Pairing Residues, Resistance Genes and Microbial Community Structure to Understand Off-Farm Impact of Antibiotic Use on Dairy Farms

Progress report for ONE22-412

Project Type: Partnership
Funds awarded in 2022: $29,057.00
Projected End Date: 08/31/2024
Grant Recipient: University of Connecticut
Region: Northeast
State: Connecticut
Project Leader:
Dr. Christine Georgakakos
SUNY College of Environmental Science and Forestry
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Project Information

Project Objectives:

Our broad objective is to understand the transport and impact of antimicrobials in the environment from animal agriculture. We will achieve this by adding additional analyses (antibiotic residues, resistance genes, and soil microbial communities) to an existing project (USDA NIFA AFRI Workforce & Development Postdoctoral Fellowship), allowing us to draw broader impact assessments of antimicrobial usage on dairy farms in Connecticut.  Specifically, our objectives are to:

(1)  Understand linkages between surface water antibiotic residues, soil microbial communities, and antibiotic resistant genes in soil on dairy farms.

(2)  Partner with appropriately compensated farmers to ground this study in on-farm applicability across farm sizes.

(3)  Communicate the results of this work to scientific, agricultural, and public audiences to increase cross-sector understanding and support.


Antimicrobial resistance is a global threat, with action and surveillance plans outlined by the United Nations Environmental Program, the European Union, and the Pan American Health Organization among others (UNEP, 2022).  Despite championing a one-health approach to addressing this issue, the environmental perspective is lacking from policy-level decisions. The conversation is being driven by human and livestock health professionals while complete environmental assessment of the legacy and impact of antibiotic residues, resistant genes, and bacteria in soil and water systems is given less consideration. Where there has been research in this realm, rarely does it connect multiple or all three of these environmental contaminants and legacy effects to inform agricultural land management.

Anthropogenically sourced antibiotics move into the environment through waste streams.  These streams can be from residential wastes via septic systems or municipal wastewater treatment, hotspots tend to be focused around hospital discharges, and through livestock wastes. Because the vast majority of antibiotics sold are for use in animal agriculture, it is critical to understand this pathway. Farm sustainability has depended on the judicious use of antimicrobials in animals and the health of the landscapes. Because of the potential for legacy contamination, and soil microbial and aquatic organism impact, the use of antimicrobials may leave an imprint on the landscape longer than exceeds the awareness of the experts who manage these fields. The long-term goal of this work is to understand the movement and impact of agriculturally sourced antimicrobials through the landscape to inform management practices that limit the chronic impacts of these medical tools and ensure farm sustainability through improved understanding of soil microbial community health and antibiotic residue transport. This proposal supports the development of improved resiliency on farms to address and deal with new microbial diseases and reduce antimicrobial resistance in the disease populations by lowering the selective pressure for those individuals on farms that contain residues in their waste streams. This understanding will benefit not only the sustainability of the farms partnering in this study, but will provide a basis for other farms to work from to ensure sustainability across the region.

To address the above challenges, we will establish paired samples through time with surface water antibiotic residues, soil microbial community composition, and antibiotic resistance genes presence to understand the interactions between these three distinct data sources over the course of the year. In addition, we overlay these samples with a parallel project addressing human dimensions of these contaminants and stable water isotopes as temporal water tracers in the landscape (USDA Workforce and Education Development Grant; PI: Dr. Georgakakos). The fusion of these components will allow for holistic watershed wide understanding of antimicrobials in Woodstock, CT, an agriculturally dominated watershed in Northeast Connecticut.

Under our existing USDA funding, we are obtaining surface water samples downstream of three farms to analyze for antibiotic residues, and calculating antibiotic residue loads to the watershed with the addition of stream discharge measurements. Under this funding, we will expand this monitoring campaign to sample more frequently, and with soil samples from active pasture and field crop fields where manure is spread to assess the impact of residues on the resistance genes present in the soil and the structure of the soil microbial community. Together, these data will allow us to calculate off-farm transport of antibiotic residues in surface water, and pair these measurements with the impact of that transport on the surrounding soil health. We will share detailed results with each of the farms involved, and provide a discussion of possible solutions to off-farm antibiotic transport if results suggest such a management change would be beneficial.


Click linked name(s) to expand/collapse or show everyone's info
  • Dr. Jane Lucas (Researcher)
  • Matthew Peckham - Producer
  • William Rees - Producer
  • Lucas Young - Producer


Materials and methods:

The study design of this and our parallel funded proposal pair five data types to allow a wholistic understanding of the hydrologic flow paths and impact of antimicrobials on the surrounding soils. We will collected stable isotope water data from surface water, soil water, and precipitation to understand the residence time of water through each system and will survey watershed residents (Fall 2024) to assess human perception and impact on these contaminants.  We will sampled antibiotic residues in surface waters, antibiotic resistance genes and microbial communities in soil. The isotope and human dimensions components along with a portion of the residues were funded under our existing USDA grant. This proposal allows for the addition of genetic and microbial community analysis and the expansion of antibiotic residue sample analysis to address new, unanswered questions. We will sample three farms with in same watershed (supplementary material, Figure 1).  We will also sample the region down stream of all three farms for stable water isotopes and antibiotic residues, through our previously funded project.


