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

Final 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
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Project Information


The purpose of this project was to investigate how the fungicide azoxystrobin and the soilborne plant pathogen Rhizoctonia solani affect the rhizosphere microbiome of table beet (Beta vulgaris spp. vulgaris). Across the United States, current management of soilborne plant pathogens includes a reliance on fungicides to protect seedlings during germination and later stages of crop growth. The strobilurin fungicide, azoxystrobin, is applied in-furrow every year to table beet fields in New York to control disease caused by R. solani. The effect of this practice on the soil and rhizosphere microbiome is largely unknown. Both fungicides and pathogen growth in the soil present a disturbance to microbial communities and may affect crop health and resilience by altering important microbial interactions within the rhizosphere and soil environment. Protecting microbial hub taxa can help to maintain healthy microbiome function when challenged by biotic and abiotic stressors. By combining data from microbial communities and Rhizoctonia disease evaluations from table beet field trials, this project provided insights into how these factors are related to plant health in a field trial setting, and supplies preliminary information for future investigations of microbiome-mediated management of R. solani and other soilborne plant pathogens.  

Field trials were conducted in each of two years (2021 and 2022) to evaluate the effect of both in-furrow azoxystrobin application and post-emergent R. solani inoculation on microbial communities in bulk soil and the table beet rhizosphere. Soil samples were collected during the 2 to 4 leaf stage and again at root maturity from plots receiving one of four treatments: in-furrow azoxystrobin, post-emergent R. solani inoculum, in-furrow azoxystrobin plus post-emergent R. solani inoculum, or nontreated. Rhizoctonia disease incidence and severity was collected during the growing season and at harvest. The 16S rRNA gene (for the bacterial community) and the internal transcribed spacer region (for the fungal community) from rhizosphere and bulk soil samples were sequenced. The DADA2 pipeline was used to assess microbial diversity and community composition.

The azoxystrobin and R. solani treatments did not significantly affect alpha or beta diversity of the microbial communities. Instead, sample type (rhizosphere and bulk soil) was the main driver of community composition. The most abundant bacterial and fungal phyla were Proteobacteria and Ascomycota, respectively. While relative abundance in the bacterial community was unaffected by either treatment, sample type, or sampling time, relative abundance of the fungal class Saccharamycetes was increased in the table beet rhizosphere. Alpha diversity was negatively correlated with disease incidence and severity in 2021, but not in 2022. Overall, there were few consistent relationships across years between disease incidence and severity and abundance of microbial taxa. The abundance of Acidobacteria and Bacteroidota was negatively correlated with R. solani abundance, and both phyla were enriched in the table beet rhizosphere. The identification of these and other microbial taxa associated with plots that had low disease incidence and severity may lead to further research into the potential for sustainable management of R. solani by supporting healthy and diverse microbial communities.

Table beet farmers in New York have indicated the need for both early-season disease control and management programs that can reduce the impact of Rhizoctonia damping off and root rot. Growers are interested in reducing fungicide use while maintaining control against R. solani. Although azoxystrobin application was not associated with differences in the microbial community in this trial, the results can be used to support future projects and additional funding applications that address the management of R. solani and other soilborne plant pathogens.

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


The purpose of this project was 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 supports 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) and 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. While no resistance has been reported in NY, other states and cropping systems have reported azoxystrobin resistance and reduced sensitivity to azoxystrobin within the R. solani population (Arabiat and Khan 2016; Blazier and Conway 2004; Olaya et al. 2012). 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.


Arabiat, S., and Khan, M. F. R. 2016. Sensitivity of Rhizoctonia solani from sugar beet to fungicides. Plant Dis. 100:2427-2433. DOI: 10.1094/PDIS-04-16-0525-RE.

Bartlett, D. W., Clough, J. M., Godwin, J. R., Hall, A. A., Hamer, M., and Parr-Dobrzanski, B. 2002. The strobilurin fungicides. Pest Manag. Sci. 58:649-662. DOI: 10.1002/ps.520.

Blazier, S. R., and Conway, K. E. 2004. Characterization of Rhizoctonia solani isolates associated with patch diseases on turfgrass. Proc. Okla. Acad. Sci. 84:41-51.

Olaya, G., Buitrago, C., Pearsaul, D., Sierotzki, H., and Tally, A. 2012. Detection of resistance to QoI fungicides in Rhizoctonia solani isolates from rice (Abstr.). Phytopathology 102:S4.88. DOI: 10.1094/PHYTO-102-7-S4.1.

U.S. Department of Agriculture National Agricultural Statistics Service (USDA-NASS). 2017. NASS - Quick Stats. Accessed 1 March 2021.



