Monitor Animal Welfare Research Articles

Validating Ultra-Wideband Positioning System for Precision Cow Tracking in a Commercial Free-Stall Barn.

UWB positioning systems offer innovative solutions for precision monitoring dairy cow behaviour and social dynamics, yet their performance in complex commercial barn environments requires thorough validation. This study evaluated the TrackLab 2.13 (Noldus) UWB system in a dairy barn housing 44-49 cows. We assessed stationary tag positioning using ten fixed tags over seven days, proximity detection between eight cows and ten stationary tags, and moving tag positioning using three tags on a stick to simulate cow movement. System performance varied by tag location, with reliability ranging from 4.09% to 96.73% and an overall mean accuracy of 0.126 ± 0.278 m for stationary tags. After the provider updated the software, only 0.62% of measures exceeded the declared accuracy of 0.30 m. Proximity detection between moving cows and stationary tags showed 81.42% accuracy within a 2-m range. While generally meeting specifications, spatial variations in accuracy and reliability were observed, particularly near barn perimeters. These findings highlight UWB technology's potential for precision livestock farming, welfare assessment, and behaviour research, including social interactions and space use patterns. Results emphasise the need for careful system setup, regular updates, and context-aware data interpretation in commercial settings to maximise benefits in animal welfare monitoring.

Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning

Monitoring animal welfare, disease prevention, vaccination administration, production supply, and ownership management all depend on accurate cattle identification. Ear tag-based cattle identification techniques are frequently used in livestock farm management. These are not used to identify specific cattle on large-scale farms. However, ear tags can come off, which makes it challenging to identify a particular individual. Ear tags are susceptible to fraud, can be copied, and run the risk of being damaged. Long-term animal identification is impossible with lost tags. For this purpose, a data set was created by taking images of cattle in their natural environment. The dataset, which contains 15,000 records from 30 different cattle, was divided into three sections: training, validation, and testing. Deep learning algorithms InceptionResNetV2, MobilenetV2, DenseNet201, Xception, and NasNetLarge were used in this study to identify specific cattle faces. The DenseNet201 algorithm achieved the highest test accuracy of 99.53% with a validation accuracy of 99.83%.

Introducing the Mellorater-The Five Domains Model in a Welfare Monitoring App for Animal Guardians.

When monitoring an animal's welfare, it helps to have comprehensive and day-to-day information about the animal's life. The goal is to ensure that animal guardians (carers, keepers, and owners) use such information to act in the animals' best interests. This article introduces the Mellorater, an animal welfare monitoring app based on the 2020 Five Domains Model. This framework provides a means of capturing comprehensive information about the world in which individual animals exist. The Mellorater asks animal guardians to rate their agreement with 18 statements covering any focal animal's nutrition, environment, health, and behavioural interactions using a five-point Likert scale. No specialist training is required other than following straightforward instructions on using the app, which are provided. The Mellorater is not proposed as a validated welfare auditing tool because it relies on reflective self-reporting and, thus, is vulnerable to the user's subjectivity. If users' subjectivity is stable over time, then the longitudinal data may be considered useful proxies for trends in quality of life. That said, it has the potential to be used by trained auditors if scientifically validated, species-specific indicators are applied. The Mellorater collects anonymous data and has been approved for a study to explore how the use of such scales may differ among guardians of different species and in different contexts. In this paper, we conduct the following: (1) summarise the app's purposes; (2) clarify its capabilities and limitations; and (3) invite animal welfare scholars, veterinarians, health and welfare professionals, and animal guardians to use it.

Evaluation of candidate data-based welfare indicators for veal calves in Switzerland.

