M. Goharshahi1, L. Lidauer2, A. Berger2, F. Kickinger2, M. Öhlschuster2, W. Auer2, D. Klein-Jöbstl1, M. Drillich1 and M. Iwersen1
1Clinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, University of Veterinary Medicine Vienna, Austria; 2Smartbow GmbH, Weibern, Austria
Email: Michael.Iwersen@vetmeduni.ac.at
Introduction One of the most important diseases in cattle worldwide is diarrhea in new born calves. Besides animal welfare issues, economic losses caused by costs for diagnosis, treatment, increased labor, increased risk for sequelae, impaired development of the calf, and, potentially, a total loss of the animal are a burden for the farmers. One important measure in minimizing the costs and the suffering of animals is an early detection of disease. Diseases are often associated with behavioral changes. Hence, a continuously monitoring of animal behaviors by use of a sensor system has the potential of early detection of disease. The aim of this study was to describe movement and activity patterns of calves suffering from diarrhea in comparison with healthy controls. For this, calves were equipped with a 3D-accelerometer integrated into an ear tag SMARTBOW (SB, Smartbow GmbH, Weibern, Austria). The SB system is commercially available for animal localization, estrus detection, and health monitoring in dairy cows. So far, it is not used in calves.
Materials and Methods The study was conducted in a commercial dairy farm in Slovakia, housing more than 2,500 calves and young stock. A total of 310 healthy female Holstein calves were enrolled at birth and followed up to 28 days of life, i.e. during the period of greatest risk for diarrhea. All animals were equipped with a SB tag in the middle of the right ear at the first day of life.
Acceleration data were recorded with a frequency of 10 Hz and sent in real-time to the receivers (SB WallPoints). The WallPoints were connected with a local farm server on which data were processed and analyzed. Based on algorithms for cows, standing and lying times as well as ‘activity’ measures provided by SB were used as output parameters in our study.
Additionally, all calves were clinically examined once per day. Based on the scoring system by Larson (1977), calves with a fecal score of 3 (spreads readily to about 6 mm depth) or 4 (i.e., orange juice, liquid consistency, splatters) on at least two consecutive days, were categorized as ‘diarrheic’. At the time of diarrhea diagnosis, a healthy control calf matching best in age was also selected and examined. Fecal samples of diarrheic and control calves were tested for the most important pathogens with regard to newborn calf diarrhea (i.e. rotavirus, bovine coronavirus, Cryptosporidium parvum, and Escherichia coli F5) by an on-farm test (FASTest D4T bovine, Megacor Diagnostik GmbH, Hoerbranz, Austria). For statistical analysis, the SPSS software package (version 24, IBM Corporation, Armonk, NY) was used. Wilcoxon nonparametric test for two-related-sample groups were used and a p‐ value < 0.05 was considered as significant.
Results In total, 13 calves left the herd before 28 days of live, because they died or were sold. In the remaining 297 calves, diarrhea occurred in 152 animals, resulting in an incidence of diarrhea of 51.2% (95% CI: 45.3–56.7%). Cryptosporidium parvum, rotavirus, coronavirus and E. coli F5 have been detected in 88.3%, 32.1%, 5.1%, and 1.3% of diarrheic calves, and in 56.4%, 29.5%, 7.7%, and 3.8% of control calves. For Cryptosporidium parvum the differences between diarrheic and control calves were significant (p < 0.05).
In Figure 1, SB-recorded median lying times (minutes per hour) for diarrheic and control calves are presented for the week before and after the visual diagnosis of diarrhea (day 0). SB-data showed that lying time increased in diarrheic calves already 2 days before visual observation of diarrhea. These differences were significant from day -1 to day 4. On day 0, average lying times per day were 72 minutes longer in diarrheic calves compared to their controls. ‘Activity’ estimated by SB (Figure 2) started to decrease from day -3 and showed maximum differences compared with controls on day 0 (p < 0.05).
Conclusions Based on algorithms developed for dairy cows, significant differences in lying times and ‘activity’ between diarrheic and healthy calves were observed by the SB system. These differences were detected already 1 day before visual diagnosis of diarrhea. Future research should focus on developing an algorithm for detecting calves at risk for diarrhea and on testing its accuracy.
