Farmers also shared that in some circumstances, such as in early spring, they are not able to realize the full potential of a winter cover crop if they are forced to mow the cover crop early to plant cash crops and ensure the harvest timeline of a high-value summer vegetable crop. The cover crop approach to soil fertility takes “persistence,” as one farmer emphasized; another farmer similarly pointed out that the benefits of cover cropping “are not always realized in the crop year. You’re in it [organic agriculture] for the long haul, there is no quick fix.” Indeed, farmers who choose to regularly plant cover crops to build soil fertility, rather than just add N-based fertilizers, reported that they came up against issues of land tenure and access to land, market pressures, and long-term economic sustainability.To build on conversations about soil fertility, farmers also provided responses to interview questions that asked them to elaborate on the usefulness of available soil tests to gauge soil fertility more broadly—and then more specifically, the usefulness of soil tests in informing their soil fertility program and/or management approaches on-farm. Overall, only three of 13 farmers reported regularly using and relying on soil tests to inform their soil fertility program or aspects of their farm operation. These farmers offered very short responses and did not elaborate. For example, one farmer shared that they “test twice a year in general,” and that they “rely on the results of the soil tests to tweak [their] fertility program.” Another farmer said briefly, “We use soil tests… we utilize them to decide what to do to try to improve the soil.” A third farmer admitted that though he “used to do a soil test every year, indoor plant table literally used to spend hundreds of dollars per year on soil tests,” he found that the results of soil tests did not change year-to-year and were, as he put it, very “stable.” This particular farmer no longer regularly uses or relies on soil testing for their farm operation.
The remaining ten farmers confirmed that they had previously submitted a soil test, usually once and most often to a local commercial lab in the region. These farmers expressed a range of sentiments when asked about the usefulness of soil tests, including disappointment, distrust, or both, particularly in the capacity of soil tests to inform soil fertility on their farm. Some farmers said directly, “I just don’t trust soil tests,” or “frankly, I don’t believe a lot in soil testing because it’s too standardized,” while other farmers initially stated they had used “limited” or “infrequent” soil tests, and then later admitted that they did not use or rely on soil tests on their farm operation. These farmers tended to focus on the limitations of soil tests that they encountered for their particular farm application. Limitations of soil tests discussed by farmers varied. Farmers stated that soil tests often confirmed what they already knew about their soil and did not add new information. For this reason, some farmers used results from a soil test as a guide, while other farmers found results to be redundant and therefore less useful to their farm operation. Because issues with soil fertility were sometimes linked to inherent soil characteristics within a particular field, such as poor drainage or heavily sandy soil, farmers found that soil tests were not able to provide new insight to overcome these environmental limitations. “I’m not able to correct that environmental limitation [ie, poor drainage] by adding more nitrogen,” one farmer emphasized. A different farmer echoed this sentiment, saying that “I’m not going to magically get rid of issues that soil tests show… I can only slightly move the needle, no matter what I do.” Most farmers recognized that soil tests produced inconsistent results because of differences in timing and location of sampling.
As one farmer noted, “You can take the same sample a couple months apart from the same field and get very different results.” Likewise, another farmer shared that, “I still struggle with the fact that I can send in two different soil tests and get two very different results. To me that seems like the science is not there.” Farmers also emphasized that each of their “fields are all so different” with “a lot of irregularity in [their] soil.” According to several farmers, soil tests did not account for variations in soil texture and soil structure, despite their observations of the influence of both edaphic characteristics on soil test results. For example, one farmer pointed out that fields that were plowed or were previously furrow irrigated created marked differences in soil test results. Similarly, another farmer shared that if a sample for soil testing was taken from an irregular patch in a field with heavier clay, differences in soil texture across samples skewed soil test results. If a systematic sampling approach was not considered, several farmers emphasized that results of soil tests might be “misleading.” Another source of inconsistency that farmers voiced stemmed from variation in protocols used across different labs that processed soil samples. One farmer stated that in their experience, “soil tests are not really accurate, because if I use a different lab, a different person [ie, consultant] doing the soil test, it’s all different.” For example, one farmer pointed out that they do not use soluble forms of nitrogen, and instead relied on their animal rotations and cover crops to supply nutrients as part of their fertility program; this farmer emphasized that, “I think we need to get to a place with soil testing where it would be more applicable or be more accurately useful for a farm like mine. Farmer explanations of their selection of Field A or Field B were remarkably consistent across respondents. Selection of Field A was primarily based on crop productivity across all farms.
