Antibiotics in the Dairy Farm Environment: Understanding Antibiotic Transport to Improve Farm Sustainability

Progress report for GNE19-201

Project Type: Graduate Student
Funds awarded in 2019: $11,782.00
Projected End Date: 05/31/2022
Grant Recipient: Cornell University
Region: Northeast
State: New York
Graduate Student:
Faculty Advisor:
Dr. Todd Walter
Cornell University
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Project Information

Project Objectives:

This work aims to characterize the adsorption and transport processes of antibiotics, and help predict transport of these compounds from agricultural sites to the surrounding geological and ecological systems. This work is to be done in parallel with an interview-based study on farmer perceptions of antibiotic impact on farms. The two studies together will create a more holistic understanding of antibiotic impacts on dairy farms. The interview study has been fully funded and is only discussed here where overlap applies.

Objective 1: Determine adsorption coefficients for two antibiotics (erythromycin and ampicillin) to manure and soil.

Objective 2: Model expected transport of antibiotics given experimental results in objective 1, and determine if the model holds for a laboratory column experiment. Adjust model appropriately if other factors (i.e. biological degradation) are shown to be important in controlling transport.

Objective 3: Test the model outcome from objective 2 at the farm scale with environmentally relevant antibiotic concentrations.

Objective 4: Lower communication barriers between farmers, scientists, policy makers and the public.


Sustainable farming practices contribute to farm longevity and management of environmental impacts. Economic and environmental sustainability go hand-in-hand to generate stable, productive farms that minimize negative environmental impacts while supporting the livelihoods of farmers.  Animal and soil health are two sustainability focus areas that draw significant farmer attention. Maintaining good animal health translates to carefully tracking environmental conditions, reading animal health indicators, and addressing illnesses as they arise. Soil health involves a complicated fusion of understanding nutrient cycling, crop nutrient needs, organic matter incorporation and decomposition, and the microbial community’s interaction and role in each of those processes. Though animal and soil health are not generally closely tied to one another outside of nutrient management, management of manure from animals treated with antibiotics or other drugs may link the two systems more closely than previously anticipated.

Many animals absorb antibiotics poorly, with some animals excreting 50-90% of the ingested compound depending on antibiotic type (Kim et al., 2011; Alcock, 1999; Feinman & Maheson, 1978). The excreted compounds can then move with water and sediments and interact with surrounding microbial communities. The extent of this interaction, and risks it poses, is not well understood. We must therefor understand the fate and transport of these excreted compounds to trace potential interaction pathways. Understanding antibiotic transport off farms with manure will help us better understand the risks these chemicals may pose to humans, livestock, and soil and aquatic organisms.

Much of our current food system depends on the judicious use of antibiotics in livestock agriculture, and will continue to do so into the foreseeable future. However, the degree of reliance upon these compounds is highly variable across farms, management styles, and sizes, according to preliminary results from an interview-based study of farmers on the topic (Georgakakos et al, in prep). This variability suggests that risk of antibiotic contamination and spread of resistance in the environment may not be homogenous across all farms.

Antibiotic transport is relevant to livestock farms of all varieties as well as urban watersheds.  I will focus on antibiotic transport on dairy farms due to dairy farm prevalence in New York State.  According to the 2017 New York State Dairy Statistics Annual Summary, 623 thousand dairy cows were registered in NY State in 2017 on nearly 4,300 farms (NYS Dept. Ag. & Markets 2017).  Management of the manure and the associated contaminants generated by an industry of this size is critically important.  Understanding the transport of antibiotics will help us engineer our farm manure management systems to minimize the distribution of these potent compounds on a farm-by-farm basis.

The class of compounds referred to as emerging contaminants such as pesticides, pharmaceuticals, and personal care products are increasingly gaining attention for their detection in surface waters.  These substances interact with the environment and humans in very impactful ways, despite their sometimes low concentrations and limited regulation.  It is therefore important we understand the adsorption underpinnings of those interactions to assess potential risk and regulate sustainable usage.

This work develops previously undetermined parameters for two antibiotics and tests those parameters environmentally.  The current state of scientific knowledge allows us to predict those parameters, but analysis has not been done to test those predictions put forth by software like EPISuite generated by the EPA.  Comparing predicted values to experimental values will allow calibration of predictive models and more accurate overall transport prediction.

Understanding antibiotic transport is important to farmers concerned about antibiotic resistance on their farms.  Spread of antibiotic resistance is a growing concern across livestock operations, and a rising concern in manure management.  This research will help determine if there are actions farmers can take to reduce the spread of resistance genes and residues. Because this work is in conjunction with an interview-based study, recommendations for management strategies based on farm size or manure management strategy may also be possible.