Welfare assessment protocols have been developed for dairy cows and veal calves during the past decades. One practical use of such protocols may be conducting welfare assessments by using routinely collected digital data (i.e., data-based assessment). This approach can allow for continuous monitoring of animal welfare in a large number of farms. It recognises changes in the animal welfare status over time and enables comparison between farms. Since no comprehensive data-based assessment for veal calves is currently available, the purposes of this review are (i) to provide an overview of single existing data-based indicators for veal calves and (ii) to work out the necessary requirements for data-based indicators to be used in a comprehensive welfare assessment for veal calves in Switzerland. We used the Welfare Quality Protocol® (WQ) for veal calves and the Terrestrial Animal Health Code from the World Organisation of Animal Health for guidance throughout this process. Subsequently, routinely collected data were evaluated as data sources for welfare assessment in Swiss veal operations. The four WQ principles reflecting animal welfare, i.e., 'good feeding', 'good housing', 'good health' and 'appropriate behaviour' were scarcely reflected in routinely available data. Animal health, as one element of animal welfare, could be partially assessed using data-based indicators through evaluation of mortality, treatments, and carcass traits. No data-based indicators reflecting feeding, housing and animal behaviour were available. Thus, it is not possible to assess welfare in its multidimensionality using routinely collected digital data in Swiss veal calves to date. A major underlying difficulty is to differentiate between veal calves and other youngstock using routine data, since an identifying category for veal calves is missing in official Swiss databases. In order to infer animal welfare from routine data, adaptations of data collection strategies and animal identification are required. Data-based welfare assessment could then be used to complement on-farm assessments efficiently and, e.g., to attribute financial incentives for specifically high welfare standards accordingly.

A scoping review of the use of bioacoustics to assess various components of farm animal welfare

Recording and analyzing animal sounds, including vocal and non-vocal cues, is a relevant tool to access and monitor different aspects of animal physiology, behaviour and general welfare in a remote, non-invasive and continuous fashion. This field, known as bioacoustics, is considerably growing, notably due to the development of machine learning techniques. Yet, applications of these methods in animal husbandry remain limited, and their potential may not be fully acknowledged. This review aims to present the potential of bioacoustic tools to monitor various aspects of the welfare of farm animals, and focuses on studies published since 2010. The first section of the review introduces the concept of bioacoustics, describes the different parameters that can be extracted from the sounds of animals, and explains how they can be analysed. The potential of these measures to inform about the physical characteristics, individuality, health, physiology, behaviour, and affective states of different species of farm animals are then presented, through a contemporary overview of the research field. Although this analysis identifies a variety of applications of bioacoustics tools in relation to the assessment and monitoring of farm animal welfare, it also points out the current scarcity of methods implemented in practice and available to farm management. Barriers to the development of bioacoustics measures in conventional farming are also discussed, and future directions for this field of research are suggested.

Automated collection of facial temperatures in dairy cows via improved UNet

In cattle, facial temperatures captured by infrared thermography provide useful information from physiological aspects for researchers and local practitioners. Traditional temperature collection requires massive manual operations on relevant software. Therefore, this paper aimed to propose a tool for automated temperature collection from cattle facial landmarks (i.e., eyes, muzzle, nostrils, ears, and horns). An improved UNet was designed by replacing the traditional convolutional layers in the decoder with Ghost modules and adding Efficient Channel Attention (ECA) modules. The improved model was trained on our open-source cattle infrared image dataset. The results show that Ghost modules reduced computational complexity and ECA modules further improved segmentation performance. The improved UNet outperformed other comparable models on the testing set, with the highest mean Intersection of Union of 80.76% and a slightly slower but still good inference speed of 32.7 frames per second. Further agreement analysis reveals small to negligible differences between the temperatures obtained automatically in the areas of eyes and ears and the ground truth. Collectively, this study demonstrates the capacity of the proposed method for automated facial temperature collection in cattle infrared images. Further modelling and correction with data collected in more complex conditions are required before it can be integrated into on-farm monitoring of animal health and welfare.