Farmers also selected a field for this category because a particular field maintained good soil moisture or because a particular field did not need as much N-based fertilizer added each season compared to all other fields. Farmers also cited several reasons for selecting their low fertility fields. These fields tended to have patchy growth, low crop productivity, or in some cases, required additional N-based fertilizer to be added each season to meet production goals. Table 3 shows a comparison of soil indicators for fertility for Field A and Field B across all farms. Ammonium concentrations were low across all farms, and ranged from 0.10 – 2.79 µg-N g-soil-1 for Fields A and 0.16 – 2.09 µg-N g-soil-1 for Fields B. Net mineralization rates were also low, and ranged from 0.08 – 1.51 µg-N g-soil-1 day-1 for Fields A and 0.05 – 1.08 µg-N g-soil-1 day-1 for Fields B. Net nitrification rates were markedly higher, and ranged widely from 1.53 – 21.45 µg-N g-soil-1 day-1 for Fields A and 2.71 – 25.18 µg-N g-soil-1 day-1 for Fields B. Nitrate concentrations were similar to values commonly found in organic agricultural systems in the region, and ranged from 2.56 – 18.12 µg-N g-soil-1 for Fields A and 4.46 – 23.24 µg-N g-soil-1 for Fields B . No differences were detected between Field A and Field B among these four soil indicators. Across all farms, total soil nitrogen ranged from 0.07 – 0.21 mg-N kg-soil-1 for Fields A and 0.11 – 0.23 mg-N kg-soil-1 for Fields B . Total N values were significantly different between Fields A and Fields B , hydroponics flood table with a mean value of 0.12 mg-N kg-soil-1 for Field A and a mean value of 0.15 mg-N kg-soil-1 for Field B. Total organic carbon was not significantly different between Fields A and B, and ranged from 0.77 – 2.40 mg-C kg-soil-1 for Fields A and 0.87 – 2.43 mg-C kg-soil-1 for Fields B. POXC values were in the typical range for organic agricultural systems in the region, and ranged from 225 – 707 mg-C kg-soil-1 for Fields A and 276 – 899 mg-C kg-soil-1 for Fields B . Soil protein values ranged from 2.21 – 7.51 g g-soil-1 for Fields A and 1.86 – 8.91 g g-soil-1 for Fields B. PCA indicated strong relationships among several key management variables; the results of the PCA also provided strong differentiation among farms along the first two principal components, which together accounted for 77.4% of the variability across farms . The first principal component explained 55.1% of the variation, and the second component explained 22.3% of the variation observed across all farms. Both components had eigenvalues greater than 1.0. Additional N-based fertilizer represented the management variable most associated with PC 1—followed by tillage, and inversely ICLS. While crop diversity, cover crop frequency, and crop rotation patterns also contributed to the overall variation explained by PC 1, these management variables were weaker in comparison to N-based fertilizer additions, ICLS, and tillage. On the other hand, variables with the strongest contribution to PC 2 were crop diversity, cover crop frequency, and crop rotation patterns.
Figure 1 summarizes the spatial distribution of all farms based on PCA results with PC 1 as the x-axis and PC 2 as the y-axis. As shown in Figure 3, the results of the nearest neighbor analysis order each farm from 1 to 13, and provide a basis for visualization of the gradient in management. Therefore, this gradient in management, strongly driven by the amount of external N-based fertilizer applied on-farm, served as the basis for further visual comparison of Fields A and FieldsB across all farms . As shown in Figure 2a, the difference in soil ammonium concentration between fields was low among farms on the low end of the gradient. At the middle and high end of the gradient, farms showed greater soil ammonium concentrations in Field B compared to Field A—with the exception of two farms. Farm by farm, net N mineralization rates followed trends identical to soil ammonium concentrations. Soil nitrate concentrations varied widely among farms and did not produce any consistent trends ; however, a majority of farms showed greater soil nitrate concentrations in Field B compared to Field A regardless of the management gradient. Like net N mineralization rates, net N nitrification rates followed trends analogous to nitrate concentrations farm by farm. For both mineralization and nitrification rates, a majority of farms showed greater rates in Field B compared to Field A, regardless of the gradient in management. Differences between Field A and Field B for total N, total C, and POXC followed identical trends farm by farm . Among farms on the high end of the gradient, the difference in total C between fields was consistently low . Similarly, the difference between fields in soil protein values were also consistently low at the high end of the gradient . Radar plots provided further comparison of Field A and Field B across all eight indicators for soil fertility along the gradient in management developed above . As mentioned, because the level of N-based fertilizer input was a strong driver of the management gradient, radar plots were divided to reflect low, medium, and high N-based fertilizer inputs. Shown in Figure 3L is the high overlap in soil indicators, with the exception of net N mineralization and nitrification rates, between Field A and B. However, among farms with medium N-based fertilizer input , the overlap of soil indicators between fields is minimal; Field B tended to show higher concentrations of soil ammonium and soil nitrate than Field A, while Field A tends to show higher values for total N, total C, POXC, and soil protein among these farms. Among high input farms , differences between fields were less evident in terms of soil ammonium concentration, total N, total C, POXC, and soil protein, though soil nitrate concentrations and net N mineralization and nitrification rates did show noticeable differences in values between the two fields.The results presented above are reflective of the perspectives, observations, and experiences of a sample of organic farmers in Yolo County, California, USA, and offer an enhanced understanding of soil health and fertility from this particular node of the organic movement . Here, we focus less, as prior studies have commonly done, on a comparative analysis that quantitatively compares farmers perception of soil health to results of soil laboratory analyses ; instead, we lead the discussion with farmer knowledge of soil health and fertility, and explore emergent synergies with ongoing soil health research and soil indicator results.