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• It is possible to identify individual cows based on their morphological appearance. • A real-time computer vision system for automatic recognition of cows is presented. • Indications about quantity and characteristics of the images were assessed. Precision Livestock Farming relies on several technological approaches to acquire in the most efficient way precise and up-to-date data concerning individual animals. In dairy farming, particular attention is paid to the automatic cow detection and tracking, as such information is closely related to animal welfare and thus to possible health issues. Computer vision represents a suitable and promising method for this purpose. This paper describes the first step for the development of a computer vision system, based on deep learning, aiming to recognize in real-time the individual cows, detect their positions, actions and movements and record the time history outputs for each animal. Specifically, a neural network based on deep learning techniques has been trained and validated on a case study farm, for the automatic recognition of individual cows in videos recorded in the barn. Four cows were selected to train and validate a YOLO neural network able to recognize a cow starting from the coat pattern. Then, precision-recall curves of the identification of individual cows were elaborated for both the specific target classes and the whole dataset in order to assess the performances of the network. By means of data augmentation techniques, an enlarged dataset has been created and considered in order to improve the performance of the network and to provide indications to increase detection efficiency in those cases where data acquisition is not easy to be carried out for long periods. The mean average precision of the detection, ranging from 0.64 to 0.66, showed that it is possible to properly identify individual cows based on their morphological appearance and that the piebald spotting pattern of a cow's coat represents a clearly distinguishable object for a computer vision network. The results also led to obtain indications about the quantity and the characteristics of the images to be used for the network training in order to achieve efficient detections when facing with applications involving animals. [ABSTRACT FROM AUTHOR]

The Spanish market offers a greater variety of Iberian pork products. The aim of this paper is to determine the perception of consumers of several aspects of Iberian pig production and animal welfare depending on the consumers' characteristics. Consumers from two Spanish regions (n = 403) answered a questionnaire about their beliefs and the importance of pig production, their purchase intentions and their willingness to pay. Consumers were segmented according to their level of knowledge about Iberian pig production. The results of this work indicate that consumers have poor knowledge about Iberian pig production. Even so, consumers show a remarkable preference for Iberian products, especially when the animals are reared freely and in natural conditions, giving great importance to animal welfare. Consumer preferences indicate the importance of emphasizing Iberian traditional pig product characteristics on the label to promote their purchase choices. • Consumers are increasing their demands about animal welfare. • In general, Spanish consumers have no knowledge of Iberian pig production system. • Consumers show a generally positive opinion about the Iberian pig. • Meat from Iberian pigs, extensive system and a low price are preferred by consumers. [ABSTRACT FROM AUTHOR]

• We discuss the validity of specific behavioural patterns measured by accelerometers. • Welfare assessment studies based on accelerometer technology are provided. • Aspects to be considered before application of accelerometer systems are discussed. • We present ideas for the future application of accelerometers in welfare assessment. Welfare assessment has traditionally been performed by direct observation by humans, providing information at only selected points in time. Recently, this assessment method has been questioned, as 'Precision Livestock Farming' technologies may be able to deliver more valid, reliable and feasible real-time data at the individual level and serve as early monitoring systems for animal welfare. The aim of this paper is to describe how accelerometers can be used for welfare assessment based on the principles of the Welfare Quality assessment protocol. Algorithm development is based mainly on the detection of behavioural traits. So far, high accuracies have been found for movement and resting behaviours in cows and pigs, while algorithm development for feeding and drinking behaviours in pigs lag behind progress in cows where valid algorithms are already available. Welfare studies have used accelerometer technology to address the effects on behaviour of diet, daily cycle, enrichment, housing, social mixing, oestrus, lameness and disease. Additional aspects to consider before a decision is made upon its use in research and in practical applications include battery life and sensor location. While accelerometer systems for cows are already being used by farmers, application in pigs has mainly remained at the research level. [ABSTRACT FROM AUTHOR]

