However, farmers possess wide and deep place-based knowledge of their soils that has the potential to advance work on soil health beyond its currently limited scope . Inclusion of farmer knowledge is integral if one outcome of ongoing research on soil health is to address both social and ecological resilience. Farmers are uniquely positioned to share their on the-ground social realities and their local ecological knowledge of their soils and farming systems . To be clear, inclusion of farmers in this research arena is essential if only to contribute farmer knowledge and farmer voices to the existing body of work—which to date has been lacking . This call for inclusion of farmer knowledge represents: 1) a departure from the majority of prior research in the US that tends to emphasize the advancement of research and policy agendas aimed at behavioral change ; and 2) simultaneously, a shift towards explicit inclusion of farmer knowledge in the knowledge-making of emergent soil health research. While farmer knowledge is certainly important and underutilized, grow rack consideration for quantitative assessments of soil health remains a critical component of advancing soil health. Available indicators to quantify soil health already exist and are widely applied both on farms and in scientific studies.
These soil indicators prioritize so-called “principles of soil health” to assess health through evaluating soil function, usually emphasizing metrics for organic matter quality, nitrogen availability, soil biological activity, and water cycling . Currently, our understanding of how local farmer knowledge of soil health and management might interact with available soil health metrics is limited. Farmer inclusive research evaluating soil metrics is generally sparse—with only a handful of studies on mostly non-organic farms in the midwestern US . Yet, if a central goal of soil health research is to further develop the concept of soil health, and also to better understand the key management practices associated with this concept, then examining the ways in which local farmer knowledge can interact with quantitative soil metrics evaluated in the field may offer a complementary approach to prior work . In this study, we focus on a functional expression of on-farm soil health related to crop production—soil fertility. Soil fertility is generally defined as the capacity of a soil to supply the nutrients needed for crop growth, and is therefore linked to crop nutrient availability . More broadly, soil fertility underpins the productivity of agricultural systems, and has social and environmental implications related to fertilizer application and nutrient management . Ongoing efforts to measure soil fertility have placed particular emphasis on how farmers consider key nutrients, such as nitrogen, as part of their farm management .
Although metrics for quantifying aspects of soil fertility have existed for several decades now, it is less understood how—if at all—these commonly available metrics for soil fertility actually inform farmers and their fertility programs . Moreover, there currently is a gap in the literature in mapping how these knowledge spheres—farmer knowledge of soil fertility and soil indicators for soil fertility—interact to co-produce new insights to evolve this component of soil health research.To investigate these questions, we applied a case study approach, engaging in on-farm research of 13 organic farms and their respective farm owners in Yolo County, California, USA—a region where this type of farmer inclusive soil health research has been limited to date. We used qualitative, in-depth field interviews in combination with quantitative field sampling and subsequent laboratory analysis. This research focused on Yolo County in particular, because of its unique role as a hub for innovative, high-value organic vegetable production . These thirteen organic farmers specifically—because of their historical relationship to their land and their intimacy with the physical place they farm—collectively represented a salient case study through which to understand soil health and fertility from a grounded perspective. More broadly, we led this work with a Farmer First approach in order to give voice to organic farmers of this region, and to provide a model for future inclusivity of farmer knowledge in the growing body of work on soil health.We conducted our experiment on 13 farms in Yolo County, California, on unceded Patwin speaking Wintun Nation tribal lands—located along the western side of the Sacramento Valley between late March 2019 and December 2020. The region is characterized by Mediterranean type climate with cool, wet winters and hot, dry summers.
Precipitation in the 2019 water year 2019 was 807 mm—the fifth wettest winter on record. The mean maximum and minimum temperatures were 33.9oC and 15.5oC, respectively for July 2019. Mean annual maximum and minimum temperatures for 2019 were 24oC and 9.8oC, respectively. All farm sites were on similar parent material . Most farms were situated on either loam, clay loam, or silty clay loam. All 13 farms selected for this soil health study were located in Yolo County . The organic farms represent a majority of the farms in the region with a diversified array of vegetable and fruit crops that sell to a variety of consumer markets, including farmers’ markets, wholesale markets, and restaurants. The 13 farmers interviewed represent 13 individuals who oversee management and operations on their farms. These individuals were most often the primary owner and operator of the farm, and made key management decisions on their farm. To identify potential participants for this study, we first consulted the USDA Organic Integrity database and assembled a comprehensive list of all organic farms in the county . Next, with input from the local University of California Cooperative Extension Small and Organic Farms Advisor for Yolo County, we narrowed the list of potential farms by applying several criteria for this study: 1) organic operation on the same ground for a minimum of 5 years; 2) a minimum of 10 years of experience in organic farming; and 3) a focus on growing diversified fruit and vegetable crops. These requirements significantly reduced the pool of potential participants. In total, 16 farms were identified to fit the criteria of this study . These 16 farmers were contacted with a letter containing information about the study and its scope. To establish initial trust with farmers identified, we worked directly with the local UCCE advisor. Thirteen farmers responded and agreed to participate in the entirety of the study .Because this research is informed by a Farmer First approach—which emphasizes multiple ways of knowing and challenges the standard “information transfer” pipeline model that is often applied in research and extension contexts—farmers were viewed as experts and crucial partners in this research . As a result, farmers were considered integral to field site selection, and were not asked to change their management or planting plans. In addition to the Farmer First approach, greenhouse grow tables we intentionally used a two-tiered interview process, in which we scheduled an initial field visit and then returned for an in-depth, semi-structured interview at a later date—after summer field sampling was complete. The overall purpose of the preliminary field visit was to help establish rapport and increase the amount and depth of knowledge farmers shared during the semi-structured interviews. The initial field visit typically lasted one hour and was completed with all 13 participants. Farmers were asked to walk through their farm and talk generally about their fields, their fertility programs, and their management approaches. The field interview also provided an opportunity for open dialogue with farmers regarding specific management practices and local knowledge . Because local knowledge is often tacit, the field component was beneficial to connect knowledge shared by each farmer to specific fields and specific practices. During the initial field visit, field sites were selected in direct collaboration with farmers. First, each farmer was individually asked to describe their understanding of soil health and soil fertility. Based on their response, farmers were then asked to select two field sites within their farm: 1) a field that the farmer considered to be exemplary in terms of their efforts towards building soil fertility ; and 2) a field the farmer considered to be a challenge in terms of their efforts towards maintaining soil fertility .
