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Abstract Background This study aimed to validate slaughterhouse indicators collected during meat inspection as an alternative to on-farm animal welfare indicators. For this purpose, the assessments of twelve on-farm and seven slaughterhouse indicators of 628 pigs from three different farms were combined into three indices, differentiated between on-farm and slaughterhouse: (1) limb health, (2) other organ health, and (3) respiratory health. At first, an assessment at animal-level using agreement parameters was carried out to ascertain whether the same welfare or health issues were identified on-farm and at slaughterhouse, taking the production period (farrowing, rearing and fattening period) and the last weeks before slaughtering into account. Second, the connection of slaughterhouse findings on the individual on-farm health indices was examined using logistic regressions, to determine whether certain welfare issues can be better monitored using slaughterhouse indicators. Results Acceptable agreement was determined using the Prevalence-Adjusted Bias-Adjusted Kappa (PABAK) for the farrowing and fattening period, but not for the rearing period. A more detailed analysis of the weeks before slaughter shows that there is still a poor agreement 8 weeks before slaughter and an acceptable agreement 4 weeks before slaughter. This indicated the slaughterhouse indicators pneumonia, pleuritis and pericarditis as possible estimators of fever and deviant behavior on-farm and the slaughterhouse indicators bursitis and joint inflammations as possible estimators of lameness. In the second part of the analysis, the connection of slaughterhouse findings on the individual on-farm health indices was investigated; a significant influence of the farm on the limb and respiratory indices and no significant influence of the slaughterhouse findings could be determined, provided that all weekly assessments during the lifetime of the pigs have been taken into account. However, an influence of the slaughterhouse findings on the respiratory index and on the other organ index could be determined if only the weekly assessments four and eight weeks before slaughter, respectively, were taken into account. Conclusions In general, the possible suitable indicators detected by the PABAK, could replace some health-related indicators but a complete substitution of on-farm welfare assessment is not possible. In addition, the traceability over time must be investigated further.

The recent trend towards fewer farms with increasing herd sizes and the public's growing demand for documentation, transparency, high welfare and legislature in the European Union emphasise the need for smart farming solutions to support farmers. The aim of this review was to describe tools and systems which are able to digitally document treatments and medicine used in pig and milk production on-farm. The systems described were categorised into systems documenting at the time of application, systems working with mobile applications or web- and computer-based operating systems, either working with or without individual identification. Additionally, the tools and systems were revised towards their ability to fulfil the requirements of European legislation. Treatment data have contributed in many research approaches to topics such as health and production surveillance, herd health management, animal welfare, animal breeding and production performance. The advantages of digital solutions lie in the simplification of work procedures and the availability of data, which provide the basis for analyses and informed decisions, leading to improved farm productivity. When comparing on-farm documentation of medicine usage and veterinary records of use and sales data, on-farm data are inferior in accuracy. Nevertheless, the need for on-farm documentation of medicine usage has been highlighted several times. References to specific systems in scientific journals and external evaluations of systems are rare. The implementation of a digital documentation system producing data of quality represents the first step. Further steps should include data analyses, interpretation and the connection of different data sources, following the approach of precision livestock farming and the Internet of Things.

The activity of pigs is an important indicator of animal welfare and is useful as an input parameter of an early warning tool for behavioural changes caused by e.g. suboptimal housing conditions or health issues. Methods for activity monitoring include video analysis of human observations (V_HO) and dynamic background subtraction (V_BG) or passive infrared motion detectors (PIR). This study analyses the relationship between these methods at the beginning and end of piglet rearing in two different housing environments. Thus, the aim of the present study was to determine whether the representation of activity within a pen using V_BG or PIR, above the activity area (PIR_AA) and the faeces area (PIR_FA), is comparable to V_HO. The trial used data from video analysis (V_BG and V_HO) and PIR (PIR_AA and PIR_FA) at the ninth and tenth day (T1) as well as at the 44th and 45th day of piglet rearing (T2) in two barren pens (28 pigs per pen) and two enriched pens (18 pigs per pen). Spearman's rank correlation coefficient (RS) was calculated to determine the correlation between V_HO and V_BG to PIR above the activity area as well as faeces area for each period (T1, T2) and pen. In comparison with V_HO, V_BG showed a RS of 0.94 to 0.96, PIR_AA showed a RS of 0.75 to 0.94 and PIR_FA showed a RS of 0.81 to 0.96. When comparing with V_BG, PIR_AA showed a RS of 0.79 to 0.90 and PIR_FA showed a RS of 0.86 to 0.93. In a further comparison, the number of active phases per method was identified according to the median per method, pen and period. The number of active phases documented by V_HO compared to V_BG and PIR (PIR_AA and PIR_FA) was determined using a regression analysis. In the detection of active phases from V_HO to V_BG, PIR_AA and PIR_FA, V_BG and PIR_AA showed good accuracy (V_BG R² = 0.94, Slope = 1.02; PIR_AA R² = 0.89, Slope = 0.97). Therefore, V_BG and PIR_AA are suitable methods to determine activity in a pen.

