Pasture management in Australia’s dairy industry requires the manual shifiting of temporary electric fences to maintain pasture quality and growth. Virtual fencing presents an alternative to save time and labour costs. We used automated virtual fence (VF) collars to determine the variation in learning of the virtual fence stimuli, and evaluated the success of the technology to contain cows in a predetermined area of pasture. Twelve Holstein-Friesian non-lactating multiparous dairy cows were fitted with the collars, and a VF was used to restrict cows to two grazing allocations (G1 and G2) across six days. Cows received an audio tone (AT) when they approached the virtual fence, and a paired electrical pulse (EP) if they continued forward. The VF contained cows within predetermined areas for 99% of time, but cows spent the least time near the fence (p < 0.01). The number of stimuli reduced through time, demonstrating the ability of cows to learn the VF (p = 0.01). However, the mean number of EP per day ranged from 1 to 6.5 between individuals (p < 0.01). Therefore, successful containment may have a welfare cost for some individuals. Further work should focus on this individual variation, including measures of welfare.

The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

Castration involves the removal of the testes and is performed to improve product quality and management of male calves. The procedure has been proven to cause significant pain and stress, and despite several attempts to reduce the impact of castration on animal welfare, there has yet to be a practical and affordable option made available for farmer application. To address this issue, we conducted 2 trials (n = 18 and 27) to examine the efficacy of topical anesthetic Tri-Solfen (TA) to alleviate the pain of surgical castration. Angus bull calves (135.8 ± 5.7 kg) aged 3 to 4 mo were randomly allocated to 3 treatment groups, including surgical castration, castration in combination with TA, and uncastrated controls. In Trial 1, pain-related behavior was assessed using a customized numerical rating scale (NRS) over 4 h. In Trial 2, pre- and postoperative skin sensitivity of the wound and periwound areas was assessed using an electronic von Frey anesthesiometer (IITC Life Sciences, Woodland Hills, CA) and von Frey monofilaments (300 g). Sampling was repeated at 1 min and 2, 4, 6, and 24 h after castration. Pain threshold was measured as maximum pressure (g) exerted by the electronic anesthesiometer to invoke animal reflex, and responses to the von Frey monofilaments were scored from 0 to 3 using a NRS on the basis of local and central motor reflexes. Calves treated with TA displayed significantly less pain-related behaviors up to 3.5 h after castration than untreated calves (P < 0.001) and did not differ from uncastrated controls. Topical anesthetic–treated calves also exhibited significantly greater pain threshold of the wound (559.2 ± 14.3 g) and surrounding skin (602.8 ± 16.5 g) than untreated calves (446.0 ± 18.9 and 515.3 ± 20.4 g, respectively; P < 0.001). Control and TA-treated calves had significantly lower mean response scores to von Frey stimulation than untreated calves (0.333, 0.978, and 4.289, respectively; P < 0.001). Results indicate that TA effects rapid and prolonged pain alleviation in calves up to 24 h after castration. Topical anesthesia may present a cost-effective, practical, on-farm approach to pain alleviation and is proposed as a potential tool for reducing the welfare impact on the beef animal in routine husbandry procedures. Copyright © 2013. American Society of Animal Science .

In this paper, we develop innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work is built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle based on Deep Learning (DL) is generated using the sensor data acquired from the farm IoT system. The physiological cycle of cattle can be monitored in real time, and the state of the next physiological cycle of cattle can be anticipated using this model. The basis of this work is the vast amount of data that is required to validate the legitimacy of the digital twins model. In terms of behavioural state, this digital twin model has high accuracy, and the loss error of training reach about 0.580 and the loss error of predicting the next behaviour state of cattle is about 5.197 after optimization. The digital twins model developed in this work can be used to forecast the cattle’s future time budget.