 It is possible that antibiotics and other emerging contaminants begin to be regulated in the near future as they continue appearing in drinking water sources. This work will help farmers adjust to those regulations should they be applied. 

A parallel interview study compliments the research of this proposal by assessing farmer perspectives of the impact of antibiotics on their operations.  By pairing these quantitative and qualitative research methods, a more complete perspective of antibiotics on dairy farms is developed. This complementary data will help determine risks of antibiotic contamination on farms of different sizes, and will suggest management practices that could reduce that transport



Alcock, RE, Sweetman, A, Jones KC. (1999).  Assessment of organic contaminant fate in waste water treatment plants 1: selected compounds and physiochemical properties.  Chemosphere. 38, 2247-2262

Feinman, SE, & Matheson, JC (1978). Draft environmental impact statement: subtherapeutic antibacterial agents in animal feeds. Food and Drug Administration Department of Health, Education and Welfare Report.

Georgakakos, CB, Hicks, B, & Walter, M.T. (in prep) Antibiotics in the dairy farm environment: farmer perspectives. J. Dairy Science.  

Kim, K. R., Owens, G., Kwon, S. I., So, K. H., Lee, D. B., & Ok, Y. S. (2011). Occurrence and environmental fate of veterinary antibiotics in the terrestrial environment. Water, Air, & Soil Pollution214(1-4), 163-174.

NYS Dept. of Agriculture & Markets.  Division of Milk Control and Dairy Services. (2017) New York State Dairy Statistics 2017 Annual Summary.



Materials and methods:

Objective 1: Determine adsorption coefficients for two antibiotics (erythromycin and ampicillin) to manure and soil.


Objective 1 will be addressed by running batch experiments in the lab (experiments 1a and 1b). All batches will be buffered to a slightly acidic pH. The pKa of erythromycin is 8.8 (McFarland et al., 1997) and those of Ampicillin are 2.5 and 7.3 (IARC, 1990).  Buffering to a slightly acidic pH ensures that the majority of the compounds of interest is in a constant form, and mimics the conditions found in manure. The batches will be run with 5 replicates each, with the following variations:

  1. Antibiotic dissolved in DI water.
  2. Antibiotic dissolved in DI water solution with autoclaved, dried, and sieved manure free of antibiotics.
  3. DI water with autoclaved, dried, and sieved manure free of antibiotics.


Two experiments will be conducted under these conditions: The first (experiment 1a) will determine the equilibrium time for adsorption to manure, and the second will determine the distribution coefficient by generating an isotherm over a range of antibiotic concentrations. 


Experiment 1a will be conducted by sampling each batch at intervals of 0, 3, 8, 24, 48, and 96 hours.  The desired goal is to reach equilibrium between the dissolved and adsorbed phases of the antibiotic for the design of experiment 1b.


The experimental protocol for experiment 1a is as follows:

  1. All amber glass jars will be washed, sonicated and autoclaved before use.
  2. Manure will be oven dried and bulk density determined. Manure will then be twice autoclaved to sterilize before use in the experiment.
  3. Stock solutions will be generated for manure slurry and antibiotic solutions
    1. Manure slurry stock solution – generate a 0.195 g/L stock solution. 10mL will be used for each sample bottle, diluted up to 150mL with additions of DI water and antibiotic solution.
    2. Antibiotic stock solution – generate a 0.01g/L stock solution. 5mL, 10ml, and 100mL will be added to each sample bottle in the various treatments.
    3. Treatment 1 (0.00033g/L), Treatment 2 (0.000667g/L) and Treatment 3 (0.00667g/L) were determined following an initial test of manure erythromycin solutions on the ELISA kit.
  4. 10 mL aliquots of manure solution will be added to each jar.
  5. 5 mL, 10 mL, and 100mL aliquots of erythromycin stock solution will be added to each jar. Remaining jar volume to 150mL will be filled with DI water.
  6. Jars will be labeled for sampling at time = 0, 3, 8, 24, and 48, 96 hours.
  7. Jars will be placed on a dark shaker for duration of experiment.
  8. No jar will not be sampled from twice, ensuring all samples are from same volume with no changes to solution components for duration of experiment.
  9. Samples will be filtered through a 0.20 μm filter immediately after sampling and frozen until analysis.



Experiment 1b will be conducted for time required to reach equilibrium.  The goal of generating an isotherm is to obtain the relationship between aqueous and adsorbed reactant concentrations. The relationships outlined below will then be calculated for each antibiotic. The adsorption isotherm will be compared to a linear, Langmuir, and Freundlich Isotherms to better understand the underlying mechanisms influencing adsorption.


