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: 11/30/2022
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 erythromycin and ampicillin to manure and soil. [Completed]

Objective 2: Model expected transport of erythromycin given experimental results in objective 1, and determine if the model holds for a laboratory column experiment. 

Objective 3: 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 results from an interview-based study of farmers on the topic (Georgakakos et al, 2021). 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. (2021) Farmer perceptions of dairy farm antibiotic use and transport pathways as determinants of contaminant loads to the environment. Journal of Environmental Management281, 111880. Doi: 10.1016/j.jenvman.2020.111880 

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.



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  • Dr. Damian Helbling (Educator and Researcher)
  • Dr. C. Enid Martínez (Educator and Researcher)


Materials and methods:

Objective 1 & 2: 


Soil and manure collection

We collected soil from the top 10 cm of a fallow control plot at the Cornell Recreation Connection (Freese Road, Ithaca NY) agricultural research plots in the Caneseraga CaB soil series, an agricultural soil characteristic of New York’s Finger Lakes region. We collected manure samples from four organic dairy farms in the region. On a per cow basis, farm 1 was 100% grass-fed with 0 kg grain/day, while farms 2, 3, and 4 fed 1.4 kg grain/day (3 lb/day), 4.5 kg grain/day (10 lb/day), and 17.2 kg grain/day (38 lb grain/day), respectively, at the time of manure collection. This diversity of feeding practices is representative of farms in the region. We combined manure from the four farms in a 1:1:1:1 by dry mass ratio to achieve an average manure composition for our experiments. All manures were individually analyzed for bulk parameters. Manure and soil were twice autoclaved at 135°C for 25 min to sterilize, then oven dried before experimental use.


 Bulk parameter characterization

We measured bulk solid parameters to understand the environment of the adsorption experiments and allow comparison between treatments. We obtained bulk density, pH, particle size distribution, electrical conductivity, organic matter loss on ignition, surface area, pore size, and zeta potential for both manure and soil substrates (Table 2). We also obtained percent sand, silt and clay for the soil. We tested both oven-dried and autoclaved then oven dried samples for the manure and the soil to assess the effect of substrate preparation on bulk parameters.


We calculated bulk density of the soil by extracting a 270 cm3 cylindrical soil core (7.3 cm x 6.5 cm) and obtained total solids content of fresh manure by measuring 250 cm3 of fresh manure and recorded the wet weights. Samples were oven dried at 65°C for 24 hr before recording dry weights (U.S. Soil Quality Institute, 1998). Using an Accument Research AR50 Dual Channel pH/Ion Conductivity Meter (S/N AR 81202286), we measured pH and electrical conductivity. We prepared pH samples from 15 g dry soil and manure, sieved through a 2 mm sieve, and rehydrated in 30 mL deionized (DI) water after a 30 min equilibration period (Robertson et al., 1999). We prepared electrical conductivity samples in a 1:5 ratio by mass of dry soil or manure to water and left to equilibrate for 1 hr prior to analysis (Rayment & Higginson, 1992).  Particle size distribution, specific surface area, and pore size was obtained using the Brunauer-Emmett-Teller (BET) method (Micrometrics ASAP 2640) from dry, sieved (2mm) samples after subjecting the samples to a 100°C vacuum for 24 hr.  We calculated percent loss on ignition by subjecting 5 g of oven dried soil or manure to 500°C for 2 hr. Solutions of 0.1 mg/mL were prepared for zeta potential analysis following Darrow et al. (2020) for soils. We obtained zeta potential from a Malvern Panalytical (zs90) Zetasizer set to a reflective index (RI) of 2 and absorbance index (A) of 1 for soil, and a RI of 1.4 and an A of 0 for manure (RI and A adjusted from estimates in Darrow et al., 2020 and Lafon et al., 2006).