This study examined relevant variables for predicting the prevalence of pigs with a tail lesion in rearing (REA) and fattening (FAT). Tail lesions were recorded at two scoring days a week in six pens in both REA (10 batches, 840 scoring days) and FAT (5 batches, 624 scoring days). To select the variables that best explain the variation within the prevalence of pigs with a tail lesion, partial least squares regression models were used with the variable importance in projection (VIP) and regression coefficients (β) as selection criteria. In REA, five factors were extracted explaining 60.6% of the dependent variable’s variance, whereas in FAT five extracted factors explained 62.4% of the dependent variable’s variance. According to VIP and β, seven variables were selected in REA and six in FAT with the tail posture being the most important variable. In addition, skin lesions, treatment index in the suckling phase, water consumption (mean), activity time (mean; CV) and exhaust air rate (CV) were selected in REA. In FAT, additional musculoskeletal system issues, activity time (mean; CV) and exhaust air rate (mean; CV) were selected according to VIP and β. The selected variables indicate which variables should be collected in the stable to e.g., predict tail biting.

Understanding behavior is important in terms of welfare assessments to be able to evaluate possible changes in behavior among different husbandry systems. The present study applied principal component analysis (PCA) to reveal relationships between behavioral indicators to identify the main components of sows' behavior promoting feasibility of welfare assessments by providing possibilities for variable reduction and aggregation. The indicators of the Welfare Quality® protocol's principle to assess behavior were repeatedly applied by two observers on 13 farms in Northern Germany. This included Qualitative Behavior Assessments (QBA) to evaluate animals' body language using 20 pre-defined adjectives, assessments of social and exploratory behavior, stereotypies, and human–animal relationship tests. Two separate PCA were performed with respect to the QBA: (1) adjectives were included as independent variables and (2) adjectives were pre-aggregated using the calculation rules of the Welfare Quality® protocol for fattening pigs since a calculation for sows does not yet exist. In both analyses, two components described sows' behavior. Most variance was explained by the solution with adjectives as independent variables (51.0%). Other behavioral elements not captured as indicators by the protocol may still be important for all-inclusive welfare assessments as the required variance of 70% was not achieved in the analyses. Component loadings were used to determine components' labels as (1) “satisfaction of exploratory behavior” and (2) “social resting”. Both components reflected characteristics of sows' natural behavior and can subsequently be used for variable reduction but also for development of component scores for aggregation. As defined for PCA, component 1 explained more variance than component 2. PCA is useful to determine the main components of sows' behavior, which can be used to enhance feasibility of welfare assessments.

Equine headshaking syndrome (EHS) is characterised as non-physical and involuntary movement of the horse’s head and neck. Although EHS is clinically simple to diagnose, its aetiopathogenesis often remains unclear. The aim of this study was to gain an overview of signalment and therapy possibilities used in France and Switzerland. To do this, an online survey was developed and distributed via newsletters. A total of 933 complete, answered surveys from France (n = 804) and Switzerland (n = 129) were evaluated. The median age in France was 12.4 years (CH = 14.3). Mostly geldings were affected (58.5%FRA, 57.4%CH). There was an association with Warmbloods in Switzerland (55.8%CH), but in France, in addition to Warmbloods (34.4%FRA), Thoroughbreds (27.2%FRA) were also affected. Moreover, horses affected by EHS often show stereotypical behaviour (15.7%FRA, 14.7%CH). A total of 38.4%FRA and 67.4%CH of horse owners utilised therapy measures, with nose covers being most commonly used (19.9%FRA, 30.2%CH). Horse owners resorted to alternative treatments that had not previously been studied in context with EHS (15%FRA, 20.9%CH). Conservative treatments, such as medication, were used by 5.4%CH and 1.9%FRA. This study provides an overview of the status of horses affected by EHS in France and Switzerland and thus offers a fundamental step to understanding the consequences of welfare issues associated with EHS.

