The effects of azoxystrobin on rhizosphere microbiology and microbiome-mediated susceptibility to Rhizoctonia solani AG 2.2 in table beet

Progress report for GNE21-249

Project Type: Graduate Student
Funds awarded in 2021: $14,741.00
Projected End Date: 07/31/2023
Grant Recipient: Cornell University
Region: Northeast
State: New York
Graduate Student:
Faculty Advisor:
Dr. Sarah Pethybridge
Cornell University
Expand All

Project Information

Project Objectives:

Objective 1:

Investigate how azoxystrobin impacts the structure and diversity of the rhizosphere microbiome in table beets

Hypothesis 1A:          

The table beet rhizosphere microbiome will be less diverse in soils with azoxystrobin applications compared to soils without azoxystrobin.

Hypothesis 1B:

In the table beet microbiome, hub taxa, or dominating species, will clearly differentiate between soils with and without azoxystrobin



Objective 2:

Determine how azoxystrobin affects the ability of R. solani to cause disease in table beets

Hypothesis 2:

Differences in the beet rhizosphere microbiome caused by previous azoxystrobin applications will correlate to different levels of Rhizoctonia root rot following inoculation.


The purpose of this project is to assess the impact of the standard conventional fungicide azoxystrobin on the rhizosphere microbiome of table beets (Beta vulgaris ssp. vulgaris) and the microbiome-mediated susceptibility of table beets to root disease caused by Rhizoctonia solani. Plant disease management programs must evaluate returns from investment in chemical controls in contexts of profitability, productivity, and environmental sustainability. A comprehensive understanding of the relationships between plant pathogens, rhizosphere microbiome communities, disease epidemics, and pesticides is essential to meet these goals. By maximizing the benefits from diverse and healthy soil microbiota and assessing fungicide risk in context of previously uncharacterized effects on the rhizosphere microbiome, this project will support steps to reduce environmental and ecological risks related to agriculture.

New York state is the second-leading producer of table beets in the United States, with over 3,000 acres in cultivation annually (USDA-NASS 2017). Table beets are grown on both large operations encompassing hundreds of acres and small farms growing for direct-to-consumer markets. As a result of high demand, table beet farmers and processors are risk-averse. Damping-off caused by R. solani can result in poor seedling emergence. These reduced crop stands contribute to undesirable variability in crop size and rejected produce due to disease, causing processors to reduce payments to farmers. Epidemics later in the season can even result in fields being passed over. The decision to use fungicides to suppress plant pathogens and prevent crop loss is frequently based on risk avoidance, irrespective of biotic, edaphic and abiotic factors. However, fungicides adversely affect off-target microbiota, environmental health, and health and livelihoods of farmers and their families.

Azoxystrobin, like other strobilurins, is a quinone outside inhibitor (QoI), since it interferes with fungal respiration at the outer quinone binding site of the cytochrome bc1 complex (Bartlett et al. 2002). This specific mode of action poses a reduced safety risk to humans and animals but promotes resistance development to this product and cross-resistance among other strobilurins. Therefore, the development of azoxystrobin resistance in pathogen populations is considered high-risk, especially since table beet farmers apply azoxystrobin in-furrow at planting every year on an annual basis. While no resistance has been reported in NY, other states and cropping systems have reported azoxystrobin resistance within the R. solani population (Blazier and Conway 2004; Olaya et al. 2012; Arabiat and Khan 2016). By supporting a shift to sustainable disease control that utilizes existing soil microbes, this project will have broad impacts on the quality of life for farmers, their employees, and the farming community.


Click linked name(s) to expand/collapse or show everyone's info
  • Dr. Julie Kikkert (Educator)


Materials and methods:

Experiment set-up (PRE SARE Funding Period)

This small plot field experiment will be located on a research farm at Cornell AgriTech (Geneva, NY). The experimental design will be a randomized complete block (RCB) with four replications of each of the four treatments. The treatments will be two levels of two factors: (i) in-furrow azoxystrobin, (ii) in-furrow azoxystrobin plus R. solani inoculum, (iii) R. solani inoculum alone, and (iv) a control treatment with neither azoxystrobin nor R. solani. Plots will consist of two adjacent 3.05-m sections of row, with two rows between adjacent plots and 1.5 m of buffer plants between plots in the same row. Following industry guidelines, 10-5-10 fertilizer will be applied to all plots and incorporated prior to planting at 336 kg/hectare, with more 10-5-10 fertilizer applied in bands at planting at a rate of 393 kg/hectare. Herbicides and in-season fertilizer will not be applied.

