Updated April 26, 2021:
A manuscript entitled “Influence of Fusarium virguliforme temporal colonization of corn, tillage, and residue management on soybean sudden death syndrome and soybean yield.” has been published in plant disease (online). In this study, we compared two levels of residue removals and two tillage systems in corn and soybean rotation system field experiments in Iowa, Indiana, Michigan, Wisconsin and Ontario to investigate the effect of corn residue on SDS development. Corn and soybean roots were sampled at consecutive time points between 1 and 16 weeks after planting (WAP). DNA was extracted from all roots and analyzed by real-time qPCR for F. virguliforme quantification. Trials were rotated between corn and soybean, containing a two x two factorial of tillage (no-tilled or tilled) and corn residue (with or without). In 2016, low (ca. 100 fg/10 mg root tissue) F. virguliforme was detected in the inoculated IA, IN and MI locations, and non-inoculated WI corn fields. However, in 2017 greater levels of F. virguliforme DNA were detected in IA, IN and MI across sampling time points. Tillage practices showed inconsistent effects on F. virguliforme root colonization and SDS foliar symptoms among trials and locations. Yet, residue management did not alter root colonization of corn or soybean by F. virguliforme. Plots with corn residue had greater SDS foliar disease index in Iowa in 2016. However, this trend was not observed across the site-years, indicating corn residue may occasionally increase SDS foliar symptoms depending on the disease level, soil and weather factors. Two PhD students Grazieli Araldi Da Silva from Iowa State University and Amy Baetsen-Young from Michigan state university, who worked in this project, graduated.
A manuscript entitled “A gated recurrent units (GRU)-based model for early detection of soybean sudden death syndrome through time-series satellite imagery” has been published in Remote sensing journal (Remote Sens. 12:3621; https://doi.org/10.3390/rs12213621). This work was done in collaboration with Dr. Guiping Hu from Department of Industrial and manufacturing systems engineering, ISU to test if SDS can be detected using aerial images and machine learning algorithm. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.
A manuscript entitled “Relationship between sudden death syndrome caused by Fusarium virguliforme and soybean yield: A meta-analysis” was published in Plant Disease (Plant Dis. 104:1736-1743). A total of 52 uniform field experiments conducted in Illinois, Indiana, Iowa, Michigan, Wisconsin, and Ontario Canada from 2013 to 2017 comparing crop protection products against SDS were analyzed using meta-analytic models to summarize the relationship. For each study, correlation and regression analyses were performed separately to determine correlation coefficients (r), intercept (ß0) and slope (ß1) and then summarized using meta-analysis. The overall mean correlation coefficient was -0.39 indicating yield was negatively correlated with FDX. That means yield will be decreased with increasing SDS foliar symptoms. The correlation was affected by disease level and cultivar with a greater magnitude in higher disease levels and with susceptible cultivars. The mean ¯ß1 was -21 kg/ha/%. In relative percent term, for every unit of FDX increase yield will be decreased by 0.5%. The result was presented in the annual American Phytopathological Society meeting held on August 3-7 2019 in Cleveland Ohio.
We completed field experiments evaluating integrated management plan for SDS. In this study, we evaluated how integration of different management options (seed treatment, low planting population, and resistant cultivar) effects on root rot, SDS, and soybean yield. Treatments included industry standard susceptible and resistant cultivars with base, base + ILEVO, and base + Saltro seed treatment at three different seed rates (110, 000 and 140,000, and 170,000 seeds/a). Assessments was made on plant population, root rot severity and root dry weight, SDS incidence and severity over time, and yield. At each location, we collected weather data (soil temperature, rainfall) and recorded soybean growth stages at each disease assessment time. Data analysis and manuscript writing is in progress. Manuscript will be published in peer-reviewed journal soon.