There are efficient sampling methods to accurately estimate behaviour with a moderate or long duration. For short behaviour, observing animals continuously is recommended although there is no recommended minimum observation time. In most studies, sampling method and observation time per day is determined by practical considerations. Thus, this study analysed the validity of behavioural observations in different observation periods using continuous sampling (CS) or time sampling (TS) based on biting behaviour. Tail-biting and ear-biting of weaned piglets in six pens were continuously observed for 12 h per day for 4 days to form a reference. Shorter observation periods of CS and TS were simulated by taking subsets of this reference. The amount of behaviour per hour of each observation period was compared to the reference and to other observation period of the same kind and length. Four different measurements were defined to calculate accuracy scores (AS; 0–1; higher values are better). Comparison to the reference shows better AS for observation periods with longer observation time in total (0.5 h of CS: 0.2; 6 h of CS: 0.6). Additionally, TS covers longer time periods without decreasing AS. However, focus on activity time results in an overestimation of irregular behaviour. Comparing AS among observation periods of the same kind and length show overall low agreement. This study indicated problems of different observation periods of CS and TS to accurately estimate behaviour. Therefore, validity of behavioural observations should be analysed in greater detail to determine optimal sampling methods.

The aim of this study was to analyze the utilization of different stable areas of a total of 52 group-housed horses as well as their preferred stable parts and the use of resources. The study was situated in a “HIT Active Stable®” in Northern Germany for a period of 227 observation days. After dividing the whole farm area in a grid of 3 × 3 m, the dataset was examined with and without the pasture area. Furthermore, linear mixed models were applied. On average, horses used 53.2 ± 19 different squares per hour. The observation day (p < 0.001) and the covariate age (p < 0.001) had significant effects on the different squares visited per hour. No significant effects were found for sex (p = 0.30) and breed (p = 0.65) as only geldings and no stallions were part of the group and the distribution of the breeds was unfavorable. The random effect animal showed that the horse-individual estimates from −19.2 to 17.6 different squares visited per hour were quite large. Furthermore, it could be shown that the horses used resources such as feed stalls with a frequency of up to 0.14% more than other paddock areas without resources. Open lying halls with tarp skin were also preferred over the metal hall. The shelters were only partly popular. Use could be visualized with the help of heat maps. This study gives a good overview of the use of individual areas and resources and possible improvements.

With increasing herd sizes came an enhanced requirement for automated systems to support the farmers in the monitoring of the health and welfare status of their livestock. Cattle are a highly sociable species, and the herd structure has important impact on the animal welfare. As the behaviour of the animals and their social interactions can be influenced by the presence of a human observer, a camera based system that automatically detects the animals would be beneficial to analyse dairy cattle herd activity. In the present study, eight surveillance cameras were mounted above the barn area of a group of thirty-six lactating Holstein Friesian dairy cows at the Chamber of Agriculture in Futterkamp in Northern Germany. With Mask R-CNN, a state-of-the-art model of convolutional neural networks was trained to determine pixel level segmentation masks for the cows in the video material. The model was pre-trained on the Microsoft common objects in the context data set, and transfer learning was carried out on annotated image material from the recordings as training data set. In addition, the relationship between the size of the used training data set and the performance on the model after transfer learning was analysed. The trained model achieved averaged precision (Intersection over union, IOU = 0.5) 91% and 85% for the detection of bounding boxes and segmentation masks of the cows, respectively, thereby laying a solid technical basis for an automated analysis of herd activity and the use of resources in loose-housing.

