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 , rolling flood tables 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 . 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.We found across all 27 farm sites sampled that gross N mineralization rates ranged from 0.05 – 4.82 µg-NH4+ -N g-soil-1 day-1 and gross N nitrification rates ranged from 0.55 – 5.90 µg-NO3- -N gsoil-1 day-1 . We determined net N mineralization rates ranged from 0.07 – 1.51 µg-NH4+ -N g-soil-1 day-1 , while net N nitrification rates had a wider range from 1.53 – 25.18 µg-NO3- -N g-soil-1 day-1 . We visually compare the six key N cycling variables—pools of inorganic N , and net and gross N rates—across the three farm types . Despite the variation in net and gross N mineralization and nitrification rates, using the farm types developed above, we found that N cycling variables were not significantly different across the three farm types for all six variables examined—based on ANOVA results . Given the variation in gross N rates reported above, we further explored the drivers of this variation in gross N rates using mixed modelling approaches.
Table 10 shows results provide for the linear mixed models used for the prediction of potential gross ammonification rates . Soil ammonium concentration and % sand were significant predictors of gross mineralization rates. While not significant, indicators for SOM were selected and also included in the model, based on AIC results. We also provide results from the selected linear mixed model used for prediction of potential gross nitrification rates in Table 11. As shown, indicators for SOM emerged as the sole significant covariate . While not significant, crop abundance was also selected and included in the model, as determined by AIC results.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. To determine drivers of this variation, we explored key predictors for soil N cycling and found that SOM indicators influenced gross N mineralization and nitrification rates, in particular gross nitrification rates.Each of the four indicators for soil organic matter used in this study—total soil N, total organic C, POXC, and soil protein—showed a strong correlation with farm type, and collectively, flood and drain tray created a gradient in soil quality . Farm Type I consistently showed the highest values for total soil N, total organic C, POXC, and soil protein, which suggests sites in this farm type had higher soil quality compared to Farm Type II and III; similarly, Farm Type II consistently showed intermediate values for all four indicators for soil organic matter. Lastly, Farm Type III consistently showed the lowest values across all four indicators, which suggests sites in this latter farm type had lower soil quality compared to the other two farm types. These initial results highlight the usefulness of establishing farm typologies based on indicators for soil organic matter as a novel approach to study gradients in soil quality on organic farms. The three farm types generated based on soil organic matter levels served as a key starting point for further analysis of the role of management in relation to soil quality. Accordingly, not only were the three farm types identified in this study significantly different based on indicators for soil organic matter levels, but the farm types also aligned with general trends in management among sites, which indicated a link between soil organic matter levels and management. In particular, as the four indicators for soil organic matter collectively serve as a proxy for soil quality, our results suggest that soil quality indicators may show responsiveness to the impacts of short-term management. In our study, crop diversity, crop rotational complexity, and tillage emerged as the strongest drivers of farm type differences, as shown by LDA coefficients . These results also coincided with average values for management variables compared across all three farm types , though variables for ICLS and cover crop application overlapped considerably across all three farms.
These cursory findings extend results from ongoing work from others , including a recent 4-year study by Sprunger et al. —which focused on organic corn systems in the Midwest. Sprunger et al. likewise reported strong links between soil metrics such as total N, total C, soil protein, and POXC—and on-farm management practices, such as crop rotation patterns, manure and cover crop application, and tillage. While extensive work has been done on organic corn and grain systems in the midwestern region of the US, our study provides new insight on the applicability of these common soil metrics in entirely different organic farming systems and climate regions—specifically on high-value vegetable farms operating in the dry, hot Mediterranean climates of northern California. Our results also underscore the usefulness of on-farm interviews in developing management variables that are potentially linked to soil indicators . Whereas most previous studies have frequently utilized mail-in surveys that rely on binary responses from farmers to understand management , our study, following Guthman and others, highlights the uneven gradient in management practices that exists among organic farms and the importance of in-depth interviews . For example, rather than simply noting the presence or absence of tillage at a field site, our study accounted for the number of tillage passes per season that a farmer implemented on a particular field site, which required soliciting a range of responses from each farmer to create a congruent metric across all field sites. As displayed in Table 6, the mean values for frequency of tillage and crop abundance differed across the three farm types in our study; these management variables strongly separated Farm Type I from the other two farm types and weakly correlated with soil quality. On the other hand, crop rotational complexity generally separated all three farm types, but did not correlate with increasing soil quality. These results suggest that while certain management practices may increase soil organic matter pools as frequency decreases, some management practices may require finding a “sweet spot” to achieve higher soil organic matter levels. Relatedly, the implementation of ICLS did not appear to be as strong of a source of differentiation among the three farm types. One reason for this weak link between soil organic matter levels and ICLS may be due to the lack of a temporal component in the development of this soil metric. For example, some farms may have recently rotated livestock on their fields, while other farms may not have rotated livestock for several years on that particular field; our metric does not capture such spatial and temporal differences. Though limited studies on organic systems in California currently exist, previous studies in the midwestern US have found that the integration of livestock does increase organic matter levels on-farm ; however, based on our results, crop diversity, crop rotational complexity, and frequency of tillage present stronger influences than cover crop application and ICLS in differentiating working organic farms—at least in this particular context.While management is undoubtedly an important driver of soil organic matter levels, our findings also suggest that soil texture may play a more significant role than management in determining levels of SOM than originally considered. Though management explained 18% of the variance among the three farm types, further analysis showed that soil textural class was the more dominant factor as shown in Figure 5; in fact, soil texture class was 44% greater than management in explaining the three farm types. This important result from our study complements parallel findings from Sprunger et al. , who also determined that soil textural class, rather than management, explained the largest amount of variation among the soil indicators they measured on their midwestern US-based organic corn systems . Our combined findings provide an initial indication that regardless of the organic system— ie, crop, climate, and/or geography—soil texture is the more dominant determinant of soil indicators for soil quality rather than the diverse management practices applied to these systems .