Batch equilibrium adsorption experiments

Oven dried manure and/or soil were added to each batch and volume brought up to 150 mL with deionized water after erythromycin addition. To determine solids ratios for experimental use, differing solid masses (0.01 g, 0.05 g, 0.5 g, 5 g, 15 g) were tested to determine detectable adsorption of erythromycin (at 667 ppb) for both soil and manure (Figure S1).  Preliminary manure tests resulted in no adsorption across the treatments, leading to the two-phased equilibrium experiment discussed below. Soil solids ratio of 5 g/150 mL was chosen as the first solid ratio tested with significant adsorption. For the combined manure and soil experiment (SM treatment), 5 g soil with 0.5 g manure was used. All batch experiments were in 250 mL amber glass vials. All amber glass vials were sonicated and autoclaved prior to usage and left on a rotating shaker in the dark for the duration of the experiment. Control reactors with no solids were prepared in the same conditions as treatment reactors. Because preliminary erythromycin showed no adsorption to manure-only treatments, no additional manure-only experiments were conducted.


Soil adsorption equilibration time

For determination of adsorption equilibration time, 10 mL of 10 ppm erythromycin solution was added to each batch reactor and total volume brought to 150 mL with a final concentration of 667 ppb. Each batch was sampled at 0 hr, 3 hr, 12 hr, 24 hr, 36 hr, 48 hr, 72 hr, 96 hr, 120 hr, and 144 hr. Samples were collected using a syringe and filtered through a 0.30 μm glass fiber filter immediately after sampling. Triplicate batch reactors were prepared for each equilibration time. We diluted samples to approximately 15 ppb erythromycin for analysis using an erythromycin enzyme-linked immunosorbent assay (ELISA) kit. All samples were analyzed within 24 hrs of filtration using a MyBioSource erythromycin ELISA kit (cat #: MBS282249) and a Molecular Devices M2 microplate reader. 


We defined the adsorption equilibration time to be the first sample that was not statistically different from both the previous (t-1) and next (t+1) samples using a Wilcox Rank Sum Test (with p-value < 0.1 indicating statistical difference). We also obtained first order reaction kinetics (Eq. 1),

  qt = qe (1- e-k1t)                     (Eq. 1)

where qt is the amount of erythromycin adsorbed at time t, qe is the amount of erythromycin adsorbed at equilibrium, and k1 is the kinetic rate constant.


Adsorption equilibrium isotherms

After establishment of the adsorption equilibrium time, erythromycin at 19 concentrations (10 ppb, 20 ppb, 40 ppb, 80 ppb, 100 ppb, 200 ppb, 400 ppb, 800 ppb, 1000 ppb, 2000 ppb, 5000 ppb, 10,000 ppb, 25,000 ppb, 40,000 ppb, 50,000 ppb, 65,000 ppb, 75,000 ppb, 100,000 ppb, and 150,000 ppb) was added to 5 g soil in 250 ml amber glass vials in triplicate (S treatment). Batch reactors were left on a shaker for 72 hr, the suspension filtered through a 0.3 μm glass fiber syringe filter, and analyzed within 24 hr using an ELISA kit and microplate reader. All samples were diluted (as needed) to the linear detection range of 0.2 - 25 ppb from the initial concentration prior to analysis.


The SM treatment was also equilibrated for 72 hr at initial erythromycin concentrations of 10 ppb, 100 ppb, 400 ppb, 1000 ppb, 5000 ppb, 10,000 ppb, 25,000 ppb, 50,000 ppb, 100,000 ppb, and 150,000 ppb.  We combined 5 g soil and 0.5 g manure by dry mass in amber glass vials for a total volume of 150 mL.


We tested five non-linear and linearized Freundlich and Langmuir models to S and SM adsorption isotherm data to determine best model fit. If models generated non-realistic (i.e. negative) adsorption parameters they were excluded from further analysis. From the remaining models the model with the greatest number of significant parameters and the smallest standard square error (SSE) was chosen as the best fit. Discussed here are the Langmuir non-linear regression and one Langmuir linearization which fit the data best (eq. 2, 3) (See supplementary material for all model analyses).


  q = qmax * K * (c / (1+K*c)                   (Eq. 2)

 c/q = a + b*c                                            (Eq.3)

         qmax = 1/b ; K = 1 / (qmax * a)    (Eq. 3.1)


In Eq. 2 and 3, q is the equilibrium adsorption density (mg adsorbate)/(mg adsorbent), qmax is the maximum adsorption density, c is aqueous concentration (mg/L), and K is the equilibrium coefficient for adsorption between the adsorbate (erythromycin) and adsorbent (solids) (). In the modified linear model (Eq.  3), a and b, are the intercept and slope of the linearized models, respectively. Linearized model parameters were then converted to parameters with physical meaning (Eq. 3.1).