Meat consumption has increased significantly in the last 50 years. This trend raises various health and environmental issues, as well as moral concerns regarding farm animal welfare. In this paper, we discuss the regulation of meat consumption in developed countries. Specifically, we discuss possible justifications for this regulation in terms of environmental, health and animal welfare considerations, as well as the effect of fiscal, informational and behavioral regulatory instruments. Finally, we present a list of challenges that policy makers and food scholars may need to confront in the future. [ABSTRACT FROM AUTHOR]

This paper summarises the factors that led to the formation of the Advisory Council on the Welfare Issues of Dog Breeding and sets out some of the challenges facing the Council. Common to all types of dog is the irresponsible way in which they can be produced, purchased and owned, and the considerable welfare problems that may arise as a direct consequence of these actions. In pedigree and purebred dogs, specific welfare problems may also be the result of the manner in which some of them are bred, as well as a variety of breed-related and inherited disorders and a number of other complex issues. These include the breeding strategies adopted, including cross-breeding for so-called 'designer dog' hybrids, and the influence of showing and judging on the production of over-exaggerated conformational features. The Advisory Council's science-based collaborative approach is key to tackling these issues. [ABSTRACT FROM AUTHOR]

• Frequent weighings can provide an early-warning system for weight gain anomalies. • In areas with natural water sources, a reward more attractive than water is needed to ensure voluntary passage of cattle through the scales. • An average of five usable recordings per animal were obtained each week. This project aimed to evaluate a novel method for monitoring the health of grazing cattle. The method studied in this paper is based on an unmanned, automatic precision weighing system that can be used on pasture, and which will alarm farmers when animals show abnormal weight gain curves. The project focused primarily on the detection of pasture-borne nematode parasite infections, which clearly reduce the weight gain of calves, but the method could be further developed to include other diseases that impair animal performance. The early detection of nematode infections would both improve animal welfare and minimize the use of anthelmintics. This study included a total of 63 first season grazing steers born from dairy cows that were allocated to one of two permanent semi-natural pasture enclosures for 20 weeks. Both enclosures had two identical scales except that one scale was powered with line current and one scale was equipped with solar panels. The automatic weighing stations were situated in close proximity to a source of water, salt and minerals so that animals would voluntarily pass through them on a regular basis. LiveStock PlannerⓇ software was used to identify usable data. The number of usable recordings per animal throughout the grazing season varied from 547 to 2677 amongst the four weighing stations, with an average of five usable recordings per animal obtained each week. The herds were guided to the weighing area on a daily basis during the first eight weeks of the grazing period to ensure that the steers received enough water. After this period, manual observations were used to verify that all animals entered the watering area without problems. The findings showed that the weighing technique worked well; hence, the developed method demonstrates great potential as an automatic supervision tool for grazing cattle. However, even though the summer of 2016 was quite dry, there was still water available in ditches and puddles. Accordingly, water alone was not a strong enough motivation to get cattle to pass through the weighing station into the watering area. Hence, in grazing areas with natural water sources, a reward more attractive than the water, salt and minerals used in this study is needed to ensure frequent voluntary passage of cattle through the scales. [ABSTRACT FROM AUTHOR]

