . LDA is most frequently used as a pattern recognition technique; because LDA is a supervised classification, class membership must be known prior to analysis . The analysis tests the within group covariance matrix of standardized variables and generates a probability of each farm sites being categorized in the most appropriate group based on these variable matrices . To characterize soil texture, we used soil texture class . To characterize soil management, we used crop abundance, tillage frequency, and crop rotational complexity—the three management variables with the strongest gradient of difference among the three farm types. A confusion matrix was first applied to determine if farm sites were correctly categorized among the three clusters created by the cluster analysis. Additional indicator statistics were also generated to confirm if the LDA was sensitive to input variables provided. The LDA was carried out using the MASS R package. To build on the results of the LDA, we performed a variation partitioning analysis to determine the level of variation in soil organic matter indicators explained by the soil texture variables, soil management variables, and their interactions .

VPA was performed using the vegan package in R . Using indicator variables for soil organic matter levels, we performed a k-means cluster analysis to develop a meaningful classification of farms. Scree plot results indicated that three clusters produced the most consistent separation of field sites. As shown in Figure 1, the two dimensional cluster analysis produced a strong first dimension , which explained 86.7% of the separation among the 27 field sites. Total N, total C, POXC, and soil protein variables strongly explained this separation of farm types, as shown by the lack of overlap among the clusters along the Dimension 1 axis. Histogram results provide a visual summary of linear difference among the three clusters and further confirms minimal overlap among clusters; however, Cluster I and Cluster II fields showed low dissimilarity between values 0 and -2 . Results from the average distance-based linkages of the dendrogram analysis similarly further established the accuracy of field site groupings determined by the cluster analysis. These results indicated that Cluster II sites were more closely related to Cluster III sites compared to Cluster I sites . ANOSIM showed strongly significant global differences among the three clusters , where a value of 1 delineates 0% overlap between clusters. Overall, ANOSIM verified the farm types obtained from the cluster analysis. In addition, ANOSIM pairwise t-tests that compared each individual cluster in pairs confirmed strongly significant dissimilarities between Cluster I and Cluster III sites . ANOSIM pairwise t-tests also indicated that Cluster I sites were significantly divergent from Cluster II sites; however, Cluster I and Cluster II showed less dissimilarities than Cluster II and Cluster III sites . ANOSIM pairwise t-test results were in congruence with the results provided by the histogram . Classification of farm sites using k-means clustering closely matched differences in on-farm management approaches . It is important to note that while general trends between clusters and management emerged, the management practices analyzed here do not fully encompass the management regimes of each farm field site, and are intended to be exploratory rather than definitive. Several general trends emerged across the three farm types . For instance, Farm Type I, comprised of six field sites, consisted of fields with higher crop abundance values and fields that more frequently planted cover crops compared to Farm Type III.

