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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Agroecosystems Management Research » Research » Research Project #434868

Research Project: Prairie Strips as a Sustainable Mitigation Strategy to Retain Antimicrobial Resistant Organisms

Location: Agroecosystems Management Research

Project Number: 5030-13000-011-62-N
Project Type: Non-Funded Cooperative Agreement

Start Date: Jun 1, 2018
End Date: Nov 30, 2018

Demonstrate the feasibility of prairie strips as a conservation practice to mitigate the spread of antimicrobials, antimicrobial resistant genes (ARG), and antimicrobial resistant bacteria (ARB) to surface, drainage, and groundwater systems. Our overarching hypothesis is that prairie strips reduce the presence of manure-associated ARBs and ARGs in downstream surface water and soils.

The work will be conducted on a farm where prairie strips have been installed to intercept runoff from a 80 acre field. Liquid swine manure is applied in the fall prior to corn planting. To assess the use of prairie strips for reducing the spread of ARBs and ARGs into surrounding soils and waters, we will evaluate the distribution of antibiotics and manure-associated bacteria and ARGs along several soil transects including areas upstream and within prairie strips. Control samples will also be obtained prior to manure application. Antibiotics, ARB, and ARG will also be measured in manure collected at application and in runoff water samples. Runoff will be generated using rainfall simulation equipment. A proxy measurement of total ARB will be Enterococcus measured using selective media and Enterococcus resistant to tylosin and tetracycline. The ARG measured will include tylosin resistance and tetracycline resistance using qPCR. We will sequence extracted DNA to detect and quantify 16S rRNA genes (total microbiome) to detect additional potential ARBs and provide context to their presence in the total microbial community. Tetracycline and tylosin will be extracted from soil and water samples. Data will be analyzed using an upstream/downstream study design, and a paired t-test will be conducted comparing differences between upstream and downstream concentrations.