CURRENT PROJECTS are listed by Research Area, the objectives of the Research Area and researchers involved.
CORN RESEARCH AREA
The Corn RAC established the following objectives for 2021 – 2022 research efforts for the National Predictive Modeling Tool Initiative (NPMTI):
- Objective 1. To establish the associations among inoculum intensity, disease development, and weather in small plot trials for assessment of gray leaf spot (GLS), northern corn leaf blight (NCLB), tar spot (TS) and Gibberella ear rot (GER).
- Objective 2. To establish the associations among initial inoculum, disease development, and weather in commercial corn fields for assessment of GLS and NCLB.
The following researchers will be participating:
Tom Allen, Ph.D., Extension/Research Professor, Mississippi State.
Kaitlyn Bissonnette, Ph.D., Assistant Extension Professor, Plant Sciences, University of Missouri.
Mark Busman, Ph.D., Research Chemist, USDA-ARS, Peoria.
Martin Chilvers, Ph.D., Associate Professor, Field Crop Pathology, Michigan State.
Pierce Paul, Ph.D., Professor, Extension Specialist, Plant Pathology, Ohio State.
Paul “Trey” Price, Ph.D., Associate Professor, Agronomic Crops, Louisiana State.
Alison Robertson, Ph.D., RAC Chair, Professor and Extension Field Pathologist, Iowa State.
Damon Smith, Ph.D., RAC Co-Chair, Associate Professor and Extension Specialist, University of Wisconsin – Madison.
Darcy Telenko, Ph.D., Assistant Professor, Botany and Plant Pathology, Purdue University.
Kiersten Wise, Ph.D., Professor, Extension Specialist, University of Kentucky.
For a copy of the complete Corn RAC statement of work (SOW) click here.
COTTON RESEARCH AREA
The Cotton Research Area Committee (RAC), established the following objectives for 2021 – 2022 research efforts as part of the National Predictive Modeling Tool Initiative (NPMTI):
- Objective 1. To create DNA detection tools for cotton pathogens that can be multiplexed and deployed in air sampling systems.
- Objective2. 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 4 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, Professor, Biological Science, University of Tulsa.
Tom Allen, Ph.D., Extension/Research Professor, Mississippi State.
Kaitlyn Bissonnette, Ph.D., Assistant Extension Professor, Plant Sciences, University of Missouri.
Kater Hake, Ph.D., RAC Co-Chair, Cotton Inc.
Heather Kelly, Ph.D., RAC Chair, Associate Professor, Plant Pathology, University of Tennessee.
Bob Kemerait, Ph.D., Professor, Plant Pathology, University of Georgia – Tifton.
Kathy Lawrence, Ph.D., Professor, Entomology and Plant Pathology, Auburn University.
Cecilia Monclova, Ph.D., Assistant Professor & Extension Plant Pathology, Texas A&M.
John Mueller, Ph.D., Professor, Plant Pathology, Clemson University.
Paul “Trey” Price, Ph.D., Associate Professor, Agronomic Crops, Louisiana State.
Ian Small, Ph.D., Assistant Professor, Plant Pathology, University of Florida – Quincy.
Terry Spurlock, Ph.D., Associate Professor, Plant Pathology, University of Arkansas.
For a copy of the complete Cotton RAC statement of work (SOW) click here.
WHEAT RESEARCH AREA
The Wheat Research Area Committee (RAC), established objectives for 2021 – 2022 research efforts for the National Predictive Modeling Tool Initiative (NPMTI) as follows:
- Objective 1. To develop a database of historical disease epidemics in the U.S 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, causal agent of Septoria nodorum blotch (SNB).
- 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, Ph.D., Assistant Professor, Plant Pathology, Kansas State.
Mary Burrows, Ph.D., Professor, Extension Plant Pathology, Montana State.
Emmanuel Byamukama, Ph.D., Associate Professor, Extension Plant Pathologist, South Dakota State.
Erick DeWolf, Ph.D., RAC Co-chair, Professor, Plant Pathology, Kansas State.
Cecilia Monclova, Ph.D., Assistant Professor & Extension Plant Pathology, Texas A&M.
Tim Murray, Ph.D., Professor, Extension Plant Pathologist, Washington State.
Pierce Paul, Ph.D., Professor, Extension Specialist, Plant Pathology, Ohio State.
Uta Stuhr, Ph.D., Associate Extension Specialist Plant Pathology, Montana State.