We compare the Kuratorium für Technik und Bauwesen in der Landwirtschaft e.V. (KTBL) protocol, a German protocol for sows and piglets developed for farm’s self-inspection, to the Welfare Quality® protocol for sows and piglets (WQ). The KTBL protocol introduces new indicators for piglets to be assessed at pen level (face lesions, carpal joint lesions, undersized animals). The reliability of their assessment at pen level was analysed by comparison to assessments at individual level. Both protocols were applied by one observer in 65 farm visits. The protocols are highly similar, although the composition varies (WQ protocol: focus on animal-based, KTBL protocol: focus on management-based indicators). Consequently, the WQ protocol detected more welfare issues (e.g., welfare issues related to appropriate behaviour: 62.9% (WQ) vs. 21.0% (KTBL protocol)). The comparison between pen and individual level of piglets’ indicators was determined using Spearman’s rank correlation coefficient (RS), intraclass correlation coefficient (ICC) and limits of agreement (LoA). Carpal joint lesions and undersized animals (RS 0.73/0.80 ICC 0.55/0.57 LoA −0.12 to 0.03/−0.01 to 0.01) are reliably assessed at pen level but face lesions (RS 0.19 ICC 0.18 LoA −0.42 to 0.03) are not. Concluding, we present advantages and disadvantages of the KTBL protocol and introduce indicators for piglets which may enhance existing protocols.

Sensors delivering information on the position of farm animals have been widely used in precision livestock farming. Global Positioning System (GPS) sensors are already known from applications in military, private and commercial environments, and their application in animal science is increasing. However, as trade-offs between sensor cost, battery life and sensor weight have to be made, GPS based studies scheduling long data collection periods and including a high number of animals, have to deal with problems like high hardware costs and data disruption during recharging of sensors. Furthermore, human–animal interaction due to sensor changing at the end of battery life interferes with the animal behaviour under analysis. The present study thus proposes a setting to deal with these challenges and offers the software tool “HerdGPS-Preprocessor”, because collecting position data from multiple animals nonstop for several weeks produces a high amount of raw data which needs to be sorted, preprocessed and provided in a suitable format per animal and day. The software tool “HerdGPS-Preprocessor” additionally outputs contact lists to enable a straight analysis of animal contacts. The software tool was exemplarily deployed for one month of daily and continuous GPS data of 40 horses in a loose-housing boarding facility in northern Germany. Contact lists were used to generate separate networks for every hour, which are then analysed with regard to the network parameter density, diameter and clique structure. Differences depending on the day and the day time could be observed. More dense networks with more and larger cliques were determined in the hours prior to the opening of additional pasture.

Documented lesions of slaughtered pigs provide a high-density data-pool that could be valuable for the purpose of animal health monitoring and breeding. However, data quality and structure hamper the application of statistical methods. The present study provides an approach that enables statistical analysis and evaluates the comparability of lesion prevalence among abattoirs. The German Quality and Safety database provided data of recorded lung, pleura, liver, and heart lesions. Filter criteria were used to improve the data structure. Data of n = 8,004,769 animals, recorded in nine abattoirs over a period of 18 months, were analyzed. Lesion prevalences were successfully modeled by applying a generalized linear mixed model. To examine prevalence differences, the coefficient of variation (CV) on a six-monthly basis was calculated, and a grand mean test (GMT) of significance was applied. High variations in estimated prevalence occurred on abattoir, six-monthly and organ basis. The highest variation occurred in the lung (CV = 64.7%), whereas liver lesions showed the lowest variation (CV = 21.8%). The GMT enabled the visualization of these variations between abattoirs, organs and over time. Concerning the assessment of the comparability of prevalences, it provides a promising tool to monitor changes in lesion examination and to address divergent abattoirs.

Besides the direct transport of animals, also indirect transmission routes, e.g., contact via contaminated vehicles, have to be considered. In this study, the transmission routes of a German pig trade network were illustrated as a monopartite animal movements network and two bipartite networks including information of the transport company and the feed producer which were projected on farm level (n = 866) to enable a comparison. The networks were investigated with the help of network analysis and formed the basis for epidemiological models to evaluate the impact of different transmission routes on network structure as well as on potential epidemic sizes. The number of edges increased immensely from the monopartite animal movements network to both projected networks. The median centrality parameters revealed clear differences between the three representations. Furthermore, moderate correlation coefficients ranging from 0.55 to 0.68 between the centrality values of the animal movements network and the projected transportation network were obtained. The epidemiological models revealed significantly more infected farms for both projected networks (70% to 100%) compared to the animal movements network (1%). The inclusion of indirect transmission routes had an immense impact on the outcome of centrality parameters as well as on the results of the epidemiological models.