Effect of the daily duration of calf contact on the dam's ultradian and circadian activity rhythms

Cow-calf contact systems are attracting increasing interest among farmers and some are already being implemented into dairy farms. However, a comprehensive assessment of animal welfare in these systems is lacking. One reason for this is the large amount of time required for behavioral observations. However, the increased use of sensors in herd management assistance systems offers new opportunities for automated monitoring of animal welfare. For example, accelerometers can be used to collect activity data for a specific pattern analysis. In this study, ultradian and circadian rhythms of cows were analyzed. The degree of functional coupling (DFC; range of values: 0–1) expresses the extent to which the activity is cyclic to 24 h, and therefore harmonically synchronized with the periodicity of the environment. A DFC of 1 indicates complete adaptation of the cows' activity rhythm to the 24-h day. Additionally, the diurnality index (DI) is used to examine the distribution of diurnal and nocturnal activity. A DI of 1 indicates complete diurnal activity, whereas −1 indicates complete nocturnal activity. The rhythms of healthy and well-adapted animals show high adaptation to the 24-h day, whereas external or endogenous effects can interfere with these rhythms. Although contact with their calves allows cows to behave more naturally, it is possible that calves demanding their mothers' attention may affect the cows' rhythmicity, similar to other external factors. To test this hypothesis, 2 herds of German Holstein cows housed in a mirrored loose housing system were included in the study, which was conducted over 2 experimental periods. Three treatments were applied, differing in contact between cow and calf. The contact dams had either whole-day or daytime contact with their calves, and the no-contact cows were separated from their calves directly postpartum. Accelerometers were used to record and analyze the cows' activity between 59 and 83 DIM, thus excluding the calving and weaning phases. Generalized linear mixed models were used to estimate the effect of treatment (no, daytime, and whole-day contact) on DFC and DI, considering the effects of estrus, deviation of milking start in the evening, and parity (primi- vs. multiparous). Finally, the harmonic period lengths of the activity patterns were extracted to analyze the distribution of the primarily expressed period lengths of the different treatments. In general, the average activity patterns of the cows did not differ between the treatments. However, dams with whole-day contact showed a lower activity peak before milking but a higher activity after evening milking. Nevertheless, the DFC and DI were similar in each group. During estrus, the chance of a maximum DFC decreased and the DI increased. Whole-day contact dams showed the most significant harmonic periods (33 per cow). Nevertheless, the primarily expressed period length (3.4 h) was equal in each treatment. In conclusion, neither contact with the calf nor its daily duration affected the ultradian and circadian rhythms of dams compared with cows separated from their calf.

The ability of a handheld near-infrared spectrometer to do a rapid quality assessment of bovine colostrum, including the immunoglobulin G concentration

Portable infrared-based instruments have made significant contributions in different research fields. Within the dairy supply chain, for example, most of portable devices are based on near-infrared spectroscopy (NIRS) and are nowadays an important support for farmers and operators of the dairy sector, allowing fast decision-making, particularly for feed and milk quality evaluation and animal health and welfare monitoring. The affordability, portability, and ease of use of these innovative devices have been pivotal factors for the implementation in dairy farms. In fact, pocket-sized devices enable non-expert users to perform quick, low cost and non-destructive analysis on various samples without complex preparation. As bovine colostrum (BC) quality is mostly given by the Ig G (IgG) level, evaluating the ability of portable NIRS tools to measure antibodies concentration is advisable. In this study we used the wireless device SCiO manufactured by Consumer Physics Inc. (Tel Aviv, Israel) to collect BC spectra and then attempt to predict IgG concentration and gross and fine composition in individual samples collected as soon as possible after calving (<6 h) in primiparous and pluriparous Holstein cows farmed in 9 Italian farms. Chemometric analyses revealed that SCiO has promising predictive performance for colostral IgG concentration, total Ig concentration, fat, and AA (R2CV ≥ 0.75). Excellent accuracy was observed for dry matter, protein, and S prediction in cross-validation and good prediction ability in external validation (R2CV ≥ 0.93; R2V ≥ 0.82). Nonetheless, SCiO's ability to discriminate between good- and low-quality samples was satisfactory. The affordable cost, the accurate predictions, and the user-friendly design coupled with the increased interest in colostrum quality within the dairy sector may boost the collection of extensive BC data for management and genetic purposes in the near future.

Deep learning strategies with CReToNeXt-YOLOv5 for advanced pig face emotion detection.

