Skip to main content
ARS Home » Crop Production and Protection » Research » Research Project #437843

Research Project: Establishing the Infrastructure to Develop Prediction Tools for Diseases & Affecting Cotton to Better Inform Management Decisions (Tulsa)

Location: Crop Production and Protection

Project Number: 0500-00102-001-007-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Jun 5, 2020
End Date: Jun 4, 2024

The first objective is to create DNA detection tools for cotton pathogens that can be multiplexed and deployed in air sampling systems. The second objective is to establish the association between initial inoculum, disease development and weather in sentinel plots to build and validate pathogen models.

The Ali lab at The University of Tulsa will conduct all experiments as outlined under Objectives 1 d. (create DNA detection tools for cotton pathogens that can be multiplexed and deployed in air sampling systems – specially to optimize aerial spore traps for detection of insect vectored DNA and RNA viruses) and Objective 2 (establish a SENTINEL PLOT with active and passive sampling of air borne spores to build and validate pathogen models) of the project. The laboratory work in Objective 1 will be conducted in the PI’s lab at the University of Tulsa using samples collected by PI Ali and other project PI’s. The field plot experiment in Objectives 2 well be established in a replicated cotton variety trial in coordination with State Cotton Extension Specialist, Seth Byrd. Dr. Ali will supervise and work along with the students assigned to the project to collect, ship and/or process plant tissue and aerosol samples, quantify target spot and other pathogens that infest the sites, and monitor cotton growth and weather conditions following the protocols detailed in the proposal. The TU PI and students will also work with PIs in the other cotton participating states to organize, process, mine, and analyze weather, disease, and spore density data to quantity associations among measured responses, and eventually (likely in year 2), test and validate various risk assessment models.