In 2020, we conducted field experiments in Illinois, Indiana Iowa, Michigan, Wisconsin, and Ontario, Canada to determine how fungicide and nematicide seed treatments, in-furrow and foliar applications will affect SDS and SCN. Three separate field experiments were conducted in each state in 2019 to i) test the efficacy of seed treatment fungicides for SDS management ii) evaluate the efficacy of nematicides seed treatments against SCN and SDS and iii) develop integrated management plan for SDS. For experiment 1, we evaluated seed treatments including fungicides (ILEVO and Saltro) and fungicides+nematicides (ILEVO + BioSt, Mertect + BioSt, Mertect + Heads Up + BioSt) applied on seed of SDS susceptible and resistant cultivars at each location. Nematicides: BioSt, Aveo, Clariva, ILEVO, Trunemco, Saltro, Saltro+ Clariva, VOTIVO, and Nemastrike were evaluated for experiment 2. Different combinations of seed treatment, cultivar, and seeding rate were tested for experiment 3. Experiment 2 was conducted in additional locations; Missouri, Kentucky, Nebraska, North Dakota, Arkansas and Delaware too. Data were collected on plant population, foliar SDS incidence and severity using standard protocols, and yield. Root rot and root dry weight were collected only from few treatments and in few locations because of restrictions surrounding the COVID pandemic. We also collected soil samples for SCN counts and HG tying at planting at each location. SCN counting and HG typing from those spring samples was processed at SCN diagnostics at University of Missouri, Columbia. Data collection from all of these experiments is done. We are currently analyzing data and summarizing results. Results from these experiments will be presented in next annual American Phytopathological Society meeting.
Muhammad Mohsin Raza, a PhD student co-advised by Dr. Leonor Leandro and Dr. Daren Mueller, graduated in 2019 estimating yield loss due to SDS and early detection of SDS in field using machine learning algorithms for his graduate research. In the preliminary analysis from the first two years of the plant and patch level field studies, we have found that time of disease onset, i.e. the date when foliar symptoms are first visible, is highly predictive of final disease severity and yield. For example, yield loss can be predicted with an accuracy of 80-90% based on the day of onset. The regression models developed for these data suggest that for each week that disease onset is delayed, yield increases by 0.7 to 7.0 bushels/acre, depending on the field. This new information opens the opportunity to test if time of disease onset can be used to differentiate among resistance levels in soybean genotypes and to compare the effectiveness of different management tools. In 2020, we conducted a field experiment in Iowa, where six commercial cultivars that vary in their SDS resistance rank were planted to test if time of disease onset is a more reliable indicator of field resistance than final severity ratings that is currently being used. Data were collected on time of foliar symptom onset, disease progress, and yield data. The cultivars were planted in replicated plots without artificial inoculation with F. virguliforme. Foliar disease was monitored on a weekly basis and yield was collected at the end of the season however no foliar disease symptoms were observed in 2020 because of dry weather.
In 2018 and 2019, several hundred individual plants with different visual ratings of SDS from low to high were tagged in three farmer’s fields located in the Boone, Hamilton and Webster counties of the state of Iowa and the border rows of our 2018 microplot experiment at Iowa State University’ Hinds Research Farm in Story County. Disease was rated multiple times at weekly interval in those plants. Two hundered soybean plants with a range of SDS foliar symptoms were arbitrarily sampled (fifty plants from each farmer field and the border rows of the experimental plot at R6 growth stage for the Fv population density (in soybean root tissues and soil) study. At the end of the season, the remaining labeled plants were harvested individually and yield component data including a total number of pods per plant, the total number of seeds per plant, total seed weight per plant and 100-seed weight per plant were collected from individual plants to correlate yield with the SDS severity. Result showed that the disease onset time is highly correlated with the final disease severity and yield. Plot with earlier disease onset has greater disease severity at the end of the season and greater yield loss. Data analysis and writing manuscripts is in process and will be submitted for publication soon.
