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, cannabis grower supplies 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, 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. Field sampling occurred over the course of four weeks in July 2019. 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 . Sub-samples were composited on site and mixed thoroughly by hand for 5 minutes before being placed on ice and immediately transported back to the laboratory.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 sub-sample of this soil was then weighed and dried for 48 hours at 105oC. The difference following draining and oven drying of a sub-sample 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, dry racks for weed 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. The amount 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. The semi-structured 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. In person interviews were conducted in the winter, between December 2019 – February 2020; three interviews were conducted in December 2020. All interviews were recorded with permission from the farmer and lasted about 2 hours.
To develop interview questions for the semi-structured interviews , we established initial topics and thematic sections first. 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. We used an openended, qualitative approach that relies on in-depth and in-person interviews to study farmer knowledge . In the semi-structured interview, farmers were asked a range of questions that included: their personal background with farming and the history of their farm operation, their general farm management approaches, as well as soil management approaches specific to soil health and soil fertility, such as key nutrients in their consideration of soil fertility, and their thoughts on soil tests . A brief in-person survey that asked several key demographic questions was administered at the end of the semistructured interviews. 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 . Through structured analysis of the interview transcripts, key themes were identified and then a codebook was constructed to systematically categorize data related to soil health and soil fertility . We summarize these results in table form. To unpack differences between Fields A and Fields B across all farms, we applied a multi-step approach. We first conducted a preliminary, global comparison between Fields A and Fields B across all farms using a one-way analysis of variance to determine if Fields A were significantly different from Fields B for each indicator for soil fertility. Then, to develop a basis for further comparison of Fields A and Fields B, we considered potential links between management and soil fertility. To do so, we developed a gradient among the farms using a range of soil management practices detailed during the initial farm visit. These soil management practices were based on interview data from the initial farm visit, and were also emphasized by farmers as key practices linked to soil fertility. The practices used to inform the gradient included cover crop application, amount of tillage, crop rotation patterns, crop diversity, the use of integrated crop and livestock systems , and the amount of N-based fertilizer application. Cover crop frequency was determined using the average number of cover crop plantings per year, calculated as cover crop planting counts over the course of two growing years for each field site. To quantify crop rotation, a rotational complexity index was calculated for each site using the formula outlined by Socolar et al. . To calculate crop diversity, we focused on crop abundance, the total number of crops grown per year at the whole farm level was divided by the total acreage farmed. To determine ICLS, an index was created based on the number and type of animals utilized . Lastly, we calculated the amount of additional N-based fertilizer applied to each field . In order to group, visualize, and further explore links with indicators for soil fertility, all soil management variables were standardized , and then used in a principal components analysis using the factoextra package in R . In short, these independent management variables were used to create a composite of several management variables. Principal components with eigenvalues greater than 1.0 were retained. To establish the gradient in management, we plotted all 13 farms using the first two principal components, and ordered the farms based on spatial relationships that arose from this visualization using the nearest neighbor analysis . To further explore links between management and soil fertility, we used the results from the PCA to formalize a gradient in management across all farms, and then used this gradient as the basis for comparison between Field A and Field B across all indicators for soil fertility.