Jake Westlin, RAC Chair, National Association of Wheat Growers (NAWG).
For a copy of the complete Wheat RAC statement of work (SOW) click here.
COLLABORATING ORGANIZATIONS
The Research Area Committees (RAC) of the National Predictive Modeling Tool Initiative are supported by two collaborating organizations: Los Alamos National Laboratory (LANL), and the National Agricultural Genotyping Center (NAGC).
LANL has developed a suite of forecasting tools to anticipate human health trends. The LANL models will be adapted to forecast plant disease outbreaks, as well as identify potential new disease threats. Over time, baseline background levels of various pathogens can be established, which will help with anomaly detection and serve as an early warning system for our nation’s food security.
LANL is one of the founding agencies behind the creation of the NAGC, which launched in 2016. NAGC is a producer-directed, not-for-profit, 501(c)(5) corporation that focuses on commercializing the research of others by making scientific discoveries available to producers and their advisors. NAGC has experience managing complex multi-state Initiatives. It administers NPMTI as the Networking & Facilitation Office.
NAGC initiated the concept of post-harvest sampling of soil and crop residues to quantify pathogen loads so that appropriate management decisions can be made prior to the next growing season. The concept was enhanced by Dr. Alison Robertson of Iowa State University with the addition of in-season monitoring for pathogens using air samplers that monitor for pathogen spores. NAGC then led the successful charge to get the enhanced proposal funded through the U.S. congressional appropriations process, which became known as the National Predictive Modeling Tool Initiative.
NAGC is the lead laboratory for NPMTI in the development of assays for detection and quantification of new pathogens. It makes NAGC-developed plant pathogen assays available on an open-source basis to extension researchers at cooperating land grant universities. NAGC will also provide diagnostic testing services to each RAC without charge, thus allowing the RAC to focus its resources on field-based research, data collection, and analysis.
Los Alamos National Laboratory:
LANL has established objectives for 2021 – 2022 research efforts for the National Predictive Modeling Tool Initiative (NPMTI) as follows:
- Objective 1. Refine analytics and models for the purposes of analytics and models for the purposes of crop disease forecasting.
- Develop algorithms for Wheat blotch diseases and specific corn and cotton diseases identified by crop partners (e.g., tar spot, areolate mildew).
- Refine LANL disease database for corn, cotton, and wheat-based on user testing on an ongoing basis.
- Objective 2. Develop a lateral flow-based assay for detection of mycotoxins in the field.
- Perform literature review of current state of the art for mycotoxin detection in the field. Collect requirements from corn and wheat partners for mycotoxin type, technical, and operational specifications for a lateral flow or alternative fieldable platform.
- Begin development of assay(s) required for mycotoxin detection.
- Work to design lateral flow or alternative fieldable platform for mycotoxin detection (one or more depending on requirements).
The LANL Research Team on the National Predictive Modeling Tool Initiative is led by:
Sheila Van Cuyk, Ph.D. – Research Scientist, Bioscience Division, LANL & NPMTI project chair.
Alina Deshpande, Ph.D. – Group Leader, Biosecurity and Public Health, LANL & NPMTI co-chair.
For a copy of the complete LANL RAC statement of work (SOW) click here.
National Agricultural Genotyping Center:
NAGC has established objectives for 2021 – 2022 research efforts for the National Predictive Modeling Tool Initiative (NPMTI), as follows:
- Objective 1. To develop validated, quantifiable assays for diseases of concern to corn, cotton, and wheat RACs.
- Objective 2. To standardize testing among and across all labs that are currently (or may someday) be involved in testing services for the NPMTI and review and refine current tests.
- Objective 3. To provide testing services for 6,000 total samples at no cost to the RACs to help researchers focus resources on field-based study, data collection, and analysis.
The NAGC team on the National Predictive Modeling Tool Initiative is led by:
Zack Bateson, Ph.D. – NAGC Research Scientist & NPMTI project chair.
Megan O’Neil – NAGC Laboratory Manager & NPMTI project co-chair.
For a copy of the complete NAGC statement of work (SOW) click here.
NEW TOOLS
AIDO4Crops
Visit AIDO4Crops here!
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 up to ten years ago, AIDO4Crops provides users with the ability to easily identify previous growing seasons that have similar disease trends to the current season.
AIDO4Crops 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 these values to identify the most similar historical occurrence and provides extensive data on this 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. This 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.
Point of Contact: For questions and comments about AIDO4Crops, please contact deshpande_a@lanl.gov