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. 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, vertical grow system 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.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, 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, vertical grow system 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. A plot with axis loadings is provided to visualize the results of the LDA and display differences across farm groups visually. 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, 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.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.