These sites used lower impact machines and applied a lower number of tillage passes compared to Farm Type II and III. In contrast, Farm Type II, also comprised of six field sites, and Farm Type III, comprised of fifteen field sites, represented fields on the lower end of crop abundance values and sites that applied cover crop plantings at a lower frequency than Farm Type I. Farm Type III on average applied a higher number of tillage passes and on average were on the lower end of ICLS index compared to both Farm Type I and Farm Type II. In general, Farm Type II used management approaches that frequently overlapped with Farm Type III, and less frequently overlapped with Farm Type I. Overall, farm types significantly differentiated based on indicators for soil organic matter levels . For all four indicators displayed in Figure 2, hydroponic shelf system differences among the three farm types were highly significant . As visualized in the side-by-side box plot comparisons for all four indicators for soil organic matter levels, Farm Type I consistently showed the highest mean values across all four indicators, while Farm Type III consistently showed the lowest mean values across all four indicators. Farm Type I had mean values of 0.21 mg-N kg-soil-1 for total soil N, 2.3 mg-C kg-soil-1 for total organic C, 787 mg-C kg-soil-1 for POXC, and 7.4 g g-soil-1 for soil protein; compared to Farm Type I, Farm Type III had means values 43% lower for total soil N, 48% lower for total organic C, 58% for POXC, and 66% lower for soil protein. Compared to Farm Type I, Farm Type II had mean values 38% lower for total soil N, 26% lower for total organic C, 28% lower for POXC, and 30% lower for soil protein than Farm Type I. Standard errors for all four indicators are shown in Figure 2.Results of the LDA showed that both linear discriminant factors are most strongly explained by soil texture , as shown by the LDA loadings . Management practices all equally, but weakly, influenced LD1 and LD2 . LD1, which explained 66.3% of the variance, was effective at separating the Farm Type I and Farm Type III . However, Farm Type II overlapped with both Farm Type I and Farm Type III for LD1. In contrast, LD2, which explained 33.6% of the variance, did not display a definitive separation between the Farm Type I and Farm Type III; however, LD2 was effective at separating Farm Type II from Farm Type I and Farm Type III. LDA accurately discriminated between the three farm types, with an overall accuracy of 90.1% , as shown in Table 8. Model accuracy was high for all three farm types . The model had the greatest sensitivity to Farm Type II and Farm Type III , and low sensitivity to Farm Type I . Both Farm Type I and Farm Type III displayed minimal confusion with Farm Type II, as the comparison of training and validation data details . We determined the proportion of variation in the three farm types accounted for by management and by soil texture . Soil textural class contributed 28% of unique variation , while management contributed 18% of unique variation . The shared contribution for all predictors was 1%, and the overall contribution of all predictors was 47%.We found across all 27 farm sites sampled that gross N mineralization rates ranged from 0.05 – 4.82 µg-NH4+ -N g-soil-1 day-1 and gross N nitrification rates ranged from 0.55 – 5.90 µg-NO3- -N gsoil-1 day-1 . We determined net N mineralization rates ranged from 0.07 – 1.51 µg-NH4+ -N g-soil-1 day-1 , while net N nitrification rates had a wider range from 1.53 – 25.18 µg-NO3- -N g-soil-1 day-1 . We visually compare the six key N cycling variables—pools of inorganic N , and net and gross N rates—across the three farm types . Despite the variation in net and gross N mineralization and nitrification rates, using the farm types developed above, we found that N cycling variables were not significantly different across the three farm types for all six variables examined—based on ANOVA results . Given the variation in gross N rates reported above, we further explored the drivers of this variation in gross N rates using mixed modelling approaches.

Table 10 shows results provide for the linear mixed models used for the prediction of potential gross ammonification rates . Soil ammonium concentration and % sand were significant predictors of gross mineralization rates. While not significant, indicators for SOM were selected and also included in the model, based on AIC results. We also provide results from the selected linear mixed model used for prediction of potential gross nitrification rates in Table 11. As shown, indicators for SOM emerged as the sole significant covariate . While not significant, crop abundance was also selected and included in the model, as determined by AIC results.This on-farm study found significant differentiation among the organic farm field sites sampled based on soil organic matter levels—and created a gradient in soil quality among the three farm types. While we found that differences in soil quality were generally aligned with trends in management among sites, soil texture—rather than management—emerged as the stronger driver of soil quality. Though initially, we found that net and gross N cycling rates were not significantly different across farm types, gross N cycling rates showed considerable variation among farm types. To determine drivers of this variation, we explored key predictors for soil N cycling and found that SOM indicators influenced gross N mineralization and nitrification rates, in particular gross nitrification rates. Each of the four indicators for soil organic matter used in this study—total soil N, total organic C, POXC, and soil protein—showed a strong correlation with farm type, and collectively, created a gradient in soil quality . Farm Type I consistently showed the highest values for total soil N, total organic C, POXC, and soil protein, which suggests sites in this farm type had higher soil quality compared to Farm Type II and III; similarly, Farm Type II consistently showed intermediate values for all four indicators for soil organic matter. Lastly, Farm Type III consistently showed the lowest values across all four indicators, which suggests sites in this latter farm type had lower soil quality compared to the other two farm types.

