Location: National Programs
Project Number: 0500-00102-001-009-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 5, 2020
End Date: Jun 4, 2022
Objective:
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 third objective is to install passive air samplers in commercial cotton fields located near the Sentinel plots and capture daily weather data. The fourth objective is to create a cotton yield epidemiology model for Target Spot based on disease progress and pathogen load. The fifth objective is to archive all data, models, and samples from the previous objectives to allow future investigators to improve models and tools and to retrospectively identify invasive pathogens or virulent strains.
Approach:
The Lawrence lab at Auburn University will conduct experiments as outlined under Objective 1 (Create DNA detection tools for cotton pathogens that can be multiplexed and deployed in air sampling systems - field collection of Areolate Mildew spores), 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 field experiment in Objectives 2 well be established at the Plant Breeding Unit of the E. V. Smith Research and Education Center near Tallassee, AL). Dr. Lawrence will supervise and work along with the Graduate 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. My Graduate Student and I 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 test and validate various risk assessment models.
