Hostname: page-component-77f85d65b8-6bnxx Total loading time: 0 Render date: 2026-03-29T22:03:11.940Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of milk electrical conductivity for the differentiation of mastitis causing pathogens in Holstein cows

Published online by Cambridge University Press:  04 October 2019

S. Paudyal ,
P. Melendez ,
D. Manriquez ,
A. Velasquez-Munoz ,
G. Pena ,
I. N. Roman-Muniz  and
P. J. Pinedo Open the ORCID record for P. J. Pinedo [Opens in a new window]
S. Paudyal
Affiliation:
Department of Animal Sciences, Colorado State University, 350 W. Pitkin St., Fort Collins, CO 80523, USA
P. Melendez
Affiliation:
Department of Clinical Sciences, College of Veterinary Medicine, University of Missouri, 1520 East, Rollins St, Columbia, MO 65201, USA
D. Manriquez
Affiliation:
Department of Animal Sciences, Colorado State University, 350 W. Pitkin St., Fort Collins, CO 80523, USA
A. Velasquez-Munoz
Affiliation:
Department of Animal Sciences, Colorado State University, 350 W. Pitkin St., Fort Collins, CO 80523, USA
G. Pena
Affiliation:
Zoetis, 10 Sylvan Way, Parsippany, NJ 07054, USA
I. N. Roman-Muniz
Affiliation:
Department of Animal Sciences, Colorado State University, 350 W. Pitkin St., Fort Collins, CO 80523, USA
P. J. Pinedo*
Affiliation:
Department of Animal Sciences, Colorado State University, 350 W. Pitkin St., Fort Collins, CO 80523, USA
*
†E-mail: pablo.pinedo@colostate.edu
Get access

Abstract

Mastitis is one of the most prevalent and costly diseases in dairy cattle. Key components for adequate mastitis control are the detection of early stages of infection, as well as the selection of appropriate management interventions and therapies based on the causal pathogens associated with the infection. The objective was to characterize the pattern of electrical conductivity (EC) in milk during intramammary infection, considering specific mastitis-causing pathogen groups involvement. Cows (n = 200) identified by an in-line mastitis detection system with a positive deviation ≥15% in the manufacturer’s proprietary algorithm for EC (high electrical conductivity (HEC)) were considered cases and enrolled in the study at the subsequent milking. One control (CON) cow, within normal ranges for EC, was matched to each case. A composite milk sample was collected aseptically from each cow for bacteriological culture. Milk yield (MY) and EC were recorded for each milking during ±7 days relative to enrollment. Milk cultures were categorized into gram positive (GP), gram negative (GN), other (OTH) and no growth (NOG). Data were submitted for repeated-measures analysis with EC as the dependent variable and EC status at day −1, bacteriological culture category, parity number, stage of lactation and days relative to sampling as main independent variables. Average (± standard error (SE)) EC was greater in HEC than in CON cows (12.5 ± 0.5 v. 10.8 ± 0.5 mS/cm) on the day of identification (day −1). Milk yield on day −1 was greater in CON than in HEC (37.6 ± 5.1 v. 33.5 ± 5.2 kg). For practical management purposes, average EC on day −1 was similar for the different bacteriological culture categories: 11.4 ± 0.6, 11.7 ± 0.5, 12.3 ± 0.8 and 11.7 ± 0.5 mS/cm in GN, GP, OTH and NOG, respectively. Parity number was only associated with day −1 EC in HEC group, with the greatest EC values in parity 3 (12.3 ± 0.3 mS/cm), followed by parity 2 (11.9 ± 0.2 mS/cm), parity >3 (11.6 ± 0.5 mS/cm) and primiparous cows (11.2 ± 0.2 mS/cm). An effect on EC for the interaction of day relative to identification by pathogen gram category was observed. The same interaction effect was observed on daily MY. Overall, the level of variation for MY and EC between- and within-cows was substantial, and as indicated by the model diagnostic procedures, the magnitude of the variance in the cows in the CON group resulted in deviations from normality in the residuals. We concluded that characteristic temporal patterns in EC and MY in particular pathogen groups may provide indications for differentiation of groups of mastitis-causing pathogens. Further research to build detection models including EC, MY and cow-level factors is required for accurate differentiation.

