Improving Knowledge of Soilborne Pathogens in PA Soybean Production Systems

This Research Spotlight addresses the Field and Forage Extension Plant Pathology and Epidemiology Laboratory's research on soilborne soybean pathogen profiles, and their composition and genetic architecture in soybean farmer fields across the state, with specific focus on the significance of fungicide seed treatments of soybean and factors that influence yield in different portions of the field.

Research was conducted in a total of 22 locations in 17 different counties in PA in 2018 (Figure 1) with further sites to be included in 2019.

Figure 1: Map of Pennsylvania showing 17 counties where the fungicide seed treatment trial and the yield-limiting factor trial was conducted in 2018.

1). Effect of Apron Maxx seed treatment on soybean seedling diseases, seedling vigor, and yields.

Though extensively used in PA, fungicide seed treatment-associated positive yield responses are variable in soybean. A study was conducted to look at the impact of Apron Maxx (Mefenoxam + Fludioxonil) on seedling diseases, seedling vigor, and yields of soybean grown in Pennsylvania.

Method

On-farm field and small plot trials were conducted in seven counties (Ahern, Armstrong, Lancaster, McKean, Centre, Somerset, and Tioga). At each location, plots were arranged in randomized complete block design. At R1 growth stage, 15 seedlings from each plot (Apron or control) were carefully uprooted to quantify the incidence of root rots. Seedling height (SH), tap root length (TRL), root/shoot weight (RW/SW: dry basis) were measured as seedling vigor indicators. Test weight (lbs/bu) and yield (bu/ac) were measured at harvest.

Results

Root rots were absent in both Apron Maxx and control plots at all locations. Non-significant differences were observed between control and Apron for SH, TRL, RW, or SW at all locations. Apron did not significantly increase test weight or yield compared to control at all locations (Figure 2).

Figure 2. Soybean fungicide seed treatment trial R1 stage disease trait assessment results. "Apron "= Apron Maxx and the "Control" was non-treated soybean seed.

Conclusion

The study showed that Apron Maxx did not positively impact soybean seedling vigor and yields. Our research continues to focus on an improved understanding of the pathogen profile as linked to the need for seed treatment fungicides to help Pennsylvania soybean farmers make rational decision making on the use of fungicide seed treatments to maximize profits. 

2). Yield-limiting factor study addressing the association between soil and root microbiomes and within-farm-spatial-variation (WFSV) of soybean yields.

Within farm spatial variation is a key yield limiting factor in soybean growing states but the drivers of this are yet unknown. Our current microbiome research is built on the hypothesis that potential differences of soil and root associated bacterial and fungal communities contribute to the spatial variation within farms and thus on the soybean yield.

In the current study, the associated microbial communities in plant roots, rhizosphere soil and bulk soil from various soybean farmer fields across PA with high vs. low yield were explored through analyzing the root and soil microbiomes at different soybean growth stages. The research was conducted using two approaches whereby soil and plant samples were collected at V1 and R8 soybean growth stages for the first approach and at V1, R1, R6 and post-harvest stage for the second approach (Figures 3 and 4).

Figure 3. All soybean sampling stages used for microbiome analysis

Figure 4: The sampling strategy followed for yield-limiting study.

The samples were processed in the laboratory and DNA extraction was followed by several different steps as shown in Figure 5 before sequencing and data analysis was conducted.

Figure 5. The pipeline for molecular characterization of the soil and root microbiome.

Results

The relative abundance of Ascomycota and Zygomycota appeared to be different between high and low sites in bulk soil and root samples at R8 while Chytridiomycota, Zygomycota, and Basidiomycota were different between high and low sites in rhizosphere at R8. The global analyses suggested that fungal community differences can potentially contribute to within-farm-spatial-variation of soybean yields in Pennsylvania.

On the bacterial perspective, there were no noticeable differences in relative abundances of bacterial communities at the phyla level across high and low sites at V1 and R8. However, in-depth analysis will be performed to see whether specific bacterial taxa drive within-farm-spatial-variation of soybean yield.

3). Understanding the pathogen profiles and their impact on seedling diseases in PA.

Attempts to control of seedling disease without a complete picture of the soilborne pathogen profile is a challenge that requires further research to make more informed management decisions. Proper knowledge on the pathogen profile and composition of a farmer field would therefore better explain the reasons underlying seedling disease and existing yield issues.

To improve our understanding of pathogen profiles in different farms and locations, all soil samples that were collected from soybean farmer fields across Pennsylvania from the fungicide seed treatment study and yield-limiting factor study were characterized for four important soilborne soybean pathogen groups: Pythium spp., Phytophthora spp., Fusarium spp., and Rhizoctonia spp. For each pathogen group, isolations were conducted using selective media in the laboratory.

A total of 551 Pythium spp., 456 Phytophthora spp., 635 Fusarium spp. and 258 Rhizoctonia spp. were isolate from the collected soil samples.  On average, 25 Pythium spp., 21 Phytophthora spp., 29 Fusarium spp. and 12 Rhizoctonia spp. isolates were found per farmer field.  Figure 6 shows selected isolates from each pathogen group found in the current study.