Understanding the substance of farmer knowledge serves as a first step to conserve this essential knowledge base in practice; however, it is equally critical to document the particularities of farmer expertise in local contexts to provide essential knowledge for other contemporaneous and future generations of farmers, scientists, and policymakers alike. Moving forward, there is therefore a need to elevate the importance and value of farmer knowledge across multiple disciplines such that farmer knowledge is considered “expert” knowledge throughout alternative agriculture . While other studies attempt to integrate the artificial binary between “formal” and “informal,” or “expert” and “non-expert” knowledge and view the two forms of knowledge as complementary , in this paper we maintain that farmer knowledge is scientifically valid, expert knowledge and therefore warrants formal, standalone documentation within the scientific literature . While it is true that the terms “traditional,” “folk,” and/or “indigenous” knowledge are applied in certain contexts, in this paper, the term “local knowledge” is most appropriate , as farmer participants were all white and all either first- or second generation settlers on unceded Patwin-speaking Wintun Nation tribal lands in Yolo County, CA. To frame this paper, we apply Agrawal’s definition of local knowledge as knowledge that is “integrally linked with the lives of people, always produced in dynamic interactions among humans and between humans and nature, and constantly changing.” This definition of local knowledge recognizes the key elements of local knowledge: 1) It is produced by people and among people; 2) It is always produced in relationship with nature; and 3) It is a dynamic process. More broadly defined, local knowledge involves dynamic processes and complex systems of experiences, practices, and skills developed and sustained by people in their environmental and socioeconomic realties . Further, plant growing rack local knowledge may develop even within one or two generations of place-based experience . In the US, there exists a handful of studies documenting rural local knowledge and rancher local knowledge .

Very few studies explicitly examine local knowledge in the context of alternative agricultural or organic systems, referred to as “farmer knowledge” in the literature. This type of knowledge is a subset of local knowledge that enables knowledge holders to farm alternatively in their specifical local contexts. To date, most formal studies on farmer knowledge tend to focus on farmer decision making as it relates to the adoption of new practices . Few studies exist at the intersection of local knowledge, alternative agriculture, and soil management. To consider this gap, we focus this study on a significant epicenter for alternative agriculture in the United States: Yolo County, California, which represents unceded Patwin-speaking Wintun Nation tribal lands. This region in northern California is unique in that it is among the handful of places in the country that emerged as a catalyst and knowledge hub for the organic agriculture movement and where a large concentration of high value, innovative organic production farms continue to thrive today. Due to a unique set of historical and ecological circumstances, the region experienced an influx of organic farmers beginning in the 1970s . During this decade, Yolo County—in combination with Santa Cruz, CA—became a significant node in the organic movement. Its emergence as a significant node was in part due to Yolo County’s proximity to the San Francisco Bay Area and the University of California, Davis—which provided key institutional support—and also partially due to the existence of largely prime agricultural lands combined with a temperate climate ideal for growing year-round. As a result, Yolo County became one of a few of places where regulations for organic production first evolved and experimentation with organic farming first emerged . Following the farm financial crisis of the 1980s, land prices in the County sharply dropped ; this economic window provided an opportunity for a new generation of farmers to insert a more ecologically-minded approach to farming.

Many of these farmers arrived to Yolo County relatively new to farming —often young, educated white urbanites with a desire to farm alternatively to the industrial agribusinesses that had historically dominated the landscape of Yolo County since the early 1900s . When these so-called “back-to-the-land” farmers arrived, many were particularly interested in soil fertility—a conscious effort to avoid “mining the soil” and address ongoing issues with soil degradation in agriculture . While initially these back-to-the-landers lacked historically- and ecologically specific knowledge of the lands they cultivated , over the last three decades or more, it is highly probable that they have individually amassed a wealth of local, place-based knowledge of their specific management contexts and soil landscapes . In this sense, farmer knowledge of soil management presents a particularly salient entry point for further examination in the context of Yolo County specifically. How did these particular farmers address the challenge of soil management in their region? What have they individually and collectively learned about soil management, in theory and in practice? Such questions are particularly important to consider given that—from a pedological and agricultural perspective—soils are heterogenous across landscapes. For example, even at the scale of a single field, differences in micro-environments, management histories, inherent soil characteristics, and time of year can all dramatically influence how a particular field can be most effectively managed. Addressing this challenge in soil management and understanding the nuances of soil management are fundamental to organic systems—where deep place-based knowledge of soil landscapes is the basis for building and sustaining healthy soils on-farm—and more broadly, resilient agriculture. Yet, farmer knowledge of soil management is still generally under-researched, particularly in the United States and particularly among organic farmers.