To assess the impact of manure on adsorption of erythromycin to soil, we conducted a two-phased equilibration experiment. In the first phase, we equilibrated manure in 4 treatments (0.01 g, 0.05 g, 0.5 g, and 5 g) with erythromycin at 667 ppb for 72 hours. In the second equilibration, we added the 0.3 μm filtrate of the first phase to batch reactors with 5 g soil/150 mL. The second for an additional 72 hr. Samples were analyzed after the second phase. Following from this experiment, SM batch experiments utilized 0.5 g manure solids.  We applied a semi-log transformation to the data after the second phase to generate a linear model:

c = a + b * log m                  (Eq. 4)

where c is the aqueous concentration of erythromycin after second equilibration, m is the initial mass of manure used in the first equilibration, and a and b are the empirically derived intercept and slope, respectively, of the resulting model.


Soil column experiments

We used 7 cm diameter columns, continuously infiltrated from below using a pump. We infiltrated about 17 liters of 500 ppb erythromycin solution through each column. Average infiltration rates ranged from 28 mL/min to 31 mL/min. Treatments were run in triplicate. All columns contained 200.00 g homogenized soil (S treatment). Columns in soil-manure (SM) treatment contained 20.00 g manure in addition to 200.00 g soil.  We determined infiltration volume from a combination of initial column tests and high and low estimates of fitted adsorption isotherm models. Volumetric outflow was recorded continuously throughout the experiment for each column independently. Samples were filtered, diluted, and analyzed within 12 hours of experiment completion. Eight samples per column were analyzed for pH, 30 samples per column were analyzed for aqueous antibiotic concentration.  A non-parametric smooth local regression (loess) model was used to visualize the column results.


Data analysis

The ELISA analysis method is a targeted approach, well-equipped to assess the presence of a single compound in laboratory samples.  This method required a standard microplate device, making it accessible across laboratories lacking high performance liquid chromatography – mass spectrometry (HPLC-MS) set ups. Because ELISA methods are designed with a detection range of 0.2-25 ppb, this method is ideal for scenarios where low concentrations are expected.  Sample dilution to reach the detection range may have introduced some of the noise in the data. We believe some of the variability between our replicates may be attributed to the high dilution ratios required to reach detection range of the ELISA.

All data were analyzed in R-studio (version 1.4.1106 for Mac).  Model parameters were tested for statistical significance using a t-test.



Objective 3

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:

Batch adsorption studies

All Figures, tables, and captions for results: SAREReportFiguresTablesCaptions

Adsorption equilibration time

We applied a first order reaction model and determined qe and k1 (Eq. 1) to be 1.47x10-5 mg/mg (p-value = 3.64x10-5) and 6.399x10-2 hr-1 (p-value = 0.020) respectively for the concentration tested (667 ppb), with both parameters significant predictors at the p-value <0.05 level. We found the adsorption equilibration time for erythromycin to adsorb to Caneserga soil was approximately 72 hours (Figure 2). The 72 hr sample was not statically different from either the previous (48 hr, p-value = 0.4) or the next sample (96 hr, p-value = 0.4).  Some additional adsorption was observed in the 144 hr samples, and may be indicative of an additional, slower adsorption mechanism. The control reactors experienced some reduction (~50 ppb) in erythromycin concentration over the test period, but this change was not statically significant (p-value = 0.2) in a wilcox rank sum test between the first and last control sample, and therefore not considered in further analysis.


When testing adsorption of erythromycin to pure manure substrate, we observed no change in aqueous concentration over any of the range of solids ratios tested (Figure S.1). Therefore, adsorption equilibration time to adsorb to manure was not determined.  All subsequent tests contained a mixture of manure and soil solids.



Figure 1: Soil-only (S) equilibration time between erythromycin and Caneseraga soil (blue) compared to control reactors (yellow) with no soil. Error bars represent the range of the 3 replicates used to obtain the points plotted.