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  • Dr. Julie Kikkert (Educator)


Materials and methods:

Small plot replicated field experiments were located on a research farm at Cornell AgriTech (Geneva, NY) in 2021 and 2022. The experimental design was a randomized complete block (RCB) with four replications of each of the four treatments. The treatments were 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 consisted 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 N-5 P-10 K fertilizer was applied to all plots and incorporated one day prior to planting at 392 kg/ha. Boron (2.2 kg/ha) was also applied at this time to meet the specific nutritional needs for table beets. Additional 10 N-5 P-10 K fertilizer was applied in bands at planting at a rate of 336 kg/ha. Herbicides and in-season fertilizer were not applied. Weed control during the season was conducted by hand weeding and a propane weed burner in the medians of each row. 

Seeds were planted on 20 May 2021 and 18 May 2022 at a rate of 55 seeds/m, with 76.2 cm between rows. In-furrow azoxystrobin applications were 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 was 24 ml/3.05 m. After treatment applications and/or seeding, soil (approximately 1 to 2 cm) was raked over the furrow for coverage. 

An R. solani isolate, collected from a symptomatic table beet root in western New York and previously confirmed as a pathogen of table beet, was used to produce inoculum. R. solani is a widespread, non-quarantined pathogen; consequently; biosafety protocols were not required for containment. The isolate was grown on sterilized barley grain for approximately 8 weeks, dried, and coarsely ground. The grain was applied to the plots at a rate of 70 kg/ha four weeks after planting (36 and 37 days after planting (DAP) in 2021 and 2022, respectively) to minimize residual azoxystrobin effects (Adetutu et al. 2008). An approximately 2.5 cm-deep furrow was created adjacent to the table beet seedlings; inoculum was then spread evenly in the furrow before the furrow was closed by raking. The four-week waiting period reflected the time to ensure the majority of azoxystrobin had broken down in the soil. By temporally separating the azoxystrobin applications and the R. solani inoculations, this experiment allowed the microbial community to respond and adapt to each disturbance separately.

Overhead sprinkler irrigation was applied on 4 and 40 days after planting (DAP) in 2021 and 5 DAP in 2022 to encourage optimal plant growth and disease development.


Objective 1: Microbial community diversity and composition

The table beet rhizosphere microbiome was sampled on 28 and 70 DAP in 2021 and 34 and 75 DAP in 2022. At each date, three subsamples were collected per plot and combined into one composite sample per plot. For each subsample, three seedlings or plants were removed from the soil. Soil that remained attached to the roots after gentle shaking was operationally defined as the rhizosphere, and contains the rhizosphere microbiome. Sterile gloves were used to remove the rhizosphere, and 10-15 g of rhizosphere soil was mixed. Three subsamples of bulk soil, collected 10 cm away from table beet plants, but still within the plot, were combined into one composite sample. Rhizosphere and bulk soil samples were preserved in plastic bags stored first on ice for transport to the lab, then at -20°C prior to DNA extraction. 

DNA extraction was performed three times, using the DNeasy Powerlyzer PowerSoil kit (Qiagen, Germantown, MD) and 0.25 g soil as the starting material. In total, each of the 16 plots yielded three subsamples, one composite sample, and three repetitions of DNA extraction for rhizosphere and bulk soil. The resulting DNA solutions were then stored in PCR-grade water at -80°C until use.

All three DNA extractions per sample were pooled and polymerase chain reaction (PCR) amplification of microbial DNA was performed in triplicate for each sample. Bacterial and fungal amplification was conducted separately for the bacterial and fungal libraries. For bacteria, 16S rRNA gene amplification used 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 used the ITS1f (forward) and ITS4r (reverse) primers with the same dual-indexing scheme. After pooling replicates and normalizing PCR product concentrations, replicates were pooled prior to sequencing at the Cornell Genomics Facility (Ithaca, NY). 16S amplicon libraries were sequenced with the paired end 2 × 250 bp V2 kit (Illumina, Inc.), and the ITS libraries were sequenced with the 2 × 300 bp V3 kit (Illumina, Inc.). 

The software DADA2 v1.26.0 (Callahan et al. 2016) in R version 4.2.3 (R Core Team 2023) was used to filter out short and low-quality reads and determine amplicon sequence variants (ASVs). Chimeric and plant-based sequences were removed, and the remaining ASVs were assigned to taxonomic groups using the RDP naïve Bayesian classifier (Wang et al. 2007). Reference databases were the SILVA database (Quast et al. 2013) for the 16S library and the UNITE database for the ITS library (Nilsson et al. 2018). Subsequent analysis was completed in R, using the packages phyloseq (McMurdie and Holmes 2013), vegan (Oksanen et al. 2022), and DESeq2 (Love et al. 2014).