Figure 2 Median ‘activity’ measures day-7 to day 7 in
relation to visual diagnosis of diarrhea (day 0). observation date of diarrhea in diarrheic and control calves.
Figure 1 Median lying times from day-7 to day 7 in
relation to visual diagnosis of diarrhea (day 0). observation date of diarrhea in diarrheic and control calves.
Session 06: Precision livestock farming methods to control animal health and welfare
ZellDiX – A new approach to predict udder health by using DHI results and cell differentiation
A. Bartel1, F. Querengässer1, E. Gass2, F. Onken2, C. Baumgartner3 and M. Doherr11Institute for Veterinary Epidemiology and Biostatistics, Free University Berlin, Germany; 2German Association for Performance and Quality Testing, Bonn, Germany; 3Bavarian Association for Raw Milk Testing, Wolnzach, Germany
Email: Alexander.Bartel@fu-berlin.de
Introduction Management of udder health is a challenging aspect on every dairy farm. Somatic cell counts (SCC), obtained from monthly DHI testing, are helpful to monitor udder health at individual animal and herd level. In Germany, six farm-level parameters using DHI based SCC results are currently provided by a monthly report. They are a useful tool to reflect the present udder health status. However, to our knowledge, there are no standardized indicators available based on DHI results to predict the cow-level udder health status in the future. The German ZellDiX project aims to enhance the informative value of DHI results by evaluating the additional value of differential somatic cell count (DSCC) (Pilla et al. 2013) and by establishing statistical models that reliably predict future udder health.
Materials and Methods Using a new generation of high throughput devices (Damm et al. 2017), SCC as well as DSCC was routinely analyzed from DHI samples of over 900,000 cows from 19,160 farms in two German federal states. Over the course of two years, more than 10 million measurements were available. Our data set consisted of highly diverse farms in respect to size and management type. We fitted two statistical models, one for chronic SCC elevations and one for stable good udder health. Chronic elevations were defined as cell counts above a defined threshold in the next 2 DHI measurements. In order to meet different farmers’ needs, results were derived for SCC thresholds between 200,000 and 700,000 cells/ml. Cows with stable good udder health had SCC values below 100,000 cells/ml in the next two DHI measurements. For mathematical modeling, we used generalized additive models (GAM) (Wood 2008) with cubic regression splines. Both models were 10-fold cross-validated and tested using internal and external validation data. In addition to these predictions, GAMs allowed to identify biases in the underlying data set and the impacts of individual parameters.
Results The predictions of the models accurately reflected the real probability, independent of region, size and breed composition of a farm as tested by multiple validation approaches. The AUC of the chronic udder impairment model (Fig. 1) at a SCC threshold of 400,000 cells/ml was 0.868 [95% CI 0.866 – 0.870] with a calibration slope of 0.995 [95% CI 0.983 – 1.006]. For the stable udder health model (Fig. 2) the AUC was 0.780 [95% CI 0.779 – 0.781] and the calibration slope was 0.993 [95% CI 0.990 – 0.996].
Figure 1 ROC-Curve of the chronic udder impairment model Figure 2 ROC-Curve of the stable udder health model
Conclusions Using individual predictions for the risk of chronic cell count elevations can support treatment or culling decisions, while ranking animals could help farmers to prioritize resources. Identification of animals with a high probability of stable low cell counts can serve as a comparable indicator of udder health. On farm level, this can be a parameter for effective management. From April 2019 on, we will use the models in practice on cooperating pilot farms.
References
Damm, Malin, Claus Holm, Mette Blaabjerg, Morten Novak Bro, and Daniel Schwarz. 2017. “Differential Somatic Cell Count—A Novel Method for Routine Mastitis Screening in the Frame of Dairy Herd Improvement Testing Programs.” Journal of Dairy Science 100 (6): 4926–40. doi:10.3168/jds.2016-12409.