Though documentation of farmer knowledge of soil management in alternative agriculture exists, most studies focus within the “development” context . Similarly, research on indigenous knowledge of soil is frequently approached from an ethnopedological or traditional ecological knowledge perspective , and lacks focus on production and/or organic agriculture. To date, farmer knowledge of local soil landscapes and related soil management practices remains entirely undocumented in Yolo County. Yet, the unique historical and ecological context makes farmer knowledge of soil health and soil management in this region especially important to document; this knowledge is potentially foundational as organic farmers adapt their farming approaches and management in the face of increasing social, economic, and environmental uncertainties. This research is informed by a Farmer First approach, which recognizes farmers as experts and crucial partners in researching and innovating solutions for resilient, alternative agriculture . The Farmer First approach recognizes multiple knowledge forms and challenges the standard “information transfer” pipeline model that is often applied in research and extension contexts . We used an open-ended, qualitative approach that relied on in-depth and in-person interviews to study farmer knowledge. Such methods are complementary to surveys that use quantitative methods for capturing a large sample of responses . Because they are more open-ended, qualitative approaches allow for more unanticipated directions ; however, indoor vertical garden system as Scoones and Thompson point out, removing local knowledge from its local context and attempting to fit it into the constrictive framework of Western scientific rationality is likely to lead to significant errors in interpretation, assimilation, and application. While interviews are not able to capture the quantity of farmer input that surveys do, in-depth interviews allow researchers to access a deeper knowledge base that has inherent value—despite limitations in scalability and/or transferability—as participants respond in their own words, using their own categorization, and perceived associations . Such in-depth interviews are therefore essential to research on farmer knowledge and local knowledge .In-person interviews were conducted in the winter, between December 2019 – February 2020; three interviews were conducted in December 2020. We used a two-tiered interview process, where we scheduled an initial field visit and then returned for an in-depth, semi-structured interview. The purpose of the preliminary field visit was to help establish rapport and increase the amount and depth of knowledge farmers shared during the semi-structured interviews. Farmers were asked to walk through their farm and talk more generally about their fields, their management practices, and their understanding of the term “soil health.” The field interview also provided an opportunity for open dialogue with farmers regarding management practices and local knowledge . Because local knowledge is often tacit, the field component was beneficial to connect knowledge shared to specific fields and specific practices.