A global strategic plan for the elimination of dog-mediated human rabies deaths by 2030 was announced in 2018. The cost-effectiveness of annual mass dog vaccination programmes, as a control and elimination method, has been advocated on many occasions. Complementary methods, such as animal birth control (ABC) activities, have received less attention. This paper provides a case-study of a programme operated by Help in Suffering (HIS) in Jaipur, India from 1994/95 until 2016/17 comprising both ABC and additional vaccination-only activities. The availability of cost data alongside information on dog numbers, dog bites and human rabies cases provided an exceptionally detailed and unique retrospective dataset recording actual events and expenditures. Updated to 2016/17 prices, the total cost of the programme was 658,744 USD. Since 2007/2008, activity costs have been separated and returned costs of 10.78 USD per dog, both sterilised and vaccinated, and 1.86 USD per dog, vaccinated only. Over the course of the programme, the number of disability-adjusted life years (DALYs) due to premature death and the distress associated with dog bites was estimated to be 36,246 fewer than would have been expected if HIS had not been operating, based on a counterfactual scenario using pre-intervention values. Linking the DALY figure to the cost of the activities undertaken by HIS yields a cost of 26 USD per DALY averted. Discounted at 3%, the DALYs averted equate to 16,587 at a cost of 40 USD per DALY averted. Both cases make it a very cost-effective intervention, in relation to the threshold of investing one year's gross domestic product (GDP) per DALY averted (1981 USD in 2016/17). The monetary benefit from fewer dog bites and clinical human rabies cases requiring treatment amounted to 5.62 million USD after discounting, which, if attributed to Help in Suffering, yields a monetary benefit-cost ratio of 8.5. Thus, the potential monetary benefits greatly outweigh the programme costs, even without considering the DALYs averted. If a modest notional monetary value of one year's GDP is assigned to represent the human capital or production value of DALYs averted, the discounted societal economic benefit reaches 38.48 million USD and implies a benefit-cost ratio of 58.4. These economic analyses demonstrate that ABC activities in combination with additional vaccination efforts can be a cost-effective control measure for dog-mediated human rabies. [ABSTRACT FROM AUTHOR]

• Deep learning-based system for cattle behavior recognition in video. • Our framework involves appearance features and spatio-temporal information. • We introduce our cattle behavior dataset with videos recorded on different farms. • Our system can recognize 15 types of hierarchical cattle activities in realtime. • Our results can facilitate management operations by understanding cattle behavior. Behavior is an important indicator for understanding the well-being of animals. This process has been frequently carried out by observing video records to detect changes with statistical analysis, or by using portable devices to monitor animal movements. However, regarding animal welfare, the use of such devices could affect the normal behavior of the animal, and present limitations in its applicability. This paper introduces an approach for hierarchical cattle behavior recognition with spatio-temporal information based on deep learning. Our research extends the idea of activity recognition in video and focuses specifically on cattle behavior. Our framework involves appearance features at frame-level and spatio-temporal information that incorporates more context-temporal features. The system can detect (class) and localize (bounding box) regions containing multiple cattle behaviors in the video frames. Additionally, we introduce our cattle behavior dataset that includes videos recorded with RGB cameras on different livestock farms during day and night environments. Experimental results show that our system can effectively recognize 15 different types of hierarchical activities divided into individual and group activities, and also part actions. Qualitative and quantitative evaluation evidence the performance of our framework as an effective method to monitor cattle behavior. [ABSTRACT FROM AUTHOR]

• High piglet mortality in outdoor production conflicts with the organic principles. • A consistent problem related to mortality appears to be large litter sizes. • Optimise breeding strategies by using less prolific genetics. • Select for improved maternal behaviour and piglet robustness. The current level of piglet mortality in production systems with outdoor farrowing is high and conflicts with the ethical principles of organic farming. Over the last decade, there has been a rise in scientific studies addressing the level of piglet mortality in outdoor pig production and the causes for mortality. Therefore, the aim of this paper was to review current literature on risk factors for piglet mortality in outdoor pig production, to identify causes of mortality and possibilities for improving piglet survival. A consistent problem related to outdoor piglet mortality appears to be the use of highly prolific sow hybrids. Large litter sizes are accompanied by long parturitions and the birth of piglets displaying signs of lowered viability. Management interventions such as use of birth assistance and caretaking of less viable piglets, as is common in indoor systems, is more difficult to apply in outdoor production. Therefore, based on the present review, we suggest that use of less prolific sow genetics, in systems with outdoor farrowing, may be one way to increase piglet survival. Sows used in outdoor systems should give birth to piglets displaying signs of high vitality and the sows should be able to nurse their own litter. This solution also conforms to the organic mind-set of operating a system with the least possible human intervention. [ABSTRACT FROM AUTHOR]

