To understand crop available N more holistically, there is a need to measure actual flow rates of soil N—in addition to—static pools of inorganic N . Soil indicators that adequately capture N availability to crops are therefore necessary to move beyond the legacy of the Law of the Minimum in organic agriculture. Unpacking the soil processes that mediate flows of N may ultimately provide a more accurate characterization of soil N cycling and in turn, N availability to crops. Unfortunately, gross N mineralization and nitrification rates are very difficult to measure in practice, particularly on working organic farms . While net N flows are easier to measure in comparison to gross N flows and can provide a useful measure of N cycling dynamics as a complement to measurements of inorganic N pools, net N flows still pose serious limitations— namely that net rates cannot detect plant-soil-microbe interactions and therefore are not adequate as metrics for determining crop available N . In particular, relying on net N flows as a measure of N availability does not account for the ability of plants to compete for inorganic N, and assumes plants take up inorganic N only after microbial N demands are satisfied . It is also possible that measuring soil organic matter pools could help indicate N availability because SOM supports microbial abundance and activity, and because SOM is also the source of substrates for N mineralization . Several studies have proposed measuring soil organic matter levels to complement measuring inorganic N pools, understand soil N cycling, dry racks for weed and infer N availability . Assessing the total quantity of organic carbon and nitrogen within soil organic matter represents one established method for measuring levels of soil organic matter, and is morereadily measurable than gross N rates.

Additional indicators for quantifying “labile” pools of organic matter, such as POXC and soil protein, have also become more widely studied in recent years, and applied on organic farms as well . When used in combination with more established soil indicators that measure organic C and N pools , this suite of indicators may potentially provide added insight to understanding crop available N . Importantly, applied together these four indicators for soil organic matter levels may also more readily and accurately serve as a proxy for soil quality—generally defined as a soil’s ability to perform essential ecological functions key to sustaining a farm operation . Despite the availability of these soil indicators, very few studies have systematically examined the way in which SOM levels on working farms compare to N cycling processes, and specifically how SOM levels compare to microbially mediated gross N rates. Further, it is still unclear to what degree the interactions between soil edaphic characteristics and soil management influence N cycling and N availability to crops . For instance, soil texture may play a mediating role in N cycling, where soils high in clay content may limit substrate availability as well as access to oxygen, which in turn, may restrict the efficiency of N cycling . In this sense, it is important to understand the role that soil edaphic characteristics play in order to identify the underlying baseline limits imposed by the soil itself. Equally important to consider is the role of soil management in mediating N cycling. Compared to controlled experiments, soil management regimes on working farms can be more complex and nonlinear in nature due to multiple interacting practices applied over the span of several years, and even multiple decades. To date, a handful of studies conducted on working farms have examined tradeoffs among different management systems , though few such studies examine the cumulative effects of multiple management practices across a gradient of working organic farms. However, understanding the cumulative effects of management practices is key to link soil management to N cycling on working farms .

Likewise, it is important to examine the ways in which local soil edaphic characteristics may limit farmers’ ability to improve soil quality through management practices. Though underutilized in this context, the development of farm typologies presents a useful approach to quantitatively integrate the heterogeneity in management on working organic farms . Broadly, typologies allow for the categorization of different types of organic agriculture and provide a way to synthesize the complexity of agricultural systems . Previous studies that make use of farm typologies found that differences in total soil N across farms are largely defined by levels of soil organic matter. To address these questions, we conducted field research at 27 farm field sites in Yolo County, California, USA, and used four commonly available indicators of soil organic matter to classify farm field sites into farm types via k-means cluster analysis. Using farm typologies identified, we examined the extent to which soil texture and/or soil management practices influenced these measured soil indicators across all working organic farms, using Linear Discriminant Analysis and Variation Partitioning Analysis . We then determined the extent to which gross N cycling rates and other soil N indicators differed across these farm types. Lastly, we developed a linear mixed model to understand the key factors most useful for predicting potential gross N cycling rates along a continuous gradient, incorporating soil indicators, on-farm management practices, and soil texture data. Our study highlights the usefulness of soil indicators towards understanding plant-soil-microbe dynamics that underpin crop N availability on working organic farms. While we found measurable differences among farms based on soil organic matter, strongly influenced by soil texture and management, these differences did not translate for N cycling indicators measured here. Though N cycling is strongly linked to soil organic matter, indicators for soil organic matter are not strong predictors of N cycling rates.All farm sites were on similar parent material according to soil survey data .

