Yield models and BER models treated weekly harvests as repeated measures, adding random effects of plot within block and harvest number. For hurdle models, random effects were treated as correlated between the conditional and hurdle portions of the model. Because PDW was measured at three time points, the initial PDW model treated the time points as a repeated measure and added a random effect of plot within block. However, given the nonlinear relationship between PDW and fruit quality described by farmers, further models used only PDW at the 6th harvest when fruit quality was at its peak and therefore did not include any repeated measures.The initial model for each outcome variable included plant spacing and PC1 for soil texture , along with PC1 for GWC and PCs 1 and 2 for nutrients at all four depths , as well as the interaction between texture and GWC. In this initial model, only one depth showed a statistically clear relationship with each outcome variable . To improve model interpretability, we then replaced the two PC’s from the depth of interest with the scaled transplant values of nitrate, ammonium and phosphate at that depth, also adding the ratio of nitrate to ammonium and an ammonium-squared term to allow for non-linearities in outcome response to nitrogen levels. Because all nutrient variables had variance inflation factors over 5 in this model , we dropped nutrient PC’s for each depth that was not of interest, indoor grow rack leaving only the transplant nutrient values at the depth of interest in the model. All nutrient VIF values were below 5 in the resulting model.
Transplant nutrient levels were used rather than midseason/harvest both because they are the most relevant to farmer management and because their interpretation is more clear than later timepoints, when low levels can either indicate lower initial nutrient levels, or that plants have more thoroughly depleted those nutrients.Two fungal community descriptors were calculated for each soil depth and root fungal community: the Shannon index and the count of OTUs in the class Sordariomycetes, which was identified as an indicator of dry farm soils . Counts were scaled, and both community descriptors were added to the final model described in the “Variable selection” section to determine the impact of fungal community structure while controlling for water, nutrients, and texture. Because the metrics between roots and the two depths of soil fungal communities were highly correlated, three separate models were run: one with both fungal community metrics from 0-15cm, one with metrics from 30-60 cm, and one with root community metrics. Of the AMF taxa that were identified to the species level in soils and roots, none was a species present in the inoculum. After removing samples that did not contain any AMF taxa, PERMANOVAs using Bray distances showed a statistically clear difference between community composition in inoculation vs. control roots but not bulk soils when stratifying by field and controlling for water, nutrients, and texture. No AMF taxa were significantly enriched in the inoculation or control condition.
Taken together, these AMF community results suggest that the inoculum shifted the root fungal community at transplant and did not persist in bulk soils for the 9 weeks before DNA samples were taken.A PERMANOVA using Bray distances showed statistically clear differences in fungal community composition in irrigated, dry farm, and non-cultivated bulk soils as well as communities at 0-15cm and 30-60cm when stratifying by field and controlling for water, texture and their interaction, which also significantly differentiated between communities . Though dry farm, non-cultivated and irrigated soils each had more unique taxa than taxa shared with another location, dry farm and non-cultivated soils each had nearly twice as many unique taxa as taxa shared with a single other location, while irrigated soils had more taxa shared with dry farm soils than unique taxa . Abundance analysis showed that there were 466 taxa that significantly discriminated between the three soil locations. We then set the LDA threshold to 3.75 to highlight only the most stark differences, resulting in 13 discriminative taxa . All of the taxa identified as being enriched in dry farm soils were sub-taxa of Sordariomycetes, a fungal class that is highly variable in terms of morphology and function. We therefore identified Sordariomycetes as a dry farm indicator taxa, or a sort of dry farm “signature”. We included the Sordariomycetes count in models as an indication of how much the soil had shifted towards a dry farm-influenced community . AMF taxa were notably absent as discriminative taxa and PERMANOVA did not show a difference in AMF community composition between the two depths, suggesting that AMF are not limited in their dispersal down to 60cm.