Weighted and unweighted alpha diversity metrics, which quantify diversity within treatments, were calculated for each treatment at both sampling dates, including ASV richness, Shannon diversity, H (Shannon 1948), Simpson diversity, D (Simpson 1949), and Pielou evenness, J (Pielou 1966). The homoscedasticity and normality of alpha diversity data were assessed with Bartlett tests (Bartlett 1937) and Shapiro-Wilk tests (Royston 1995), respectively. ANalysis Of VAriance (ANOVA) was used to compare the means of the diversity indices. Post hoc Fisher’s protected least significant difference tests were conducted when required using the lsd.test function in the package agricolae (de Mendiburu 2021). The effect of sample type and time on alpha diversity was determined by a Kolmogorov-Smirnov test (Massey 1951). Bray-Curtis dissimilarity indices were used to measure diversity differences between treatments, or beta diversity. Bray-Curtis differences were calculated with the distance function in the R package phyloseq (McMurdie and Holmes 2013), then PERmutational Multivariate Analysis Of VAriance (PERMANOVA; Anderson 2001) was used to compare the effect of treatment on microbial community composition.


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, table 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 affected. Disease symptoms were expected to occur in this trial, even on plots that received azoxystrobin due to incomplete disease control. 

Table beet root samples from R. solani-inoculated plots were rated for disease at approximately weekly intervals, beginning at emergence and continuing until harvest. Plant populations were recorded on each sampling date by counting all living plants within each row and calculating the average number of plants per row length. Additionally, disease incidence and severity data were collected from table beet roots at harvest (75 DAP and 86 DAP in 2021 and 2022, respectively. Once all plants were removed from the plot, disease incidence was calculated based on the presence or absence of lesions. Plants within an arbitrarily chosen 1-m transect from each row provided assessments of foliage, root size, root weight, and disease severity. Foliage was separated from roots at the crown and fresh weight of foliage and total root weight was recorded. A sub-sample (20 to 30%) of the fresh foliage per plot was dried at approximately 65°C for 72 h. Shoulder diameter of all roots from each of the 1-m transects was measured using electronic calipers (Insize USA LLC, Loganville, GA). 

Exterior and interior disease symptoms of all mature roots from each 1-m transect were assessed, and scores were combined into a single continuous rating for each root for parametric analysis (Wigg and Goldman 2020). Mature roots from 1-m transects were each assessed visually for exterior symptoms on a 0 to 5 scale: 0 = no diseased tissue; 1 = 1 to 10% diseased tissue; 2 = 11 to 30% of root affected; 3 = 31 to 60% of root affected; 4 = 61 to 99% of root affected; and 5 = root completely rotted and dead. A visual scale was used to manage intra- and inter-rater reliability and produce accurate and consistent rating of root rot severity. Next, each root was bisected twice and internal disease symptoms were rated using the same scale. Interior and exterior ratings were combined using the following formula: (Exterior Rating × 0.25) + (Interior Rating × 0.75) = Weighted Average Disease Rating (Wigg and Goldman 2020). This weighted average reflects how surface lesions are less damaging to overall beet quality than interior rot. This method may also offset the variability associated with small sample sizes of mature beets. The relationship of disease incidence and severity to microbial community characteristics was assessed by calculating Spearman's correlation coefficient and visualized using the R package phylosmith (Smith 2023). The abundance of Rhizoctonia reads was calculated by summing all reads assigned to the genus Rhizoctonia, and this total was also compared to microbial community characteristics.


Adetutu, E. M., Ball A. S., and Osborn A. M. 2008. Azoxystrobin and soil interactions: Degradation and impact on soil bacterial and fungal communities. J. Appl. Microbiol. 105:1777-1790. 10.1111/j.1365-2672.2008.03948.x.

Anderson, M. J. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26:32-46. DOI: 10.1111/j.1442-9993.2001.01070.pp.x.

Bartlett, M. S. 1937. Properties of sufficiency and statistical tests. Proc. Math. Phys. Eng. Sci. 160:268-282. DOI: 10.1098/rspa.1937.0109.

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J., and Holmes, S. P. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 137:581-583. DOI: 10.1038/nmeth.3869.

De Mendiburu, F. 2020. ‘agricolae’: Statistical procedures for agricultural research. R package version 1.3-3.

Kozich, J. J., Westcott, S. L., Nielson, T. B., Highlander, S. K., and Schloss, P. D. 2013. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79:5112-5120. DOI: 10.1128/AEM.01043-13.

Love, M. I., Huber, W., and Anders, S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:550. DOI: 10.1186/s13059-014-0550-8.

Massey, F. J. 1951. The Kolmogorov-Smirnov test for goodness of fit. J. Amer. Stat. Assoc. 46:68:78. DOI: 10.1080/01621459.1951.10500769.

McMurdie, P. J., and Holmes, S. 2013. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217. DOI: 10.1371/journal.pone.0061217.