All fields had soil textural class that was either loam, clay loam, or silty clay loam, based on soil texture analyses. 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 Yolo County . Next, with input from the University of California Cooperative Extension Small Farms Advisor for Yolo County, we narrowed the list of potential farms by applying several criteria for this study: 1) grow fruit, vegetables, and other diversified crops; 2) located within Yolo County; 3) at least 10 years of experience in organic farming; 4) at least five years of farming on the same land. This significantly reduced the pool of potential participants to 16 possible farms. In the end, 13 organic farms and 1 local research station agreed to an initial field interview in early summer 2019 and field sampling in mid-summer 2019. Farmers who agreed to participate were not asked to change their management or planting plans.During the initial field visits in June 2019, two field sites were selected in collaboration with farmers on each participating farm; these sites represented fields in which farmers planned to grow summer vegetables. Therefore, only fields with all summer vegetable row crops were selected for sampling. At this time, farmers also discussed management practices applied for each field site, including information about crop history and rotations, bed prepping if applicable, tillage, organic fertilizer input, and irrigation . Because of the uniformity of long-term management at the field station , hydroponic rack system only one treatment was selected in collaboration with the Cropping Systems Manager—a tomato field in the organic corn-tomato-cover crop system. Since the farms involved in this study generally grew a wide range of vegetable crops, we designed soil sampling to have greater inference space than a single crop, even at the expense of adding variability. Sampling was therefore designed to capture indicators of nitrogen cycling rates and nitrogen pools in the bulk soil at a single time point. Fields were sampled mid-season near peak vegetative growth when crop nitrogen demand is the highest. Using the planting date and anticipated harvest date for each crop, peak vegetative growth was estimated and used to determine timing of sampling. We collected bulk soil samples that we did not expect to be strongly influenced by the particular crop present. This sampling approach provided a snapshot of on-farm nitrogen cycling. 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 site across three rows of the same crop, away from field edges. Within the transect area, three composite samples each based on 5 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. To determine bulk density , we hammered a steel bulk density core sampler approximately 30cm from a plant at a depth 20cm below the soil surface and recorded the dry weight of this volume to calculate BD; we sampled three replicates per site and averaged these values to calculate final BD measurements for each site. 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 and gross N mineralization and nitrification . Air dried samples were measured for gravimetric water content and BD. Gravimetric water content was determined by drying fresh soils samples at 105oC for 48 hrs. 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, air dried samples were 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 100g soil on top. 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. Air dried samples were sieved to 2mm, ground, and then analyzed for total soil N and total organic C using an elemental analyzer at the Ohio State Soil Fertility Lab ; additional soil data including pH and soil protein were also measured at this lab. Soil protein was determined using the autoclaved citrate extractable soil protein method outlined by Hurisso et al. . Additional 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 0.5-mL 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 .To measure gross N mineralization and nitrification in soil samples, we applied an isotope pool dilution approach, adapted from Braun et al. . This method is based on three underlying assumptions listed by Kirkham & Bartholomew : 1) microorganisms in soil do not discriminate between 15N and 14N; 2) rates of processes measured remain constant over the incubation period; and 3) 15N assimilated during the incubation period is not remineralized. To prepare soil samples for IPD, we adjusted soils to approximately 40% WHC prior to incubation with deionized water. Next, four sets of 40g of fresh soil per sub-sample were weighed into specimen cups and covered with parafilm. Based on initial NH4 + and NO3 – concentrations determined above, a maximum of 20% of the initial NH4 + and NO3 – concentrations was added as either 15N-NH4 + or 15N-NO3 – tracer solution at 10 atom%; the tracer solution also raised each sub-sample soil water content to 60% WHC.