The aims of this research were to determine the uptake of a national strategy to reduce lameness in the UK flock, known as the Five-Point Plan (5 P P); explore the association between footrot vaccination (Footvax®) use and 5 P P adoption; investigate the management practices associated with farmer-reported percentage lameness through risk factor analysis; and identify the population attributable fractions of these management practices. In 2014, the 5 P P was launched to provide a practical, farm-level framework to help farmers reduce lameness to reach Farm Animal Welfare Committee (FAWC) targets. No published studies have explicitly explored its uptake in UK flocks nor its association with lameness prevalence. Understanding what parts of the 5 P P farmers adopt and which elements contribute towards the greatest reduction in lameness are integral in informing future strategies. Between November 2018 and February 2019, 532 UK sheep farmers completed a cross-sectional online and paper-based survey. The geometric mean of farmer-reported percentage lameness in ewes was 3.2 % (95 % CI: 2.8–3.6). Farmers adopted a median of 3 points of the plan, but was only fully-adopted by 5.8 % of farmers. The number of points adopted increased with flock size, with larger commercial flocks more likely to cull and vaccinate against footrot, but smaller, pedigree flocks were more likely to treat individual lame sheep. Vaccination was poorly associated with the uptake of other points of the 5 P P. Eight flock management factors were associated with significantly higher percentage lameness in ewes; not carrying out measures to avoid lameness transmission, not quarantining bought in stock, not treating individual lame sheep within three days, maintaining an open flock and foot trimming were all associated with a higher risk of lameness in flocks studied. In addition, using Footvax® for ≤5 years was associated with a higher risk of lameness, although vaccination could be a consequence of high flock lameness or these farmers were not implementing other effective managements, such as treating promptly. The highest PAFs were calculated for trimming lame sheep (16.9 %), maintaining an open flock (13.5 %) and not carrying out measures to avoid lameness transmission (11.8 %). We provide new evidence documenting the benefits of adopting parts of the 5 P P on reducing lameness prevalence in UK flocks, although uptake of these measures could be improved in flocks. Encouraging uptake of these measures could make an important contribution towards reducing the prevalence of lameness and reaching 2021 FAWC ≤ 2% lameness prevalence targets. [ABSTRACT FROM AUTHOR]

• Sheep face corpus dataset of normal and abnormal sheep. • Different pre-trained architectures of deep learning performance for small datasets. • State-of-the-art models and their usages for protection of animal life's. • Layer freezing, regularization of architectures and augmentations of datasets to achieve higher accuracy. Digital image recognition has been used in the different aspects of life, mostly in object classification and detections. Monitoring of animal life with image recognition in natural habitats is essential for animal health and production. Currently, Sheep Pain Facial Expression Scale (SPFES) has become the focus of monitoring sheep from facial expression. In contrast, pain level estimation from facial expression is an efficient and reliable mark of animal life. However, the manual assessment is lack of accuracy, time-consuming, and monotonous. Hence, the recent advancement of deep learning in computer vision helps to classify facial expression as fast and accurate. In this paper, we proposed a sheep face dataset and framework that uses transfer learning with fine-tuning for automating the classification of normal (no pain) and abnormal (pain) sheep face images. Current state-of-the-art convolutional neural networks (CNN) based architectures are used to train the sheep face dataset. The data augmentation, L2 regularization, and fine-tuning has been used to prepare the models. The experimental results related to the sheep facial expression dataset achieved 100% training, 99.69% validation, and 100% testing accuracy using the VGG16 model. While employing other pre-trained models, we gained 93.10% to 98.4% accuracy. Thus, it shows that our proposed model is optimal for high-precision classification of normal and abnormal sheep faces and can check on a comprehensive dataset. It can also be used to assist other animal life with high accuracy, save time and expenses. [ABSTRACT FROM AUTHOR]