Baseline soil characteristics will be assessed in the spring, prior to applying the treatments. These tests will provide preliminary data on soil nutrient content, pH, organic matter, total active carbon, aggregate stability, and soil respiration. These tests are part of the Standard Soil Health Analysis Test from the Cornell Soil Health Laboratory (Ithaca, New York). Additional analysis of the baseline samples will provide preliminary data on soil microbiome composition and community structure before the treatments are applied. Each of the four baseline samples will be a composite sample consisting of eight subsamples per block, taken at regular intervals along transects parallel to rows. Each subsample will be a soil core 1 cm in diameter and 10 cm deep. Subsamples will be stored on ice in the field and during transport. After combination and homogenization of the subsamples, DNA extractions will be performed from 0.5 g of the baseline composite sample, repeated three times. All DNA extractions from soil will use the DNeasy Powerlyzer PowerSoil kit (Qiagen, Germantown, MD), following the manufacturer’s instructions. This DNA will be stored at -80°C until further processing.

Seeds will be planted at a rate of 55 seeds/m, with 76.2 cm between rows. In-furrow azoxystrobin applications will be made using a CO2-pressurized backpack sprayer, with the spray directed in a 20-cm wide band directly over the open furrow. The application rate will be 24 ml/305 m. After treatment applications and/or seeding, soil (1 to 1.5 cm) will be raked over the furrow for coverage. Direct measurement of residual azoxystrobin will be done by collecting eight subsamples (1 × 10 cm soil cores) per block immediately after furrow closing and again immediately prior to inoculation four weeks later. Azoxystrobin levels in these samples will be measured by liquid chromatography/mass spectrometry to assess degradation of the fungicide.

A R. solani isolate associated with diseased table beet in a New York field that has been confirmed as a pathogen of table beet in greenhouse trials will be used for inoculum. R. solani is a widespread, non-quarantined pathogen; consequently; biosafety protocols are not required for containment. Inoculum will be grown on sterilized barley grain for 8 weeks, dried, and coarsely ground. The grain will be applied to the plots at a rate of 70 kg/hectare four weeks after planting to minimize residual azoxystrobin effects (Adetutu et al. 2008). The four-week waiting period reflects the time to ensure that the majority of azoxystrobin has broken down in the soil, limiting confounding effects from the active fungicide. By temporally separating the azoxystrobin applications and the R. solani inoculations, this experiment will allow the microbial community to respond and adapt to each disturbance separately.

Overhead irrigation will be used on all plots to promote plant growth and disease development. Weed management will be done by hand in and around the plots, and by tractor cultivation between rows.

Planting and experiment set-up were completed on 20 May 2021 as described above. Fertilizer (393 kg/ha 10-5-10) was banded at planting following incorporation of 336 kg/ha 10-5-10 and 2.2 kg/ha Boron, a day earlier on 19 May 2021. Boron was included to meet the specific nutrient requirements of table beets. Baseline soil samples were collected immediately after planting, then the in-furrow applications of azoxystrobin were made.  

Inoculations with Rhizoctonia solani on dried barley were made on 17 June 2021. A furrow about 2.5 cm deep was made next to each row in the plot, and the dried barley inoculum was applied at a rate of 70 kg/ha, or 32 g/plot.  

Plots were weeded during the season by hand, and a propane weed burner was used between rows. Care was taken to not damage beet leaves. Overhead irrigation was used as needed on 24 May and 29 June 2021.

The entire small plot field experiment was repeated during the summer of 2022, including treatment applications and sampling as described above. Beets were planted and the plots were established on 18 May 2022. Post-emergent inoculations of Rhizoctonia solani on dried barley were made on 24 June at the same rate of 70 kg/ha or 32 g/plot. Hand weeding was done within the plots throughout the season. Overhead irrigation (1.27 cm) was applied to the field on 23 May. 

