Abstract

The study aims to verify the usefulness of new intervals-based algorithms for clinical interpretation of animal behavior in dairy cows around calving period. Thirteen activities associated with feeding-ruminating-locomotion-behaviors of 42 adult Holstein-Friesian cows were continuously monitored for the week (wk) -2, wk -1 and wk +1 relative to calving (overall 30’340 min/animal). Soon after, animals were retrospectively assigned to group-S (at least one spontaneous diseases; n = 24) and group-H (healthy; n = 18). The average activities performed by the groups, recorded by RumiWatch® halter and pedometer, were compared at the different weekly intervals. The average activities on the day of clinical diagnosis (dd0), as well as one (dd-1) and two days before (dd-2) were also assessed. Differences of dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were calculated. Variables showing significant differences between the groups were used for a univariate logistic regression, a receiver operating characteristic analysis, and a multivariate logistic regression model. At wk +1 and dd0, eating- and ruminating-time, eating- and ruminate-chews and ruminating boluses were significantly lower in group-S as compared to group-H, while other activity time was higher. For ΔD2 and Δweeks, the differences of eating- and ruminating-time, as well as of eating-and ruminate-chews were significantly lower in group-S as compared to group-H. Concerning the locomotion behaviors, the lying time was significantly higher in group-S vs. group-H at wk +1 and dd-2. The number of strides was significantly lower in group-S compared to group-H at wk +1. The model including eating-chews, ruminate-chews and other activity time reached the highest accuracy in detecting sick cows in wk +1 (area under the curve: 81%; sensitivity: 73.7%; specificity: 82.4%). Some of the new algorithms for the clinical interpretation of cow behaviour as described in this study may contribute to monitoring animals’ health around calving.

Highlights

  • The peripartum period represents a crucial phase for dairy cows’ life cycle because of the significant increase of energy requirements and the severe metabolic adjustments mainly due to the exponential fetal growth, calving and the onset of lactation [1–3]

  • The current retrospective study offers clinical considerations concerning the usefulness of novel algorithms of digitally recorded data of feeding, ruminating and locomotion behaviors in dairy cows naturally experiencing the around calving period

  • The study revealed that the combined use of RumiWatch1 halter (RWh) and RumiWatch1 pedometer (RWp) may represent a supportive instrument for clinical interpretation of cows’ behavior, showing alterations of several feeding, rumination and locomotion behaviors at the very same day as the disease was first clinically diagnosed

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Summary

Introduction

The peripartum period represents a crucial phase for dairy cows’ life cycle because of the significant increase of energy requirements and the severe metabolic adjustments mainly due to the exponential fetal growth, calving and the onset of lactation [1–3] Throughout this period of physiological and behavioral changes, the natural drop in dry matter intake, as parturition approaches, makes the adaptation phase even more difficult, culminating in a negative energy balance (NEB) status; this is recognized as a risk factor for the development of puerperal diseases [2,4,5]. A successful management of this period, represents a key factor of profitable farms [6] As the latter continue to increase in size, and supervision of individual cows gets more difficult, the use of precision dairy farming (PDF) technologies may provide essential support to manage the herd. Technologies for monitoring cows’ activities and behavior may have a great impact to support the observations of skilled herdspersons and to allow for early diagnosis improving the success rate of therapeutic measures

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