This study underscores the paramount importance of facial expressions in pigs, serving as a sophisticated mode of communication to gauge their emotions, physical well-being, and intentions. Given the inherent challenges in deciphering such expressions due to pigs' rudimentary facial muscle structure, we introduced an avant-garde pig facial expression recognition model named CReToNeXt-YOLOv5. The proposed model encompasses several refinements tailored for heightened accuracy and adeptness in detection. Primarily, the transition from the CIOU to the EIOU loss function optimized the training dynamics, leading to precision-driven regression outcomes. Furthermore, the incorporation of the Coordinate Attention mechanism accentuated the model's sensitivity to intricate expression features. A significant innovation was the integration of the CReToNeXt module, fortifying the model's prowess in discerning nuanced expressions. Efficacy trials revealed that CReToNeXt-YOLOv5 clinched a mean average precision (mAP) of 89.4%, marking a substantial enhancement by 6.7% relative to the foundational YOLOv5. Crucially, this advancement holds profound implications for animal welfare monitoring and research, as our findings underscore the model's capacity to revolutionize the accuracy of pig facial expression recognition, paving the way for more humane and informed livestock management practices.

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data.

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

MOVEMENT DATA ANALYSIS AS A NOVEL PREDICTIVE TECHNOLOGY FOR TENDON REPAIR

Digital Ventilated Cages (DVC) offer an innovative technology to obtain accurate movement data from a single mouse over time [1]. Thus, they could be used to determine the occurrence of a tendon damage event as well as inform on tissue regeneration [2,3]. Therefore, using the mouse model of tendon experimental damage, in this study it has been tested whether the recovery of tissue microarchitecture and of extracellular matrix (ECM) correlates with the motion data collected through this technology.Mice models were used to induce acute injury in Achilles tendons (ATs), while healthy ones were used as control. During the healing process, the mice were housed in DVC cages (Tecniplast) to monitor animal welfare and to study biomechanics assessing movement activity, an indicator of the recovery of tendon tissue functionality. After 28 days, the AT were harvested and assessed for their histological and immunohistochemical properties to obtain a total histological score (TSH) that was then correlated to the movement data.DVC cages showed the capacity to distinguish activity patterns in groups from the two different conditions. The data collected showed that the mice with access to the mouse wheel had a higher activity as compared to the blocked wheel group, which suggests that the extra movement during tendon healing improved motion ability. The histological results showed a clear difference between different analyzed groups. The bilateral free wheel group showed the best histological recovery, offering the highest TSH score, thus confirming the results of the DVC cages and the correlation between movement activity and structural recovery.Data obtained showed a correlation between TSH and the DVC cages, displaying structural and movement differences between the tested groups. This successful correlation allows the usage of DVC type cages as a non-invasive method to predict tissue regeneration and recovery.Acknowledgements: This research is part of the P4FIT project ESR13, funded by the H2020-ITN-EJD MSCA grant agreement No.955685.

Successful hormonal and chemical induction of prostate cancer in a rat model: practical guidelines.

Prostate cancer is a very common cancer in men, affecting approximately 1.40 million men worldwide in 2020. To improve the quality of life and survival of both animals and humans, effective therapeutic approaches have been developed and evaluated using animal models. The rat model of prostate cancer induced by a multi-step protocol that consists of a sequential administration of flutamide, followed by testosterone propionate, then the administration of N-methyl-N-nitrosourea, and finally subcutaneous implantation of tubes filled with crystalline testosterone, is one of the most frequently used for prostate cancer research. However, the lack of standardization in procedures for prostate cancer induction, sample collection, and analysis represents a challenge for researchers. To address this issue, we aim to provide investigators with a detailed, step-by-step guide to implementing a rat model of prostate cancer, based on our extensive experience in this field. First, we briefly review the prostate cancer-induced protocols found in the literature, then we provide a detailed description of the prostate cancer rat model implemented by our team. After, we explore the rats' prostate monitoring during the experiment protocol through imaging modalities, such as ultrasonography, computed tomography, and magnetic resonance imaging. We also describe animal welfare monitoring based on a table of humane endpoints, as well as data collection, such as biological variables and prostate samples. In sum, this article will ensure the quality of results and enable their comparison among different researchers using this rat model. Guidelines, Male, Murinic, Rodent, Tumor.