Objective 1: Assessing Microbiome Structure

Sampling the beet rhizosphere microbiome will occur at 2 and 8 weeks after seedling emergence, about 1 week before and 5 weeks after inoculation. At each date, three subsamples will be collected per plot and combined into one composite sample per plot. For each subsample, 10 seedlings or plants will be removed from the soil. Soil that remains attached to the roots will be classified as the rhizosphere and will contain the rhizosphere microbiome. Sterile gloves will be used to remove the rhizosphere, and 25 g of rhizosphere soil will be homogenized. DNA extraction will be performed three times, again starting with 0.5 g of soil and the DNeasy Powerlyzer PowerSoil kit (Qiagen, Germantown, MD). In total, each of the 16 plots will yield three subsamples, one composite sample, and three repetitions of DNA extraction. The resulting DNA solutions will be stored at -80°C.

The 2021 sampling dates were 17 June and 29 July. The soil samples were stored at -20°C. DNA extraction was completed within two weeks and the extracted DNA was stored in PCR-grade water at -80°C. PCR and sequencing is planned for February 2022. 

Sampling in 2022 was completed as described on ___ and ____. DNA extractions are stored at -80°C. As of January 2023, PCR is underway with sample submission and sequencing planned for late January and February following necessary quality assessments.

(DURING SARE Funding Period)

Once DNA from all samples has collected, all three DNA extractions per sample will be pooled and polymerase chain reaction (PCR) amplification of microbial DNA will be performed in triplicate for each sample, including baseline, 2-week, and 8-week microbiome samples. Bacterial and fungal amplification will be done separately. For bacteria, 16S rRNA gene amplification will use the 515F forward primer and the 806R reverse dual-indexing primer set, adapted for Illumina (Illumina, Inc., San Diego, CA) sequencing (Kozich et al. 2013). Fungal amplification will use the ITS1f (forward) and ITS4r (reverse) primers with the same dual-indexing scheme. After pooling replicates and normalizing PCR product concentrations, replicates will be pooled prior to sequencing at the Cornell Genomics Facility (Ithaca, NY). 16S and ITS1 libraries will be pooled and sequenced separately. This experiment will result in 36 samples for each sequencing run.

The software QIIME 2 will be used to filter out short and low-quality reads, leaving reads from 430 to 450 bp long (Bolyen et al. 2019). Chimeric and plant-based sequences will be removed, and the remaining sequences will be sorted into taxonomic groups using the RDP naïve Bayesian classifier (Wang et al. 2007). Reference databases will be the SILVA and UNITE databases (Quast et al. 2013; Nilsson et al. 2019). Subsequent analysis will be done in R (R Core Team 2021).

A subset of 16 sample was successfully sequenced and and allowed for verification of the PCR primers and methods used. Initial sequence filtering was  done using the DADA2 pipeline rather than QIIME 2 in order to better use available computing resources. Subsequent steps were not changed. Working with the sequences from a small sample set instead of the entire project provided an opportunity to fine-tune the analysis pipeline and plan future work.

The results from metagenomic sequencing will address Hypothesis 1A and Hypothesis 1B. Illumina sequencing will provide 1-3 million reads in the 430-450 bp range after initial quality filtering. We expect that most bacterial reads will map to members of Proteobacteria (Carrión et al. 2019). Both α-diversity and β-diversity will be assessed, using Chao’s and Shannon’s Index to measure diversity within the treatments (α-diversity) and the Bray-Curtis dissimilarity index to measure diversity differences between treatments (β-diversity). Differences in microbiome composition that may be attributable to the azoxystrobin include reduced fungal abundance from the broad-spectrum activity of azoxystrobin. We also expect increased relative abundances of bacteria that can degrade azoxystrobin, such as members of Streptomyces and Amycolatopsis (Wang et al. 2020). For Hypothesis 1B, hub taxa or hub species are identified analytically based on recovered sequences by creating a correlation network between microbial species (Agler et al. 2016; Gdanetz et al. 2021). Additionally, repeated principal component analysis (PCA) with abundant taxa sequentially removed from the dataset will also reveal which species are hub taxa (Carrión et al. 2019).


