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, cannabis drying racks commercial 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 . This broader finding is significant because it supports emergent research that suggests that while management certainly contributes to soil quality, inherent characteristics of the soil in a given field may place limits on achievable organic matter levels on organic farms . Based on our findings, it is evident that even along minimal gradients in soil texture class, organic matter levels strongly differ. Soil texture is known to be a strong control on soil organic matter dynamics across diverse ecological systems—not just agricultural systems—in part because organic compounds, particularly those derived from soil microbes, are among those capable of stabilization by physical and chemical mechanisms, including aggregation, sorption on mineral surfaces, and entrapment within fine pores . At a fundamental level, soils with greater amounts of clay tend to stabilize SOM on surfaces more than soils with high sand and/or silt content , as clay particles provide greater surface area through organo-mineral associations than other particle sizes . For example, it has been shown in numerous previous studies that as clay content increases, the relative abundance of total soil N also increases . Further other studies have shown that soil texture and structure can influence SOM chemistry, and therefore, SOM stabilization . Our study takes previous research in agricultural contexts one step further to show that while management is important to consider, soil texture may be the more dominant factor; however, based on our results, it is still unclear which direction soil texture may be driving SOM. Nonetheless, our results highlight that contextualizing management in the native soil texture is essential to understand the limits of management imposed by pre-existing constraints of the soil. In practice, current emphasis in on-farm soil health research and quality assessments tends to focus on the importance of changing management to build healthy soils and improve soil quality without explicit consideration for soil texture . In this study, the gradient of soil textures across the farm fields sites was relatively limited and even so—soil texture still explained a significant component of the variance observed compared to management. Given this outcome, our findings here reinforce the importance of using soil texture as a starting point for evaluating soil quality.
Knowing the soil textural class of different fields may help farmers determine the management practices that have greatest potential for improving soil quality on farms with even small variances in soil textures; soil texture class may also help farmers better contextualize results of their soil health tests. Our study suggests that moving forward, soil texture should be more explicitly considered when making management recommendations to improve soil quality on organic farms. That said, vertical grow racks understanding the interactive effects between management and soil texture continues to be a gap in on-farm research and soil health assessment. Future studies might build on our approach and examine whether applying a similar suite of indicators to capture soil organic matter levels may yield similar connections with management in other organic farming contexts in California—and elsewhere in the US. Our study provides a potentially widely applicable method for developing a functional understanding of soil organic matter in complex agricultural landscapes. In this sense, the overall significance of the results of the cluster analysis highlights the efficacy of developing typologies to provide a useful tool for understanding the complexity of working agricultural landscapes. Importantly, the development of farm typologies allowed for additional analysis of other soil indicators for N cycling an availability—by using the farm types as a central tool for further investigation.Though the range of gross N cycling rates from this study are comparable to N cycling values reported from previous studies in organic agricultural systems , we found that farm types did not have significantly different gross N mineralization and nitrification rates—contrary to our initial hypothesis and despite that farm types strongly differentiated based on soil organic matter levels. These hypotheses were in part based on prior work with organic farms in this region that reported instances where inorganic N pools were low—well below established soil nitrate threshold sufficiency values—but that the crops themselves showed high production of, and sufficient N . Fields in which this trend was observed had the highest levels of soil C, and so in this previous study, it was hypothesized that higher rates of N production explained this observed trend. However, nitrogen bio-availability for crops is not just a function of the gross production of inorganic N by microbes but is also influenced by physical soil characteristics within the rhizosphere, such as the local soil structure and mineralogy, plant root structure and associated mycorrhizal pathways, as well as accessibility of water to plants and soil microbes . These variable conditions in the rhizosphere are not captured by measuring N cycling rates but still directly influence bio-availability of N. For these reasons, the N cycling results of this study may not follow prior findings from Bowles et al. . Still, we did observe an influence of soil organic matter levels on N cycling, particularly in terms of gross nitrification rates. As shown in the Linear Mixed Model results in Table 12, SOM indicators do appear to have an influence in predicting gross nitrification rates , even as the proportion of variation explained is modest . This slight trend is also evident in the boxplots . The weak but significant link between soil organic matter levels and gross nitrification rates is important to highlight because these results suggest that building soil organic matter presents one way to increase nitrification rates and potentially crop N availability. Because the plant-soil-microbe N cycling system is strongly influenced by soil water content and soil structure, it is possible that gross N cycling indicators lack the responsiveness that SOM indicators exhbiti especially in scenarios where improved soil quality allows for crops to continue accessing soil microsites with available N . Similarly, crops with more abundant and active mycorrhizal community associations can extend into smaller Ncontaining aggregates that may be otherwise locked up for crops with less root proliferation andhyphal associations. Additionally, it is also possible that changing microbial community composition in the soil may lead to greater immobilization of N, locking up available N but not necessarily impacting gross production of N. These plant-soil-microbe interactions that control availability of N may not be detectable solely by measuring gross N flows. While not significant, SOM indicators were also selected in the development of the LMM for gross mineralization rates as well. These results are congruent with previous research looking across ecosystem types that reported a relationship between N cycling rates and SOM indicators. For example, a meta-analysis published by Booth et al. that examined woody, grass, and agricultural ecosystems found a strong positive relationship between indicators for SOM and gross N mineralization. It is likely that in this prior study, the range of ecosystem types analyzed were sufficiently broad to detect a significant trend between indicators for SOM and N cycling. However, in our context, which encompasses agricultural systems only—it is possible that previously established trends are less detectable within this narrower range of ecosystem type.