Keywords

dairymilkingbacteriadiagnosishealth

Information

Type
Research Article
Information
animal , Volume 14 , Issue 3 , March 2020 , pp. 588 - 596
Copyright
© The Animal Consortium 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

*

The original published article contained the incorrect doi for the Supplementary material. This has subsequently been corrected.

References

Auldist, MJ, Coats, S, Rogers, GL and McDowell, GH 1995. Changes in the composition of milk from healthy and mastitic cows during the lactation cycle. Australian Journal of Experimental Agriculture 35, 427–436.CrossRefGoogle Scholar
Fernando, RS, Rindsig, RB and Spahr, SL 1982. Electrical conductivity of milk for detection of mastitis. Journal of Dairy Science 65, 659–664.CrossRefGoogle ScholarPubMed
Fernando, RS, Spahr, SL and Jaster, EH 1985. Comparison of electrical conductivity of milk with other indirect methods for detection of subclinical mastitis. Journal of Dairy Science 68, 449–456.CrossRefGoogle ScholarPubMed
Fosgate, GT, Petzer, M and Karzis, J 2013. Sensitivity and specificity of a hand held milk electrical conductivity meter compared to the California mastitis test for mastitis in dairy cattle. Veterinary Journal 196, 98–102.CrossRefGoogle Scholar
Gaspardy, A, Ismach, G, Bajcsy, AC, Veress, G, Markus, S and Komlosi, I 2012. Evaluation of the online electrical conductivity of milk in mastitic dairy cows. Acta Veterinaria Hungarica 60, 145–155.CrossRefGoogle Scholar
Godden, SM, Royster, E, Timmerman, J, Rapnicki, P and Green, H 2017. Evaluation of an automated milk leukocyte differential test and the California Mastitis Test for detecting intramammary infection in early- and late-lactation quarters and cows. Journal of Dairy Science 100, 6527–6544.CrossRefGoogle ScholarPubMed
Gonçalves, JL, Lyman, RL, Hockett, M, Rodriguez, R, dos Santos, MV and Anderson, KL 2017. Using milk leukocyte differentials for diagnosis of subclinical bovine mastitis. Journal of Dairy Research 84, 309–317.CrossRefGoogle ScholarPubMed
Haas, YD, Barkema, HW, Schukken, YH and Veerkamp, RF 2005. Associations between somatic cell count patterns and the incidence of clinical mastitis. Preventive Veterinary Medicine 67, 55–68.CrossRefGoogle ScholarPubMed
Halasa, T, Huijps, K, Østerås, O and Hogeveen, H 2007. Economic effects of bovine mastitis and mastitis management: A review. Veterinary Quarterly 29, 18–31.CrossRefGoogle ScholarPubMed
Hamann, J and Zecconi, A 1998. Evaluation of the electrical conductivity of milk as a mastitis indicator. Bulletin International Dairy Federation 334, 5–22.Google Scholar
Hassan, KJ, Samarasinghe, S and Lopez-Benavides, MG 2009. Use of neural networks to detect minor and major pathogens that cause bovine mastitis. Journal of Dairy Science 92, 1493–1499.CrossRefGoogle ScholarPubMed
Hillerton, J and Walton, A 1991. Identification of subclinical mastitis with a hand-held electrical conductivity meter. Veterinary Record 128, 513–515.CrossRefGoogle ScholarPubMed
Hogan, JS, Gonzalez, RN, Harmon, RJ, Nickerson, SC, Oliver, SP, Pankey, JW and Smith, KL 1999. Laboratory handbook on bovine mastitis. pp. 1–30. National Mastitis Council, Madison, WI, USA.Google Scholar
Janzekovic, M, Brus, M, Mursec, B, Vinis, P, Stajnko, D and Cus, F 2009. Mastitis detection based on electric conductivity of milk. Journal of Achievements in Materials and Manufacturing Engineering 34, 39–46.Google Scholar
Jensen, DB, Hogeveen, H and De Vries, A 2015. Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis. Journal of Dairy Science 99, 7344–7361CrossRefGoogle Scholar
