Cited by 206Pharmaceutical and Antibiotic Environmental ImpactsAntibiotic Use and ResistancePesticide and Herbicide Environmental Studies

The DOI matches its Crossref record.

Fate and Transport of Antimicrobials and Antimicrobial Resistance Genes in Soil and Runoff Following Land Application of Swine Manure Slurry

Open access
Joy, Stacey R., Bartelt-Hunt, Shannon L., Snow, Daniel D., Gilley, John E., Woodbury, Bryan L., Parker, David B., Marx, David B., Li, Xu·2013·Environmental Science & Technology

Abstract

Due to the use of antimicrobials in livestock production, residual antimicrobials and antimicrobial resistance genes (ARGs) could enter the environment following the land application of animal wastes and could further contaminate surface and groundwater. The objective of this study was to determine the effect of various manure land application methods on the fate and transport of antimicrobials and ARGs in soil and runoff following land application of swine manure slurry. Swine manure slurries were obtained from facilities housing pigs that were fed chlortetracyline, tylosin or bacitracin and were land applied via broadcast, incorporation, and injection methods. Three rainfall simulation tests were then performed on amended and control plots. Results show that land application methods had no statistically significant effect on the aqueous concentrations of antimicrobials in runoff. However, among the three application methods tested broadcast resulted in the highest total mass loading of antimicrobials in runoff from the three rainfall simulation tests. The aqueous concentrations of chlortetracyline and tylosin in runoff decreased in consecutive rainfall events, although the trend was only statistically significant for tylosin. For ARGs, broadcast resulted in significantly higher erm genes in runoff than did incorporation and injection methods. In soil, the effects of land application methods on the fate of antimicrobials in top soil were compound specific. No clear trend was observed in the ARG levels in soil, likely because different host cells may respond differently to the soil environments created by various land application methods.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/es4026358

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In a soil column experiment following a simulated rainfall event, ARGs responded differently in their vertical transport and distribution: some remained concentrated in the topsoil while others migrated into the bottom layers of the column.

    “ARGs in a soil column experiment following a simulated rainfall event responded differently in their vertical transport and distribution; some ARGs remained concentrated in the topsoil while others were found to migrate into the bottom layers of the column (Joy et al., 2013).”

    From Review of emerging contaminants in green stormwater infrastructure: antibiotic resistance genes, microplastics, tire wear particles, PFAS, and temperature (2024) · cites this paper as Joy et al., 2013

    The Rainfall Simulation Experiments section describes soil cores taken from field plots, not a soil-column experiment. The Discussion says ARGs were absent or at low levels in deep soil in most plots and concludes that vertical transport was not significant during the study; it suggests that ARGs detected in bottom soil before rainfall likely came from infiltration of the applied slurry. Page numbers are not available in the supplied passages. (LLM, conf=0.88)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 46

