Soybean (Glycine max) is an important leguminous crop that is grown throughout the United States and around the world. In 2016, soybean was valued at $41 billion USD in the United States alone. Increasingly, soybean farmers are adopting alternative management strategies to improve the sustainability and profitability of their crop. Various benefits have been demonstrated for alternative management systems, but their effects on soybean-associated microbial communities are not well-understood. In order to better understand the impact of crop management systems on the soybean-associated microbiome, we employed DNA amplicon sequencing of the Internal Transcribed Spacer (ITS) region and 16S rRNA genes to analyze fungal and prokaryotic communities associated with soil, roots, stems, and leaves. Soybean plants were sampled from replicated fields under long-term conventional, no-till, and organic management systems at three time points throughout the growing season. Results indicated that sample origin was the main driver of beta diversity in soybean-associated microbial communities, but management regime and plant growth stage were also significant factors. Similarly, differences in alpha diversity are driven by compartment and sample origin. Overall, the organic management system had lower fungal and bacterial Shannon diversity. In prokaryotic communities, aboveground tissues were dominated by Sphingomonas and Methylobacterium while belowground samples were dominated by Bradyrhizobium and Sphingomonas. Aboveground fungal communities were dominated by Davidiella across all management systems, while belowground samples were dominated by Fusarium and Mortierella. Specific taxa including potential plant beneficials such as Mortierella were indicator species of the conventional and organic management systems. No-till management increased the abundance of groups known to contain plant beneficial organisms such as Bradyrhizobium and Glomeromycotina. Network analyses show different highly connected hub taxa were present in each management system. Overall, this research demonstrates how specific long-term cropping management systems alter microbial communities and how those communities change throughout the growth of soybean.

Fungicides reduce fungal pathogen populations and are essential to food security. Understanding the impacts of fungicides on crop microbiomes is vital to minimizing unintended consequences while maintaining their use for plant protection. However, fungicide disturbance of plant microbiomes has received limited attention, and has not been examined in different agricultural management systems. We used amplicon sequencing of fungi and prokaryotes in maize and soybean microbiomes before and after foliar fungicide application in leaves and roots from plots under long-term no-till and conventional tillage management. We examined fungicide disturbance and resilience, which revealed consistent non-target effects and greater resiliency under no-till management. Fungicides lowered pathogen abundance in maize and soybean and decreased the abundance of Tremellomycetes yeasts, especially Bulleribasidiaceae, including core microbiome members. Fungicide application reduced network complexity in the soybean phyllosphere, which revealed altered co-occurrence patterns between yeast species of Bulleribasidiaceae, and Sphingomonas and Hymenobacter in fungicide treated plots. Results indicate that foliar fungicides lower pathogen and non-target fungal abundance and may impact prokaryotes indirectly. Treatment effects were confined to the phyllosphere and did not impact belowground microbial communities. Overall, these results demonstrate the resilience of no-till management to fungicide disturbance, a potential novel ecosystem service provided by no-till agriculture.

Tar spot is a fungal disease complex of corn that has been destructive and yield limiting in Central and South America for nearly 50 years. Phyllachora maydis, the causal agent of tar spot, is an emerging corn pathogen in the United States, first reported in 2015 from major corn producing regions of the country. The tar spot disease complex putatively includes Monographella maydis (syn. Microdochium maydis), which increases disease damage through the development of necrotic halos surrounding tar spot lesions. These necrotic halos, termed “fish-eye” symptoms, have been identified in the United States, though Monographella maydis has not yet been confirmed. A recent surge in disease severity and loss of yield attributed to tar spot in the United States has led to increased attention and expanded efforts to understand the disease complex and how to manage it. In this study, next-generation sequencing of the internal transcribed spacer-1 (ITS1) ribosomal DNA was used to identify fungal taxa that distinguish tar spot infections with or without fish-eye symptoms. Fungal communities within tar spot only lesions were significantly different from communities having fish-eye symptoms. Two low abundance OTUs were identified as Microdochium sp., however, neither were associated with fish-eye symptom development. Interestingly, a single OTU was found to be significantly more abundant in fish-eye lesions compared to tar spot lesions and had a 91% ITS1 identity to Neottiosporina paspali. In addition, the occurrence of this OTU was positively associated with Phyllachora maydis fish-eye symptom networks, but not in tar spot symptom networks. Neottiosporina paspali has been reported to cause necrotic lesions on various monocot grasses. Whether the related fungus we detected is part of the tar-spot complex of corn and responsible for fish-eye lesions remains to be tested. Alternatively, many OTUs identified as Phyllachora maydis, suggesting that different isolate genotypes may be capable of causing both tar spot and fish-eye symptoms, independent of other fungi. We conclude that Monographella maydis is not required for fish-eye symptoms in tar spot of corn.

Corn is a staple feed and biofuel crop with a value close to $3.7 billion dollars for Michigan’s economy. Knowledge about distribution and abundance of seedling pathogens in Michigan corn fields is limited. Here we used a combination of culture-based and next-generation sequencing of soil samples to determine the extent of species associated with diseased corn seedlings and those present in soil. Over 2 years, symptomatic seedlings and associated soil samples were collected from 11 Michigan fields. A total of 170 oomycete cultures were obtained from seedlings using a semiselective medium (CMA-PARPB) and identified using the internal transcribed spacer region. Thirty-three species were isolated, with Pythium inflatum (25%; clade B) and P. sylvaticum (12%; clade F) being the most abundant species. For the amplicon-based approach, the cytochrome oxidase subunit I marker (COI) mitochondrial region was amplified from soil samples and sequenced using Illumina MiSeq. The dominant Pythium clades present in the soil were F, I, D, and B and accounted for at least 75% of the abundance in all locations. Pythium clades F, I, and D were recovered with similar trends with the culture and amplicon approach; however, clade B was highly abundant in plant isolation, but not in soil. The 20 most abundant species were characterized for pathogenicity and fungicide sensitivity. P. irregulare and P. ultimum var. ultimum were the most virulent at both 15 and 20°C. Isolates were tested for their sensitivity to mefenoxam and ethaboxam. Most isolates were sensitive to both chemistries, but P. rostratifingens and P. aff. torulosum were less sensitive to ethaboxam and P. ultimum var. ultimum less sensitive to mefenoxam. The survey and isolate characterization provides a better understanding of seedling and root rot disease of corn and opportunities to improve management of this disease complex.