Kamphuis, C, Mollenhorst, H and Hogeveen, H 2011. Sensor measurements revealed: predicting the gram status of clinical mastitis causal pathogens. Computers and Electronics in Agriculture 77, 86–94.CrossRefGoogle Scholar
Khatun, M, Clark, CEF, Lyons, NL, Thomson, PC, Kerrisk, KL and Garcia, SC 2017. Early detection of clinical mastitis from electrical conductivity data in an automatic milking system. Animal Production Science 57, 1226–1232CrossRefGoogle Scholar
Kitchen, B 1981. Review of the progress of dairy science: Bovine mastitis: milk compositional changes and related diagnostic tests. Journal of Dairy Research 48, 167–188.CrossRefGoogle ScholarPubMed
Lago, A, Godden, SM, Bey, R, Ruegg, PL and Leslie, K 2011. The selective treatment of clinical mastitis based on on-farm culture results: I. Effects on antibiotic use, milk withholding time and short term clinical and bacteriological outcomes. Journal of Dairy Science 94, 4441–4456CrossRefGoogle ScholarPubMed
Littell, RC, Milliken, GA, Stroup, WW, Wolfinger, RD and Schabenberger, O 2006. SAS for mixed models, 2nd edition. SAS Institute Inc., Cary, NC, USA.Google Scholar
Maatje, K, Huijsmans, PJM, Rossing, W and Hogewerf, PH 1992. The efficacy of inline measurement of quarter milk electrical conductivity, milk yield and milk temperature for the detection of clinical and subclinical mastitis. Livestock Production Science 30, 239–249CrossRefGoogle Scholar
Mansfeld, R, Mansfeld, S, Sant, B and Hoedemaker, M 2001. New aspects regarding the use of the milk electrical conductivity as a parameter for routine diagnostics in dairy production medicine programs. In 2nd International Symposium on Bovine Mastitis and Milk Quality, 2001, Vancouver, Canada, pp. 488–489.Google Scholar
Milner, P, Page, KL and Hillerton, JE 1997. The effects of early antibiotic treatment following diagnosis of mastitis detected by a change in the electrical conductivity of milk. Journal of Dairy Science 80, 859–863.CrossRefGoogle ScholarPubMed
Milner, P, Page, KL, Walton, AW and Hillerton, JE 1996. Detection of clinical mastitis by changes in electrical conductivity of foremilk before visible changes in milk. Journal of Dairy Science 79, 83–86CrossRefGoogle ScholarPubMed
Nielen, M, Schukken, YH, Brand, A, Deluyker, HA and Maatje, K 1995. Detection of subclinical mastitis from on-line milking parlor data. Journal of Dairy Science 78, 1039–1049.CrossRefGoogle ScholarPubMed
Norberg, E, Hogeveen, H, Korsgaard, IR, Friggens, NC, Sloth, KHMN and Løvendahl, P 2004. Electrical conductivity of milk: ability to predict mastitis status. Journal of Dairy Science 87, 1099–1107.CrossRefGoogle ScholarPubMed
Paudyal, S, Melendez, P, Manriquez, D, Velasquez, A, Pinedo, P and Pena, G 2018. Use of electrical conductivity for the differentiation of mastitis-causing pathogens. Abstracts of the 2018 American Dairy Science Association Annual Meeting, Knoxville, Tennessee. Journal of Dairy Science 101 (suppl. 2).Google Scholar
Pyorala, S 2003. Indicators of inflammation in the diagnosis of mastitis. Veterinary Research 34, 565–578.CrossRefGoogle Scholar
Woolford, MW, Williamson, JH and Henderson, HV 1998. Changes in electrical conductivity and somatic cell count between milk fractions from quarters subclinically infected with particular mastitis pathogens. Journal of Dairy Research 65, 187–198.CrossRefGoogle ScholarPubMed
Supplementary material: File

Paudyal et al. supplementary material

Paudyal et al. supplementary material

Download Paudyal et al. supplementary material(File)
File 5.2 MB