Soybean sudden death syndrome (SDS) is an important disease of soybean in Kansas, resulting in millions of dollars of economic loss annually. Together, the objectives proposed in this study will help producers better manage this disease on their farms through better detection, prediction of risk, and through recommendations for cropping system adjustments (plant population and row spacing). Our proposed field trials will be used as “living classrooms” through in-field extension events.

Objective 1: Evaluate the influence of agronomic practices on soybean sudden death syndrome (SDS) through on-farm trials evaluating row spacing, plant population, and variety selection.
Objective 2: Develop a sudden death syndrome prediction tool for predicting disease prior to planting.
Objective 3: Evaluate genetic diversity of Fusarium spp. from soybeans causing SDS in Kansas.
Objective 4: Validate a rapid diagnostic tool for SDS based on loop-mediated isothermal amplification (LAMP).
Objective 5: Generate and promote data-driven best management practices based on results of objectives 1, 2, and 3.

Objective 1: Evaluate the influence of agronomic practices on soybean sudden death syndrome (SDS) through on-farm trials evaluating row spacing, plant population, and variety selection.
Deliverable: Updated best management practice recommendations for agronomics that influence disease development.
Deliverable: At least one peer-reviewed publication.
Objective 2: Develop a sudden death syndrome prediction tool for predicting disease prior to planting.
Deliverable: A risk tool to help farmers understand their SDS risk at the time of planting.
Objective 3: Evaluate genetic diversity of Fusarium spp. from soybeans causing SDS in Kansas.
Deliverable: An improved understanding of population diversity in our region.
Objective 4: Validate a rapid diagnostic tool for SDS based on loop-mediated isothermal amplification (LAMP).
Deliverable: A diagnostic tool that can be used by diagnostic labs to better diagnose SDS from farmer's fields.
Deliverable: At least one peer-reviewed publication.
Objective 5: Generate and promote data-driven best management practices based on the results of objectives 1, 2, and 3.
Deliverable: A series of extension materials and programs to improve knowledge of management practices among Kansas farmers.

Update:
In year one of this project, field trials were established in Rossville and Topeka in Kansas and one location in Iowa. for Objective 1, data are currently being analyzed but preliminary results suggest that SDS was more severe under high plant populations and narrower (15-in) row spacings. For objective 2, historical data has been organized and summarized (from multi-year K-State research trials) to evaluate the influence of pre-planting weather conditions on SDS symptom development. Previously collected isolates from SDS symptomatic plants were purified and sequenced. Preliminary phylogenetic analyses (Objective 3) suggest F. virguliforme was the only species detected. Additional isolates were collected in 2023 and are currently being processed. We are beginning work on Objective 4. Through this project, we were able to partially support on MS student and two visiting scholars. This work was shared at several soybean field days in 2023 (Objective 5).

Updated November 15, 2024:
Objective 1: Evaluate the influence of agronomic practices on soybean sudden death syndrome (SDS) through on-farm trials evaluating row spacing, plant population, and variety selection.

In year one of this project, field trials were established in Rossville and Topeka in Kansas and one location in Iowa.

Madison Kessler, the MS student working on this project, presented preliminary results at the 2024 American Phytopathological Society North Central Meeting. Madison’s poster won first place in the competition. The meeting was held in Manhattan, KS from 10-12 June 2024. Please see attached file “Objective 1_Onofre” for preliminary results.

Objective 2: Develop a sudden death syndrome prediction tool for predicting disease prior planting.

Madison Kessler presented preliminary results at the 13th International Epidemiology Workshop. The meeting took place in Foz do Iguaçu, Brazil from 9-12 April 2024. Please see attached file “Objective 2_Onofre” for preliminary results.

Objective 3: Evaluate genetic diversity of Fusarium spp. from soybeans causing SDS in Kansas.

Madison Kessler together with Luiza Adami presented preliminary results at the 2024 American Phytopathological Society North Central Meeting. The meeting was held in Manhattan, KS from 10-12 June 2024. Please see attached file “Objective 3_Onofre” for preliminary results.

During the 2022 season, symptomatic plants were sampled from 13 fields across five counties in Kansas (Fig. 1) resulting in 128 Fusarium spp. being isolated. Genetic analysis using PCR and sequencing of the translation elongation factor-1a (EF-1a) gene was conducted for all isolates. Additionally, isolates identified as part of the FSSC clade 2 were further sequenced for the nuclear rDNA intergenic spacer (IGS) region. Phylogenetic trees were constructed using the Maximum Likelihood method with bootstrap support from1000 replicates. Of the 128 isolates, 113 isolates were confirmed F. virguliforme. Of the remaining isolates six were identified as FSSC clade 5, one as FSS Cclade 3, two as FFSC, two as FOSC, and four as undescribed Fusarium spp. These data suggest that in the fields sampled, F. virguliforme is likely the dominant species causing SDS in Kansas.
Objective 4: Validate a rapid diagnostic tool for SDS based on loop-mediated isothermal amplification (LAMP).

The specificity tests were performed using 22 isolates kindly sent by Dr. Martin Chilvers (Michigan State University). Those isolates include all four species that cause Sudden Death Syndrome (SDS), plus other species that could be found in a soybean field and other Fusarium Species Complex representatives. Each sample in each replicate was performed in duplicate. We noticed that the p6 set of primer is amplifying all SDS pathogens species, however, the Fusarium phaseoli, F. cuneirostrum, and F. azukicola are also amplifying. This means that the primers is not specific for F. virguliforme and for SDS pathogens. New primers are currently being re-designed and will be included in upcoming tests.

Objective 5: Generate and promote data-driven best management practices based on results of objectives 1, 2 and 3.

Through a dynamic extension programming, objectives 1, 2, and 3 were shared during multiple occasions with Kansas Soybean Producers which included on-farm field days and winter meetings, radio and local TV interviews, YouTube, social media, and the K-State Agronomy eUpdate.

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View uploaded report 3

View uploaded report 4

In year one of this project, field trials were established in Rossville and Topeka in Kansas and one location in Iowa. for Objective 1, preliminary results suggest that SDS was more severe under high plant populations and narrower (15-in) row spacings. For objective 2, preliminary results suggest that models based on summaries of soil temperature prior to planting predicted years with severe SDS with more than 80% accuracy. Preliminary phylogenetic analyses (Objective 3) suggest F. virguliforme was the only species detected in Kansas. In Objective 4, we conducted specificity tests using 22 isolates, which included all four species that cause Sudden Death Syndrome (SDS), plus other species that could be found in a soybean field and other Fusarium Species Complex representatives. Through this project, we were able to partially support on MS student and two visiting scholars. This work was shared during multiple occasions with Kansas Soybean Producers which included on-farm field days and winter meetings, radio and local TV interviews, YouTube, social media, and the K-State Agronomy eUpdate in 2023 (Objective 5).

In this proposal we aim to evaluate SDS risk based on agronomic practices and environment, and will validate a more accurate diagnostic assay. Together, this will allow us to update our best management practice recommendations for Kansas soybean producers and improve on-farm decision making.