Nilsson, R. H., Larsson, K., Taylor, A. F., Bengtsson-Palme, J., Jeppesen, T. S., Schigel, D., Kennedy, P., Picard, K., Glöckner, F. O., Tedersoo, L., Saar, I., Koljalg, U., and Abarenkov, K. 2018. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47:D259-D264. DOI: 10.1093/nar/gky1022.

Oksanen, J., Simpson, G., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., O'Hara, R., Solymos, P., Stevens, M., Szoecs, E., Wagner, H., Barbour, M., Bedward, M.,  Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Caceres, M., Durand, S., Evangelista, H., FitzJohn, R., Friendly, M., Furneaux, B., Hannigan, G., Hill, M., Lahti, L., McGlinn, D., Ouellette, M., Ribeiro-Cunha, E., Smith, T., Stier, A., TerBraak, C., and Weedon, J. 2022. ‘vegan’: Community ecology package. R package version 2.6-4,

Pielou, E. C. 1966. The measurement of diversity in different types of biological collections. J. Theo. Biol. 13:131-144. DOI:10.1016/0022-5193(66)90013-0.

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., and Glöckner, F. O. 2013. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41:D590-D596. DOI: 10.1093/nar/gks1219.

R Core Team. 2023. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.

Royston, P. 1995. Remark AS R94: A remark on algorithm AS 181: The W test for normality. J. R. Soc. Series C. Appl. Stat. 44:547-551. DOI: 10.2307/2986146.

Shannon, C. E. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27:379-423. DOI: 10.1002/j.1538-7305.1948.tb01338.x.

Simpson, E. 1949. Measurement of diversity. Nature 163:688. DOI: 10.1038/163688a0.

Wang, Q., Garrity, G. M., Tiedje, J. M., and Cole, J. R. 2007. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73:5261-5267. DOI: 10.1128/AEM.00062-07.

Wigg, K. S., and Goldman, I. L. 2020. Variability in reaction to root and crown rot caused by Rhizoctonia solani among table beet cultivars, breeding lines, and plant introductions in controlled environment conditions. HortScience 55:1482-1494. DOI: 10.21273/HORTSCI15011-20.

Research results and discussion:

Objective 1: Microbial community diversity and composition

Bacterial 16S rRNA

2021 analysis

Treatment had no significant effect on alpha diversity indices (ANOVA, P > 0.05, Table 1). However, there were significant differences in Shannon diversity (Kolmogorov-Smirnov: D = 0.625, P < 0.001) Simpson diversity (D = 0.750, P < 0.001), Pielou evenness (D = 0.781, P < 0.001) and richness (D = 0.438, P = 0.004) between early and late collected samples. The effect of sample type (rhizosphere or bulk soil) on alpha diversity of the bacterial community was less pronounced with only Pielou evenness significantly different between rhizosphere and bulk soil (D = 0.343, P = 0.045). Neither the effect of treatment alone nor the treatment × sample type interaction had a significant effect on beta diversity, or community composition (P = 0.069 and P = 0.925, respectively). At the late sampling (70 DAP), treatment had a larger influence on bacterial community composition (PERMANOVA: R2 = 0.14, P = 0.003), followed by the effect of sample type (R2 = 0.12, P = 0.001). These effects are shown on NMDS plots, which depict weak grouping by sample type and treatment (Fig. 1A, B).

Relative abundance of bacterial phyla representing at least 1% of ASVs is shown as a bar plot of relative abundances, separated by sampling time and sample type (Fig. 2). The bacterial phyla constituting the largest portion of the bacterial communities were Proteobacteria (27.1 ± 0.8%), Acidobacteria (18.8 ± 0.6%), Bacteroidota (16.5 ± 0.4%), Actinobacteria (9.8 ± 0.3%), Verrucomicrobiota (8.6 ± 0.4%), and Planctomycetota (5.6 ± 0.2%; Fig. 2). When analyzed separately for each combination of sampling time and sample type, there were no significant differences between relative abundance of bacterial phyla based on treatment (P > 0.05 for each contrast). There were significant differences in relative abundance of bacterial phyla associated with both sampling time and sample type. Firmicutes, Proteobacteria, Bacteroidota, and Acidobacteriota, were significantly (P < 0.05) more abundant in early samples (28 DAP) compared to late samples (70 DAP), while the phyla Actinobacteriota, and Verrucomicrobiota were significantly (P < 0.05) more abundant in late samples. Firmicutes, Chloroflexi, and Myxococcota had significantly (P < 0.05) greater relative abundances in rhizosphere samples compared to bulk soil samples (Fig. 2). Bulk soil samples had significantly (P < 0.05) greater relative abundances of Proteobacteria, Bacteroidota, and Acidobacteriota than rhizosphere samples (Fig. 2).