• The data acquisition system collects point clouds when pigs freely move on it. • The accuracy of non-contact pigs' body measurement is highly improved. • A registration method using cuboid is applied on reconstructing the pig contour. • A novel method is applied on curve-fitting to effectively estimate abdominal girth. Body measurement is an important technique to acquire the livestock body parameters for precision agriculture and effective management of livestock. Traditionally, the livestock body parameters are often manually measured, which suffers from several drawbacks, including unstable accuracy, low efficiency, difficulty in measuring complex parameters, and potentially negative influence on animal welfare. Considering this, this paper presents a 3D surface reconstruction and body size measurement system based on multi-view RGB-D cameras. We use Kinect depth cameras to obtain the point clouds of the freely walking pigs from three different views (i.e., upper-view, left-view and right-view). The registration parameters are obtained by a rectangular cuboid, which can be used to reconstruct three local point clouds. Thereafter, the distribution of the point cloud projection in different directions is used to locate the measuring positions, which then contribute to the precise measurement of several key parameters, such as body length, body height, body width and abdominal girth. Moreover, the polar coordinate transform is developed to improve the measurement accuracy of the abdominal girth. The experiments performed on forty pigs have shown that our average relative errors in the body length, height, width and abdominal girth are 2.97%, 3.35%, 4.13% and 4.67%, respectively, which demonstrate the feasibility and accuracy of the proposed system. [ABSTRACT FROM AUTHOR]

• Ammonia determination with low cost sensor. • Use of IoT to increase productivity in biosystems. • Low cost embedded technology in rural properties. • Precision animal science helps to increase production and well-being. Animal welfare is a major concern these days, as well as the factors that have impact on animal productivity. Constant monitoring of environmental parameters such as temperature, humidity, levels of ammonia and luminosity is crucial because these variables directly impact poultry performance and welfare. Along these lines, it is necessary to look for solutions that allow the monitoring of these variables in a practical, reliable and cheap way. In the agribusiness, the scenario for the IoT (Internet of Things) is very promising, because it has emerged to fill this gap and deliver sensed data, widely increasing the connectivity of the devices. Therefore, this paper describes a low cost, hardware and software proposal for monitoring these environmental parameters in poultry farms. In addition, experiments were done to compare the proposed prototype with commercial devices. The measured variables showed a correlation above 0.90 between the sensors used in the prototype and the calibrated equipment, also having the cost of only 13% of that of the conventional equipment. The prototype displayed favorable results, indicating that its implementation is viable. [ABSTRACT FROM AUTHOR]

There has been an increasing demand for livestock products with credence attributes (CAs), such as environmental sustainability and high animal welfare standards. Although most livestock product CAs are strongly related to farm-level production processes, rarely have studies explored the issue from an on-farm perspective. Therefore, this paper aims to explore whether consumers' willingness-to-pay (WTP) a price premium could incentivise dairy farmers to deliver products with CAs in New Zealand. We first employed a meta-regression analysis to estimate WTP for CAs of dairy products, and then incorporated the WTP estimates into farm system models to estimate the economic and environmental (nitrogen and GHG losses) impacts for the farms. Data for the meta-regression models were sourced from a literature survey yielding 32 studies estimating WTP for CAs of dairy products. The results showed that price premium ranged from 5.3% to 47.5% for environmentally-friendly dairy products. Farm system (FARMAX), farm environmental (OVERSEER) and life cycle assessment (LCA) models were used to model the farm system changes required to deliver 'green' dairy by using data for the average Waikato dairy farm. The results showed that the economic and environmental outcomes varied over the predicted range in WTP as well as for different farm system scenarios. Findings of the study could help inform NZ farmers as to how to adjust their farm systems in response to market signals and thereby potentially gain a price premium. [ABSTRACT FROM AUTHOR]