Sample collection:

Water and soil samples were collected May 2022 through June 2023 twice monthly using grab sampling methods. This sampling scheme allowed us to see temporal changes in antibiotic residues, resistance gene abundance and soil microbial communities, and also gave us sufficient replication to average samples seasonally to determine variability. Our sampling schedule was approved by the partner farmers to ensure this study did not impact normal farm operations but would still able to capture field and farm runoff of manure-derived contaminants.


Antimicrobial residue analysis:

We have sampled surface water for antimicrobial residues and completed analysis of this component.  We also sampled residues found in manure prior to field application to obtain direct source data. Water sampling sites were located immediately downstream of farm runoff confluences. We analyzed for a suite of emerging contaminants through a two-phased approach: we surveyed farmers to obtain a list of all antimicrobials used on the farms and targeted these antibiotics in each sample.  Following this targeted analysis, we also preformed a untargeted analysis on a subset of the samples to determine other highly concentrated contaminants. This analysis was completed at the Center for Environmental Sciences and Engineering (CESE) at the University of Connecticut with the mentorship of Dr. Anthony Provatas. Using this approach, we captured the broad story of contaminants derived from both livestock and human sources as well as some breakdown products. All residues samples have been collected and analyzed, with data analysis and modeling remaining to be completed. Emerging contaminant concentrations will be paired with discharge measurements to allow calculation of contaminant loads throughout the year (Spring 2024).


Soil microbial community and antimicrobial resistance analyses:

Soils were collected twice monthly in tandem with soil and manure samples to examine microbial communities and levels of antibiotic resistance. At the time of sampling, we measured temperature, moisture level, and pH of each soil sample. We will sample microbes using Zymo Xpedition Soil/Fecal DNA Miniprep kits and protocols. We will analyze microbial communities by Illumina© sequencing libraries generated by PCR amplification of 16S bacterial and ITS fungal sequences. We will examine antibiotic resistant genes (tetM, AmpC, and Sul1) using quantitative PCR methods. All resistance analysis was be completed Fall 2023. All microbial sequences will be processed, cleaned and taxonomically assigned using the pipeline DADA2 starting in Spring 2024. Subsequent analysis will be done in R v. 3.5.2.

Research results and discussion:

We completed 28 field campaigns collecting soil and manure samples for microbial analysis, along with surface water and manure samples for antibiotic residue analysis and soil water, surface water, ground water and precipitation for stable water isotope analysis.  Each field campaign is also accompanied by collection of meta data on soil temperature, water temperature, soil moisture, and stream discharge and soil pH.


Analysis of stable water isotopes has occurred throughout the sampling period at the University of Connecticut in the Ecohydrology Lab. Analysis of microbial communities and resistance genes will occur at the Cary Institute of Ecosystem Studies this Spring. Analysis of antibiotic residues in surface water and manure is planned for this spring at the Center for Environmental Science and Engineering (CESE) at the University of Connecticut. Below are out results to date of stable water isotope analyses and meta data collected at each site. 


Together, figures 1 and 2 indicate that the three study farms are defined by unique hydrologic flow paths and estimated difference in water residence times in the environment. We will use this data to model water ages to determine molecule residence times in the landscape from precipitation event through to discharge from the site. We will then pair these residence times with antibiotic signatures in stream water to better understand risk of contaminant transport off these sites, and impact of residence times on soil microbial community and extent of antibiotic resistance gene presence.


Figures 3-6 display the meta data collected alongside soil and water samples for analysis. These factors (soil moisture and temperature) influence microbial community structure and are therefore important data to have alongside microbial community analysis.


Figure 7 and 8 depict the flow characteristics of the three streams we are studying.  Two of the three sites were dry during the months of July and August, while the third site, at Elm farm, which similarly displayed stable water isotope signatures similar to the groundwater signatures, continued to flow throughout the dry months. These differing hydrologic regimes may be caused by a variety of factors, which we will explore through modeling exercises.


Figure 9 displays the antibiotic concentrations detected in stream water at each study site across time.  This figure depicts only those antibiotics (and caffeine) that were above detection in at least one sample.  Figure 10 displays the same data as Figure 9, versus discharge to begin to understand when pulses of antibiotic residues are entering stream systems. 


Figure 11 displays all the residues detected in manure samples of the residues tested. The dramatic difference in the residues detected in manure versus those found across farms in the surface water suggests usage alone of these pharmaceuticals does not predict extent of environmental contamination downstream. 