Essentially, farmers were asked, “Can you think of a field that you would consider ‘least challenging’ in terms of building soil fertility on your farm?” and “Can you also think of a field that you would consider ‘most challenging’ in terms of building soil fertility on your farm?” . Farmers would often select several fields, and through back-and-forth dialogue with the field researcher, together would arrive at a final field selected for each category . Only fields with all summer vegetable row crops were selected for sampling. For each site, farmers delineated specific management practices, including information about crop history and crop rotations, bed prepping if applicable, the number of tillage passes and depth of tillage, rate of additional N-based fertilizer inputs, and type of irrigation applied. Following field site selection, soil sampling was designed to capture indicators of soil fertility in the bulk soil at a single time point. Fields were sampled mid-season at peak vegetative growth when crop nitrogen demand was the highest. This sampling approach was intended to provide a snapshot of on-farm soil health and fertility. Because the farms involved generally grow a wide range of vegetable crops, we designed the study to have greater inference space than a single crop, even at the expense of adding variability. As such, we collected bulk soil samples that we did not expect to be strongly influenced by the particular crop present. To sample each site, a random 10m by 20m transect area was placed on the field across three rows of the same crop. Within the transect area, three composite samples each based on five sub-samples were collected approximately 30cm from a plant at a depth of 20cm using an auger . Subsamples were composited on site and mixed thoroughly by hand for 5 minutes before being placed on ice and immediately transported back to the laboratory. Laboratory Processing Soil samples were preserved on ice until processed within several hours of field extraction. Each sample was sieved to 4mm and then either air dried, extracted with 0.5M K2SO4, or utilized to measure net mineralization and nitrification . A batch of air-dried samples were measured for gravimetric water content , which was determined by drying fresh soils samples at 105oC for 48 hours. Moist soils were immediately extracted and analyzed colorimetrically for NH4 + and NO3 – concentrations using modified methods from Miranda et al. and Forster . Additional volume of extracted samples were subsequently frozen for future laboratory analyses. To determine soil textural class, another batch of air-dried samples were further sieved to 2mm and subsequently prepared for analysis using the “micropipette” method . Water holding capacity was determined using the funnel method, adapted from Geisseler et al. , where a jumbo cotton ball thoroughly wetted with deionized water was placed inside the base of a funnel with 100 g soil on top. Deionized water was added and allowed to imbibe into the soil until no water dripped from the funnel. The soil was allowed to drain overnight . A subsample of this soil was then weighed and dried for 48 hours at 105oC. The difference following draining and oven drying of a subsample was defined as 100% WHC. Additional air-dried samples were sieved to 2mm, ground and then analyzed for total organic carbon , total soil nitrogen , soil protein, and pH at the Ohio State Soil Fertility Lab . The former two analyses were conducted using an elemental analyzer . Soil protein was determined using the autoclaved citrate extractable soil protein method outlined by Hurisso et al. . Remaining air-dried samples were sieved to 2mm, ground, and then analyzed for POXC using the active carbon method described by Weil et al. , but with modifications as described by Culman et al. . In brief, 2.5g of air-dried soil was placed in a 50mL centrifuge tube with 20mL of 0.02 mol/L KMnO4 solution, shaken on a reciprocal shaker for exactly 2 minutes, and then allowed to settle for 10 minutes. A 0.5mL aliquot of supernatant was added to a second centrifuge tube containing 49.5mL of water for a 1:100 dilution and analyzed at 550 nm. Theamount of POXC was determined by the loss of permanganate due to C oxidation .After the initial field visit and following summer field sampling, all 13 farmers were contacted to participate in a follow up visit to their farm, which consisted of a semi-structured interview followed by a brief survey.