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Title:The effect of Septoria brown spot on soybean yield and phyllosphere microbiome
Author(s):Lin, Heng-An
Director of Research:Mideros , Santiago
Doctoral Committee Chair(s):Mideros , Santiago
Doctoral Committee Member(s):Hartman, Glen; Villamil, Maria Bonita; Yannarell, Anthony
Department / Program:Crop Sciences
Discipline:Crop Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Septoria brown spot, soybean, qPCR, yield, phyllosphere microbiome, oxford nanopore
Abstract:Septoria brown spot (SBS), caused by Septoria glycines is the most prevalent soybean foliar disease in Illinois and often co-occurs with other late-season diseases, such as Cercospora leaf blight and frogeye leaf spot. Foliar fungicide applications during the reproductive stage is a common method to control these diseases. However, the application of fungicide does not always result in a yield increase. Furthermore, the effect of the fungicide applications on the phyllosphere fungal community needs further understanding. In this study, my research goals were to (i) characterize the development of SBS and its relationship with yield reduction, (ii) develop molecular markers for early identification and quantification of S. glycines, and (iii) characterize the effect of fungicide application on S. glycines and other phyllosphere organisms. I conducted replicated multi-location inoculated field trials to characterize the disease development and evaluated the relationship between SBS and soybean yield. My results showed that the yield was negatively correlated with the percentage of the disease vertical progress and chlorotic area. From the stepwise regression analysis, the percentage of vertical progress was the best predictor variable for the model. As the vertical progress reached 30% at the R6 growth stage, there was a 10% predicted yield loss. Likewise, when the symptoms reached 80% of vertical progress, a 27% yield loss was predicted. There was no significant effect of the fungicide treatments on yield. The variance component analysis of the disease components data and yield data indicated that the location was the most critical factor that affected the experiment. Power analyses showed that at least eight locations are needed to reach 80% statistical power in small-plot studies with similar disease levels to my study to obtain statistical differences in fungicide treatments. In chapter 3, I described the development and validation of a quantitative PCR (qPCR) method to accurately detect and quantify S. glycines. The assay designed with the actin gene (Ac) was specific to S. glycines for both conventional PCR and qPCR. The assay designed with the β-tubulin (Bt) gene was specific to S. glycines only on the qPCR. Both Ac and Bt assays had high qPCR reaction efficiency (95% and 98%) and sensitivity to detect 10 pg of S. glycines gDNA. The Bt assay was validated with field samples that had different necrotic areas. Symptoms of necrosis ranging from 0 to 30 % were significant and positively correlated (r = 0.87) to the S. glycines gDNA. The S. glycines gDNA was detected as early as 1-day post-inoculation in detached leaf assays. In chapter 4, I used DNA metabarcoding to understand the dynamics between Septoria and non-target species using samples collected from the inoculated and fungicide-treated fields. Full-length ITS and partial LSU region were sequenced using oxford nanopore sequencer that yielded 3,342 unique OTUs. The cultivars had a significant difference of fungal communities at the V4 growth stage. Ten fungi were identified as core components of the leaves. Although possible interactions were identified between Septoria and other fungal organisms, the inoculation treatment did not significantly impact the entire communities according to the and diversity analyses. At R5 growth stage, the fungicide application significantly shaped the fungal communities. From the relative abundance analysis, the fungicide treatment significantly decreased the proportion of most fungi compared to the control samples, but the proportion of Bipolaris, and Diaporthe increased. This study presents a comprehensive evaluation of SBS from multiple aspects. The results provide useful information for the estimation of the yield damage caused by SBS. I expect that the qPCR assays reported here could be used for disease diagnosis and to better characterize the infection process of S. glycines. Finally, I demonstrated that metabarcoding could be a tool to quantify the effect of fungicide on target and non-target organisms. I believe that understanding the disease development, yield effect, and dynamics of the phyllosphere microbiome is necessary to untangle the late-season disease complex and develop better management practices.
Issue Date:2021-04-21
Rights Information:Copyright 2021 Heng-An Lin
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05

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