After the initial field visits, all 13 farmers were contacted to participate in a follow up visit to their farm that consisted of a semi-structured interview followed by a brief survey. The semistructured interview is the most standard technique for gathering local knowledge . These in-depth interviews allowed us to ask the same questions of each farmer so that comparisons between interviews could be made. To develop interview questions for the semistructured interviews , we established initial topics such as the farmer’s background, farm history, general farm management and soil management approaches. We consulted with two organic farmers to develop final interview questions. The final format of the semi-structured interviews was designed to encourage deep knowledge sharing. For example, the interview questions were structured such that questions revisited topics to allow interviewees to expand on and deepen their answer with each subsequent version of the question. Certain questions attempted to understand farmer perspectives from multiple angles and avoided scientific jargon or frameworks whenever possible. Most questions promoted open-ended responses to elicit the full range of possible responses from farmers. In the interviews, we posed questions about the history and background of the participant and their farm operation, how participants learned to farm, and to describe this process of learning in their own words, as well as details about their general management approaches. Farmers were encouraged to share specific stories and observations that related to specific questions. Next, we asked a detailed set of questions about their soil management practices, including specific questions about soil quality and soil fertility on their farm. In this context, soil quality focused on ecological aspects of their soil’s ability to perform key functions for their farm operation ; while soil fertility centered on agronomic aspects of their soils’ ability to sustain nutrients necessary for production agriculture . A brief in-person survey that asked a few demographic questions was administered at the end of the semi-structured interviews. Interviews were conducted in person on farms to ensure consistency and to help put farmers at ease. The interviews typically lasted two hours and were recorded with permission from the interviewee. Interviews were transcribed, reviewed for accuracy, and uploaded to NVivo 12, a software tool used to categorize and organize themes systematically based on research questions . Coding is a commonly used qualitative analysis technique that allows researchers to explore, understand, and compare interviews by tracking specific themes . Through structured analysis of the interview transcripts, we identified key themes and constructed a codebook to delineate categories of knowledge. Once initial coding was complete, we reviewed quotations related to each code to assess whether the code was accurate. The final analysis included both quantitative and qualitative assessments of the coded entries. For the quantitative measure, we tallied both the number of coded passages regarding different themes or topics, and the number of farmers who addressed each theme. In addition, we examined the content of the individual coded entries to understand the nature of farmer knowledge and consensus or divergence among farmer responses for each theme. All farmers interviewed mentioned direct experience as being one of the most important modes for understanding their landscape, their farming system, and management practices essential to their farm operation. Farmers described this accumulation of experience as “learning by doing,” being “self-taught,” or learning by “trial and error” . These farmers added that in learning by experience, they made “a lot of mistakes” and/or faced “many failures” but also learned from these mistakes and failures—and importantly, that this cycle was crucial to their chosen learning process. More than half of the farmers interviewed maintained that no guidebook or manual for farming exists; while reading books was viewed as valuable and worked to enhance learning for individual farmers, to farm required knowledge that could only be gained through experience. Moreover, nearly all farmers also explicitly commented on the fact that they have never stopped learning to farm . Overall, farmers learned primarily through personal experience and over time, making connections and larger conclusions from these experiences.

The reader and the writer meet in the space of the text, but they do so incompletely. For Lévinas, the Other is both the condition for recollection and the condition for the representation, since it is in conversation with the Other that the subject can communicate.The monument Kofman creates with Rue Ordener, Rue Labat is, finally, a home, but not a private home. It is a home that acknowledges the ways in which people and spaces interpenetrate one another. Kofman’s text is, on the one hand, deeply private. She makes no metatextual statements to introduce the reader to the space she is creating, for instance, and she does not do work for the reader’s benefit, such as explaining the significance of July 16, 1942 or The Lady Vanishes. This lack of gestures towards the reader may seem unwelcoming. Yet this very lack of purposeful welcome enables the reader to interact with Kofman and her text, to help build it out by providing the historical context and following the intertextual references where they lead. The home Kofman and the reader build together is a Möbius-monument, a space of escape routes and thresholds that allow for, perhaps, a measure of mutual comprehension while simultaneously acknowledging and preserving individual subjectivity. Architect Julian Bonder employs Lévinas when he explains how the ethical “working memorials” he builds should create a space in which the visitor can enter into a conversation with the Other and with the otherness of the past . While Bonder builds physical monuments, such as the Mémorial de l’abolition de l’esclavage in Nantes, France, text may in some ways be a more appropriate medium for an ethical monument. As Ann Smock notes, text, especially for Kofman, is “sure to dispossess you,” weed growing systems robbing the writer and the reader of their subjectivity and enabling them to transcend themselves and, as Lévinas would say, to have a conversation .