Figure 6. Images of selected pure cultures of A). Pythium spp., B) Phytophthora spp., C) Fusarium spp., isolates and Rhizoctonia spp. found in selected counties in PA.
Note: Rhizoctonia is a monotypic genus.

Pure cultures of isolates were characterized in different studies to help decision making and possible manipulation of crop production practices:

3(i). Impact of metalaxyl, ethaboxam, and mefenoxam on in-vitro growth rate of Pythium isolates from Pennsylvania.

The selection of appropriate fungicides with proper concentration is critical for efficient control soil borne pathogens. A study was conducted to determine the efficacy of selected fungicide active ingredients on in-vitro growth of selected Pythium isolates that were obtained. Future work will focus on the other pathogen groups.

Method

A selection of 153 Pythium isolates recovered from soil samples collected were grown in PDA amended with 0, 10, 100, and 1000 ppm concentrations of Metalaxyl, Ethaboxam and Mefanoxam. Colony diameter was measured at different time points to compute the colony growth rate.

Figure 7. Two selected Pythium fungal isolates tested against 4 concentrations of Ethaboxam in three replicates.

Results

Statistical analysis indicated that there was a significant isolate × concentration interaction for all tested fungicides.  Among the isolates tested, 70.6%, 88.2% and 64.7% were insensitive (non-significant growth rate difference compared to counterpart control = 0 ppm) to Metalaxyl, Ethaboxam and Mefanoxam respectively. Furthermore, only 13.1%, 4.6% and 9.8% of the isolates were sensitive to Metalaxyl, Ethaboxam and Mefanoxam at 1000 ppm.

Conclusions

Findings showed that the majority of the tested isolates were unaffected by the different fungicide active ingredients even at higher concentrations. Further research is needed to determine if these primary active ingredients used in seed treatments are effective for control of Pythium spp. in Pennsylvania soybean fields.

3(ii). The relationship between soil chemical properties and population densities of pathogen groups were investigated.

In this study, we examined the possible links between soil chemical properties and pathogen population densities.

Method

Enumeration of fungal colonies from soil samples collected from all 22 locations were carried out on selective media (Fusarium = Nash and Snyder; Pythium = P5ARP; Phytophthora = P5ARP + hymexazol; Rhizoctonia = Ko and Hora). Organic matter (OM), cation exchange capacity (CEC), pH, and nutrients (P, K, Mg, Ca, Zn, Cu, S) of soil samples were determined.

Results

Pearson correlation analysis showed a significant relationship between soil Fusarium density (CFU/g) and S (r = 0.74, P < 0.0001). Pythium density was significantly correlated with K (r = 0.71, P = 0.0002) and CEC (r = 0.58, P = 0.0051). Neither Rhizoctonia nor Phytophthora densities were significantly correlated with any of the measured properties.

Conclusions

Despite their importance for enhanced crop production, S and K have the potential to increase inoculum densities of soil borne Fusarium and Pythium species respectively and could indirectly promote crop's susceptibility to soil borne diseases caused by these fungi.  

3(iii). Association of selected biological and chemical properties of soil with within-farm-spatial-variation of soybean yields were investigated.

This study was conducted to investigated to determine if variation in selected biological and chemical properties of soil contribute to spatial heterogeneity of soybean yields.

Method

Bulk soil samples from 14 locations in PA were collected from five historically high and low yielding sites per location at V1 growth stage of soybean. The plant pathogenic nematode counts (lesion, stunt, spiral, stubby root, dagger, ring, lance, and pin), fungal counts (Fusarium, Pythium, and Phytophthora species and Rhizoctonia solani), organic matter, cation exchange capacity, pH, and nutrients (P, K, Mg, Ca, Zn, Cu, S) of soil samples were determined.

Results

None of the measured variables were significantly different between high and low yielding sites. Using a multivariate statistical procedure (principle component analysis) revealed that first two principle components contribute to 46% of the total observed variation in the data set. However, this variance maximizing data point distribution failed to distinctly cluster high and low yielding sites in the principle component space.

Conclusions

Findings suggested that the underlying biological and chemical causes behind within-farm spatial-heterogeneity of soybean yields in Pennsylvania is complex. Further research is required to determine which biological and chemical properties are associated with pest and pathogens to determine the impact on yield.

This is a research and extension collaboration between Penn State Extension and the Department of Plant Pathology and Environmental Microbiology and includes: Ananda Bandara, Ryan Trexler, Brandon Wilt, Terrence Bell, Alyssa Collins, Del Voight, Adriana Murillo-Williams, Charlie White, Dilooshi Weerasooriya, and Paul Esker. Furthermore, these efforts would not be possible without many other Extension Educators, including: Casey Baxter, Zach Larson, Rachel Milliron, Anna Busch, Andrew Frankenfield, Elizabeth Bosak, Justin Brackenrich, Nicole Santangelo, Claire Coombs, and Jeff Graybill.