To determine SDS integrated management effects on F. virguliforme population level in soil and soil health, every participating states collected soil samples from UTC and ILeVO treated plots of both resistant and susceptible cultivars. Samples were sent to the University of Illinois for assessment indicators of soil health related to microbial activity and disease suppression, as well as mycorrhizal colonization potential, and total nematode community assessment. Soil health indicators will be assessed according to the Cornell Soil Health Assessment and include chemical, physical, and biological indicators: microbial biomass, enzyme activities, mineralizable C, active C, total soil protein, pH, EC, OM, available N, P, K and micronutrients, bulk density, and infiltration.
To test the difference in F. virguliforme population in soil during the season and examine if F. virguliforme population can be used to determine risk of SDS, soil samples were collected from diseased and healthy zones form two locations in 2019 and analyzed in collaboration with Pattern Ag (https://www.pattern.ag). Result showed a clear difference in F. virguliforme population in soil level in healthy and diseased zones. In 2020, we collected soil samples from the trial locations of each state at planting to determine baseline F. virguliforme population. Soil samples are being processed in Pattern Ag (https://www.pattern.ag) and in Dr. Martin Chilver’s lab at Michigan State University. Result from Dr Chilvers lab will be presented in next annual American Phytopathological Society meeting.
A manuscript titled “Predicting soybean yield and sudden death syndrome development using at-planting risk factors” has been published in plant disease (Plant Dis. 109:1710-1719). 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. SCN distributions at-planting were significantly correlated with end-of-season SDS severity and yield in 2015. Prediction models developed through multiple linear regression showed that F. virguliforme abundance (P < 0.001), SCN egg quantity (P < 0.001), and growing season (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. And an additional manuscript for detection and quantification of F. brasiliense has been published (Plant Dis 104:246-254).
A manuscript titled “Linkage Mapping for Foliar Necrosis of Soybean Sudden Death Syndrome” is published in Plant Disease (Plant Dis. 110:907-915). This study generated an F2 population derived from crossing the susceptible variety Sloan and the resistant germplasm line PI 243518, which exhibits resistance to both foliar chlorosis and necrosis. A total of 400 F2 lines were evaluated for foliar chlorosis, foliar necrosis, and overall SDS foliar symptoms, separately. Genotyping-by-sequencing was applied to obtain single nucleotide polymorphisms (SNPs) in the F2 population, and linkage mapping using 135 F2 lines with 969 high-quality SNPs identified a locus on chromosome 13 for foliar necrosis and SDS foliar symptoms. The locus partially overlaps with loci previously reported for SDS on chromosome 13, which is the third time the region from 15.98 to 21.00 Mbp has been reproduced independently and therefore qualifies this locus for a new nomenclature proposed as Rfv13-02. In summary, this study generated a new biparental population that enables not only the discovery of a locus for foliar necrosis and SDS foliar symptoms on chromosome 13 but also the potential for advanced exploration of SDS foliar resistance derived from the germplasm line PI 243518.
A manuscript entitled “Multi-location evaluation of fluopyram seed treatment and cultivar on root infection by Fusarium virguliforme, foliar symptom development, and yield of soybean” has been published in Canadian Journal of Plant Pathology (https://doi.org/10.1080/07060661.2019.1666166). The main objective of this study was to evaluate the influence of soybean cultivar and two rates of fluopyram seed treatment on root rot and foliar symptoms of SDS, root weight, grain yield and colonization of roots by F. virguliforme under multiple field conditions. Three seed treatments: (1) base seed treatment (control), (2) base treatment + standard rate of fluopyram (0.15 mg a.i/seed, and (3) base treatment + reduced rate of fluopyram (0.075 mg a.i/seed) were included. Our results showed that both rates of fluopyram significantly reduced root rot and foliar SDS disease severity and increased yield compared to the base treatment. The two rates of fluopyram did not differ in the reduction of root rot or foliar disease severity, but yield was greater with the higher rate than the lower rate in both years. Yield was negatively correlated with root rot at the R4/R5 stage and with foliar disease index. A yield benefit to fluopyram was also observed in a location where only root rot symptoms but no foliar symptoms were observed. These findings suggest that fluopyram seed treatment can reduce the root rot and the foliar phase of SDS, and both phases play an important role in SDS development and yield and should be managed accordingly.