Objective 2: Azoxystrobin-affected microbiome effects on disease development

Azoxystrobin consistently reduces crop loss from R. solani when applied in-furrow. However, the half-life of azoxystrobin can be as short as 14 days, after which the protective effects of the fungicide are greatly reduced (Adetutu et al. 2008). Depending on the length of time from planting to emergence, beet seedlings may still be vulnerable to damping off or infection at this time. Rot of mature roots, manifested as dark lesions, cracking, and crown rot, is another common outcome when older plants are infected. Therefore, significant disease development is expected to occur in this trial, even on plots previously treated with azoxystrobin. At the time of inoculation, eight soil subsamples will be collected per block, each consisting of one 10 cm soil core. The composited soil samples will be analyzed for residual azoxystrobin and compared to the samples collected immediately after application to estimate azoxystrobin degradation.

Beet root samples from R. solani-inoculated plots will be rated for disease at two-week intervals, beginning two weeks after inoculation and continuing until harvest. Plant populations will be recorded on each sampling date by counting living plants within each of two 1-m long transects. Additionally, disease incidence and severity data will be collected from table beet roots at harvest. Mature roots from 1-m transects will be bisected twice and rated for disease on a 1-9 scale, where 1 represents no disease, 2-5 represents less than 50% of root tissue affected, and 6-9 indicates over 50% of root tissue has been decayed (Ohkura et al. 2009). An additional disease rating method, adapted from sugar beet trials, provides a separate percentage score for exterior and interior root rot symptoms (Wigg and Goldman 2020). These scores are then combined into an overall rating for each root. The resulting weighted average reflects how surface lesions are less damaging to overall beet quality than interior rot. This method may offset the variability associated with small sample sizes of mature beets. The equation is described below:

(Exterior Rating × 0.25) + (Interior Rating × 0.75) = Weighted Average Disease Rating

This scale transforms two separate, discrete measures of disease severity into one continuous disease rating suitable for parametric data analysis. If this method is used, a visual scale will be used to manage intra- and inter-rater reliability and produce accurate and consistent rating of root rot severity.

When combined with microbiome composition data and gene expression analysis, these disease ratings will allow for exploration of associations between disease intensity and microbiome characteristics. Multivariate analysis, such as principal component analysis, will be performed in an R statistical environment (R Core Team 2019).

Pairwise comparison of disease development between azoxystrobin-treated plots and plots without azoxystrobin will directly address Hypothesis 2. The isolate of R. solani used in this experiment has been demonstrated to cause disease in both the field and greenhouse conditions. Therefore, we expect greater seedling losses and mean disease severity in the inoculated plots. Azoxystrobin has been shown to alter the soil microbiome, and similar changes in the rhizosphere will likely affect the ability of R. solani to cause disease (Wang et al. 2020; Zhang et al. 2019; Baćmaga et al. 2017). The results from Objective 2 are crucial to the broader impacts of this study, representing the link between microbiome structure and disease development.  

By identifying hub taxa in the beet rhizosphere microbiome that impact microbiome function and the relationship between these communities and disease caused by R. solani, this experiment will also provide preliminary data for mechanistic research on microbiome resilience.

Plant populations were recorded approximately weekly from 6 June until 4 August, 2021. All living plants within the entire plot were counted, the average of each 3.1 m row was recorded. Harvest occurred on 12 August 2021. All roots were counted and assessed for the presence of external disease symptoms. Harvest metrics were collected immediately from 2 x 1.0 m transects, including root weight, fresh leaf weight. Shoulder diameter, dry foliage weight, and root disease ratings were collected from these samples (harvested transects) over the next few days. 

All roots from both transects were visually scored using the above method first for external symptoms, then bisected and scored for internal symptoms. Using these numbers, a weighted average root disease score per root was calculated as described above. 

Plant population counts, harvest assessments, and disease ratings were completed during the 2022 field experiment. Plant populations were recorded approximately weekly from 6 June until 1 August, 2022. Plots were harvested on 12 August 2022. As in 2021, all root were counted and assessed to calculate disease incidence, but only 2 x 1.0 m transects were collected to assess root weight, fresh and dry foliage weight, shoulder diameter, and root disease ratings.