The effective control to 50% growth inhibition (EC50) is a standard statistic for evaluating dose-response relationships. Many statistical software packages are available to estimate dose-response relationships but, recently, an open source package (“drc”) in R has been utilized. This package is highly adaptable, having many models to describe dose-response relationships and flexibility to describe both hormetic relationships and absolute and relative EC50. These models and definitions are generally left out of phytopathology literature. Here, we demonstrate that model choice and type of EC50 (relative versus absolute) can matter for EC50 estimation using data from Pythium oopapillum and Fusarium virguliforme. For some P. oopapillum isolates, the difference between absolute and relative EC50 was significant. Hormetic effects changed F. virguliforme EC50 distributions, leading to higher estimates than when using four- or three-parameter log-logistic models. Future studies should pay careful attention to model selection and interpretation in EC50 estimation and clearly indicate which model and EC50 measure (relative versus absolute) was used. We provide guidelines for model choice and interpretation for those wishing to set up experiments for accurate EC50 estimation.

Microbiomes from maize and soybean were characterized in a long-term three-crop rotation research site, under four different land management strategies, to begin unraveling the effects of common farming practices on microbial communities. The fungal and bacterial communities of leaves, stems, and roots in host species were characterized across the growing season using amplicon sequencing and compared with the results of a similar study on wheat. Communities differed across hosts, and among plant growth stages and organs, and these effects were most pronounced in the bacterial communities of the wheat and maize phyllosphere. Roots consistently showed the highest number of bacterial OTUs compared to above-ground organs, whereas the alpha diversity of fungi was similar between above- and below-ground organs. Network analyses identified putatively influential members of the microbial communities of the three host plant species. The fungal taxa specific to roots, stems, or leaves were examined to determine if the specificity reflected their life histories based on previous studies. The analysis suggests that fungal spore traits are drivers of organ specificity in the fungal community. Identification of influential taxa in the microbial community and understanding how community structure of specific crop organs is formed, will provide a critical resource for manipulations of microbial communities. The ability to predict how organ specific communities are influenced by spore traits will enhance our ability to introduce them sustainably.

In the United States, sudden death syndrome (SDS) of soybean is caused by the fungal pathogen Fusarium virguliforme and is responsible for important yield losses each year. Understanding the risk of SDS development and subsequent yield loss could provide growers with valuable information for management of this challenging disease. Current management strategies for F. virguliforme use partially resistant cultivars, fungicide seed treatments, and extended crop rotations with diverse crops. The aim of this study was to develop models to predict SDS severity and soybean yield loss using at-planting risk factors to integrate with current SDS management strategies. In 2014 and 2015, field studies were conducted in adjacent fields in Decatur, MI, which were intensively monitored for F. virguliforme and nematode quantities at-planting, plant health throughout the growing season, end-of-season SDS severity, and yield using an unbiased grid sampling scheme. In both years, F. virguliforme and soybean cyst nematode (SCN) quantities were unevenly distributed throughout the field. The distribution of F. virguliforme at-planting had a significant correlation with end-of-season SDS severity in 2015, and a significant correlation to yield in 2014 (P < 0.05). SCN distributions at-planting were significantly correlated with end-of-season SDS severity and yield in 2015 (P < 0.05). Prediction models developed through multiple linear regression showed that F. virguliforme abundance (P < 0.001), SCN egg quantity (P < 0.001), and year (P < 0.01) explained the most variation in end-of-season SDS (R2 = 0.32), whereas end-of-season SDS (P < 0.001) and end-of-season root dry weight (P < 0.001) explained the most variation in soybean yield (R2 = 0.53). Further, multivariate analyses support a synergistic relationship between F. virguliforme and SCN, enhancing the severity of foliar SDS. These models indicate that it is possible to predict patches of SDS severity using at-planting risk factors. Verifying these models and incorporating additional data types may help improve SDS management and forecast soybean markets in response to SDS threats.

Current methods to quantitatively assess fungicide sensitivity for a diverse range of oomycetes are slow and labor intensive. Microtiter-based assays can be used to increase throughput. However, many factors can affect their quality and reproducibility. Therefore, efficient and reliable methods for detection of assay quality are desirable. The objective of this study was to develop and validate a robust high-throughput fungicide phenotyping assay based on spectrophotometric quantification of mycelial growth in liquid culture and implementation of quality control with Z′ factor and growth curves. Z′ factor was used to ensure that each isolate grew enough in the absence of fungicides compared with the negative control, and growth curves were used to ensure active growth at the time of concentration of a fungicide that reduces growth by 50% (EC50) estimation. EC50 and relative growth values were correlated in a side-by-side comparison with values obtained using the amended medium (gold standard) assay. Concordance correlation indicated that the high-throughput assay is accurate but may not be as precise as the amended medium assay. To demonstrate the utility of the high-throughput assay, the sensitivity of 216 oomycete isolates representing four genera and 81 species to mefenoxam and ethaboxam was tested. The assay developed herein will enable high-throughput fungicide phenotyping at a population or community level.