Kofman maps her memories and city space onto one another, using the inherent intersubjectivity of text to create a monument in their intersection where both she and the reader may dwell.Mercè Rodoreda’s El carrer de les Camèlies differs in several significant ways from Perec’s and Kofman’s texts. Rodoreda’s text is a novel, not an autobiography . It takes place in Barcelona, not Paris, and it is written from Rodoreda’s exile in Geneva, in Catalan, not in French.That this text is in certain ways so different from the other two throws into relief their intriguing similarities. All three texts involve a painful and divisive wartime period, and all three are meditations on the relationship between historical and personal memory, and on monuments of the war period and the postwar. Finally, the meditation in all three texts involves the creation of a unique textual space that lays the foundation for a more nuanced monument than a simple chronological account or a physical monument. If Kofman’s text is about intersubjective space and Perec’s text is about the network of one’s own memory , Rodoreda’s text is about balancing memory with the tendency to ossify the past: that is, it treats the question of how to remember the past but not be trapped by it. Rodoreda seeks to create a monument that incorporates the present and allows for change and growth, a living monument. She does this by constructing a synchronous network of symbols, contrasting this network with a tendency to look to the past and towards one’s “roots. “Rootedness” implies fixedness; rooted monuments do not admit polysemy or change. For Rodoreda, both textual monuments and physical monuments may have this immobile quality, which contains an element of violence or constraint. At several points in El carrer de les Camèlies, the protagonist is violently fixed with a name or a mark. In contrast to these scenes of violent inscription stands Rodoreda’s vast and polysemous symbolic network.

In El carrer de les Camèlies, writing and language can be violent, pinning meaning and identity in one place like the name etched on a tombstone, but they also have the potential to provide freedom through the circulation of symbols, a process of signification and resignification. Rodoreda thus contends with a tension between, on the one hand, those physical monuments and the acts of writing, which is also a monumentalizing practice, that pin meaning in one place and, on the other hand, those monumentalizing processes that allow for a degree of polyvalence and freedom. This tension may also be seen on the difficult border between collective and personal memory. Collective memory does not exist as such, independent of individuals. As Halbwachs writes, “the memory of the group realizes and manifests itself in individual memories” . On the other hand, the way that a collective has agreed or come to remember something, and the things it chooses to remember and forget, molds the individual’s recollection. “The past is not preserved,” writes Halbwachs, “but reconstructed on the basis of the present” . The individual has no access to the pure, preserved past, but only to the memories that he or she can create, which are shaped by the larger collective. The issue of what remains in collective memory, and how it remains there, is of particularly urgent interest to Rodoreda, writing as she was during the Time of Silence, when Republican and Catalan suffering was either officially forgotten and, when it was officially remembered, differed greatly from the way the victims remembered it . If we follow Halbwachs in thinking that memory is constructed through a kind of dialogue between “collective frameworks” and the individual, we see how an individual can influence the creation of collective memory as much perhaps as the “collective frameworks” can influenced what is remembered and how. Thus Rodoreda’s insistence on the maleability and mobility of memory is an insistence on the individual capacity and need to modify collective memory. Like the physical monuments produced by a society, language may also be considered a manifestation of the Halbwachs’ “collective frameworks.” Through that language, a group may experience, share and create a common heritage. On the other hand, that binding function of language can be restrictive.