Inspection of chicken wings and legs for animal welfare monitoring using X-ray computed tomography, visual examination, and histopathology

In broiler chickens, fractures of wings and legs are recorded at poultry slaughterhouses based on the time of occurrence. Prekilling (PRE) fractures occur before the death of animal, so the chicken was still able to experience pain and distress associated with the injury (an animal welfare issue). Postkilling (POST) fractures occur when the chickens are deceased and fully bled-out and consequently unable to feel pain (not an animal welfare issue). Current practice dictates that fractures are recognized visually and recorded by the animal welfare officers as mandated by European Union and/or national regulations. However, new potential monitoring solutions are desired since human inspection suffers from some significant limitations including subjectivism and fatigue. One possible solution in detecting injuries is X-ray computed tomography (CT) scanning and in this study we aim to evaluate the potential of CT scanning and visual inspection in detecting limb fractures and their causes. Eighty-three chicken wings and 60 chicken legs (n = 143) were collected from a single slaughterhouse and classified by an animal welfare officer as PRE, POST or healthy (HEAL). Samples were photographed and CT scanned at a veterinary hospital. The interpretation of CT scans along with photographs took place in 3 rounds (1. CT scans only, 2. CT scans + photographs, 3. photographs only) and was performed independently by 3 veterinarians. The consistency of the interpretation in 3 rounds was compared with the animal welfare officer's classification. Furthermore, selected samples were also analyzed by histopathological examination due to questionability of their classification (PRE/POST). In questionable samples, presence of hemorrhages was confirmed, thus they fit better as PRE. The highest consistency between raters was obtained in the 2nd round, indicating that interpretation accuracy was the highest when CT scans were combined with photographs. These results indicate that CT scanning in combination with visual inspection can be used in detecting limbs fracture and potentially applied as a tool to monitor animal welfare in poultry slaughterhouses in the future.

Application of Artificial Intelligence in Monitoring of Animal Health and Welfare

Blackground: With the advent of AI technology, great strides have been made in the realm of animal healthcare. This article delves into the numerous uses of AI in veterinary medicine and demonstrates its revolutionary potential in the field. Artificial intelligence (AI) algorithms have shown impressive skills in illness detection, using medical imaging analysis to help veterinarians discover and categorise diseases with greater accuracy and efficiency. Furthermore, predictive analytics algorithms use various data sources, such as electronic health records and genetic profiles, to recognise trends and forecast illness outbreaks, allowing veterinarians to remotely monitor vital signs and act swiftly paving the way for preventative measures and individualised treatment. Methods: The purpose of this article is to offer a synopsis of the many ways in which artificial intelligence (AI) is being used to improve the health and well-being of animals. Understanding the effects of AI in animal healthcare and setting the stage for its further development will be accomplished via an examination of the present state of the subject. Results: It is evident that through mountains of data from studies and clinical trials, AI is helping to speed up the discovery of novel treatments and improve the understanding of animal health. A responsible and useful application of AI in animal healthcare requires the establishment of ethical concerns, data protection, and regulatory frameworks. Method: This document provides a synopsis of the many ways in which artificial intelligence (AI) is being used to improve the health and well-being of animals.

Real-Time Monitoring of Animals and Environment in Broiler Precision Farming—How Robust Is the Data Quality?