2022 analysis

In 2022, the azoxystrobin or inoculum treatments were not significantly associated with differences in alpha diversity metrics across sampling times and sample type (P > 0.05 for all indices, Table 2). Sampling time significantly affected Shannon diversity (Kolmogorov-Smirnov: D = 0.343 P = 0.045). Sample type was found to significantly affect Shannon diversity (D = 0.438, P = 0.004) Simpson diversity (D = 0.531, P < 0.001), and Pielou evenness (D = 0.531, P < 0.001). The effect of treatment at 34 DAP on beta diversity (Bray-Curtis distances) was not significant (P = 0.36), but sample type explained the largest portion of variation in the bacterial community (PERMANOVA: R2 = 0.12, P = 0.001). There was no significant effect on community composition from treatment at 75 DAP (P = 0.91). PERMANOVA indicated that sample type also had a significant effect on community composition of the 75 DAP samples (R2 = 0.13, P = 0.001). Moreover, NMDS plots did not show separation by treatment (Fig. 1C, D).

In 2022, the bacterial phyla with the greatest relative abundance across all samples were Proteobacteria (24.7 ± 0.6%), Actinobacteriota, (15.5% ± 0.4%), Acidobacteriota, (15.2 ± 0.5%), Bacteroidota, (14.1 ± 0.5%), Verrucomicrobiota, (7.6 ± 0.2%), and Planctomycetota (5.6 ± 0.2%; Fig 3). Within each combination of sampling time and sample type, treatment did not significantly affect the relative abundances of these and other bacterial phyla (P > 0.05 for each contrast). As in 2021, there were differences in relative abundance of bacterial phyla in the microbiomes from different sampling times and sample types. Samples collected at 75 DAP had a significantly greater abundance of Bacteroidota and Verrucomicrobiota, but a significantly lesser abundance of Actinobacteriota, Acidobacteriota, and Myxococcota (P < 0.05). Rhizosphere soil samples were significantly enriched in Firmicutes, Proteobacteria, Bacteroidota, and Actinobacteriota compared to bulk soil microbiomes (P < 0.05). Within Proteobacteria, Gammaproteobacteria were significantly (P < 0.001) enriched in rhizosphere soil, and Alphaproteobacteria were significantly (P < 0.001) enriched in bulk soil microbiomes.

Fungal ITS region

2021 analysis

In 2021, alpha diversity was not significantly different between the azoxystrobin and inoculation treatments for each combination (ANOVA, P > 0.05 for all indices, Table 3).  Although the effect of treatment was not significant, there were significant effects from sample type and sampling time on some alpha diversity indices. Fungal communities from bulk soil samples had greater Shannon diversity (Kolmogorov-Smirnov: D = 0.594, P < 0.001), decreased Simpson diversity (D = 0.625, P < 0.001), and increased Pielou evenness (D = 0.594, P < 0.001), than fungal communities from rhizosphere soil. Samples from 70 DAP had significantly greater ASV richness (D = 0.344, P = 0.041) than samples from 28 DAP. NMDS ordination of Bray-Curtis distances between fungal communities showed strong separation by sample type, but not treatment (Fig. 4A, B). PERMANOVA determined that sample type (rhizosphere soil or bulk soil) had a significant effect on community composition at both 28 DAP (R2 = 0.35, P = 0.001) and 70 DAP (R2 = 0.28, P = 0.001). Treatment did not have a significant effect on fungal community composition at either sampling period (P = 0.69, P = 0.41, respectively).

The most frequently identified fungal phyla in 2021 across all samples were Ascomycota (56.4 ± 0.8%) and Basidiomycota (17.5 ± 0.5%) followed by Mucoromycota (16.6 ± 0.5%) and Mortierellomycota (8.1 ± 0.3%). The most frequently identified fungal classes were Saccharomycetes (22.9 ± 0.4%), Dothideomycetes (21.8 ± 0.2%), Mucoromycetes (16.6 ± 0.3%), Agaricomycetes (9.3 ± 0.3%), Mortierellomycetes (8.0 ± 0.2%), Tremellomycetes (7.4 ± 0.1%), Eurotiomycetes (5.2 ± 0.1%), and Leotiomycetes (4.6 ± 0.1%; Fig. 5).

In the rhizosphere community at 28 and 70 DAP, Agaricomycetes was more abundant in nontreated plots than azoxystrobin-treated plots (P = 0.029 and P < 0.001). Otherwise, there were no significant differences (P > 0.05) in relative abundance of classes between treatments. Sampling time did not significantly affect the abundance of the top fungal classes in rhizosphere samples (P > 0.05). In bulk soil, only one fungal class, Saccharomycetes, was significantly more abundant at 28 DAP than 70 DAP (P = 0.019). Saccharomycetes and Eurotiomycetes were significantly more abundant in rhizosphere samples than in bulk soil, while Mortierellomycetes and Agaricomycetes were more abundant in bulk soil (P < 0.001 for each class).