Dominance indices are often calculated using the number of won and lost fights of each animal focusing on dyadic interactions. Social network analysis provides new insights into the establishment of stable group structures going beyond the dyadic approach. Thus, it was investigated whether centrality parameters describing the importance of each animal for the network are able to capture the rank order calculated by dominance indices. Therefore, two dominance indices and five centrality parameters based on two network types (initiator-receiver and winner-loser networks) were calculated regarding agonistic interactions observed in three mixing events (weaned piglets, fattening pigs, gilts). Comparing the two network types, the winner-loser networks demonstrated highly positive correlation coefficients between out-degree and outgoing closeness and the dominance indices. These results were confirmed by partial least squares structural equation modelling (PLS-SEM), i.e., about 60% of the variance of the dominance could be explained by the centrality parameters, whereby the winner-loser networks could better illustrate the dominance hierarchy with path coefficients of about 1.1 for all age groups. Thus, centrality parameters can portray the dominance hierarchy providing more detailed insights into group structure which goes beyond the dyadic approach.

The present study’s aim was to assess the test−retest reliability (TRR) of the ‘Welfare Quality® animal welfare assessment protocol for sows and piglets’ focusing on the welfare principle ‘appropriate behavior’. TRR was calculated using Spearman’s rank correlation coefficient (RS), intraclass correlation coefficient (ICC), smallest detectable change (SDC), and limits of agreement (LoA). Principal component analysis (PCA) was used for deeper analysis of the Qualitative Behavior Assessment (QBA). The study was conducted on thirteen farms in Northern Germany, which were visited five times by the same observer. Farm visits 1 (F1; day 0) were compared to farm visits 2 to 5 (F2−F5). The QBA indicated no TRR when applying the statistical parameters introduced above (e.g., ‘playful‘ (F1−F4) RS 0.08 ICC 0.00 SDC 0.50 LoA [−0.62, 0.38]). The PCA detected contradictory TRR. Acceptable TRR could be found for parts of the instantaneous scan sampling (e.g., negative social behavior (F1−F3) RS 0.45 ICC 0.37 SDC 0.02 LoA [−0.03, 0.02]). The human−animal relationship test solely achieved poor TRR, whereas scans for stereotypies showed sufficient TRR (e.g., floor licking (F1−F4) RS 0.63 ICC 0.52 SDC 0.05 LoA [−0.08, 0.04]). Concluding, the principle ‘appropriate behavior’ does not represent TRR and further investigation is needed before implementation on-farm.

Dominance indices are calculated by considering the differences between the number of won and lost fights. Whether these differences show a significant asymmetric outcome or not is neglected. Thus, two calculation methods for the limits of significant dyads are proposed using a sign test based on the differences in won and lost fights, considering all dyadic interactions in the pen (PEN: pen individual limits), and a sign test focusing on each individual dyad (DYAD: dyad individual limits). These were compared to the data set containing all dyadic interactions (ALL). Agonistic interactions in three mixing events were video recorded for two and a half days (weaned piglets) or one and a half days (fattening pigs, gilts). Dominance indices (DI) were calculated for all data sets. Pen/dyad individual limits revealed a small number of significant dyads (weaned piglets: 12.4%/8.8%; fattening pigs: 4.2%/0.6%; gilts: 3.6%/0.4%). Pen individual limits should be selected as they allow adaption of the limits according to the fighting frequency. Spearman rank correlation coefficients of the dominance indices between the data sets were always above 0.7, implying that the rank order remained relatively stable. Information about the impact of significant dyads on sociometric measures is important to prevent misinterpretations about the social structure in animal groups and should be considered in future studies.

This study examined whether the human approach test (HAT) or novel object test (NOT), which are considered as suitable tests for assessing the level of fear or anxiety in animals, are suitable to detect a positive affective state in 297 fattening pigs from three different farms. The investigated farms consisted of a barren (farm 1, n = 160) and an enriched (farm 2, n = 106; farm 3, n = 31) husbandry. Each pig was subjected three times to the HAT and NOT during fattening (at the start, middle, and end of fattening). The pigs housed in the barren environment showed quicker approach latencies than the enriched-housed pigs (HAT: farm 1: 7.4 ± 1.1 s vs. farm 2: 57.1 ± 1.1 s, respectively, farm 3: 58.3 ± 1.3 s (end of fattening); NOT: farm 1: 4.5 ± 1.1 s vs. farm 2: 23.0 ± 1.1 s, respectively, farm 3: 9.0 ± 1.2 s (end of fattening)). The same pattern of behavior was observed for the duration of contacts in the HAT but not in the NOT (HAT: farm 1: 83.8 ± 1.1 s vs. farm 2: 6.3 ± 1.1 s, respectively, farm 3: 7.6 ± 1.3 s (end of fattening)). However, due to controversially discussed literature, it is difficult to conclude whether the described differences in the pigs’ behavior between the two housing systems might indicate useful indicators to detect their affective state.