Pitfalls and limitations

While the methods described above will provide a robust look at the root-associated microbiome of table beets, certain factors may add complications and influence the results. One of the potential pitfalls associated with this experiment is the large spatiotemporal variability associated with soil organisms. Proper subsampling and compositing will address this: each of 16 plots will yield three subsamples, and DNA extractions will be performed in triplicate on the composite samples. Adequate repetitions will reduce variability from the field and laboratory techniques. Published values for the half-life of azoxystrobin in soil are varied, and can be affected by soil moisture, temperature, and microbial activity (Adetutu et al. 2008; Bacmaga et al. 2017; Singh and Singh 2010). This project includes soil sampling both immediately after application and four weeks later to assess azoxystrobin degradation experimentally. Since this is a field-based trial, seasonal variability may be significant. Therefore, the entire experiment will be repeated for a second year, allowing for averaging of data over two years and method refinement if necessary. This will be especially helpful in case weather events reduced crop stand or deleterious affect disease development. Purchases of all Year 2 supplies and sequencing will occur during the funding period.

Research results and discussion:

Microbiome analysis remains ongoing, but data and preliminary interpretations from the 2021 and 2022 field seasons are available. The results from this data contribute towards Objective 2, which involves assessing the level of disease caused by R. solani inoculum. Data analysis comparing treatments from the 2021 and 2022 field seasons are included in Table 1 and Table 2, respectively. 

In 2021, there was no significant difference (P > 0.05) in root disease severity between treatment. However, the plot that received the post-emergent inoculum had significantly higher (P = 0.048) average disease severity. Results from the 2022 field season did not show significant differences between treatments in either disease incidence or disease severity. Nontreated plots in 2022 had seemed to have higher disease levels and reduced stand counts compared to the 2021 season. This season-to-season variation will make the analysis of relating Rhizoctonia disease to microbiome characteristics more challenging. 

Table 1. Treatment effects on crop stand on selected 2021 dates, and root disease incidence and severity at harvest, as well as harvest quality metrics collected from two one-meter transects. Disease severity data was also collected from all beets within the two one-meter transects.

Treatment Crop Stand Root Disease Incidence (%) Total Root Weight (kg) (2 x 1.0 m transect) Average Shoulder Diameter (cm) (2 x 1.0 m transect Average Disease Severity per Root (%) (2 x 1.0 m transect)
14 June 13 July 4 August


107.1 81.1 24.5 a 3.6 3.26 1.28
Inoculum 158.5 123.1 87.5 40.7 b 3.1 3.15 2.37
Azoxystrobin 173.0 113.6 111.1 18.6 a 4.1 3.33 0.61
Azoxystrobin + Inoculum 165.1 102.9 112.5 24.2 a 4.1 3.63 1.01
LSD1 - - - 15.4 - - -
P 0.507 0.727 0.193 0.048 0.226 0.427 0.1549

1 LSD = Least Significant Difference


Table 2. Treatment effects on crop stand on selected 2022 dates, and root disease incidence at harvest, as well as harvest quality metrics collected from two one-meter transects. Disease severity data was also collected from all beets within the two one-meter transects.

Treatment Crop Stand Root Disease Incidence (%) Total Root Weight (kg) (2 x 1.o m transect) Average Shoulder Diameter (cm) (2 x 1.0 m transect) Average Disease Severity per Root (%) (2 x 1.0 m transect)
13 June 5 July 1 August
Nontreated 152.8 133.6 86.9 32.1 3.48 3.82 1.03
Inoculum 109.0 154.1 108.0 33.7 4.59 3.79 1.77
Azoxystrobin 173.4 134.3 100.5 16.5 3.41 4.37 0.54
Inoculum + Azoxystrobin 176.9 150.9 119.5 25.4 4.41 3.92 0.52
P 0.311 0.535 0.142 0.153 0.073 0.9862 0.1514
Research conclusions:


Participation Summary

Education & Outreach Activities and Participation Summary

2 Webinars / talks / presentations

Participation Summary:

25 Farmers participated
20 Number of agricultural educator or service providers reached through education and outreach activities
Education/outreach description:

I plan to share project information and results with farmers, industry leaders, and academic peers both during and after this project. Both formal and informal strategies will be used to reach a broad and diverse audience. My collaborator, Dr. Julie Kikkert, will be essential for maximizing benefits from these outreach opportunities. As the Team Leader for the Cornell Vegetable Program within Cornell Cooperative Extension (CCE), she specializes in direct outreach to farmers and can help create and distribute extension publications. I have plans to present at specific events with farmers and beet processors each year, and present at academic meetings at the end of the project.