Pilla, R, M Malvisi, GGM Snel, D Schwarz, S König, C-p Czerny, and R Piccinini. 2013. “Differential Cell Count as an Alternative Method to Diagnose Dairy Cow Mastitis.” Journal of Dairy Science 96 (3): 1653–60. doi:10.3168/jds.2012-6298.
Wood, Simon N. 2008. “Fast Stable Direct Fitting and Smoothness Selection for Generalized Additive Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70 (3): 495–518. doi:10.1111/j.1467-9868.2007.00646.x.
Using cognitive bias as a welfare tool in poultry
Ľ. Košťál, Z. Skalná and K. PichováCenter of Biosciences, Slovak Academy of Sciences, Bratislava, Slovakia Email: Lubor.Kostal@savba.sk
Harding et al. [1] in their pivotal study introduced the idea of using the link between cognition and emotions for the assessment of affective states in animals. Soon after that, the first studies of affect-induced judgment bias in birds were published, using European starling as a model. Although several other inspiring papers on this subject used songbird species (starlings and in one case canaries), nevertheless, substantial numbers of published avian cognitive bias studies used poultry species, such as domestic chickens (9 papers) and Japanese quail (2 papers).
Variation in design of cognitive bias tests in these poultry studies is quite large. Most of the studies use more or less modified spatial judgement task of Burman et al. [2]. In this task, birds are trained to expect a reward (corn, mealworm) in one location and no reward (empty bowl) or punishment (puffed rice soaked in a quinine sulphate, air puff) in another. After successful discrimination training follows the judgement bias testing, assessing whether birds in positive or negative emotional states respond differently to ambiguous stimuli of intermediate spatial location. The second approach used by two chicken studies adopted a Go-Go two choice visual discrimination task of Brilot et al. [3]. In this task hens have to learn to associate combination of cues (two background colours and two symbols) with a different food reward size. Hens are trained in a two-choice (left–right) test in an arena to associate a high-value reward (four mealworms) with a black cue and a low-value reward (one mealworm) with a white cue. Birds that reach training criteria are exposed to three unrewarded ambiguous cues (25, 50, and 75% black) to assess judgement bias. The third approach is based on operant Go-NoGo discrimination task, derived from the design of pivotal study of Harding et al. [1]. This is the approach used in our laboratory. We use the custom-built Skinner boxes with touchscreen (operated by Biopsychology toolbox software) designed for quail and laying hens for training the operant visual discrimination task and subsequent judgement bias testing. A custom mealworm dispenser controlled by the software delivers the reward and white noise serves as a punishment. Circles in different shades of grey are used as cues. The last, fourth design of judgement bias tests used in poultry is trying to eliminate one of the drawbacks of all previous approaches, i.e. extensive discrimination training needed before the judgment bias tests itself. (To illustrate this difficulty, in one of the papers using the two choice visual discrimination task, one third of hens did not achieve the discrimination criterion.) To avoid these problems, Salmeto et al. [4] came up with the original idea: in their experiments with young chicks, they used naturally appetitive (mirror image of chick) and aversive (horned owl silhouette) stimuli in a straight alley maze. As ambiguous cues i n the judgement bias test, they used morphed images of a chick and an owl.
There is also large variation in the ways affective states (mood) are induced in the above-mentioned poultry studies: housing environment, enrichment, social isolation, different fearfulness, series of aversive events over several days, different temperatures, treatment with corticosterone, or suppression of depression induced by social isolation by the antidepressants differ from study to study. However, not all of the treatments caused the expected judgement biases. For example there were no significant differences in responses in judgement bias tests between laying hens housed in the basic and enriched pens, or Japanese quail housed in cages and in deep litter pens, suggesting that these environments did not induce large enough differences in the birds’ emotional state to have a significant impact on their behaviour in the tests, or that the tests used are not sensitive enough. On the other hand, corticosterone-treated broilers showed an increased expectation of punishment in the face of ambiguous information [5], that lead authors to conclude that pessimism could be a useful welfare indicator in chickens. With respect to underlying brain mechanisms, it is of interest that the judgement bias in young hens was shown to be related to dopamine turnover rate in mesencephalon, with higher activity in individuals that had a more optimistic response.
