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Rumination time and monitoring of health disorders during early lactation.

Rumination time and monitoring of health disorders during early lactation.
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Paudyal S, Maunsell FP, Richeson JT, Risco CA, Donovan DA, Pinedo PJ,


Paudyal S, Maunsell FP, Richeson JT, Risco CA, Donovan DA, Pinedo PJ, (click to view)

Paudyal S, Maunsell FP, Richeson JT, Risco CA, Donovan DA, Pinedo PJ,

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Animal : an international journal of animal bioscience 2017 11 16() 1-9 doi 10.1017/S1751731117002932
Abstract

The objective was to evaluate the association between changes in daily rumination time (dRT) and early stages of disease during early lactation and to assess the performance of two proposed disease detection indices. This cohort study included 210 multiparous Holstein cows at the University of Florida Dairy Unit. Cows were affixed with a neck collar containing rumination loggers providing rumination time. The occurrence of health disorders (mastitis, metritis, clinical hypocalcemia, depression/dehydration/fever (DDF), digestive conditions, lameness and clinical ketosis) was assessed until 60 days in milk. Two indices were developed to explore the association between dRT and disease: (i) Cow index (CIx), based on changes in dRT in the affected cow during the days before the diagnosis of health disorders; (ii) Mates index (MIx), index based on deviations in dRT relative to previous days and healthy pen mate cohorts. Subsequently, an alert model was proposed for each index (ACIx and AMIx) considering the reference alert cut-off values as the differences between average index values in healthy and sick cows for each specific disease. The sensitivity (SE) of ACIx detecting disease ranged from 42% (digestive conditions and DDF) to 80% (clinical hypocalcemia) with 84% specificity (SP). The SE of AMIx ranged from 46% (digestive conditions and DDF) to 100% (clinical hypocalcemia) with 85% SP. Consistent reductions in rumination activity, both within cow and relative to healthy mate cohorts, were observed for each health disorder at the day of diagnosis. However, the ability of the proposed algorithms for detecting each specific disease was variable.

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