Adsorption equilibrium isotherms

When comparing S and SM adsorption isotherms, it is apparent that the presence of manure increases the aqueous concentration of erythromycin and reduces the adsorbed mass (Figure 3). The two isotherms are distinct until the highest concentrations tested (100,000 ppb, 150,000 ppb).  At these highest concentrations, adsorption of erythromycin begins to converge around the same values observed in the S treatments.

Both the S and SM isotherms were best fit by Langmuir models. In the S adsorption isotherm (Figure S2), the Langmuir non-linear regression model fit best (Eq. 2), with parameters of maximum adsorption capacity (qmax) and the equilibrium coefficient (K), were 1.53x10-3 mg adsorbed erythromycin/mg soil and 8.01x10-2 L/mg erythromycin respectively . Parameters were statistically significant (p-value < 0.01) for both non-linear and linear regression models when t-test statistics were computed (Table S3). The 150,000 ppb samples did not show a large enough change to be detected.  The 150,000 ppb sample has therefore been removed from the analysis, and was not used to calculate best fit. 

Unlike the models run on the S isotherm, the tested SM isotherm (Figure S3) models had fewer statistically significant parameters (Table S3).  The linear regression model (Eq. 3) fit the data best considering models with non-negative parameter values, at least one parameter that was a significant predictor of the data, and standard square error (SSE) values (K =1.99x10-4 L/mg erythromycin, qmax = 4.63x10-2 mg adsorbed erythromycin/mg (Table 3). 

We tested the by conducting an additional adsorption experiment between an erythromycin—manure-DOM solution and same soil previously discussed. We found a semi-log relationship (Eq. 4) between manure mass and aqueous concentration (Figure 4). We found that the presence of manure hinders erythromycin adsorption to this soil. At the highest manure mass tested (5.00 g), samples resembled control batch experiments’ aqueous concentrations at equilibrium. While at the lowest manure mass tested (0.01 g), samples resembled S treatments’ aqueous concentrations. We found the adjusted R2 for this relationship to be 0.9629. The semi-log model coefficients were 527.54 (p-value = 0.0033*) and 93.56 (p-value = 0.0124*) for a and b respectively. This result suggests that at manure to soil ratios of 1:1 or greater, erythromycin is likely to remain in a more mobile, aqueous phase.


Soil Column Experiment

Initial breakthrough curves for both S and SM columns occurred between 0 and 5 L erythromycin solution passed through the columns (Figure 5). The two column treatments overlap significantly between 2.5 and 6 L infiltrated after which they diverge for the remainder of the experiment (16.6-17.6 L passed through the columns). Although there is considerable overlap between the two treatments, especially in early samples, the SM smooth loess model results in a higher aqueous concentration than the S treatment model for the entire experiment. Due to the slow adsorption kinetics determined in the adsorption equilibration time experiment, these results are expected. When the columns begin to diverge, the SM columns discharged a greater amount of erythromycin, consistent with lower adsorption extents in the isotherm experiments.


The S column from 5 L through the end of the experiment displayed additional adsorption in comparison with SM columns, that may be characterized by a secondary, slower adsorption equilibration time or an adsorption mechanism with less affinity for erythromycin. This multi-phased adsorption may be a result of the complex, non-homogenous nature of soil used, and appears to be much less extensive in the SM treatment, which approaches the influent concentration of 500 ppb for some final samples. This second phase of adsorption maybe have been captured by the 144 hr samples in the adsorption equilibration time experiment, which displayed additional adsorption in comparison to the 72 hr samples.



We found that erythromycin adsorbs to soil (S treatments), but in the presence of manure (SM treatments), erythromycin is characterized by an adsorption isotherm favoring its aqueous phase, leading to higher mobility and contamination risk. We found that primary adsorption equilibrium was established after approximately 72 hr in soil batch reactors, suggesting in-field adsorption after manure application may be lowest immediately following application, with high risk of water contamination if applied during saturated soil conditions or immediately preceding precipitation.  Following the recommendation by Menz et al. (2018), this work defines the soil-erythromycin adsorption interaction to better understand residue movement in the environment, especially under conditions of manure application, one likely source of erythromycin contamination in the environment.