Specific outreach events:

Table Beet/Processing Vegetable Field Days at Cornell AgriTech: Field days engage farmers directly by letting them come to the research fields and see treatments and outcomes in person. Additionally, I can visit individual farms and processors to present my research, complementing ongoing CCE activities.

New York Vegetable Research Association and Council Advisory meetings (December 2021): These give me a chance to hear concerns from table beet farmers and processors at the end of each season. Here, research priorities for the next year will be set. While not funded by this group, my project supports their mission of improving production and economic returns. These meetings are organized by my collaborator, Julie Kikkert, and have a commodity-specific focus. All processing table beet growers and processors are invited and usually attend. 

On December 13th, 2022, I participated in the 2021 Processing Vegetable Meeting organized by Cornell Cooperative Extension. 45 people were in attendance for the beet and carrot session, a number that included farmers, processors, crop advisors, and Extension personnel. I presented results from two early-season disease control trials and an update/preliminary observations from this SARE-funded trial.  

American Phytopathological Society (APS) Meeting: From August 6th through August 10th, 2022, I attended annual APS meeting. I shared my work with a large and diverse audience of plant pathologists and researchers from both academia and industry. I presented a poster titled "The impact of azoxystrobin on plant health, root quality, and the rhizosphere microbiome in table beets". Educational sessions and both formal and informal networking allowed me to discuss issues in microbiome research and gain perspective to aid my work.  

As of fall 2022, changes to the New York Vegetable Research Association and Council meeting formats have allowed me to focus on sharing more detailed results in written form rather than an annual presentation. This will include the factsheet and newsletter article for use by growers and Cornell Cooperative Extension personnel.

Specific Publications (Winter and Spring 2023):

Cornell Cooperative Extension Factsheet and Newsletter: I will address table beet root rot in the context of microbiome health and sustainable fungicide use with a factsheet and an associated article for the VegEdge and ENY Produce Pages newsletter.

Peer-reviewed journal articles: I plan to write a manuscripts and publish an research article, likely in the academic journal Plant Disease. This will be submitted during the end of the second year of the project. 

Annual and Final Reports: I will submit annual progress reports and the comprehensive final report to Northeast SARE using the online Grant Management System.

Project Outcomes

Project outcomes:


Knowledge Gained:


Assessment of Project Approach and Areas of Further Study:

2021 update:

The field-based portion of this trial went smoothly. Precipitation and temperatures did not vary from typical summer conditions for central NY. Soil moisture levels were appropriate for disease development. Since this project seeks to evaluate qualities of the rhizosphere microbiome in a field setting, herbicides cannot be used for weed control as these chemicals would introduce an additional, undesirable variable to the study. Hand-weeding, while successful, was labor intensive throughout the season.  

Direct quantification of azoxystrobin has in the soil has been challenging to accomplish. Soil samples are currently frozen, and residual azoxystrobin will be assessed the once the availability of equipment is ascertained and analytical-grade azoxystrobin standards are obtained. 

 Lab work remains approximately on schedule, and will be completed during the winter and early spring of 2022. 

2022 update:

A subset of samples was submitted in order to check viability of the primer design, PCR, and sequencing methods for this experiment. The returned sequences were acceptable, and provided a chance to run this small dataset through the sequence filtering and analysis pipeline. Of the 16-sample subset, all but one had at least 70% of reads pass all of the filtering and denoising steps.

The field trial during 2022 experienced an overall higher level of seedling disease, including from Rhizoctonia, during May and June. This caused observable disease symptoms in all plots, not just inoculated plots at harvest. DNA extractions were completed in the fall, and lab work remains in progress.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.