Changing climates have caused droughts that not only result in massive financial losses , but also raise major concerns for farmers’ ability to maintain continuity in their farming operations. Because California’s waters are over-allocated even in years of typical rainfall, the Sustainable Groundwater Management Act, which requires sustainable groundwater use by 2040, implies that irrigation will need to be discontinued on hundreds of thousands of cropland acres. In this backdrop, dry farming, a practice in which farmers grow crops with little to no irrigation, has quickly garnered interest from farmers and policy-makers around the state. While dry farming is an ancient practice with rich histories in many regions, perhaps most notably the Hopi people in Northeast Arizona, dry farming emerged more recently in California, with growers first marketing dry farm tomatoes as such in the Central Coast region in the early 1980’s. In a lineage that likely traces back to Italian and Spanish growers, dry farming on the Central Coast relies on winter rains to store water in soils that plants can then access throughout California’s rain-free summers, allowing farmers to grow produce with little to no external water inputs. As water-awareness gains public attention, dry farming has been increasingly mentioned as an important piece in California’s water resiliency puzzle, however, while some extension articles exist no peer-reviewed research has been published to date on vegetable dry farming in California. We therefore assembled a group of six dry farming operations on the Central Coast to collaboratively identify and answer key management questions in the dry farm community. Growers identified three main management questions that would benefit from further research: 1. Which depths of nutrients are most influential in determining fruit yield and quality? 2. Are AMF inoculants effective in this low-water system, and more broadly. 3. How can farmers best support high-functioning soil fungal communities to improve harvest outcomes?
Growers were primarily concerned with fruit yield and quality, vertical grow rack system with blossom end rot prevention and overall fruit quality being of particular interest due to the water stress and high market value inherent to this system. Managing for yields and quality present a unique challenge in dry farm systems, as the surface soils that farmers typically target for fertility management in irrigated systems dry down quickly to a point where roots will likely have difficulty accessing nutrients and water. Because plants are likely to invest heavily in deeper roots as compared to irrigated crops, we hypothesized that nutrients deeper in the soil profile would be more instrumental in determining fruit yields and quality. As deficit irrigation and drought change microbial community composition in other agricultural and natural systems, we hypothesized that dry farm management would cause shifts in fungal communities in response to dry farm management, which could in turn improve tomato harvest outcomes. Beyond general shifts in fungal communities, farmers were specifically interested in arbuscular mycorrhizal fungi inoculants, which are increasingly available from commercial sellers. Recent research has shown that AMF can help plants tolerate water stress, and we therefore hypothesized that commercial AMF inoculants might be beneficial. We organized a season-long field experiment from early spring to late fall of 2021 to answer these questions, sampling soils and collecting harvest data from plots on seven dry farm tomato fields on the Central Coast. Each farmer managed the fields exactly as they normally would, with AMF inoculation being the only experimental manipulation. We sampled soils for nutrients and water content at four depths down to one meter throughout the growing season to determine which nutrient depths influenced harvest outcomes. We also took DNA samples from soils and roots in surface and subsurface dry farm soils, as well as nearby irrigated and non-cultivated soils, sequencing the ITS2 region to analyze the fungal community to verify inoculation establishment and more broadly characterize soil fungal communities to see how fungal communities changed under dry farm management and determine whether these changes or the introduction of an inoculant influenced harvest outcomes.
We then used Bayesian generalized linear mixed models to estimate the effects of nutrient depths and fungal community metrics on yield and fruit quality data from 10-20 weekly harvests on each field. Our results highlight a tension between managing nutrients for fruit yield and quality, while fungal community metrics show promise for increasing fruit quality. The experiment was conducted on seven certified organic dry farm tomato fields in Santa Cruz and San Mateo counties in California during the 2021 growing season. Five blocks were established on each field over the course of a full growing season , for a total of 70 experimental plots. These fields are managed by six farms; one farm contributed two fields at two separate sites. Each farmer continued to manage their field for the duration of the experiment according to their typical practices. Each dry farm crop was preceded by a crop in the winter prior to the experiment, either in the form of a cover crop , or continuous winter production . All fields were disked prior to planting, and two fields additionally ripped down to 60-90cm. Each field’s plant and bed spacing, plant date, and tomato variety are listed in Table 1, along with amendments added to the soil. Fields also varied in their rotational history . The mapped soil series, measured texture, and soil pH are listed in Table 3. From March 2 to October 27 there were 15 rain events greater than 1 mm recorded at the De Laveaga CIMIS weather station , none of which occurred between the months of May and October . Monthly weather data is summarized in Table 4.A nested experimental design was used to account for management and biophysical differences across fields. Each plot contained 12 plants, and plots were divided across two beds with a buffer row between . Plots were randomly selected to be inoculated in the first experimental row and then paired with a counterpart in the second experimental row that received the opposite inoculation condition to achieve a randomized complete block design with five blocks per field. Here we refer to a pair of inoculated and control plots as a block. There were three non-inoculated buffer plants between each plot and at least twenty buffer plants at the start and end of each experimental row. Harvests began when farmers indicated that they were beginning to harvest the portion of their field that included the experimental plots. Each field was harvested once per week from its start date to its end date, grow rack with lights with the exception of Farm 5, which was harvested twice per week, in accordance with farmer desires. All red tomatoes were harvested from each plot and sorted into marketable, blossom end rot, sunburnt, or “other unmarketable” fruits and then weighed. Harvests stopped when there were no remaining tomatoes in the field or when farmers decided to terminate the field. Fruit size and quality were assessed on the third, sixth, and ninth week of harvest at a given field. Ten representative marketable tomatoes were taken from each plot, weighed, dried at 70 degrees C and then weighed again to establish the percent dry weight . PDW was used as a proxy for fruit quality, with fruits with a lower water content increasing fruit quality up to a certain point.