• This paper uses stochastic partial budgeting analysis to examine the relationship between farm animal welfare (FAW) improvements and profitability in Swedish cattle fattening operations. • Increase in space allowance and forage to concentrate (FC) ratio are considered as FAW improving strategies. • An increase in space allowance of 1 m2/animal is associated with a decrease of 18.9 and 10.8% in beef and dairy breeds, respectively. • An increase in FC ratio (from 40:60 to 65:35) is associated with a decrease of about 21–34 and 17–53% in beef and dairy breeds, respectively. • The study only focuses on direct pecuniary effects of FAW and does not account for the non-use or 'soft' values associated with FAW. There is an increased concern among many stakeholders in society that livestock production systems provide a low-quality life to the animals and therefore advocate improved farm animal welfare (FAW). On the other hand, producers are concerned that stricter welfare regulations would decrease their profits and hence their competitiveness. Given the relevance of the debate surrounding FAW especially in Sweden, and the lack of studies related to the beef sector, this study examined the impact of changes in FAW practices on economic performance of Swedish beef fattening operations. We modelled a herd of 50 cattle of either beef or dairy breed reared for meat production to examine the impact of increased space allowance, including a corresponding decrease in herd size, and increased forage-to-concentrate (FC) ratio. We found that a 1-m2/animal increase in space allowance was associated with a decrease of 18.9 and 10.8% in short-run, per animal profits in beef and dairy breeds, respectively. Sensitivity analysis suggested that an increase in weight gain and short-run loss in herd size due to increased space allowance were the most important factors. We estimated that an increase in the FC ratio from 40:60 to 65:35 was associated with a reduction in per animal profits of about 21 to 34% in beef breeds and 17 to 53% in dairy breeds, respectively. Sensitivity analysis indicated that reduced growth due to increased FC ratio was the most important factor. Our results suggest that an increase in space allowance or FC ratio in Swedish beef fattening operations may reduce farm profitability. They also imply that consequences of any policy instrument should be carefully examined to reach the goal of improved FAW along with improved profitability and sustainability for the farmer. [ABSTRACT FROM AUTHOR]

• A deep learning method based on CNN and LSTM was used to recognise pig aggression. • This study directly process video episodes rather than individual frames. • CNN features were input into LSTM framework to extract spatial-temporal features. • The proposed method could recognise aggressive episodes with an accuracy of 97.2%. • A frame skipping approach can improve the accuracy to 98.4% and halve running time. Aggression is considered as a major animal welfare problem in commercial pig farming. The aim of this study is to develop a deep learning method based on convolutional neural network (CNN) and long short-term memory (LSTM) to recognise aggressive episodes of pigs. Compared to previous studies of pig behaviours based on deep learning, this study directly process video episodes rather than individual frames. In the experiment, nursery pigs (8/pen) were mixed for 3 days and then 8 h of video was recorded in each day. From these videos, 600 aggressive 2 s-episodes were manually selected and then augmented into 2400 episodes by using horizontal, vertical and diagonal mirroring. From the videos, 2400 non-aggressive 2 s-episodes were also manually selected. 80% of the data were randomly allocated as training set and the remaining 20% as validation set. Firstly, the CNN architecture VGG-16 was used to extract spatial features. These features were then input into LSTM framework to further extract temporal features. Through fully connected layer, the prediction function Softmax was finally used to determine if the current episode is aggression or non-aggression. Using the proposed method, aggressive episodes could be recognised with an accuracy of 97.2%. This result indicates that this method can be used to recognise aggressive episodes of pigs. Additionally, this paper further investigates the validity of this method under the conditions of skipping frames and reducing the episode length. The results show that a frame skipping approach whereby 30 fps is reduced into 15 fps within each 2 s-episode can improve the accuracy into 98.4% and halve the total running time. [ABSTRACT FROM AUTHOR]