Figure 12 depicts the combined antibiotic resistance profiles detected in a subset of samples across time.  These samples are averaged across sampling locations within the farms (i.e. hay, corn, riparian sites).  Figure 13-17 depict individual resistances detected.  Again, comparison between these plots and the manure residue plots suggests usage alone does not predict resistance patterns.  


Figure 18 depicts a test for correlation between different residues in the same samples.  No residues show significant correlation, suggesting that different flow paths may be taken for different residues, distributing the impact of these contaminants over time. 

Dual Isotope plot for groundwater, surface water, and precipitation samples

Figure 1: Dual isotope space of all farms and Ground water (G), precipitation (P), stream flow (Q)samples. 

Time series of isotope data.

Figure 2: Time series of all data, including groundwater and surface water at all sites.  

Average soil moisture at each farm, measured at each of riparian, corn, and hay sites

Figure 3: Boxplots of all soil moisture data collected at each farm, not separated by season. 

Soil moisture across sites through time

Figure 4: Temporal variability in soil moisture across farms (shapes) and sites (colors).

Temperature variability across sites through time.

Figure 5: Temporal temperature patterns. Farms are represented by shapes in this figure, colors indicate site (riparian, corn, hay).

Average temperatures across sites and farms

Figure 6: Temperature averaged across all sampling points split by site at each farm. 

Discharge across sites.

Figure 7: All rating curves plotted together. Y axis is log scale. 

Individual site discharge rating curves.

Figure 8: Rating curves for each farm with best fit nonlinear regression fit. 

Antibiotic concentrations across farms and downstream sites studied.

Figure 9: Antibiotic concentrations in stream water at each site. 

Residues at each site versus discharge.

Figure 10: Concentration of antibiotics versus discharge at each site. 

Concentration of antibiotics in manure samples from each farm over time.

Figure 11: Concentration of antibiotics versus time from each farm's manure sample

Combined antimicrobial resistance through time at each farm.

Figure 12: Combined antimicrobial resistance at each farm on a subset of sampling dates.

Tetracycline resistance

Figure 13: Tetracycline resistance across time for each farm. 

Sulfonamide resistance

Figure 14: Sufonamide resistance

Vancomycin resistance

Figure 15: Vancomycin resistance

Macrolide and erythromycin resistance

Figure 16: Macrolide and erythromycin resistance 

Beta lactam resistance

Figure 17: Beta lactam resistance

Test for correlation between antibiotic residues in water samples.

Figure 18: Test for correlation between differing antibiotics


Research conclusions:

We have completed the antibiotic resistance gene data collection and have recently shipped samples for full genomic sequencing to determine microbial community structure across sites.  Completion of microbial analysis will be in Spring 2024. Antibiotic residue analysis has been completed and stream water residue concentrations included in this report. We will also be finalizing stable water isotope modeling in Spring 2024. 

Participation Summary
3 Farmers participating in research

Education & Outreach Activities and Participation Summary

13 Consultations
3 Tours
2 Webinars / talks / presentations

Participation Summary:

Education/outreach description:

The results of this work will be disseminated through several publications and in-person conversations.  Due to the one-health nature of this problem, it is critical that information is spread beyond the academic environmental contaminant silo and into the other areas using and contributing to antimicrobial resistance. These areas include agricultural communities for their use of antibiotics in livestock, scientists studying contaminant transport and environmental effects of antimicrobials, and the public which uses antibiotics for health reasons.


The Partner Farmers:

All results will be shared with the partner farmers both in the form of copies of publications as well as through an in-person, end of study meetings. These meetings will allow for a dialog about on-farm research, suggested future research directions, and results specifically applicable to the partner farmer and the agricultural community in general.


The scientific community:

Final results will be published in a manuscript submitted to the Environmental Science and Technology. All data will be made publicly available through USDA’s Ag Data Commons for future use in modeling and informing broader synthesis studies.


Agricultural community:

Several publications in local trade magazines will communicate the results of this study to the farming community.  Specifically, we will submit pieces to Hoard’s Dairyman to reach the dairy community at the national level, and cooperative extension blogs and newsletters (e.g. CCE SCNY Dairy and Field Crop Team Blog) to discriminate these results in the regions not directly involved in the study.


The public:

The results of this study will also be shared in regional newspapers (e.g. The Hartford Courant) to facilitate public understanding of on-farm systems and the one-health approach to antimicrobial resistance mitigation. We will also participate in a public in-person and virtual science conversation at the Cary Institute of Ecosystem Studies. This science conversation is an opportunity for researchers to explain their work to the broader public and answer questions.

Learning Outcomes

Key areas in which farmers reported changes in knowledge, attitude, skills and/or awareness:

As data has not yet been fully analyzed, this conversation has not yet occurred. 

Project Outcomes

5 New working collaborations
Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.