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a b s t r a c tBovine respiratory disease (BRD) complex in calves impairs health and welfare and causes severe economic losses for the Stockperson. Early recognition of BRD should lead to earlier veterinary (antibiotic/ anti-inflammatory) treatment interventions thereby reducing the severity of the disease and associated costs. Coughing is one of the clinical manifestations of BRD. It is believed that by automatically and continuously monitoring the sounds within calf houses, and analysing the coughing frequency, early recognition of BRD in calves is possible. Therefore, the objective of the present study was to develop an automated calf cough monitor and examine its potential as an early warning system for BRD in artificially reared dairy calves. The coughing sounds of 62 calves were continuously recorded by a microphone over a three-month period. A sound analysis algorithm was developed to distinguish calf coughs from other sounds (e.g. mechanical sounds). During the sound recording period the health of the calves was assessed and scored periodically per week by a trained human observer. Calves presenting with BRD received antibiotic and/or anti-inflammatory treatment and the dates of treatment were recorded. This treatment date reference served as a comparison for the investigation of whether an increase in coughing frequency could be related to calves developing BRD. The calf cough detection algorithm achieved 50.3% sensitivity, 99.2% specificity and 87.5% precision. Four out of five periods, where coughing frequency was observed to be increased, coincided with the development of BRD in more than one calf. This period of increased coughing frequency was always observed before the calves were treated. Therefore, the calf cough monitor has the potential to identify early onset of BRD in calves.

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Abstract 10Chicken weight provides information about growth and feed conversion of the flock in order to identify 11 deviations from the expected homogeneous growth trend of the birds. This paper proposes a novel method 12 to automatically measure the growth rate of broiler chickens by sound analysis. 13Through the application of process engineering, Precision Livestock Farming (PLF) can combine audio and 14 video information into on-line automated tools that can be used to control, monitor and model the 15 behaviour, health and production of animals and their biological response. 16The aim of this study was to record and analyse broiler vocalisations under normal farm conditions, to 17 identify the relation between animal sounds and growth trend. Recordings were made at regular intervals, 18 during the entire life of birds, in order to evaluate the variation of frequency and bandwidth of the sounds 19 emitted by the animals. 20Two experimental trials were carried out in an indoor reared broiler farm; the audio recording procedures 21 lasted for 38 days. The recordings were made, in an automated, non-invasive and non-intrusive way and 22 without disturbing the animals in to the broiler house. Once a week, 50 birds were selected at random and 23 their weight recorded in order to follow the growth trend in the birds. 24Sound recordings were manually analysed and labelled using the Adobe Audition CS6 software. 25 2 Analysing the sounds recorded, it was possible to find a significant correlation (P<0.001) between the 26 frequencies of the vocalisations recorded and the weight of the broilers. 27The results explained how the frequency of the sounds emitted by the animals was inversely proportional 28 to the age and to the weight of the broilers; the more they grow, the lower the frequency of the sounds 29 emitted by the animals. 30This preliminary study shows how this method based on the identification of specific frequencies of the 31 sounds, in an indoor reared broiler farm, linked to the age and to the weight of the birds, could be used as 32 an early warning method/system to evaluate the health and welfare status of the animals at farm level, 33 developing also an automated growth monitoring tool. 34

In calf rearing, bovine respiratory disease (BRD) is a major animal health challenge. Farmers incur severe economic losses due to BRD. Additional to economic costs, outbreaks of BRD impair the welfare of the animal and extra expertise and labour are needed to treat and care for the infected animals. Coughing is recognised as a clinical manifestation of BRD. Therefore, the monitoring of coughing in a calf house has the potential to detect cases of respiratory infection before they become too severe, and thus to limit the impact of BRD on both the farmer and the animal. The objective of this study was to develop an algorithm for detection of coughing sounds in a calf house. Sounds were recorded in four adjacent compartments of one calf house over two time periods (82 and 96 days). There were approximately 21 and 14 calves in each compartment over the two time-periods, respectively. The algorithm was developed using 445 min of sound data. These data contained 664 different cough references, which were labelled by a human expert. It was found that, during the first time period in all 3 of the compartments and during the second period in 2 out of 4 compartments, the algorithm worked very well (precision higher than 80%), while in the 2 other cases the algorithm worked well but the precision was less (66.6% and 53.8%). A relation between the number of calves diagnosed with BRD and the detected coughs is shown.

Current farm sizes do not allow the precise identification and tracking of individual cows and their health and behavioral records. Currently, the application of information technology within intensive dairy farming takes a key role in proper routine management to improve animal welfare and to enhance the comfort of dairy cows. An existing application based on information technology is represented by the GEA CowView system (GEA Farm Technologies, Bönen, Germany). This system is able to detect and monitor animal behavioral activities based on positioning, through the creation of a virtual map of the barn that outlines all the areas where cows have access. The aim of this study was to validate the accuracy, sensitivity, and specificity of data provided by the CowView system. The validation was performed by comparing data automatically obtained from the CowView system with those obtained by a manual labeling procedure performed on video recordings. Data used for the comparisons were represented by the zone-related activities performed by the selected dairy cows and were classified into 2 categories: activity and localization. The duration in seconds of each of the activities/localizations detected both with the manual labeling and with the automated system were used to evaluate the correlation coefficients among data; and subsequently the accuracy, sensitivity, specificity, and positive and negative predictive values of the automated monitoring system were calculated. The results of this validation study showed that the CowView automated monitoring system is able to identify the cow localization/position (alley, trough, cubicles) with high reliability in relation to the zone-related activities performed by dairy cows (accuracy higher than 95%). The results obtained support the CowView system as an innovative potential solution for the easier management of dairy cows.