Effect of forage type on swallowed bolus mass and a method for counting swallows in dairy cattle

Dry matter intake (DMI) is a primary determinant of milk production in grazing dairy cows and an ability to measure the DMI of individual cows would allow herd managers to formulate supplementary rations that consider the amount of nutrients ingested from grass. The 2 related aims of this experiment were to define the mean number of swallowed boli and mass of the swallowed boli in Holstein-Friesian dairy cattle offered a variety of forages commonly fed in the dairy industry of southeastern Australia, and to evaluate 2 indirect methods for counting the number of swallows. Twelve ruminally-fistulated, lactating Holstein-Friesian cows were randomly assigned to 3 replicated 4 × 4 Latin square designs and offered 4 forages: fresh chicory (FC), fresh perennial ryegrass (RP), alfalfa hay (AH), and perennial ryegrass silage (RS). The experiment was conducted over 28 d with each of 4 periods consisting of 7 d with 3 d of measurement. Forage diets were offered to individual cows following the partial evacuation of the rumen. The first 20 min after forage was offered constituted the measurement period, during which all swallowed boli were manually captured by samplers who placed their hand through the ruminal fistula and over the cardia entrance of the rumen of each cow. Concurrently, microphones and video cameras were used for the indirect measurement of swallows. The average swallowed bolus mass overall was 17.4 g dry matter (DM) per bolus with the lowest mass observed in cows offered FC (8.9 g DM/bolus), followed by RP (14.9 g DM/bolus), compared with cows offered AH (23.6 g DM/bolus) and RS (22.3 g DM/bolus). The swallowing rate was greater in cows offered FC (78 swallows/20 min) than in cows offered RP, AH, and RS (62.3 swallows/20 min). The audio recording method showed greater concordance (Lin's concordance correlation coefficient = 0.90) with the physical capturing of the boli through the rumen, than the video recording method did (Lin's concordance correlation coefficient = 0.54). It is concluded that the mass of the swallowed boli is related to forage type and that using a microphone attached to the cow's forehead can provide an accurate measure of the number of swallows when verified against the actual number of swallows counted by manual interception of the boli at the rumen cardia.

Effects of extrinsic factors on some rumination patterns: A review

The rumen and its activity, rumination, are the characterizing traits of the suborder Ruminantia, and it is accompanied by related feeding habits and consequent animal behavior. Several extrinsic (not related to the animal itself) factors affect rumination behavior; most are reflected in rumination timing (considering overall daily duration as well as circadian differences in rumination patterns): age, environmental factors, and diet. For these reasons, great efforts have been sustained at the research level for monitoring rumination patterns. Currently, some research outcomes are applied at the farm level; others are still at the experimental level. All of these efforts are finalized mainly for the use of rumination pattern recording as an effective prediction tool for the early detection of health and welfare problems, both in a single head and at the herd level. Moreover, knowledge of the effects of extrinsic factors on rumination physiology represents a great challenge for improving the efficiency of ruminant livestock management, from the diet to the housing system, from parasites to heat stress. The present review deals mainly with the worldwide raised ruminant species.

Recognition of Cattle's Feeding Behaviors Using Noseband Pressure Sensor With Machine Learning

Automatic monitoring of feeding behavior especially rumination and eating in cattle is important to keep track of animal health and growth condition and disease warnings. The noseband pressure sensor is not only able to accurately sense the pressure change of the cattle's jaw movements, which can directly reflect the cattle's chewing behavior, but also has strong resistance to interference. However, it is difficult to keep the same initial pressure while wearing the pressure sensor, and this will pose a challenge to process the feeding behavior data. This article proposed a machine learning approach aiming at eliminating the influence of initial pressure on the identification of rumination and eating behaviors. The method mainly used the local slope to obtain the local data variation and combined Fast Fourier Transform (FFT) to extract the frequency-domain features. Extreme Gradient Boosting Algorithm (XGB) was performed to classify the features of rumination and eating behaviors. Experimental results showed that the local slope in combination with frequency-domain features achieved an F1 score of 0.96, and recognition accuracy of 0.966 in both rumination and eating behaviors. Combined with the commonly used data processing algorithms and time-domain feature extraction method, the proposed approach improved the behavior recognition accuracy. This work will contribute to the standardized application and promotion of the noseband pressure sensors.