2022 analysis

Alpha diversity metrics of fungal communities from 2022 samples were not significantly different between treatments at each combination of sampling time and sample type (Table 4, P > 0.05 for each diversity measure). When sample time was assessed alone, ASV richness (Kolmogorov-Smirnov: D = 0.438, P = 0.004), Shannon diversity (D = 0.438, P = 0.004), and Pielou evenness (D = 0.344, P = 0.045) were all significantly greater at 75 DAP than at 34 DAP, while Simpson diversity (D = 0.563, P < 0.001) was significantly lower in the 75 DAP samples than those collected at 34 DAP. There were no significant differences in alpha diversity indices between rhizosphere and bulk soil communities in 2022 (all P > 0.05).

There was no significant separation in community composition based on Bray-Curtis dissimilarity between treatments (Fig. 4C, D). PERMANOVA indicated no significant differences in community composition associated with in-furrow azoxystrobin or R. solani inoculum at either 34 DAP or 75 DAP (P = 0.45 and P = 0.51, respectively). However, at each sampling time there was a significant influence of sample type, where rhizosphere soil samples had distinct fungal communities from bulk soil samples (R2 = 0.17, P = 0.001 at 34 DAP and R2 = 0.17, P = 0.001 at 75 DAP).

Fungal communities sampled at 34 and 70 DAP from rhizosphere and bulk soil consisted predominantly of Ascomycota (72.4 ± 0.5%), Basidiomycota (15.3 ± 0.2%), and Mucoromycota (8.3 ± 0.5%). The most frequently identified classes were Saccharomycetes (39.5 ± 0.5%), Dothideomycetes (24.1 ± 0.5%), Tremellomycetes (13.8 ± 0.1%), Mucoromycetes (8.3 ± 0.3%), Eurotiomycetes (3.8 ± 0.1%), Mortierellomycetes (8.0 ± 0.2%), and Leotiomycetes (3.4 ± 0.1%; Fig. 6). Treatment had no significant effect on the abundance of these classes (P > 0.05 for each contrast). In contrast, Dothideomycetes and Tremellomycetes were significantly more abundant at 75 DAP than at 34 DAP in the rhizosphere, and Mucoromycetes, Leotiomycetes, Eurotiomycetes, and Mortierellomycetes were significantly more abundant at 34 DAP (all P < 0.001). In bulk soil, Saccharomycetes, and Mucoromycetes were significantly more abundant at 34 DAP, while Dothideomycetes, Leotiomycetes, and Eurotiomycetes were more abundant at 75 DAP (P < 0.01). Sample type did not significantly affect the abundance of fungal classes at 34 DAP, but at 75 DAP, rhizosphere samples had a significantly higher abundance of Saccharomycetes, Tremellomycetes, and Eurotiomycetes, while bulk soil samples had a significantly higher abundance of Mucoromycetes, Dothideomycetes, and Mortierellomycetes (all P < 0.05).

Treatment had a significant effect on the relative abundance of Mucoromycota and Mortierellomycota. At 75 DAP, Mucoromycota had significantly greater relative abundance, and Basidiomycota had decreased relative abundance in bulk soil in inoculated plots compared to azoxystrobin-treated plots (P = 0.034 and P = 0.021, respectively). Moreover, at 75 DAP, Mortierellomycota was enriched in bulk soil communities following inoculation. In rhizosphere soil samples at 75 DAP, relative abundance of Mucoromycota was significantly decreased in plots receiving azoxystrobin (with or without subsequent inoculation) compared to inoculated plots (P = 0.025 and P = 0.007, respectively). When comparing sampling time alone, relative abundance of Ascomycota and Basidiomycota was greater in communities from 75 DAP than at 34 DAP (P < 0.001 for each phylum). Basidiomycota was the only fungal phylum influenced by sample type alone. Bulk soil samples had decreased relative abundance of Basidiomycota compared to rhizosphere soil samples (P = 0.046).