In conclusion, the cognitive bias paradigm is a valuable tool for the assessment of poultry welfare. Nevertheless, existing judgement bias tests need further optimization and validation, improvement of the test design, and avoiding problems such as loss of ambiguity with repeated testing etc. Other types of cognitive bias tests, such as the newly proposed tests of attention and memory bias introduced recently in some species, represent another promising perspective.
References
1. Harding, E., Paul, E., and Mendl, M., Nature, 2004, 427(6972): p. 312.
2. Burman, O.H., et al., Animal Behaviour, 2008, 76(3): p. 801-809.
3. Brilot, B.O., Asher, L., and Bateson, M., Animal Cognition, 2010, 13(5): p. 721-731.
4. Salmeto, A.L., et al., Brain Res, 2011, 1373: p. 124-130.
5. Iyasere, O.S., et al., Scientific Reports, 2017, 7(1): p. 1-12.
Session 06: Precision livestock farming methods to control animal health and welfare
Automated assessment of welfare in chickens
M. S. DawkinsDepartment of Zoology, University of Oxford, UK Email: marian.dawkins@zoo.ox.ac.uk
Introduction OPTICFLOCK is a smart system that processes data from cameras on-farm and delivers a ‘verdict’ on the welfare of each flock. The aim of the OPTICFLOCK project is to provide farmers with a management tool that enables them achieve higher welfare standards, improved efficiency and reduced need for medication. By automatically monitoring the welfare of broiler chickens throughout their lives, it gives farmers a continuous readout of the health and welfare of their flocks in real time and provides early warning signs of problems before these become serious
Materials and Methods OPTICFLOCK works by analysing optical flow patterns produced by the movement of chicken flocks. It streams images directly from IP cameras inside broiler chicken houses into a small computer that immediately processes the images.
Only numbers, not images are stored so that security is ensured. Previous research has shown that optical flow measures are correlated with key welfare outcomes for broiler flocks such as % hockburn and pododermatitis. In addition, flocks that subsequently tested positive for Campylobacter showed altered optical flow patterns when birds were less than a week old, at least two weeks before Campylobacter is normally detectable in faecal samples.
To be useful to farmers, OPTICFLOCK needs to be able to indicate in real time which flocks are at risk of developing welfare problems. By using the optical flow patterns of flocks with known welfare outcomes as a reference, a traffic light system for use on an app or PC has been developed. Figure 1 shows for one outcome measure - % hockburn – how two particular flocks (black lines) compare to the reference flocks. Here, the key reference is the daily median of kurtosis optical flow values of all reference flocks that had low (<10%) hockburn. The green area shows the Inter Quartile Range of the deviations from that median shown by reference flocks with low (10%) hockburn. The red area shows the IQR of the deviations from that same median shown by reference flocks with high hockburn (>40%).
Flock with 70% final hockburn Flock with 0% hockburn
The daily optical flow patterns of the two flocks were different from the start. The flock on the left had a final hockburn measure of 70% and the deviation of its kurtosis optical flow from the reference median fell into the IQR of high hockburn reference flocks (red).
The flock on the right, which had 0% final hockburn, spent all of its life within the IQR of the low hockburn reference flocks.
Conclusions OPTICFLOCK can indicate which flocks are at risk from high levels of hockburn in very young birds, before any marks appear. It still needs to be shown, however, that camera monitoring is any better at managing hockburn or other welfare issues than existing methods, such as careful control of temperature and humidity within the house. This and other limitations of automated assessment of flock welfare will be discussed.
References
Dawkins, M.S., Roberts, S.J., Cain, R.J. Nickson, T., Donnelly, C.A. (2017) Early warning of footpad dermatitis and hock burn in broiler chicken flocks using optical flow, body weight and water consumption. Veterinary Record 180: 448 (May 20 2017). Cite as dos 10.1136/vr.104066
Colles, F.M., Cain, R., Nickson, T., Smith, A., Roberts, S.J., Maiden, M.C.J., Lunn, D.,Dawkins, M.S. (2016) Monitoring chicken flock behaviour provides early warning of infection by human pathogen Campylobacter. Proceedings of the Royal Society B 283. 20152323.