Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part III. Metritis

M. L. Stangaferro, R. Wijma, L. S. Caixeta, M. A. Al-Abri, J. O. Giordano

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with metritis based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of metritis by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. In this manuscript, the overall performance of HIS to detect cows with all disorders of interest in this study [ketosis, displaced abomasum, indigestion (companion paper, part I), mastitis (companion paper, part II), and metritis] is also reported. Holstein cattle (n = 1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel) from at least −21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. An HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as an HIS of <86 units during at least 1 d from −5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, β-hydroxybutyrate, total calcium, and haptoglobin were determined in a subgroup of cows (n = 459) at −11 ± 3, −4 ± 3, 0, 3 ± 1, 7 ± 1, 14 ± 1, and 28 ± 1 DIM. The overall sensitivity of HIS was 55% for all cases of metritis (n = 349), but it was greater for cows with metritis and another disorder (78%) than for cows with metritis only (53%). Cows diagnosed with metritis and flagged based on HIS had substantial alterations in their rumination, activity, and HIS patterns around CD, alterations of blood markers of metabolic and health status around calving, reduced milk production, and were more likely to exit the herd than cows not flagged based on the HIS and cows without disease, suggesting that cows flagged based on the HIS had a more severe episode of metritis. Including all disorders of interest for this study, the overall sensitivity was 59%, specificity was 98%, positive predictive value was 58%, negative predictive value was 98%, and accuracy was 96%. The AHMS was effective for identifying cows with severe cases of metritis, but less effective for identifying cows with mild cases of metritis. Also, the overall accuracy and timing of the AHMS alerts for cows with health disorders indicated that AHMS that combine rumination and activity could be a useful tool for identifying cows with metabolic and digestive disorders, and more severe cases of mastitis and metritis.

Original languageEnglish
Pages (from-to)7422-7433
Number of pages12
JournalJournal of Dairy Science
Volume99
Issue number9
DOIs
Publication statusPublished - Sep 1 2016

Fingerprint

rumination
endometritis
dairy cows
cows
monitoring
Health
Milk
Mastitis
physical activity
mastitis
indigestion
displaced abomasum
milk
ketosis
haptoglobins
Abomasum
Hydroxybutyrates
blood
metabolic diseases
digestive system diseases

Keywords

  • activity
  • dairy cow
  • metritis
  • rumination

ASJC Scopus subject areas

  • Food Science
  • Animal Science and Zoology
  • Genetics

Cite this

Use of rumination and activity monitoring for the identification of dairy cows with health disorders : Part III. Metritis. / Stangaferro, M. L.; Wijma, R.; Caixeta, L. S.; Al-Abri, M. A.; Giordano, J. O.

In: Journal of Dairy Science, Vol. 99, No. 9, 01.09.2016, p. 7422-7433.

Research output: Contribution to journalArticle

Stangaferro, M. L. ; Wijma, R. ; Caixeta, L. S. ; Al-Abri, M. A. ; Giordano, J. O. / Use of rumination and activity monitoring for the identification of dairy cows with health disorders : Part III. Metritis. In: Journal of Dairy Science. 2016 ; Vol. 99, No. 9. pp. 7422-7433.
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