Research HighlightsBlowing in the Wind: Monitoring and Identifying Fungal Spores
In this article, you’ll find details on:
- Low-cost spore traps and DNA sequencing provide more efficient pathogen monitoring.
- High-tech analysis, including machine learning, can help predict disease pressure and treatment timing.
- This new approach to soybean pathogen monitoring has the potential to predict hotspots and help farmers optimize integrated pest management strategies, including fungicide application.
By Laura Temple
Diseases like frogeye leaf spot and Cercospora leaf blight plague soybean fields. Fungal spores from these and other pathogens remain in the soil and crop residue until they germinate under favorable weather conditions and are dispersed by wind and rain.
Virginia researchers are exploring a new approach to monitoring the presence and threat from these diseases that combines affordable spore traps with DNA sequencing and machine learning technology. The Virginia Soybean Board is funding the application of this approach in soybeans.
“Our goal is to build a surveillance network to monitor how plant pathogen spores travel through Virginia,” says Yuan Zeng, assistant professor at Virginia Tech. “Sharing that information with farmers will let them know what diseases could show up in their fields and help optimize fungicide applications and timings.”
She explains that traditional spore monitoring uses expensive traps to capture spores that research teams can identify and count under a microscope. Her team began testing a more efficient, cost-effective method during the 2023 growing season.
Low-Cost Spore Traps
First, Zeng’s team modified spore traps using a rotating arm, which costs about $400 in materials, just 10% of the cost of the traditional Burkhard spore trap. The new trap uses greased sterile rods attached to the rotating arm at two levels to capture fungal spores. One arm is set about 1.8 meters, or 6 feet, above the ground, while the other is just 40 centimeters, or about 15.75 inches, above the soil surface.
“The spinner arms create a small wind tunnel,” Zeng explains. “We set them at two levels because wind speeds vary, depending on the time of year and development of the crop canopy. The lower arm better captures pathogens that overwinter in the soil or on crop residue as they start traveling.”
The rods are autoclaved before being greased and set out to capture microbes traveling through the air in fields.
Her team placed two traps in each monitored field across 14 counties in Virginia after planting, alongside weather stations to monitor environmental conditions. Starting after planting, they collected the rods every two weeks to analyze the fungal populations in the test fields.
High-Tech Analysis
To determine what fungal spores are traveling through the air, Zeng’s team extracts the DNA of all the microbes found on the rods of each arm on every spore trap. The material is loaded into a sequencer that determines the identities of different DNA fragments of all the organisms present, including fungal spores, in just a few days.
“This metagenomic approach identifies everything in the air, so we get a more complete picture of potential disease threats,” she says. “We already know the genomes of many common pathogens, so we can be confident in the identification from this process. We’ve found very diverse populations of pathogens that cause diseases in soybeans, including frogeye leaf spot, Cercospora leaf blight, Rhizoctonia root and stem rot, Alternaria leaf spot and more.”
To correlate the presence of spores with disease pressure, the team collected disease severity ratings of the crop to identify disease resistance levels of soybean varieties planted in each field. This information, along with weather station data, could impact how farmers manage potential disease problems.
“We use machine learning to understand how pathogen populations shift, given weather conditions and crop growth,” Zeng explains. “Crop canopies create microclimates that impact factors like leaf wetness, which in turn influence when and how fungal spores germinate and penetrate leaves.”
Optimizing Disease Management
Zeng believes the 2023 results show great promise to use this approach to monitor and predict soybean diseases.
“For example, our traps captured frogeye leaf spot spores 47 days before they appeared on soybean leaves,” she explains. “With detailed weather information, we may be able to predict hotspots for frogeye leaf spot and help farmers time fungicide applications to just after the presence of spores, as they start attacking the crop.”
She expects that with additional years of testing and data, this approach will more accurately determine disease thresholds and how weather influences disease development and progression. Combined with hotspot predictions, she believes this information could strengthen farmers’ integrated pest management approach to managing diseases like frogeye leaf spot.
“In identified hotspots, farmers could plant a non-host crop or choose a tolerant variety,” Zeng says. “They could also use weather data to modify planting dates and harvest the crop in that field earlier or later, avoiding having the crop at a vulnerable stage when timing for a disease outbreak is likely. They could also time fungicide applications to reduce disease severity to below threshold levels to minimize fungicide use and maximize yield.”
As this model is proven and refined, she aims to make data available to farmers online.
Looking to the future, she sees potential for spore DNA to provide even more information, like if a population of frogeye leaf spot or Cercospora leaf blight pathogens carry fungicide resistance. This would also inform IPM strategies.
In addition to monitoring soybean fields, Zeng is including corn and tobacco fields in her study. Monitoring pathogenic spores across major crops in the state will support sustainable agroecosystem planning and decision-making for farmers.
Meet the Principal Investigator on this project: Yuan Zeng
Published: Apr 8, 2024
The materials on SRIN were funded with checkoff dollars from United Soybean Board and the North Central Soybean Research Program. To find checkoff funded research related to this research highlight or to see other checkoff research projects, please visit the National Soybean Checkoff Research Database.