After identifying Sordariomycetes as an indicator taxa for dry farming, we further explored whether multiple years of dry farming enhance soils’ dry farm signature by comparing fields that had not received external water inputs for multiple years and those which had received regular external water inputs the summer prior to the study. The extent to which Sordariomycetes were enhanced was measured by the difference between counts in dry farm and irrigated soils in the study year . We found that fields that had not received regular external water inputs the previous year showed a significantly higher difference in Sordariomycetes counts between dry farm and irrigated soils , indicating that multiple years without irrigation enhance a soil’s dry farm signature.Marketable yields per plot surprisingly did not correlate with plant spacing, which runs counter to current common wisdom in extension publications18. Because spacing ranged from 15-48 inches between plants , relatively consistent yields on a per-plant basis contributed to a wide range in yields on a per-area basis . As there are very few irrigated tomatoes in the Central Coast region due to its cool, moist climate, it is difficult to compare dry farm yields to what might be found in an irrigated system in the same region. However, in 2015 , the statewide average fresh market tomato harvest was 39 T/ha, a number that is surprisingly on par with the average dry farm yield in this study . Because there is a clear trade off between yield and fruit quality–the highest yielding fields also had the lowest fruit quality, ebb and flow system and increasing ammonium concentrations improve fruit quality while lowering yields–it may be difficult to increase yields above the state average while still charging consumers a premium for dry farm quality. Growers can currently charge roughly double the price per kg for dry farm-quality compared to irrigated tomatoes; therefore, short of doubling yields, current dry farmers may be reluctant to shift management to maximize yield over quality. However, these high yields do open the possibility that dry farm management could expand to industrial-scale markets that do not rely on consumer trust in high quality produce, competing instead with irrigated production if larger scale farmers adopt dry farm practices while choosing to intentionally manage for yields over quality.Only soil nutrients at 30-60cm depth showed correlations with BER, while marketable yields and fruit percent dry weight were only influenced by nutrients below 60cm. Specifically, ammonium concentrations were associated with increased fruit quality but decreased yields and incidence of blossom end rot, while nitrate was associated with increased yields. Because soils dry down quickly in dry farm fields–available water content on average decreased by 65% in the top 30cm from transplant to midseason, while decreasing by only 16% below 60cm –plants likely devote rooting efforts to exploring deeper soils that are not too dry for efficient nutrient acquisition. Farmers also make an effort to plant transplants as deeply as possible, quickly delivering roots to depths below 30 cm. Though tomatoes root adventitiously from their stems and can therefore send out roots at shallower depths, rapidly drying surface soils likely limit nutrient uptake by adventitious roots, directing resources instead towards deeper rooting. The importance of soil nutrients at transplant at 30-60cm in predicting BER incidence, as compared to 60-100cm for yields/quality, suggests that calcium uptake occurs at an earlier stage of plant development when a higher proportion of roots were likely present at 30-60cm .
Roots likely concentrated more heavily in deeper soils during fruit set and development, causing only nutrients below 60cm to show a relationship with fruit yields and PDW. Our results also show a surprising relationship between transplant ammonium levels and fruit yields/quality. Though ammonium levels are quite low below 30cm , their negative association with yields suggests that either these low ammonium concentrations were still able to inhibit calcium/water uptake and further stress plants, as seen in studies with higher ammonium concentrations, or that higher transplant ammonium levels were indicative of other soil circumstances that negatively impacted yields. One possibility is that wetter transplant soils led to higher rates of nitrification, causing decreased ammonium levels and also higher yields due to increased water availability. While GWC was included in our models and was not significant, ammonium concentrations could in some ways be a better indicator of water availability than GWC if they more fully reflect the conditions that lead to nitrification. Transplant ammonium also showed a significant positive correlation with clay content . It is possible that, within the range of textures seen in this study, plots with higher clay content at depth inhibited plants’ ability to root deeply or led to decreased plant available water. This possibility is supported by the water x texture interaction that links plots with low clay and high GWC to increased yields. We note that the plots with the highest ammonium levels were all from one field , which exerted a strong influence on results; however, excluding Field 5 from analyses does not change the direction of nutrient coefficients, or the depth at which nutrients show a significant relationship with these outcomes. Additional research is needed to understand the unexpected relationship between ammonium concentration and harvest outcomes found here. Because nitrate levels correlate positively with yields and do not show a statistically clear relationship with BER or fruit quality, it may be tempting to conclude that farmers should increase nitrate availability in dry farm soils. However, risk of nitrate leaching must be taken into account, especially in this agricultural region that suffers from severe nitrate pollution of groundwater. Three of the seven fields in our study had nitrate levels at harvest—in just the top 15cm—above the threshold considered likely to cause groundwater contamination if that nitrate were to fully leach out of the rooting zone when it mobilizes in the first large rain event of the fall/winter wet season. These levels would likely be further accentuated by the Birch effect as soils are rewetted. Because this first rain event typically occurs after plants are terminated, or is the terminating event itself, these systems may be particularly prone to nitrate loss when living roots are not present in the soil to recapture it. Though careful cover crop management, which is practiced by all of the farms in this study, can likely attenuate leaching, decisions to fertilize should be made with caution. Taken together, these results highlight two core challenges for dry farmers. First, there is a tension between fruit quality and yields, with conditions that lead to high yields decreasing fruit quality and vice versa. Second, it is difficult to manage soil fertility deep in the soil profile, especially when nutrients are prone to leaching.While a commercial AMF inoculant applied at tomato transplant changed AMF community composition in roots, it did not provide any benefit to yield outcomes, if anything lowering fruit quality. Diversified farm management likely made AMF communities in these soils more diverse with higher spore counts than would be seen in more industrialized systems. Altering the AMF community through inoculation may have disrupted or simply not altered functions that the endogenous community was as well or better-equipped to provide. This result has been seen repeatedly in field research, where commercial inoculants often fail to impact agriculturally relevant outcomes, or local AMF communities outperform exogenous ones. It is also possible that, while the inoculum established enough to shift the AMF community and lower fruit quality, inocula generally will not have a large influence on dry farm tomatoes given that they are applied to surface soils while plants focus on deeper rooting, or that the specific species in the inoculant we used were not well-suited to this system.