El Carrer de les Camèlies is a textual monument out of language that is polyvalent, and both personally and collectively significant. In creating such a monument, Rodoreda is advocating for a writing that binds a community together but does not suffocate it in doing so. This loose binding allows individuals and the memories they possess to allow the larger “collective framework” to grow and change. A few central images express the tension between Rodoreda’s synchronous symbol network and monumentalizing practices that arrest the flow of meaning and are oriented exclusively towards the past. First, she treats the paradox of a stone angel or a stone bird: the angel is an image of flight and movement, but the stone angel can do nothing of the sort. A bird is a similar image of freedom and flight, yet in El carrer de les Camèlies, birds are often associated with stones. The second set of images are a pair: the cemetery and the garden. A cemetery is filled with immobile stone monuments, promising a fixed, unchanging memory that only looks backwards21. In the garden, the flowers may, like the monuments, carry associations with them, but they are also alive, able to grow and change22. For Rodoreda, the best garden is ungated: just as the stone monument in the cemetery constricts possibilities for memory, the gated garden constrains the flowers inside, fixing them in one space like the tombs in the cemetery. The story of Cecília’s life proceeds chronologically, indoor farming systems stretching from just before the Spanish Republic to the postwar economic boom . Cecília’snarration, however, is sometimes anti-chronological, moving from the effect of an event back to the event itself. The network of symbols in the text similarly pulls against reading chronologically, inviting the reader to follow the thread of a symbol and make connections between past and present. Such a reading is more synchronous than chronological, as the appearance of a symbol recalls to mind its other appearances. At times, the reader does not understand the full weight of a symbol until later in the text, recontextualizing its previous appearances. El carrer de les Camèlies thus has two structures: the chronological structure and the one created by her dense symbolic network. Because a reading of the text necessitates considering more than one moment in the text at a time, a quick summary of the text may help orient the reader of this piece. El carrer de les Camèlies is the story of a beautiful woman, Cecília, who was found as an infant by a night watchman outside of a garden gate in Barcelona, on the street named in the novel’s title. Pinned to her clothing is a piece of paper with “Cecília Ce” written on it. Cecília grows up with adopted parents, Senyor Jaume and Senyora Magdalena.

She continually searches for clues to her parentage, in particular to the identity of her father, and runs away from home twice in search of him. Once she goes to the Liceu Opera House in search of him, thinking he may be a musician. She spends the war years with her adopted family, then runs away for good with her first lover, Eusebi, to live in a shantytown on the outskirts of Barcelona. After Eusebi is arrested and a subsequent lover dies, Cecília becomes a street prostitute and eventually a kept woman. Her living situations become progressively more oppressive and unpleasant, finally culminating in imprisonment in the apartment of a lover, Eladi, who forces her, over a long but unspecified period of time, to drink to the point of hallucination. After this episode, she is left on the street and rescued by an acquaintance who nurses her back to health. After she recovers, her fortunes change and she becomes a wealthy demimondaine in possession of her own home. Throughout, she is often pregnant, but always either aborts or miscarries the fetus; she never has a child. The text ends with Cecília in conversation with the night watchman who found her. He tells her that she had been originally left at the gate of another family on the street, but he decided that Senyor Jaume and Senyora Magdalena, childless, would prefer to raise a baby. He also confesses that he was the one who pinned the name to her clothing, naming her after a girl he had been in love with who had died.The preservation from the rapid deposition of volcanic ash results in the archaeological visibility of flora that were either growing at the time of the eruption or were collected, stored, and utilized in some manner by the ancient inhabitants. Thus far, plant material has been recovered at this site in two main forms: carbonized macrobotanical remains and plaster casts taken when archaeologists encountered voids within the volcanic ash during excavations. Using paleoethnobotany as a methodological tool at Cerén reveals significant plant-human interactions of ancient Mesoamericans that add to other studies of less wellpreserved domestic settings, where investigations have typically focused on architecture and artifacts , creating a stronger and more in-depth interpretation of ancient household life in ancient Mesoamerica. The paleoethnobotanical collection efforts at Cerén have been highly productive, revealing foodstuffs within homes, kitchens, and storage facilities as well as exterior spaces such as small household garden plots, clusters of fruit trees surrounding each structure, and extensive infields and outfields of maize, manioc, and wild and weedy plant species. The assorted array of culturally and economically useful species reveals a detailed variety of foodstuffs readily accessible to the inhabitants on a daily basis that would have been incorporated into meals and contributed towards daily life as medicine, tools, construction material, fuel, and more. Typically, the recovery of ancient plant remains in Mesoamerica is challenging due to the generally poor preservation of organic materials in the tropical environment, with carbonization leading to the best preservation conditions . The long history of paleoethnobotanical research at Cerén allows for a deeper study of the social meanings behind Mesoamerican agriculture and home gardens with an intimate view of how these ancient people interacted with and viewed their environment in the past.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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