Increasing digitalization in animal farming, commonly addressed as Precision Livestock Farming (PLF), offers benefits in terms of productivity, sustainability, reduced labor and improved monitoring of animal welfare. However, the large amounts of collected data must be stored, processed and evaluated in a proper way. In practice, challenges of continuous and exact data collection can arise, e.g., from air pollutants like dust occluding cameras and sensors, degrading material, the ever-present commotion caused by animals, workers and machines, regularly required maintenance or weak signal transmission. In this study, we analyzed the quality of multi-source spatio-temporal data from a broiler house with 8100 birds over a period of 31 months collected by the Farmer Assistant System (FAS). This is a ceiling-suspended robot equipped with several sensors and cameras that continuously collect data while moving through the barn. The data analysis revealed numerous irregularities: missing values, outliers, repetitive measurements, systematic errors, and temporal and spatial inconsistencies. About 40–50% of all records collected with the early version of the FAS had to be sorted out. The newer version of FAS provided cleaner data, although still about 10–20% of the data had to be removed. Our study has shown that where sophisticated technological systems meet a challenging environment, a thorough and critical review of data completeness and quality is crucial to avoid misinterpretation. The pipeline developed here is designed to help developers and farmers detect failures in signal processing and localize the problem in the hardware components. Scientists, industrial developers and farmers should work more closely together to develop new PLF technologies to more easily advance digitization in agriculture.

Modelling of Animal Activity, Illuminance, and Noise on a Weaned Piglet Farm.

Measuring animal activity and its evolution in real time is useful for animal welfare assessment. In addition, illuminance and noise level are two factors that can improve our understanding of animal activity. This study aims to establish relationships between animal activity as measured by passive infrared sensors, and both illuminance and noise level on a conventional weaned piglet farm. First, regression models were applied, and then cosine models with three harmonics were developed using least squares with a Generalized Reduced Gradient Nonlinear method. Finally, all the models were validated. Linear models showed positive correlations, with values between 0.40 and 0.56. Cosine models drew clear patterns of daily animal activity, illuminance and noise level with two peaks, one in the morning and one in the afternoon, coinciding with human activity inside the building, with a preference for inactivity at night-time and around midday. Cosine model fitting revealed strong correlations, both in the measurement and validation periods, for animal activity (R = 0.97 and 0.92), illuminance (R = 0.95 and 0.91) and noise level (R = 0.99 and 0.92). The developed models could be easily implemented in animal welfare monitoring systems and could provide useful information about animal activity through continuous monitoring of illuminance or noise levels.

Estimation of Owned and Street Dog Population by Quesionnire Survey and Mark-Recapture Method in Three Urban Areas: Bishoftu, Dukem and Modjo Towns

The understanding of the structure of free-roaming and owning dog population’s estimation is of extreme importance for the planning and monitoring of dog population control strategies, animal welfare and health. The methods used to estimate the abundance of this group of dogs are more complex than the ones used with domiciled owned dogs. We point to enhancements necessary for the implementation of the studies and to potential updates and revisions to the recommendations of the World Health Organization with respect to the estimation of free-ranging and owning dog populations. Rabies is a fatal viral disease of animals and people. People usually infected via bites from an infected animal (e.g. dog). Post Exposure Prophylaxis (PEP) has to initiate immediately after bite wounds of suspected rabid animals in order to avoid fatalities. The situation of rabies poorly known in Ethiopia. Questionnaire survey was conducted in selected blocks in Dukem, Bishoftu and Modjo towns in order to capture information on dog population size estimates using mark-resight surveys has provided useful baseline data for understanding the population dynamics of dogs at the study sites. Mark-resight surveys provide useful information for designing and managing the logistics of dog vaccination or assessing vaccination coverage and for evaluating the impact of neutering programs on the size and structure of dogs’ populations over time. As part of a dog born zoonosis disease risk assessment project, surveys conducted to estimate the size of the dog population and to describe dog management practices of households belonging to different ethnic groups. A markedrecapture method employed and the number of unowned dogs estimated. A total of 599 dog owning households and 276 non-dog household interviewed from at selected 19 blocks at study sites. Owned dogs were more likely male, local breed and for the purpose of used to guard. These households kept their dogs either fully restricted, semi and free roaming but full restriction have reported only at the higher percentage of the total dog households. By counted method, study the ownerless dogs higher than owning and semi-restricted dogs.

Real-Time Monitoring of Grazing Cattle Using LORA-WAN Sensors to Improve Precision in Detecting Animal Welfare Implications via Daily Distance Walked Metrics.

