Soybean growers across the US face several common yield limiting diseases that can result in annual losses from $2.2 to $4.6 million. Decision making tools provide a method to target fungicide applications, simultaneously improving fungicide efficacy and proactively delaying the development of fungicide resistance. With funding from soybean checkoff funds, the development of the Sporecaster app has been successful in helping farmers in northern states make fungicide application decisions for management of white mold. The Sporecaster platform has since been used to develop a framework for frogeye leaf spot for northern soybean growing regions. Given the importance of frogeye leaf spot to Southern, Mid-southern, and Atlantic regions, the present proposal aims to expand the frogeye leaf spot prediction framework to these regions. In addition, the proposal aims to consolidate research activities across these regions for two other common disease problems, Cercospora leaf blight and target spot, laying the groundwork for comparable prediction tools for these diseases. The project will use historical datasets of fungicide trials, new uniform fungicide trials, spore trapping networks, and improved detection tools for predominant foliar pathogens of soybean to develop the data necessary for developing prediction tools. Each of these will independently provide information relevant to soybean farmers and other stakeholders involved in soybean production, and will also be combined into higher impact decision making tools relevant across the U.S.

Objective 1: Expand the development and validation of frogeye leaf spot prediction tool to new regions, using the Sporecaster framework.
Objective 2: Develop and conduct experiments to adapt the Sporecaster framework for Cercospora leaf blight prediction.
Objective 3: Develop and conduct experiments to adapt the Sporecaster framework for target spot prediction.
Objective 4: Communicate results of the research to farmers and other stakeholders involved with soybean production.

1. Peer-reviewed publications that detail the development of prediction models and tools for Cercospora leaf blight, frogeye leaf spot, and target spot.
2. Peer-reviewed publication that reports on the most efficacious and economically viable fungicides for management of Cercospora leaf blight.
3. Peer-reviewed publication that reports on the most efficacious and economically viable fungicides for management of target spot.
4. Peer-reviewed publication that detail the geographical and temporal distribution of spores of important foliar fungal pathogens of soybean that will improve our epidemiological and biological understanding of these pathogens.
5. Updated foliar disease information on SRIN.
6. Updated Extension publications on frogeye leaf spot and new Extension publications on Cercospora leaf blight and target spot.

Updated September 1, 2023:
Uniform soybean foliar fungicide field trials in eleven different states have been planted, treatments have been applied, and disease evaluations are currently ongoing. The current levels of foliar disease pressure range from low to high with frogeye leaf spot and Septoria brown spot being the two most commonly-observed diseases in the trials so far.

Spore traps have been deployed in soybean fields across eleven different states. Spore samples are being collected at two different heights weekly. Currently, spore samples are being stored in a refrigerator and will be sent in bulk to the Smith and Thomas-Sharma Labs where they will be quantified using quantitative-PCR (qPCR) assays. Initial discussions to standardize TaqMan spore assays across labs were conducted. Thomas-Sharma's Lab will provide serial dilutions of spores of the three Cercospora species that cause Cercospora leaf blight to the Smith Lab. Isolates for these are being grown out for sporulation. The Thomas-Sharma Lab also will provide the Taqman qPCR assay for testing. All DNA extraction will be conducted using the FastDNA Spin Kit, used by the Smith Lab.

The Smith Lab continues to work on the "Frogspotter" app to forecast risk of frogeye leaf spot. Field data from this current year will be added to adjust the model as needed.

Updated January 31, 2024:
Uniform soybean foliar fungicide field trials were conducted in eleven different states, in which eleven treatments and a nontreated check were evaluated. Disease pressure varied from location to location, and data from locations were dived into two categories: low disease pressure and moderate-high disease pressure. Results from these trials showed that the average yield response of all fungicide treatments relative to the nontreated check was 5.3 bu/A in the moderate-high disease pressure locations, compared to 1.8 bu/A in the low disease pressure locations. The data from the uniform fungicide trial were used to help revise the 2024 edition of the Crop Protection Network Soybean Foliar Fungicide Efficacy Guide. In addition, the data from these trials are being used to test, adjust, and optimize disease prediction models.

Spore traps were deployed in soybean fields across eleven different states. Spore samples were collected at two different heights weekly. The samples were sent in bulk to the Smith Laboratory (University of Wisconsin), where DNA currently is being extracted. Once extracted, the Smith and Thomas-Sharma (Louisiana State University) Labs will use quantitative-PCR (qPCR) assays to quantify foliar pathogens. Currently, qPCR protocols are being validated for the three Cercospora species that cause Cercospora leaf blight in the Thomas-Sharma Lab, and qPCR protocols are being developed in the Smith Lab for the frogeye leaf spot pathogen. These quantitative data will be used to better inform traditional disease forecasting models and machine-learning tools used to improve the accuracy of forecasting models.

The Smith Lab continues to work on the "Frogspotter" app to forecast risk of frogeye leaf spot. A beta model was tested in 2023. The model currently uses a 21-day average of maximum air temperature and 10-day average of total daily hours with max relative humidity > 75% (wetting variable), and daily risk indices are calculated in the tool. The disease data collected from the uniform fungicide trials are currently being used to retrain the model, and further field validation will occur during the 2024 growing season.

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Uniform soybean foliar fungicide field trials were conducted in eleven different states, in which eleven treatments and a nontreated check were evaluated. Disease pressure varied from location to location, and data from locations were dived into two categories: low disease pressure and moderate-high disease pressure. Results from these trials showed that the average yield response of all fungicide treatments relative to the nontreated check was 5.3 bu/A in the moderate-high disease pressure locations, compared to 1.8 bu/A in the low disease pressure locations. The data from the uniform fungicide trial were used to help revise the 2024 edition of the Crop Protection Network Soybean Foliar Fungicide Efficacy Guide. In addition, the data from these trials are being used to test, adjust, and optimize disease prediction models.

Spore traps were deployed in soybean fields across eleven different states. Spore samples were collected at two different heights weekly. The samples were sent in bulk to the Smith Laboratory (University of Wisconsin), where DNA currently is being extracted. Once extracted, the Smith and Thomas-Sharma (Louisiana State University) Labs will use quantitative-PCR (qPCR) assays to quantify foliar pathogens. Currently, qPCR protocols are being validated for the three Cercospora species that cause Cercospora leaf blight in the Thomas-Sharma Lab, and qPCR protocols are being developed in the Smith Lab for the frogeye leaf spot pathogen. These quantitative data will be used to better inform traditional disease forecasting models and machine-learning tools used to improve the accuracy of forecasting models.

The Smith Lab continues to work on the "Frogspotter" app to forecast risk of frogeye leaf spot. A beta model was tested in 2023. The model currently uses a 21-day average of maximum air temperature and 10-day average of total daily hours with max relative humidity > 75% (wetting variable), and daily risk indices are calculated in the tool. The disease data collected from the uniform fungicide trials are currently being used to retrain the model, and further field validation will occur during the 2024 growing season.

This project will provide validated tools that soybean farmers across the U.S. can utilize to make important disease management decisions.