Differing from the organic carbon partitioning coefficient predicted by EPI Suite™ and previous soil adsorption experiments, which indicated Though the soils we used did have 9.8% organic matter present, the addition of manure significantly increased mobility of erythromycin. Our results suggest that adsorption sites previously taken by erythromycin are instead occupied by a component from the manure, the manure alters the form of erythromycin to make it less likely to sorb to the soil, or that erythromycin—DOM-manure complexes formed, which were small enough to pass through the filter and unable to sorb to the soil.  These DOM-erythromycin complexes could have effectively made the contaminant more mobile and would be expected given the propensity of this compound to partition into organic carbon. We did not explicitly test this mechanism. DOM has been shown to influence contaminant adsorption to minerals and through complexation reactions yielding compounds with higher or lower affinities for adsorption sites (Polubesova & Chefez, 2014), pushing the solid – aqueous-phase contaminant equilibrium toward the aqueous phase, as observed in our study. Clarithromycin and roxithromycin, two related macrolide antibiotics, have both showed similarly reduced adsorption to minerals in the presence of DOM (Feitosa-Felizzola et al., 2009). Humic acid-DOM was reported to reduce adsorption of clarithromycin and roxithromycin onto  manganese oxide and ferrihydrite minerals, likely by competing with the mineral surfaces for the macrolide antibiotics (Feitosa-Felizzola et al., 2009), a possible mechanism that may similarly influence erythromycin. To the authors knowledge, this is the first-time manure has been shown to inhibit adsorption of erythromycin to soils. 

Other antibiotics have previously displayed biphasic adsorption, as seen in our column experiments. Tetracycline was observed to have a biphasic adsorption to a highly porous human-hair-based substrate, filling external adsorption sites prior to less accessible internal adsorption sites (Ahmed et al., 2017). Biphasic mineralization of erythromycin has been observed and associated with desorption of the compound from sediments (Kim et al, 2004b), but no prior studies have noted a biphasic adsorption of the erythromycin to soils to the authors knowledge.


Study implications

We found that erythromycin was more mobile in SM treatments in comparison with S treatment. This conclusion held across adsorption isotherm experiments and column experiments. Therefore, erythromycin contamination sourced from agricultural manure application may pose a higher risk of entering surface and ground water supplies than previously concluded from S experiments (Pan & Chun, 2016). To assess risk of antibiotic contamination in other antibiotic classes and with other dominant functional groups, manure and SM interactions should be investigated similar to and beyond those preformed here to describe mechanisms of adsorption. Additionally, organic matter derived from differing sources may interact uniquely with each contaminant due to differing DOM compositions. Reducing transport, and, more broadly antibiotic environmental impact, requires a deeper understanding than our current knowledge of the controls of sorption of erythromycin to environmentally relevant compounds and soil surfaces. 

Because erythromycin sorbs readily to soils, best management practices (BMPs) that reduce sediment transport may also reduce erythromycin transport. However, because of continuous flushing of sediments in riparian zones and other frequently saturated areas (such as sediment control BMPs), conditions may favor the aqueous phase of the compound, increasing mobility and reducing removal of erythromycin during high flow conditions. Control of erythromycin residue transport may be more effective on the manure management level, e.g., breaking down this compound through heat treatment (Oliver et al., 2020) or biological degradation prior to field application of manure. Kim et al. (2004b) found natural biological degradation of erythromycin was controlled by desorption of erythromycin from stream sediments, suggesting microbial degradation could be enhanced if a system was designed to allow degradation in manure storage prior to field application.

Research conclusions:

We found that erythromycin adsorbs more strongly to agricultural soil in the absence of manure, remaining more mobile in the presence of manure. We found that the characteristic adsorption isotherms that fit both environments were Langmuir isotherms, with the nonlinear regression fitting the soil-only (S) adsorption isotherm best and a linearization of the Langmuir fitting the SM condition best. In our column experiment we observed more passage of erythromycin in the SM condition, as would be expected from the differences in their isotherms, and possible biphasic adsorption breakthrough curve. If we are to reduce the transport of compounds such as erythromycin, we must consider these physical and chemical processes alongside biological utilization and human decision-making pathways to predict impact and risk of transport. 

Participation Summary
4 Farmers participating in research

Project Outcomes

2 Grants applied for that built upon this project
1 Grant received that built upon this project
$150,000.00 Dollar amount of grant received that built upon this project
2 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.