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In the near future farmers may be able to use data-driven decision-support tools to prevent diseases, reduce costly crop inputs and determine the best crops to plant in each growing season.
Ultimately the National Predictive Modeling Tool Initiative will be a nationwide, multi-crop-disease-forecasting data tool.
Cotton research hopes to create DNA-detection tools for cotton pathogens that can be multiplexed and deployed in air-sampling systems.
The fungus Phyllachora maydis causes tar spot disease in susceptible varieties of corn.
The fungal-pathogen tar spot survives the winter in leftover infested crop residues and on soil surfaces. Presence of other host plants are currently unknown.
Tar spot or fungal fruiting structures called ascomatum appear as small, raised, black spots scattered across the upper- and lower-leaf surfaces, and even on husks and leaf sheaths during severe infections. Sausage-shaped spore cases are visible under a microscope.
After rain or periods of high humidity, the stromata extrude spores which accumulate in a gelatinous mass on the stromata. In turn, rain and wind disperse these spores that can travel up to 80 yards. Pustules made by the tar spot fungi cannot be scraped off by fingernails; the feature helps to distinguish between tar spot and common-southern rust spores during the late growing season.
MADISON, Wis. – Scientific advances in predictive modeling now allow long-range weather forecasting, pathogen identification, spore transport across states and regions, and more. In the near future farmers may be able to use data-driven decision-support tools to prevent diseases, reduce costly crop inputs and determine the best crops to plant in each growing season.
Damon Smith
The Corn Research Area Committee established the following objectives for its 2021-2022 research efforts for the National Predictive Modeling Tool Initiative.
Objective 1. To establish the associations among inoculum intensity, disease development and weather in small plot trials for assessment of gray leaf spot, northern corn leaf blight, tar spot and Gibberella ear rot.
Objective 2. To establish the associations among initial inoculum, disease development and weather in commercial corn fields for assessment of gray leaf spot and northern corn leaf blight.
The following researchers will be participating.
• Tom Allen, Mississippi State University
• Kaitlyn Bissonnette, University of Missouri
• Mark Busman, USDA-ARS at Peoria, Illinois
• Martin Chilvers, Michigan State University
• Pierce Paul, Ohio State University
• Paul “Trey” Price, Louisiana State University
• Alison Robertson, Iowa State University
• Damon Smith, University of Wisconsin
• Darcy Telenko, Purdue University
• Kiersten Wise, University of Kentucky
Visit agpmt.org/5-year-action-plan/corn-disease-management/ and agpmt.org/wp-content/uploads/2021/09/Corn-SOW.pdf for more information.
The Wheat Research Area Committee established the following objectives for its 2021-2022 research efforts for the National Predictive Modeling Tool Initiative.
Objective 1. To develop a database of historical disease epidemics in the United States that will serve as a foundation for the modeling effort of cereal rust and leaf blotch epidemics at the state and regional levels.
Objective 2a. To quantify associations among pathogen-inoculum density, disease development and weather variables in small plot trials, initially focused on Parastagonospora nodorum, the causal agent of Septoria nodorum blotch.
Objective 2b. To quantify associations among airborne inoculum concentration on onset, development and spread of leaf, stripe and stem rust in small plot trials.
Objective 3. To quantify associations among pathogen inoculum, disease development and weather variables in commercial fields, focused on the cereal rust and wheat blotch complexes of disease.
The following researchers will be participating.
• Kelsey Anderson, Kansas State University
• Mary Burrows, Montana State University
• Emmanuel Byamukama, South Dakota State University
• Erick DeWolf, Kansas State University
• Cecilia Monclova, Texas A&M University
• Tim Murray, Washington State University
• Pierce Paul, Ohio State University
• Uta Stuhr, Montana State University
• Jake Westlin, National Association of Wheat Growers
Visit agpmt.org/5-year-action-plan/wheat-disease-management/ and agpmt.org/wp-content/uploads/2021/09/Wheat-SOW.pdf for more information.
The Cotton Research Area Committee established the following objectives for its 2021-2022 research efforts as part of the National Predictive Modeling Tool Initiative.
Objective 1. To create DNA-detection tools for cotton pathogens that can be multiplexed and deployed in air-sampling systems.
Objective 2. To monitor commercial fields with active and passive sampling of airborne spores to build and validate pathogen models.
Objective 3. To monitor commercial fields with passive sampling of airborne spores near four sentinel plots to validate models and demonstrate the utility of pathogen sampling.
Objective 4. To conduct seed-treatment trials to relate soil-borne pathogens, environmental conditions and seed-treatment pesticides to cotton-stand establishment.
Objective 5. To create cotton-epidemiology models for target spot, Ramularia, and seedling disease to predict disease progression, crop impact and pathogen load.
The following researchers will be participating.
• Akhtar Ali, University of Tulsa
• Tom Allen, Mississippi State University
• Kaitlyn Bissonnette, University of Missouri
• Kater Hake, Cotton Inc.
• Heather Kelly, University of Tennessee
• Bob Kemerait, University of Georgia
• Kathy Lawrence, Auburn University
• Cecilia Monclova, Texas A&M University
• John Mueller, Clemson University
• Paul “Trey” Price, Louisiana State University
• Ian Small, University of Florida
• Terry Spurlock, University of Arkansas
Visit agpmt.org/5-year-action-plan/cotton-disease-management/ and agpmt.org/wp-content/uploads/2021/09/Cotton-SOW.pdf for more information.
AIDO4Crops – Analytics for Investigation of Disease Outbreaks for Crops – is a decision-support tool designed to enhance situational awareness during unfolding disease outbreaks by providing detailed background information on disease trends from historical growing seasons.
Using curated data from as much as 10 years ago, AIDO4Crops provides users with the ability to easily identify previous growing seasons that have similar disease trends to the current season. It uses a similarity algorithm to find the closest matching historical growing season to ongoing situations in the field. The tool asks a series of questions to collect data for on-going field conditions.
The same information has been collected for each historical growing season. The similarity algorithm uses those values to identify the most similar historical occurrence and provides extensive data on that occurrence. The data presented in various tabs of AIDO4Crops include a geographical map of occurrences, a time series of occurrences and detailed textual descriptions of the crop conditions. That allows users to find years that exhibited similar disease trends to their current growing season, and then use the historical details described in AIDO4Crops to understand whether to reuse the same approaches for controlling crop diseases.
Visit aido4crops.bsvgateway.org or email deshpande_a@lanl.gov for more information.
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Using curated data from as much as 10 years ago, AIDO4Crops provides users with the ability to easily identify previous growing seasons that h…
Tar spot or fungal fruiting structures called ascomatum appear as small, raised, black spots scattered across the upper- and lower-leaf surfaces, and even on husks and leaf sheaths during severe infections. Sausage-shaped spore cases are visible under a microscope.
After rain or periods of high humidity, the stromata extrude spores which accumulate in a gelatinous mass on the stromata. In turn, rain and wind disperse these spores that can travel up to 80 yards. Pustules made by the tar spot fungi cannot be scraped off by fingernails; the feature helps to distinguish between tar spot and common-southern rust spores during the late growing season.
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