The experimental protocol for experiment 1b is as follows:

  1. All amber glass jars will be washed, sonicated and autoclaved before use.
  2. Manure will be oven dried and bulk density determined. Manure will be twice autoclaved to sterilize before use.
    1. A stock manure suspension will be generated as outlined above in experiment 1a.
  3. Sand will be inspected under microscope to assess cleanliness of particles. Sand will be treated with an acid wash, sodium dithionite, and  H2O2 to remove any carbonates, oxides, and organic contaminants respectively which may influence results.
  4. Soil used will have organic matter content analyzed, FeO2 content quantified, and if possible other mineralogy. Soils will be twice autoclaved before experimental use.
  5. Control Manure and erythromycin solutions will be made to assess changes in composition over the course of the experiment.
  6. Manure stock solution will be added to each jar as outlined in experiment 1a.
  7. Antibiotic stock solution will be prepared, and pipetted at various volumes to a range of erythromycin concentrations.
  8. Jars will be labeled with concentration and sampled at the equilibrium time obtained in experiment 1a.
  9. Jars will be placed on a dark shaker for duration of experiment at 20°C.
  10. Samples will be taken at equilibrium, determined by the results of experiment 1a.
  11. Samples will be filtered through a 0.20 μm filter immediately after sampling and frozen until analysis



To plot the isotherm, it is first necessary to calculate the adsorption density (q) of the antibiotic.

q = (mass of antibiotic sorbed) / mass of sorbent

Once the change concentration is obtained, the mass absorbed will be calculated as follows:

q = (delta c_a)*(V_solution) / M_sorbent

The equations that characterize each of these relationships are shown below:


The linear isotherm is characterized by the equation  q = K*c_a where K is the binding affinity of sorbent for sorbate.


The single-site Langmuir isotherm is characterized by the equation  q = q_max*K*ca / (1+K*ca) where qmax  is the maximum possible sorption sites.


The multi-site Langmuir isotherm is characterized by the equation q = Σ(q_max_j*K_j*ca / (1+K_j*ca) ( where constants remain the same as for the Single-site Langmuir, simply summed over all sites.


The Freundlich isotherm is characterized by the equation q = k*can  where k is the binding affinity of sorbent for sorbate with variable units and n is the distribution of binding energies at surface (0.1 < n < 1).


All coefficients will be obtained using non-linear regression analysis in R. Models will be compared to determine closeness of fit to data.  This analysis will then allow an interpretation of the mechanisms controlling adsorption to manure. 


Objective 2: Model expected transport of antibiotics given experimental results in objective 1, and determine if the model holds for a laboratory column experiment. Adjust model appropriately if other factors (i.e. biological degradation) are shown to be important in controlling transport.


Objective 2 will be addressed using a packed column experiment.  Because manure is composed of various sized and functional organic matters, I anticipate that I will not be able to assume rapid equilibration with adsorbed and dissolved antibiotic concentrations. Adsorption onto a solid is considered to involve a series of steps (Benjamin & Lawler, 2013):

  • Transport though bulk solution and into adsorbent boundary layer.
  • Transport through boundary layer to absorbent surface.
  • Binding of adsorbate to external layer of adsorbent.
  • Diffusion into interior of adsorbent via pore diffusion.


The generalized mass balance for transport of the adsorbate through a column can be described by:

In this equation, c is the dissolved concentration of antibiotic, ε is the porosity of the material, DL is the combined dispersion and diffusion term, ρp, app the apparent density of the particles, q the adsorption density, and rL the reaction term.  Assuming plug flow and no degradation or formation reactions, the dispersion and reaction terms may be neglected.  However, this equation assumes rapid equilibration between adsorbed and dissolved adsorbate.


If we assume the adsorbate needs to diffuse into a spherical particle, the governing mass balance equation assuming plug flow becomes:


where kL is the mass transfer rate coefficient through the particle boundary layer, crP is the concentration at the center of the particle, and rP is the radius of the particle.  The experimental data obtained during this column experiment will help elucidate which of these models is a closer fit to for observed concentration distributions.  For eqn (2) we would also need to assume particle radius or a distribution of particle radii that would represent the organic matter in manure.


The velocity of the Mass Transfer Zone (MTZ) through the column may be estimated according to the following equation:


where q*in is the adsorption density on the isotherm relating to cin. This velocity can then be used to calculate the time required for the MTZ to move through the reactor, as a function of the speed the water moves and as a function of the increase in time resulting from adsorption. This time in conjunction with geometry of the reactor can be used to design a flow rate and sampling regime that will fit our compounds. 


I will test homogenous soil columns under both biotic and abiotic conditions in the following treatments in 5 replicates each:

  • Sand control
  • Sand + Manure – sterile
  • Sand + Manure – non-sterile
  • Sand + soil - sterile
  • Sand + soil – non-sterile
  • Sand + soil + Manure – non-sterile
  • Sand + soil + Manure – sterile


Sampling times will be estimated using the time expected for the MTZ to reach the end of the column, compared with model predictions.