Objective 2: Azoxystrobin-affected microbiome effects on disease development

Crop stands ranged from 46 to 56 plants/m at 25 DAP in 2021 and 109 to 177 plants/m at 26 DAP in 2022 (Table 5), and were not significantly different between the treatments (P = 0.507 and P = 0.311 in 2021 and 2022, respectively). At harvest in 2021, crop stands ranged from 26.6 to 36.9 plants/m (Table 5). Plots that received in-furrow azoxystrobin, including the azoxystrobin and inoculum treatment, had 33% higher crop stands prior to harvest at 77 DAP, but this was not significantly different from plots that did not receive azoxystrobin (P = 0.192). No significant differences between treatments were observed for root weight (P = 0.226) and average root shoulder diameter (P = 0.427). Plots that received R. solani inoculum alone had significantly higher disease incidence than other treatments (P = 0.048). Plots that received azoxystrobin and the post-emergent R. solani inoculum had similar disease incidence compared to plots receiving azoxystrobin alone and nontreated plots. Average disease severity per root was generally low, ranging from 0.61% in azoxystrobin-treated plots to 2.37% in inoculated plots. Although average disease severity was highest in plots that received inoculum alone, this was not significantly different from other plots (P = 0.155). 

In 2022, final crop stands were between 28.5 and 39.2 plants/m (Table 5). Differences in crop stand between the treatments were not significant at harvest (75 DAP; P = 0.142). Root weight ranged from 3.5 to 4.4 kg and was not significantly different between treatments (P = 0.073). As in 2021, plots that received only R. solani inoculum had the highest root disease incidence and severity; however, differences among treatments in disease incidence and severity were not significant (P = 0.153 and P = 0.154, respectively; Table 5). In 2022, disease severity per root was low and ranged from 0.52% in azoxystrobin-treated plots to 1.77% in inoculated plots. Disease incidence and severity were positively correlated in both years (2021: Spearman’s ρ2 = 0.84, P < 0.001 and 2022: R2 = 0.67, P = 0.006 for 2021). R. solani was isolated from 80% and 90% of symptomatic roots in 2021 and 2022, respectively.

In 2021, there were significant correlations between alpha diversity and Rhizoctonia incidence and severity, but only at certain combinations of sampling time and only for rhizosphere soil samples. For rhizosphere soil at 28 DAP, disease incidence and severity were positively correlated to fungal ASV richness (Spearman’s R2 = 0.56, P < 0.023 and R2 = 0.61, P < 0.012). Disease severity was negatively correlated with Shannon diversity and Pielou evenness of the bacterial community (R2 = -0.61, P = 0.01 and R2 = -0.59, P = 0.016). At 70 DAP, Shannon diversity (R2 = -0.65, P = 0.007), Simpson diversity (R2 = -0.78, P < 0.001), and Pielou evenness (R2 = -0.71, P = 0.002) were negatively correlated to disease incidence. Shannon diversity (R2 = -0.54, P = 0.031), Simpson diversity (R2 = -0.71, P = 0.003), and Pielou evenness (R2 = -0.70, P = 0.003) were also negatively correlated to disease severity at 70 DAP. In 2022, there were no significant correlations between disease incidence or severity and alpha diversity of fungal communities (P > 0.05). ASV richness of the rhizosphere bacterial communities at 28 DAP was negatively correlated with disease severity (R2 = -0.61, P = 0.014). Correlations between the relative abundance of Rhizoctonia solani and disease incidence or severity were not significant in any sampling time or sample type.

Correlations between relative abundance of microbial taxa at the genus level (fungi) or phylum level (bacteria) to each of disease incidence, disease severity, and the relative abundance of ASVs belonging to R. solani revealed the presence of certain bacterial and fungal taxa was correlated to root disease. In 2021, bacterial phyla that were negatively correlated with disease incidence and severity included Actinobacteria, Chloroflexi, Desulfobacteria, Gemmatimonadota, and Latescibacterota. Acidobacteria, (Fig. 7A). Bacteroidota, and Bdellovibrionota were negatively correlated with the abundance of ASVs belonging to R. solani. Fungal genera with the largest positive correlation to disease incidence and severity were Mrakia and Tausonia. The genera Bipolaris, Didymella, Periconia, Podila, Mortierella, and Preussia were positively correlated with Rhizoctonia abundance for all treatments, including plots that did not receive inoculum (Fig. 8A). Bacterial phyla that were negatively correlated with disease incidence and severity in 2022 included Bacteroidota, Planctomycetota, and the NB1-j phylum (Fig. 7B). NB1-j was positively correlated with the relative abundance of R. solani in the nontreated plots. In 2022, the fungal phyla Leptodiscella, Talaromyces, and Tausonia were positively correlated with disease incidence and severity (Fig. 8B). Tremateia was negatively correlated to disease incidence in nontreated plots. The genera Alternaria, Cladosporium, Epicoccum, and Microdochium were most strongly positively correlated with Rhizoctonia abundance, especially in the nontreated plots.