• Memory becomes a concern when creating models based on data from electronic medical records. • Processing the data in parts or sampling can allow analysis on a PC. • The relationship between weight and signalment in cats was explored. • Weight of domestic cats peak in middle age, with intact cats weighing less. Large amounts of animal health data are available to researchers, but are often stored in different formats and information silos. Analysis of this existing information can provide new insights into the health and welfare of animals and possibly reduce the need to collect additional data. The objective of this study was to develop a method of managing and analyzing large amounts of data on a personal computer that can be run within 24 h to limit the time and resources spent deploying models on larger servers. This paper describes an overall approach that makes use of existing methods for data acquisition and modeling, but adapts and combines them in a way that allows manipulation and analysis of large volumes of data on a PC. This included a total of five steps: removing errors; removing data points outside the scope of a specific hypothesis; creating descriptive statistics; developing explanatory and/or predictive models; and assessing the fit or accuracy of the models created. The approach was developed using electronic medical records for 19,416,753 feline patients from 3972 anonymized veterinary clinics in the United States and Canada, recorded between January 1981 and June 2016. Data regarding patient signalment (age, sex, breed, reproductive status) and body weight were extracted from the records and used to create linear regression models to describe body weight in cats of different ages, breeds, genders and reproductive status. Ordinary least squares linear regression and stochastic gradient descent linear regression were compared to determine their effectiveness and suitability for creating predictive models with large datasets, using 10 fold cross validation. This approach could be used to build workflows to create models to determine exploratory and predictive properties of health parameters for animals and people. The ability to work with large datasets on a PC or equivalent technology was demonstrated. Significant interactions were present among sex, reproductive status and age. A peak in weight occurred between 6 and 9 years depending on the sex, reproductive status and breed. The predictive ability of the two models was similar, with both producing a root mean square error of 1.45 and a mean absolute error of 1.09, and mean error that was approximately zero on the validation dataset. [ABSTRACT FROM AUTHOR]

• Mechanical harvesting is less influenced by light, weight and outdoor temperature. • The tested harvesting machine can reduce stress if well- adjusted and conducted. • The 'abdomen' technique should be preferred over inverted manual catching techniques. • Stress-induced behavioural reactions of broilers increase the risk for lesions. Harvesting of broilers is a stressful event, whether done mechanically or manually. The use of harvesting machines might compromise animal welfare less than manual harvesting methods because it is less influenced by environmental and flock-specific factors. In this paper, a mechanical and a manual harvesting method are compared in regard to stress-induced behavioural and physiological reactions. The stationary person test and the avoidance distance touch test were applied before harvesting to estimate pre-treatment fear of humans in the flocks. We then recorded the behaviour of the flocks during harvesting and evaluated stress-induced behaviour such as wing flapping and escape behaviour. Furthermore, we took blood samples at the abattoir and analysed the corticosterone concentration and heterophil/lymphocyte ratio. In a statistical analysis, all assessed parameters were related to environmental and flock-specific factors, as well as to the risk of lesions such as haematomas and fractures. Our aim was to figure out if the use of a harvesting machine, in this case the Apollo Generation 2, puts less stress on broilers during harvesting than manual catching. The applied behaviour tests indicate the excitability and fear of humans of the flocks but are complex to interpret. Compared with mechanical harvesting, manual harvesting was more influenced by environmental and flock-specific factors such as average weight (odds ratio [OR] = 1.49; 95% confidence interval [CI] [1.11; 2.22]) or catching duration (OR = 1.79; 95% CI [1.44; 2.19]). The results verified a correlation between stress-induced behaviour and the occurrence of lesions. The risk for haematomas was influenced by escape behaviour during manual harvesting (OR = 1.07; 95% CI [1.02; 1.13]) and by bumps against the containers (OR = 1.06; 95% CI [1.05; 1.07]) and flips (OR = 6.23; 95% CI [3.99; 9.27]) during mechanical harvesting. The risk for fractures during mechanical harvesting was influenced by the occurrence of flips (OR = 2.74; 95% CI [1.07; 5.67]). The risk for wing flapping was twice as high during manual harvesting as during mechanical harvesting (OR = 2.11; 95% CI [1.82; 2.44]). The blood parameters showed no correlations with the initial behaviour test results and the assessed stress-induced behaviour during harvesting. Corticosterone concentration was strongly influenced by light intensity (beta = 3.75; 95% CI [2.55; 4.95]) and outdoor temperature (beta = 46.34; 95% CI [39.18; 53.27]) during manual harvesting. The results showed weak points of both harvesting methods, and we offer suggestions to improve animal welfare during harvesting. [ABSTRACT FROM AUTHOR]

