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, grow vertical 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. 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 , 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 . 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. 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 . 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, vertical grow systems 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. This approach increased the production pool as little as possible while also ensuring sufficient enrichment of the NH4 + and NO3 – pools with 15N-NH4 + and 15N-NO3, respectively, to facilitate high measurement precision . Due to significant variability of initial NH4 + and NO3 – pool sizes in each soil sample, differing amounts of tracer solution were added to each sample set evenly across the soil surface. To begin the incubation, each of the four sub-samples received the tracer solution via evenly distributed circular drops from a micropipette. The specimen cups were placed in a dark incubation chamber at 20oC. After four hours , two sub-sample incubations were stopped by extraction with 0.5M K2SO4 as above for initial NH4 + and NO3 – concentrations. Filters were pre-rinsed with 0.5 M K2SO4 and deionized water and dried in a drying oven at 60°C to avoid the variable NH4 + contamination from the filter paper. Soil extracts were frozen at -20°C until further isotopic analysis. Similarly after 24 hrs , two sub-sample incubations were stopped by extraction as previously detailed, and subsequently frozen at -20°C. At a later date, filtered extracts were defrosted, homogenized, and analyzed for isotopic composition of NH4 + and NO3 – in order to calculate gross production and consumption rates for N mineralization and nitrification. We prepared extracts for isotope ratio mass spectrometry using a microdiffusion approach based on Lachouani et al. . Briefly, to determine NH4 + pools, 10mL aliquots of samples were diffused with 100mg magnesium oxide into Teflon coated acid traps for 48 hours on an orbital shaker. The traps were subsequently dried, spiked with 20μg NH4+ -N at natural abundance to achieve optimal detection, and subjected to EA-IRMS for 15N:14N analysis of NH4 + . Similarly, to determine NO3 – pools, 10mL aliquots of samples were diffused with 100mg magnesium oxide into Teflon coated acid traps for 48 hours on an orbital shaker. After 48 hours, acid traps were removed and discarded, and then each sample diffused again with 50mg Devarda’s alloy into Teflon coated acid trap for 48 hours on an orbital shaker. These traps were dried and subjected to EA-IRMS for 15N:14N analysis of NO3 + . Twelve dried samples with very low spiked with 20μg NH4+ -N at natural abundance to achieve optimal detection.In addition to the soil biogeochemical variables described above, farmers were also interviewed to determine specific soil management practices on their farms. Farmers were asked to describe the number of tillage passes they performed per field per season; the total number of crops per acre that the farm produced during one calendar year at the whole farm level; the degree to which the farm utilized integrated crop and livestock systems on the farm; crop rotational complexity for each field; and the frequency of cover crop plantings for each field. To calculate the frequency of tillage, we tallied the total number of tillage passes per season for each field. To calculate crop abundance, the total number of crops grown per year at the whole farm level was divided by the total acreage farmed. To capture the use of ICLS, we created an index based on the number of and type of animals utilized. Specifically, the index was calculated by first adding the number of animals used in rotation on farm for each animal type and then dividing by the total number of acres for each farm. These raw values were then normalized, creating an index range from 0 to 1 . Lastly, to quantify crop rotational complexity, a rotational complexity index was calculated for each site using the formula outlined by Socolar et al. . 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.