Hopes of dry land: Managing soils to improve fruit yield and quality in dry farm tomatoes

All field and lab work will be carried out in accordance with UC Berkeley COVID protocols.

Objective 1: Coordinate field trials through on-farm participatory research to engage farmers in research design and outcomes

In Fall 2020-Spring 2021, I interviewed eleven Central Coast farmers to better understand dry farming and identify research priorities for farms in the area. All farmers either identified fungal inoculants as an area of keen interest or were excited when I suggested it as a research option, yet none had previously experimented with applying fungal inoculants on their operations. Farmers were additionally curious about broader questions of what soil health means in the context of dry farming, where field outcomes often shirk conventional wisdom about best practices. Of the eleven, eight farmers (representing six farms) had operations large enough to accommodate field trials, and all were enthusiastic to join the project.

During field visits to each farm, I collaborated with growers to develop a research plan that fits the system and addresses conditions and questions relevant to each participating farm. We decided that each farm could feasibly host ten 12-plant plots, which I then manipulated and sampled from (growers had no change to their management of these plots compared to the rest of the field). After my initial intervention to inoculate half of the plots, I sampled soils (see Objective 2) and collected yield data to give farmers desired information on the potential benefits of fungal inoculation and other soil management techniques (see Objective 3). 

Funding from Western SARE has allowed me to collect the full set of data that farmers have identified as being of particular interest: soil nutrients, inoculant efficacy, field fungal communities, yields, and fruit quality (see Objective 2). I continued communicating closely with farmers over the course of the growing season and after sampling was complete to ensure that their observations about the plots are included in data interpretation and further refinement of research questions. I also shared preliminary data and results with farmers, which was well-received; all farmers indicated that they would use the information provided in subsequent management decisions.

Project Team Members: Myself (YS); Advisor Tim Bowles (TB); Ag Advisor Jim Leap (JL); Producers Joel Schirmer, Thomas Broz, James Cook, Larry Palla, Barbara Palla, Verónica Mazariegos-Anastassiou, and Darryl Wong. 

Project Team Roles: 

• Identify research questions of interest: YS, JL, Producers
• Preliminary experimental design: YS, TB
• Iterative experimental refining: YS, TB, Producers

Objective 2: Characterize fungal communities and quantify the utility of fungal inoculants 

Soil management shapes fungal communities, which in turn impact agricultural yields and crop quality. Practices such as cover cropping, crop rotation, and fertilization contribute to mycorrhizal fungal diversity, and the resulting communities vary in hyphal growth rate, turnover rate, spore production, and more. Interactions between these traits, host plants, and the abiotic environment influence how “helpful” a mycorrhizal community will be, with more diverse communities showing the best yield outcomes.  In addition, a history of dry soils (i.e., lack of irrigation) in a field may prime native beneficial microbial communities. Farmers are therefore interested in understanding which management practices (inoculation, rotational history, fertilization, etc.) will produce diverse fungal communities that are best suited to the crops growing in their fields. Our research explores these questions on seven dry farm fields on California’s Central Coast. These fields grow Early Girl, New Girl, and Dirty Girl tomatoes and belong to six producer collaborators (one farm is contributing two fields). 

I used a nested experimental design to account for management and biophysical differences across fields, establishing 10 plots at each field site (70 plots total). Experimental plots (12 plants each) were distributed across two adjacent rows with or without a buffer row between them, depending on the size of the field (Figure 1). Each experimental row began and ended with at least ten buffer plants, and there were three buffer plants between experimental plots. Each experimental plot ranged from 13-44 ft in length depending on the spacing between each plant, but always contained 12 plants at transplant. Buffers between plots were similarly variable in length (3-8 ft), and always contained 3 plants at transplant. Inoculation was distributed in a randomized complete block design to account for heterogeneity in biophysical environments across the field. To accomplish this, each of the plots in rows 2 and 4 was paired with its closest counterpart in the opposite row (forming a block), and inoculant was randomly applied to one of the plots in each pair within three days of when farmers transplanted their first dry farm tomatoes (April-May). 

I inoculated with Valent’s MycoApply Ultrafine Endo, a mix of four species of arbuscular mycorrhizal fungi: Glomus intraradices, Glomus mosseae, Glomus aggregatum, and Glomus etunicatum. These species are known for having an aggressive growth strategy, which can benefit crops by establishing quickly but may also disrupt already high-functioning native communities. This inoculum is one of several commercially available mycorrhizal inoculants and was shown to decrease water stress in preliminary field trials with processing tomatoes in Summer 2020. 