To enhance feasibility, the Animal Welfare Indicators (AWIN) assessment protocol for horses consists of two levels: the first is a visual inspection of a sample of horses performed from a distance, the second a close-up inspection of all horses. The aim was to analyse whether information would be lost if only the first level were performed. In this study, 112 first and 112 second level assessments carried out on a subsequent day by one observer were compared by calculating the Spearman’s Rank Correlation Coefficient (RS), Intraclass Correlation Coefficients (ICC), Smallest Detectable Changes (SDC) and Limits of Agreements (LoA). Most indicators demonstrated sufficient reliability between the two levels. Exceptions were the Horse Grimace Scale, the Avoidance Distance Test and the Voluntary Human Approach Test (e.g., Voluntary Human Approach Test: RS: 0.38, ICC: 0.38, SDC: 0.21, LoA: −0.25–0.17), which could, however, be also interpreted as a lack of test-retest reliability. Further disagreement was found for the indicator consistency of manure (RS: 0.31, ICC: 0.38, SDC: 0.36, LoA: −0.38–0.36). For these indicators, an adaptation of the first level would be beneficial. Overall, in this study, the division into two levels was reliable and might therewith have the potential to enhance feasibility in other welfare assessment schemes.

The aim of the present study was to automatically predict the onset of farrowing in crate-confined sows. (1) Background: Automatic tools are appropriate to support animal surveillance under practical farming conditions. (2) Methods: In three batches, sows in one farrowing compartment of the Futterkamp research farm were equipped with an ear sensor to sample acceleration. As a reference video, recordings of the sows were used. A classical CUSUM chart using different acceleration indices of various distribution characteristics with several scenarios were compared. (3) Results: The increase of activity mainly due to nest building behavior before the onset of farrowing could be detected with the sow individual CUSUM chart. The best performance required a statistical distribution characteristic that represented fluctuations in the signal (for example, 1st variation) combined with a transformation of this parameter by cumulating differences in the signal within certain time periods from one day to another. With this transformed signal, farrowing sows could reliably be detected. For 100% or 85% of the sows, an alarm was given within 48 or 12 h before the onset of farrowing. (4) Conclusions: Acceleration measurements in the ear of a sow are suitable for detecting the onset of farrowing in individually housed sows in commercial farrowing crates.

Centrality parameters in animal trade networks typically have right-skewed distributions, implying that these networks are highly resistant against the random removal of holdings, but vulnerable to the targeted removal of the most central holdings. In the present study, we analysed the structural changes of an animal trade network topology based on the targeted removal of holdings using specific centrality parameters in comparison to the random removal of holdings. Three different time periods were analysed: the three-year network, the yearly and the monthly networks. The aim of this study was to identify appropriate measures for the targeted removal, which lead to a rapid fragmentation of the network. Furthermore, the optimal combination of the removal of three holdings regardless of their centrality was identified. The results showed that centrality parameters based on ingoing trade contacts, e.g. in-degree, ingoing infection chain and ingoing closeness, were not suitable for a rapid fragmentation in all three time periods. More efficient was the removal based on parameters considering the outgoing trade contacts. In all networks, a maximum percentage of 7.0% (on average 5.2%) of the holdings had to be removed to reduce the size of the largest component by more than 75%. The smallest difference from the optimal combination for all three time periods was obtained by the removal based on out-degree with on average 1.4% removed holdings, followed by outgoing infection chain and outgoing closeness. The targeted removal using the betweenness centrality differed the most from the optimal combination in comparison to the other parameters which consider the outgoing trade contacts. Due to the pyramidal structure and the directed nature of the pork supply chain the most efficient interruption of the infection chain for all three time periods was obtained by using the targeted removal based on out-degree.