Current Projects

CURRENT PROJECTS are listed by Research Area, the objectives of the Research Area, the researcher involved, and how a particular researcher is fulfilling the Research Area’s objectives.

CORN RESEARCH AREA

Seven scientists are members of the Corn Research Area Committee (RAC) they will be working to develop predictive modeling tools for Northern Corn Leaf Blight (NCLB) and Gray Leaf Spot (GLS).  The Corn RAC established the following objectives for 2020 – 2021 research efforts for the National Predictive Modeling Tool Initiative (NPMTI):

  • Objective 1. To establish the association between inoculum intensity, disease development and weather in small plot trials.
  • Objective 2. To establish the association between initial inoculum, disease development and weather in commercial corn fields.

Researchers listed here are participating in support of the Corn RAC objectives. As noted, it is a collaborative effort among the researchers

corn-4457379_1920cropped
cotton-175477_1920cropped

COTTON RESEARCH AREA

The Cotton Research Area Committee (RAC), which is currently comprised of 12 scientists and leadership from Cotton Inc., created the following Statement of Work (SOW) to guide their activities: to develop and demonstrate community-wide tools for cotton pathogen prediction and management. The SOW is supported by 5 objectives for 2020 – 2021 research efforts as part of the National Predictive Modeling Tool Initiative (NPMTI):

  • Objective Create DNA detection tools for cotton pathogens that can be multiplexed and deployed in air sampling systems.
  • Objective Establish Sentinel Plots with active and passive sampling of air borne spores to build and validate pathogen models.
  • Objective Establish Commercial Fields with passive sampling of air borne spores near four Sentinel Plots to validate models and demonstrate the utility of pathogen sampling.
  • Objective Create cotton yield epidemiology model for Target Spot based on disease progression and pathogen load.
  • Objective Archive all data, models and samples from Tasks 1, 2, 3 & 4 to allow future investigators to improve models and tools and to retrospectively identify invasive pathogens or virulent strains.

Researchers listed here are participating in support of the Cotton RAC objectives. As noted, it is a collaborative effort among the researchers.

WHEAT RESEARCH AREA

The Wheat Research Area Committee (RAC), which is comprised of 7 scientists and leadership from the National Association of Wheat Growers (NAWG), represents both Spring and Winter Wheat interests. A focus of the Wheat RAC is rust research in its three forms: stem, stripe and leaf. The Wheat RAC established objectives for 2020 – 2021 research efforts for the National Predictive Modeling Tool Initiative (NPMTI), as follows:

  • Objective 1. Develop a database of historical disease epidemics in the U.S that will serve as a foundation for the modeling effort.
  • Objective 2a. Quantify associations among pathogen inoculum density, disease development, and weather variables in small plot trials.
  • Objective 2b. Quantify associations among airborne inoculum concentration on onset, development, and spread of leaf, stripe and stem rust in small plot trials.
  • Objective 3. Quantify associations between pathogen inoculum, disease development and weather variables in commercial fields.

Researchers listed here are participating in support of the Wheat RAC objectives. As noted, it is a collaborative effort among the researchers.

ears-1452991_1920cropped

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).

Los Alamos National Laboratory

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.

Field results and data in aggregate will be sent to LANL for input into a forecasting model. With this information, LANL will help to transition technologies that are not being developed elsewhere. For example, LANL will provide backbone modeling of factors affecting plant disease risk (such as crop varieties, tillage type (if any), geographic location, historic weather patterns and other environmental conditions, etc.) on high performance computers to determine ecosystem impacts. In addition, LANL will help to define disease thresholds for quantifiable outcomes, which will become more robust over time.

Standardization is key to the success of NPMTI. LANL will house and aggregate, to a common protocol, the data that is submitted. LANL also will leverage appropriate LANL tools for decision support. The information will be available to NPMTI cooperators, while operating under standard IP rights, where appropriate. A successful forecasting tool of this magnitude and breadth is contingent on delivering simplicity (user-friendly interface for crop consultants, advisors and farmers) out of complexity (advanced predictive modeling).

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.

 

The LANL Research Team on the National Predictive Modeling Tool Initiative is led by:

  • Sheila Van Cuyk, Ph.D. – Research Scientist in the Bioscience Division, LANL & NPMTI project chair
  • Alina Deshpande, Ph.D. – Group Leader, Biosecurity and Public Health, LANL & NPMTI co-chair

National Agricultural Genotyping Center

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.

Los Alamos National Laboratory 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 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.

 

The NAGC team on the National Predictive Modeling Tool Initiative is led by:

  • Zack Bateson, Ph.D. – Research Scientist & NPMTI project chair
  • Megan O’Neil – Laboratory Manager & NPMTI project co-chair