I inoculated experimental plants by mixing the inoculum with water to create a slurry that can be poured at the base of each plant. Valent confirmed that this is an effective inoculation technique, though several others can be used for ease of application (e.g., drenching root balls in a water-inoculum mixture before transplanting).

Mid-way through the season, I sampled one plant per plot to collect roots, which were split into two sample sets: one for fungal colonization quantification, and one for DNA analysis to determine which fungal species are colonizing the roots. Roots for colonization were stained and dyed, and fungal presence is being quantified via microscopy. Roots for DNA analysis were ground and extracted with a kit, and DNA was quantified. These samples were sent to the University of Minnesota's Genomic Center for Illumina MiSeq sequencing using ITS2 primers. Fungal community data were used to determine whether the inoculated species are in fact colonizing plant roots, and for further explorations of soil health (see Objective 3).

I used Bayesian generalized mixed effect models to explore the relationship between yields/fruit quality and inoculation, fungal species diversity (Shannon index), and soil nutrients/water at varying depths, with random effects of field, block, and plot to account for management differences and spatial heterogeneity. See Objective 3 for further fungal analysis.

Project Team Members: Myself (YS), Producers, two Undergraduate Assistants (UA)

Project Team Roles: 

• Plot setup: YS, Producers
• Sample collection: YS
• Root analyses (colonization and DNA): YS, UA
• Communication of results: YS, Producers

Objective 3: Identify soil management strategies that enhance fruit yield and quality and evaluate their profitability

Though irrigated tomato plants usually cluster their roots at the surface of the soil (close to the water source), dry farm tomatoes send their roots into deeper soils to scavenge for water and nutrients (40). It was therefore important to collect data about nutrients across the soil profile to determine which nutrient depths are most influential for dry farm tomatoes. Because high levels of phosphorus (P) and nitrogen (N) can also inhibit plant-fungus associations, it is be important to consider both the minimum and maximum nutrient thresholds for optimal dry farming performance.

Soil samples were taken at three points over the course of the field season: once at transplant (within two weeks of plant date), once mid-season (9 weeks after transplant), and once during harvest (18 weeks after transplant).  I sampled at four depths (0-15cm, 15-30cm, 30-60cm, 60-100cm) at all soil sampling events. In the lab, samples were stabilized (air dried or KCl extractions) and stored for further analysis.

WSARE funds allowed me to test soil samples for available N (nitrate and ammonium) and available P (Olsen P) via colorimetry. I also measured gravimetric water content from each soil sample to track water availability in each plot throughout the top meter of the soil profile. 

When fruits began to mature, I coordinated with farmers to harvest and weigh the tomatoes from each plot once per week. Funds from WSARE allowed me to hire two undergraduate students to assist with the harvest, making it possible to conduct weekly harvests for the ~10 weeks of production on each field. Tomatoes from each plot were sorted into marketable, blossom-end rot, and sunburnt fruit, and then weighed. I also collected marketable fruit samples from each plot at harvests 3, 6, and 9 to test for tomato quality via fruit water content.

I used Bayesian generalized mixed effect models to explore relationships between each response variable (total marketable fruit, fruit lost to blossom-end rot, fruit water content) and inoculation, soil nutrients (N, P, and N:P ratio at each sampling depth), soil water content, field legacy (years since last irrigation), and fungal species diversity (from Objective 2). Random effects of field, block and plot accounted for unmeasured attributes such as climate, management differences, and spatial heterogeneity in field soils. I treated marketable and blossom-end rot fruit as repeated measures due to the 10+ consecutive weeks of sampling. 

To better understand the economic structures and profitability behind dry farming, I also conducted semi-structured interviews with the same farmers who participated in the field study.

Project Team Members: Myself (YS), Advisor Tim Bowles (TB), Producers, two Undergraduate Assistants (UA)

Project Team Roles: 

• Sample collection: YS, UA (at harvest)
• Fruit harvest: YS, UA
• Soil lab analyses (N, P, gravimetric water content, DNA extraction): YS, UA
• Data analysis: YS, TB
• Communication of results: YS, Producers