Biological Systems Engineering


Date of this Version



Meyers MA, Durso LM, Gilley JE, Waldrip HM, Castleberry L, Millmier-Schmidt A. Antibiotic resistance gene profile changes in cropland soil after manure application and rainfall. J. Environ. Qual. 2020;49:754–761.


This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License


Land application of manure introduces gastrointestinal microbes into the environment, including bacteria carrying antibiotic resistance genes (ARGs). Measuring soil ARGs is important for active stewardship efforts to minimize gene flow from agricultural production systems; however, the variety of sampling protocols and target genes makes it difficult to compare ARG results between studies. We used polymerase chain reaction (PCR) methods to characterize and/or quantify 27 ARG targets in soils from 20 replicate, long-term no-till plots, before and after swine manure application and simulated rainfall and runoff. All samples were negative for the 10 b-lactamase genes assayed. For tetracycline resistance, only source manure and post-application soil samples were positive. The mean number of macrolide, sulfonamide, and integrase genes increased in post-application soils when compared with source manure, but at plot level only, 1/20, 5/20, and 11/20 plots post-application showed an increase in erm(B), sulI, and intI1, respectively. Results confirmed the potential for temporary blooms of ARGs after manure application, likely linked to soil moisture levels. Results highlight uneven distribution of ARG targets, even within the same soil type and at the farm plot level. This heterogeneity presents a challenge for separating effects of manure application from background ARG noise under field conditions and needs to be considered when designing studies to evaluate the impact of best management practices to reduce ARG or for surveillance. We propose expressing normalized quantitative PCR (qPCR) ARG values as the number of ARG targets per 100,000 16S ribosomal RNA genes for ease of interpretation and to align with incidence rate data.