Final Report GNE21-249 tables and figures

Research conclusions:

Overall, the in-furrow azoxystrobin treatments were not associated with significant changes to the rhizosphere microbiome of table beet. In 2021 and 2022, alpha and beta diversity was similar between treatments with and without in-furrow azoxystrobin. R. solani inoculum, applied post-emergent, resulted in increased disease incidence but was not consistently associated with changes to the bacterial or fungal community. Instead, most of the differences in microbial diversity and community composition were attributed to sample type (rhizosphere or bulk soil). Sampling time also affected microbial communities, but these changes to community composition were not associated with treatment. There were significant correlations between alpha diversity indices and disease incidence and severity, but not consistently between years. Certain taxa were negatively correlated with the number of reads assigned to R. solani, including Acidobacteriota and Bacteroidota. Future studies may focus on these phyla to find bacteria beneficial to reduced root disease in table beet. Results from this study are inconclusive as to the detrimental impact of azoxystrobin on any otherwise beneficial components of the rhizosphere microbial community.

In the short-term, results from this study are unlikely to affect on-farm decision making since azoxystrobin is still useful for reducing crop loss caused by Rhizoctonia damping off and root rot. In addition to providing data to support additional projects researching microbiome-mediated management of root disease, this project can raise awareness on the impact of pesticide use on soil microbial communities and other off-target effects. 

Participation Summary

Education & Outreach Activities and Participation Summary

1 Journal articles
4 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:

Project information and results were shared with farmers, industry leaders, and academic peers. Both formal and informal strategies were 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.

In December 2021, I participated in the annual Processing Vegetable Meeting in Batavia, New York. These commodity-specific meetings are organized by my collaborator, Julie Kikkert and Cornell Cooperative Extension. Forty-five 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 preliminary results from this SARE-funded trial. This event serves as the annual meeting for the New York Vegetable Research Association and Council Advisory, and is where research priorities for the next year are set. While not funded by this group, my project supports their mission of improving production and economic returns. 

In August 2022, I attended the annual American Phytopathological Society (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. In December 2022, this work was included as part of a short online presentation at the annual meeting for the Multistate Research Project W4147: Managing Plant Microbe Interactions in Soil to Promote Sustainable Agriculture. Results were also presented at the student-organized symposium in June at Cornell AgriTech in Geneva, NY, for an audience of graduate students, staff and faculty from Cornell AgriTech, and undergraduate researchers. 

A comprehensive manuscript has been written and is being formatted and prepared for submission to the APS-published academic journal, PhytoFrontiers

This work may also be included as part of an enhanced Cornell Cooperative Extension factsheet, complementing existing materials covering Rhizoctonia diseases of table beet.

Project Outcomes

1 Grant applied for that built upon this project
1 Grant received that built upon this project
$119,262.00 Dollar amount of grant received that built upon this project
Project outcomes:

This project will contribute to future agricultural sustainability by providing information about the off-target impacts of azoxystrobin . Despite inconclusive results in some aspects of this project, growers understand the need for continued research for long-term change and improvement in the cropping system.

Knowledge Gained:

This project was instrumental in advancing my own skills as a scientist in and out of the lab. The replicated field trial, soil DNA extraction and amplicon library preparation, and analysis of DNA sequence data allowed me to hone skills in diverse areas, including project management and large-scale data processing

Notably, this project provided preliminary research that supported a successful application to the USDA-NIFA Predoctoral Fellowship program. This grant was approved in the summer of 2023, and a total of $119,262 was awarded. 

My education at Cornell University, including the opportunity to complete this project as an NE SARE Graduate Student Grant, strongly influenced my commitment to public-sector research and outreach. In the fall of 2023, I will complete my Ph.D. degree and begin a career as an Assistant Professor and Extension Plant Pathologist at North Dakota State University in Fargo, North Dakota. This is an Extension-focused position supporting the sugarbeet industry in North Dakota and Minnesota. The mission of Northeast SARE to advance economic, environmental, and community sustainability related to agriculture will continue to impact my work in plant pathology research and Extension. 

Assessment of Project Approach and Areas of Further Study:

In 2021 and 2022, precipitation and temperatures did not vary from typical summer conditions for central New York. Soil moisture levels were appropriate for disease development. However, 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. The overall disease severity per root was low in both years, indicating that the post-emergent inoculation method resulted in a higher number of table beets with minor lesions. In contrast, separate trials with in-furrow R. solani inoculation (also conducted in 2021 and 2022) typically had higher disease severity following the same amount of inoculum application. Clearly, inoculation procedures must be matched to the unique questions and hypotheses for each project.

To build stronger links between the rhizosphere microbiome and table beet root health, future basic and translational research may focus on bacterial or fungal taxa most likely to have a beneficial impact on plant health. Rhizoctonia diseases impact table beet and other crops each year, and fungicide use is regarded as a necessity by risk-averse growers and vegetable processors in New York. In the table beet industry, growers and processors recognize the importance of reduced reliance on pesticides and have indicated support for research projects that help them achieve productivity and sustainability goals. 


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.