Animal welfare monitoring relies on sensor accuracy for detecting changes in animal well-being. We compared the distance calculations based on global positioning system (GPS) data alone or combined with motion data from triaxial accelerometers. The assessment involved static trackers placed outdoors or indoors vs. trackers mounted on cows grazing on pasture. Trackers communicated motion data at 1 min intervals and GPS positions at 15 min intervals for seven days. Daily distance walked was determined using the following: (1) raw GPS data (RawDist), (2) data with erroneous GPS locations removed (CorrectedDist), or (3) data with erroneous GPS locations removed, combined with the exclusion of GPS data associated with no motion reading (CorrectedDist_Act). Distances were analyzed via one-way ANOVA to compare the effects of tracker placement (Indoor, Outdoor, or Animal). No difference was detected between the tracker placement for RawDist. The computation of CorrectedDist differed between the tracker placements. However, due to the random error of GPS measurements, CorrectedDist for Indoor static trackers differed from zero. The walking distance calculated by CorrectedDist_Act differed between the tracker placements, with distances for static trackers not differing from zero. The fusion of GPS and accelerometer data better detected animal welfare implications related to immobility in grazing cattle.

Development and evaluation of an animal health and welfare monitoring system for veterinary supervision of pullet farms.

Regular welfare monitoring throughout rearing of pullets may help to identify problems early and take counteractions timely, which helps in guaranteeing good welfare. The aims of our observational study were (i) to establish and test a welfare monitoring system that can be used during (short) routine veterinary and technical staff visits for pullet flocks, (ii) to use the monitoring system to investigate variability between flocks and (iii) to analyse factors that potentially affect pullets' body weight, uniformity in body weight and mortality. The developed monitoring system tries to minimise the time required while not losing important information. Age-specific recording sheets comprise animal-based indicators of welfare and relevant environmental factors (housing, management, care) to allow for identifying causes of problems and targeted action. Finally, the system was implemented in a cross-sectional study and data collected in 100 flocks (67 organic, 33 conventional) on 28 rearing farms in Austria. Linear mixed models were used to identify factors influencing body weight, uniformity and mortality, both including all flocks (A) and only organic flocks (O) and a linear regression model with all flocks to investigate associations within animal-based indicators. High variability was found between flocks in animal-based indicators. Body weight was higher when the pre-rearing period was shorter (p≤0.001, A&O), with higher intensities of light (p=0.012, O), with only one compared to more stockpersons (p≤0.007, A&O), with a higher number of flock visits per day (p≤0.018, A&O), and a lower avoidance distance (p=0.034, A). Body weight uniformity increased, with age and decreased with the duration of the light period (p=0.046, A), and, amongst others, was higher on organic farms (farming type; p=0.041). The latter may reflect a more uniform level of welfare due to a lower stocking density and lowered effects of social competition. Within organic flocks mortality was lower if pullets had access to a covered veranda (p=0.025) resulting in an overall lower stocking density inside the barn, while in the model including all farms mortality was higher in cases where a disease had been diagnosed. We conclude that our monitoring system can easily be implemented in regular veterinary and technical staff visits, but could also be used by the farmers'. Several easy-to-record animal-based indicators of animal welfare could be analysed more frequently to increase early detection of problems. Implementation of such a routine-based monitoring system with easy-to-assess animal-based parameters and input measures can contribute to better animal health and welfare in pullets.

Zoo Animal Welfare Assessment: Where Do We Stand?

Zoological institutions, such as zoos and aquariums, have made animal welfare a top priority, as it is not only a moral obligation but also crucial for fulfilling their roles in education and conservation. There is a need for science-based tools to assess and monitor animal welfare in these settings. However, assessing the welfare of zoo animals is challenging due to the diversity of species and lack of knowledge on their specific needs. This review aims to discuss the advantages and disadvantages of existing methodologies for assessing zoo animal welfare through: (1) A critical analysis of the main approaches to zoo animal welfare assessment; (2) A description of the most relevant animal-based welfare indicators for zoo animals with a particular focus on behavioural and physiological indicators; and (3) An identification of areas that require further research.