A manuscript titled” Effect of seed treatment and foliar crop protection products on sudden death syndrome and yield of soybean” has been published in Plant Disease (Plant Dis. 103:1712-1720). Briefly, in this manuscript seed treatment fungicides, ILeVO and Mertect; seed treatment biochemical pesticides, Procidic and HeadsUp; foliar fungicides, Fortix; and an herbicide, Cobra were evaluated in Illinois, Indiana, Iowa, Michigan, South Dakota, Wisconsin, and Ontario for SDS management in 2015 and 2016. Overall, fluopyram provided the highest level of control of root rot and foliar symptoms of SDS among all the treatments. Foliar application of lactofen reduced foliar symptoms in some cases but produced the lowest yield. In 2015, fluopyram reduced the foliar disease index (FDX) by over 50% in both cultivars and provided 8.9% yield benefit in susceptible cultivars and 3.5% yield benefit in resistant cultivars compared to the base seed treatment (control). In 2016, fluopyram reduced FDX in both cultivars by over 40% compared to the base seed treatment. For yield in 2016, treatment effect was not significant in the susceptible cultivar while in the resistant cultivar, fluopyram provided 3.5% greater yield than the base seed treatment. In this study, planting resistant cultivars and using fluopyram seed treatment were the most effective tools for SDS management. Although, plant resistance provided an overall better yield-advantage than using fluopyram seed treatment alone.
An extension article entitled “Seed Treatment and Foliar Fungicide Impact on Sudden Death Syndrome and Soybean Yield’ has been published in Crop Protection Network (CPN-5002| doi.org/10.31274/cpn-20191206-0). This publication was based on two research articles effect of seed treatment and foliar crop protection products on sudden death syndrome and yield of soybean (Plant Dis. 103:1712-1720) and benefits and Profitability of Fluopyram-Amended Seed Treatments for Suppressing Sudden Death Syndrome and Protecting Soybean Yield: A Meta-Analysis (Plant Disease 102:1093-1100). A summary report from field experiment testing the effect of SCN seed treatments on SCN, SDS and yield was presented in Southern Soybean Disease Workers Meeting held at Pensacola, Florida in March 4-5. A poster was presented in virtual annual APS meeting 2020. We presented our research reports at Group Meetings, winter meetings, ICM conferences, on Crop Protection Network, many state or province level talks, seminars, media interviews, talk in field days and conferences for farmers and also published in state newsletter articles, several media releases etc. Our information was also uploaded to SRII. The result from this study will have directly benefited soybean farmers in the North Central region and also establish foundation to address future research and management questions.
This project provides farmers with unbiased information on products that can be used as part of an IPM plan for SDS. As an example, some companies promote their products for SDS management (e.g., Mertect, HeadsUp, Fortix), but after careful evaluation, we are able to show farmers that these products actually are not effective at management of SDS. We also evaluated Syngenta's new product in 2020, providing an unbiased dataset of over 60 locations across the U.S. comparing ILEVO and Saltro. We will continue to evaluate products as they become commercially available.
The group is working towards identifying the risk of SDS in fields. We are still several steps away, but progress is being made in correlating inoculum and disease levels. We are also looking at other soil health metrics to fine tune this relationship.
We evaluated other management strategies that could be incorporated into an IPM program. For example, there is some evidence that corn residue plays a role in SDS severity. We evaluated the effect of removing corn residue on SDS. We found that removing corn residue (leaves, stalks) did not affect SDS levels. We also evaluated SCN seed treatments since SCN can affect SDS severity. We found that SCN seed treatments do not affect SDS levels. We initiated a study looking at an IPM approach, combining several management strategies to maximize SDS control.
We take several different strategies for making sure the work being done on SDS is being communicated. We write journal articles to make sure the research community is aware of what we are doing, we write articles for the soybean checkoff community and Extension articles for ag business/extension agents through the Crop Protection Network. We also communicate about our research directly with farmers and ag business through extension events, social media, and media press releases.