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      Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data

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          Abstract

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          Tail biting is a major animal welfare issue within modern pig production, and tail biting should be prevented whenever possible. If the farmer could get an alarm when a pen of pigs is at high risk of developing tail damage, the farmer would be able to take timely action to prevent tail damage in specific pens. In the current investigation, a method for prediction of tail biting events was developed and tested in a real-life setting. The method used changes in pigs’ drinking behaviour and in the temperature of the pen. The method was able to alarm the farmer about 12 of the 14 tail biting events prior to serious tail damage. However, the farmer did also get false alarms on 30% of the days without tail biting events, which is not optimal. Thus, the farmer could use the alarms as indications of which pens to pay greater attention to. The next step could be to expand the method to include behavioural changes that are more specific to tail biting such as changes in the pigs’ tail posture.

          Abstract

          Tail biting in pigs is an animal welfare problem, and tail biting should be prevented from developing into tail damage. One strategy could be to predict events of tail biting so that the farmer can make timely interventions in specific pens. In the current investigation, sensor data on water usage (water flow and activation frequency) and pen temperature (above solid and slatted floor) were included in the development of a prediction algorithm for tail biting. Steps in the development included modelling of data sources with dynamic linear models, optimisation and training of artificial neural networks and combining predictions of the single data sources with a Bayesian ensemble strategy. Lastly, the Bayesian ensemble combination was tested on a separate batch of finisher pigs in a real-life setting. The final prediction algorithm had an AUC > 0.80, and thus it does seem possible to predict events of tail biting from already available sensor data. However, around 30% of the no-event days were false alarms, and more event-specific predictors are needed. Thus, it was suggested that farmers could use the alarms to point out pens that need greater attention.

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          Most cited references24

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          Relationships between tail biting in pigs and disease lesions and condemnations at slaughter.

          Two matched case-control studies were performed at an abattoir with a capacity of 780 pigs per hour, each study using the approximately 7000 pigs slaughtered on one day. In the first study, the severity of tail biting and pneumonia were recorded in pigs with bitten or intact tails. In the second study, the tail score, sex, and the presence of pleuritis, externally visible abscesses and trimming were recorded in pigs with bitten or intact tails. In study 1, there was no significant association between the tail score and the percentage of lung tissue affected by lesions typical of enzootic pneumonia, but there was a significant association between the severity of tail biting and the prevalence of lungs with abscesses and/or pleuritic lesions (P<0.0001). In study 2, there were significant associations between the severity of tail biting, and the prevalence of external carcase abscesses and carcase trimming; the carcases of castrated males had evidence of tail biting more frequently than the carcases of females (P<0.05).
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            Tail biting and production performance in fattening pigs

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              Automatic early warning of tail biting in pigs: 3D cameras can detect lowered tail posture before an outbreak

              Tail biting is a major welfare and economic problem for indoor pig producers worldwide. Low tail posture is an early warning sign which could reduce tail biting unpredictability. Taking a precision livestock farming approach, we used Time-of-flight 3D cameras, processing data with machine vision algorithms, to automate the measurement of pig tail posture. Validation of the 3D algorithm found an accuracy of 73.9% at detecting low vs. not low tails (Sensitivity 88.4%, Specificity 66.8%). Twenty-three groups of 29 pigs per group were reared with intact (not docked) tails under typical commercial conditions over 8 batches. 15 groups had tail biting outbreaks, following which enrichment was added to pens and biters and/or victims were removed and treated. 3D data from outbreak groups showed the proportion of low tail detections increased pre-outbreak and declined post-outbreak. Pre-outbreak, the increase in low tails occurred at an increasing rate over time, and the proportion of low tails was higher one week pre-outbreak (-1) than 2 weeks pre-outbreak (-2). Within each batch, an outbreak and a non-outbreak control group were identified. Outbreak groups had more 3D low tail detections in weeks -1, +1 and +2 than their matched controls. Comparing 3D tail posture and tail injury scoring data, a greater proportion of low tails was associated with more injured pigs. Low tails might indicate more than just tail biting as tail posture varied between groups and over time and the proportion of low tails increased when pigs were moved to a new pen. Our findings demonstrate the potential for a 3D machine vision system to automate tail posture detection and provide early warning of tail biting on farm.
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                Author and article information

                Journal
                Animals (Basel)
                Animals (Basel)
                animals
                Animals : an Open Access Journal from MDPI
                MDPI
                2076-2615
                19 July 2019
                July 2019
                : 9
                : 7
                : 458
                Affiliations
                [1 ]Department of Animal Science, Aarhus University, Blichers Allé 20, DK-8830 Tjele, Denmark
                [2 ]Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 2, DK-1870 Frederiksberg C, Denmark
                [3 ]Department of Large Animal Sciences, University of Copenhagen, Grønnegårdsvej 2, DK-1870 Frederiksberg C, Denmark
                Author notes
                [* ]Correspondence: mona@ 123456anis.au.dk
                Author information
                https://orcid.org/0000-0003-0958-8763
                https://orcid.org/0000-0002-9437-0605
                Article
                animals-09-00458
                10.3390/ani9070458
                6681100
                31330973
                861744e9-9e43-4656-93ac-04c38b1ae346
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 26 June 2019
                : 15 July 2019
                Categories
                Article

                sus scrofa domesticus,precision livestock farming,computational ethology,drinking behaviour,water flow,pen temperature,dynamic linear models,artificial neural network,bayes’ theorem,bayesian ensemble

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