Extension research has linked dry farm fruit quality with lower fruit water content, as opposed to specific compounds that are elevated in dry farm tomatoes, and we expect PDW to correlate highly with the concentration of flavors previously found to create dry farm fruits’ superior quality. After eliciting quality categorization from farmers in the study, we determined that fruit quality increases up to a PDW of 8%, peaks between 8 and 12%, and falls above 12%.Soil samples were taken three times over the course of the field season: once at transplant , once mid-season , and once during harvest . Each sample was then divided into fresh soil , dried at 60 degrees C , and dried at 105 degrees C . Ammonium and nitrate levels were measured after using 2M KCl to extract samples from transplant , midseason , and harvest samples using colorimetry. As soil pH was close to neutral, Olsen P37 was used to measure plant-available phosphate on samples from transplant and midseason . Gravimetric water content was assessed for all samples. Samples from transplant were composited by depth at each field, and texture was assessed using a modified pipette method38. At transplant, a soil core was taken with a bucket auger down to one meter from a central plot in each field and used to calculate bulk density at each depth increment. We then took a weighted average of GWC at each plot to calculate available water using bulk density and a pedotransfer function based on soil texture. Potentially leachable soil nitrate levels were calculated for each field using nitrate concentrations from the top 15cm at the harvest sampling event, which occurred within the first three weeks of harvest. Though the plants continued to grow for the duration of the harvest, it is unlikely that nitrate from the top 15cm were used due to the soil’s low water content, and no precipitation orirrigation occurred for the duration of harvest. Bulk density in the top 15cm was assumed to be 1.2 g soil/cm3 as experimental bulk density was measured with 1m of soil and likely overestimated the bulk density at the surface of the soil.Soil sub-samples taken from 0-15cm and 30-60cm at midseason were set aside for DNA analysis. In addition to the experimental plots, samples were also taken from both depths at the nearest irrigated crop production areas and non-cultivated soils, such as hedgerows, field sides, etc. . Gloves were worn while taking these samples and the auger was cleaned thoroughly with a wire brush between each sample. Roots were also collected from one plant per plot and were dug out using a trowel from the top 15 cm of soil. These samples were stored on-site in an ice-filled cooler and transferred to a -80 degree C freezer immediately upon returning to the lab . Roots were later washed in PBS Buffer/Tween20 and ground using liquid N.The ITS2 rRNA region was selected for amplification and fungal community analysis. This region has been successfully utilized in recent AMF community studies. Though AMF-specific primers exist , we chose the more general ITS2 fungal primers for several key reasons. First, in the field, SSU primers detect more taxa in nonGlomeraceae families but give lower resolution in the Glomeraceae family. Because the four species in our inoculant are in the Glomeraceae family and this family is dominant in agricultural systems and clay soils, we prioritized species resolution in Glomeraceae over other families. More broadly, the higher variability in the ITS2 region can lead to more unassigned taxa, but does not run as much of a risk that distinct taxa will be lumped together. Third, and of particular importance in our root samples, these primers are better able to select for fungal over plant material than other ITS primer options. Finally, ITS2 allowed us to also examine the broader fungal community in our samples, whereas SSU and LSU options are AMF specific and cannot be used to characterize other fungi.We modeled all yield and fruit quality data with Bayesian generalized mixed effect models. Due to zero-inflated data, we used hurdle models for yields and blossom end rot , while percent dry weight was always non-zero and therefore did not require a hurdle. To pick a model family, we modeled the non-zero data from each outcome variable with gaussian, lognormal, and gamma families, using Bayesian leave-one out estimates of the expected log pointwise predictive densities to compare model fits. Gamma models showed the best fit for each outcome variable and were therefore used for all linear models.In addition to the variables of interest, each model had a random effect of field and block within field. Yields were modeled using the total marketable fruit weight harvested from each plot at each harvest point, while BER was modeled using the proportion of fruits that were classified as non-marketable due to BER from each plot at each harvest point.