  1. Wang L. L., 2011, Microb. Ecol., 63, 32, 10.1007/s00248-011-9921-9 · DOI
  2. Sarmah A. K., 2006, Chemosphere, 65, 725, 10.1016/j.chemosphere.2006.03.026 · DOI · Paper page
  3. Chee-Sanford J. C., 2009, J. Environ. Qual., 38, 1086, 10.2134/jeq2008.0128 · DOI
  4. Heuer H., 2011, Curr. Opin. Microbiol., 14, 236, 10.1016/j.mib.2011.04.009 · DOI · Paper page
  5. Dolliver H., 2008, J. Environ. Qual., 37, 1227, 10.2134/jeq2007.0392 · DOI
  6. Kay P., 2004, Environ. Toxicol. Chem., 23, 1136, 10.1897/03-374 · DOI
  7. Kreuzig R., 2005, Environ. Toxicol. Chem., 24, 777, 10.1897/04-019R.1 · DOI
  8. Davis J. G., 2006, J. Environ. Qual., 35, 2250, 10.2134/jeq2005.0348 · DOI
  9. Kim S.-C., 2010, J. Hazard. Mater., 175, 836, 10.1016/j.jhazmat.2009.10.086 · DOI
  10. Sassman S. A., 2005, Environ. Sci. Technol., 39, 7452, 10.1021/es0480217 · DOI
  11. Hu D., 2007, Environ. Toxicol. Chem., 26, 884, 10.1897/06-197R.1 · DOI
  12. Werner J. J., 2009, J. Agric. Food Chem., 57, 6932, 10.1021/jf900797a · DOI
  13. Eichhorn P., 2004, Anal. Chem., 76, 6002, 10.1021/ac0494127 · DOI
  14. Heuer H., 2008, Soil Biol. Biochem., 40, 1892, 10.1016/j.soilbio.2008.03.014 · DOI
  15. Byrne-Bailey K. G., 2009, Antimicrob. Agents Chemother., 53, 696, 10.1128/AAC.00652-07 · DOI
  16. Brooks J. P., 2009, J. Environ. Qual., 38, 218, 10.2134/jeq2008.0029 · DOI
  17. Gilley J. E., 2013, ASABE Annual International Meeting
  18. Humphry J. B., 2002, Appl. Eng. Agric., 18, 199, 10.13031/2013.7789 · DOI
  19. Sharpley A., 2003, J. Environ. Qual., 32, 2172, 10.2134/jeq2003.2172 · DOI
  20. Zhu J., 2001, J. Chromatogr., 928, 177, 10.1016/S0021-9673(01)01139-6 · DOI
  21. Snow D. D., 2003, Analysis of Emerging Contaminants using Liquid Chromatography/Mass Spectrometry (LC/MS/MS), 161, 10.1021/bk-2003-0850.ch010 · DOI
  22. Zhu J., 2001, J. Chromatogr., 177, 10.1016/S0021-9673(01)01139-6 · DOI
  23. Aga D. S., 2005, J. Agric. Food Chem., 53, 7165, 10.1021/jf050415+ · DOI
  24. Govaerts C., 2003, Rapid Commun. Mass Spectrom., 17, 1366, 10.1002/rcm.1058 · DOI
  25. Koike S., 2007, Appl. Environ. Microbiol., 73, 4813, 10.1128/AEM.00665-07 · DOI
  26. Ghosh S., 2009, Appl. Microbiol. Biotechnol., 84, 791, 10.1007/s00253-009-2125-2 · DOI
  27. Koike S., 2010, Microb. Ecol., 59, 487, 10.1007/s00248-009-9610-0 · DOI
  28. Yoshida Y., 2011, FEMS Microbiol. Lett., 320, 33, 10.1111/j.1574-6968.2011.02291.x · DOI
  29. Murphy T., 2008, Biotechnol. Lett., 30, 1665, 10.1007/s10529-008-9743-0 · DOI
  30. Suzuki M. T., 2000, Appl. Environ. Microbiol., 66, 4605, 10.1128/AEM.66.11.4605-4614.2000 · DOI
  31. Ji X., 2012, J. Hazard. Mater., 235, 178, 10.1016/j.jhazmat.2012.07.040 · DOI
  32. Zhao L., 2010, Sci. Total Environ., 408, 1069, 10.1016/j.scitotenv.2009.11.014 · DOI
  33. Martinez-Carballo E., 2007, Environ. Pollut., 148, 570, 10.1016/j.envpol.2006.11.035 · DOI
  34. Zhou L.-J., 2013, The Science of the total environment, 444, 183, 10.1016/j.scitotenv.2012.11.087 · DOI
  35. Wang Q. Q., 2006, J. Agric. Food Chem., 54, 157, 10.1021/jf052216w · DOI
  36. Dolliver H., 2008, J. Environ. Qual., 37, 1245, 10.2134/jeq2007.0399 · DOI
  37. Yu Z. T., 2005, Appl. Environ. Microbiol., 71, 6926, 10.1128/AEM.71.11.6926-6933.2005 · DOI
  38. Chen J., 2007, Appl. Environ. Microbiol., 73, 4407, 10.1128/AEM.02799-06 · DOI
  39. Chen J., 2010, Microb. Ecol., 60, 479, 10.1007/s00248-010-9634-5 · DOI
  40. Kay P., 2005, Chemosphere, 59, 951, 10.1016/j.chemosphere.2004.11.055 · DOI
  41. Teixido M., 2012, Environ. Sci. Pollut. Res., 19, 3087, 10.1007/s11356-012-0954-5 · DOI
  42. Clay S. A., 2005, J. Environ. Sci. Health, Part B, 40, 841, 10.1080/03601230500227533 · DOI
  43. Hamscher G., 2002, Anal. Chem., 74, 1509, 10.1021/ac015588m · DOI
  44. Pavli V., 2006, Biol. Pharm. Bull., 29, 2160, 10.1248/bpb.29.2160 · DOI
  45. Alexander T. W., 2011, BMC Microbiol., 11
  46. Boes J., 2005, Prev. Vet. Med., 69, 213, 10.1016/j.prevetmed.2005.02.007 · DOI

Cited by

Sorted by citations

Metadata

Authors
Joy, S. R., Bartelt-Hunt, S. L., Snow, D. D., Gilley, J. E., Woodbury, B. L., Parker, D. B., Marx, D. B., & Li, X.
Affiliations
  • University of Nebraska–Lincoln (US)
  • Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska 68588, United States
  • Agricultural Research Service (US)
  • United States Department of Agriculture (US)
Corresponding author
Li, Xu
Venue
Environmental Science & Technology
Published
2013-10-14
Volume
47
Issue
21
Pages
12081-12088
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
OpenAlex
W2001516227
Cited by
206
References
0
Citations by year
2026: 5 · 2025: 13 · 2024: 15 · 2023: 14 · 2022: 20 · 2021: 18 · 2020: 25 · 2019: 22 · 2018: 20 · 2017: 22 · 2016: 10 · 2015: 8 · 2014: 13 · 2013: 1
FWCI
8.01
Type
journal article
Language
en
Open access
Locations
  • Environmental Science & Technology (publishedVersion)
  • PubMed (publishedVersion)
  • Lincoln (University of Nebraska) (submittedVersion)
Topics
  • Physical Sciences › Environmental Science › Pollution › Pharmaceutical and Antibiotic Environmental Impacts
  • Health Sciences › Medicine › Infectious Diseases › Antibiotic Use and Resistance
  • Physical Sciences › Environmental Science › Pollution › Pesticide and Herbicide Environmental Studies
Keywords
antimicrobial agents, antimicrobial resistance genes, runoff, rainfall simulation, chlortetracycline, tylosin, bacitracin, erm genes, manure incorporation, manure injection, broadcast application, soil contamination, antibiotic use in livestock
MeSH terms
Agriculture, Animals, Anti-Infective Agents, Drug Resistance, Microbial, Manure, Rain, Soil, Soil Microbiology, Swine, Water Pollutants, Chemical, Groundwater

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review