 Objective 3: Test the model outcome from objective 2 at the farm scale with environmentally relevant antibiotic concentrations.


Objective 3 will be addressed using a field-scale experiment, during which the coefficients determined in the lab can be compared with practice-scale data. I will identify two small farms that use the antibiotics of concern, and hydrologic and soil data will be collected.  These farms will be monitored specifically during manure application periods (generally after harvest in fall and before planting in spring).  One farm will be chosen for the presence of a riparian buffer (or other management practice) and the other chosen for the lack of the same practice.


The goal of experiment 3 is to identify uncertainty between lab-scale parameters and field-scale transport of these compounds.  A model will be generated in R, using water and soil samples for calibration. A sensitivity analysis will be carried out on the lab derived parameters to assess robustness of the parameter values. The two farms will be monitored continuously during the period of interest using an ISCO water sampler and soil samples once per week following manure application.  By running this experiment on two farms, the calibrated parameters on one farm may be applied to the second farm to assess regional validity.


The findings of Experiment 3 will be shared with farmers during focus groups outlined in Question 4 and help motivate discussion.  By doing so I hope to strengthen the farmer-researcher relationship and established bi-directional trusted communication lines.


Objective 4

Communicate the results with scientists, farmers, policy makers, and the public to help lower communication barriers between each of these entities.


Based on preliminary results from the interview-based study mentioned above, farmers feel that communication between the public, the scientific and engineering community, and policy makers is disconnected from farmers.  Objective 4 aims to share information from this study with each of these entities, through the outreach activities described below.   


Scientific Community: All results will be submitted for publication in an academic journal and included with my PhD dissertation. I will continue to present this work at conferences, and specifically will attend the American Geophysical Union Annual Meeting in December to do so.


Stakeholders: I will also publish the results and context of this study in a dairy trade magazine, such as Progressive Dairyman, to communicate the results of this work to dairy farmers.  I will also individually send each of these documents to the farmers who were interviewed in my parallel interview-based study on perspectives of antibiotics. In conjunction with the interview study, there will be focus groups of small groups of similarly sized diary farms.  After the focus group portion of the meeting, results of these studies will be shared with attendees and discussion allowed to continue. 


The Public: The non-farming public will be reached through a series of newspaper articles discussing the topics of this work.  Preliminary results from our interview based study indicate farmers believe the general public is ill-informed and disconnected from farms, so this series of newspaper articles is aimed to help alleviate some of that perceived disconnect.


Policy Makers: The articles and broad outcomes of this work will be shared with New York State lawmakers, especially those representing the regions where farms are located that participated in the parallel interview-based study. Letters to lawmakers will include both the scientific results of this study as well as perspectives from farmers obtained

Research results and discussion:

Each objective for this project builds on the previous objectives.  Therefore, the following timetable reflects this process with the ultimate goals of contributing the scientific literature on the topic and share the information with the public, policy makers, and farmers. Due to internal university delays, the funds were not approved for usage until December 2019, pushing the original timetable back. The approximate timetable for this project is to be completed within one year, lasting from January 2020 through September 2020. Following setbacks in lab availability due to COVID-19 university closures and lab access, the timetable for this project has been significantly adjusted, and a no-cost extension approved.  The experiments will now take place starting Janurary 2021. 


Objective 1  will involve batch experiments conducted in January and February 2021.  Results will inform objectives 2 and 3 and be included in manuscript 1.  

Soil and Manure characteristics have been obtained for the samples to be used in November and December 2020. These include pH, electrical conductivity, percent sand, silt, and clay, zetapotential, particle size distribution, and particle surface area. These characteristics will be used to describe the environment of the study so to make these results more widely useful. 


Objective 2 requires completion of objective 1 before beginning. Planning for the column experiments of objective 2 will begin in August while Objective one is completed, column experiments for objective 2 will run February 2021, followed by model calibration.  Objectives 1 and 2 will be submitted as manuscript 1 upon completion. Manuscript will be drafted in March 2021.


Objective 3 is a field experiment that will be conducted in March and April 2020 to capture spring manure spreading.  Objective 3 will culminate in manuscript 2 to be submitted for peer review.  Manuscript 2 will be drafted in May and June 2020. 


Objective 4 is to be completed after laboratory experiments are finished.  Objective 4 will culminate in 3 deliverables, an article for the public to discuss the context of this research and key findings in a local newspaper, a letter for policy makers to discuss implications of results, and an article for a trade magazine to share results with dairy farmers.

Participation Summary
4 Farmers participating in research
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.