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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
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
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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.

Type
Research Article
Copyright
© The Animal Consortium 2019 

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Footnotes

*

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

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