• Perinatal mortality is defined as death of a full term calf either during parturition or shortly after (up to 48 h). • Major risk factors for perinatal mortality are calf sex (male), primiparous dams, assistance at calving, and twin calves. • Assessing the generalisability of many papers is challenging without data on how the risk factors affect population risk. Dairy calf mortality is an area of interest worldwide as animal welfare continues to increase in importance to the consumer. Perinatal mortality, defined as death of a full term calf either during parturition or shortly after (up to 48 h), is a major component of overall calf mortality with estimates of losses in large scale multi-farm studies suggesting that 2.4–9.7% of calves are lost across a wide range of different systems. In order to reduce perinatal mortality, we need to understand the key risk factors which influence its prevalence. This review explores the literature on risk factors associated with perinatal mortality, with a particular focus on the limitations of the published literature, applicability of the data, and the challenges faced when trying to compare data across studies. Overall, calves that are male, born to primiparous dams, are twins or had to be assisted during the calving process, are at a higher risk of mortality. [ABSTRACT FROM AUTHOR]

• An animal welfare indicator for dairy farms is developed. • We compare an animal welfare indicator and farmer's job-satisfaction and stress. • A link exists between farmer welfare, farmers stress and animal welfare. • High job satisfaction and farm expansion are associated with animal welfare. The aim of this paper is to explore whether and how dairy farmer's occupational well-being and stress are associated with animal welfare and farm expansion. A web-administered questionnaire was used to collect 914 dairy farmer's opinions on their quality of life, working situation and mental health. Factor analysis was used to describe farmer's occupational well-being and stress, and farmers who expand their farming operations. A structural equation model (SEM) was used to explore the association of the occupational well-being and stress with animal welfare. Animal welfare was measured by an animal welfare indicator, based on variables listed in the international standard that was available in the Norwegian Animal Recording System. The findings show that high farmer occupational well-being and a low level of stress have a direct positive association with the animal welfare indicator. Contrary, low occupational well-being and high level of stress is negatively associated with the animal welfare indicator. Finally, farmer's degree of loneliness and optimism, satisfaction with income and determination to continue production, is associated with animal welfare indirectly through farm expansion. In this study farm expansion was positively associated with the animal welfare indicator. [ABSTRACT FROM AUTHOR]

Highlights • Finishing bulls had more synchronised behaviour in pastures than in pens. • This was the case for lying, eating and standing, as well as for them together. • The higher level of synchronisation may imply more natural social behaviour. • The effects of allelomimicry and concurrence on the difference are discussed. Abstract The objective of the present study was to compare the synchronisation of the lying, feeding and standing behaviour of bulls housed in an uninsulated barn and in a pasture. Dairy (Ayrshire and Holstein-Friesian) and beef (Hereford) bulls were housed in June-July in either partly bedded pens in an uninsulated barn (PEN bulls; five groups of four or five bulls with 6.4 or 8.0 m2/bull) or in a pasture (PAS bulls; five groups of four or five bulls with 680, 1000 or 1250 m2/bull). The PEN bulls were fed grass silage ad libitum. For concentrate supplementation both the PEN and PAS bulls received rolled barley 4.4 kg dry matter/animal/day. The behaviour of the bulls was observed for two 24-h periods. The animals were 15–17 months old during the observations. Instantaneous sampling with a 6-min sampling interval (IS-6 min) was used originally for collecting the data. Lying, eating and standing (=all other activities performed in a standing posture) were recorded during the IS-6 observations. The Fleiss' Kappa coefficient was used as an index of behavioural synchronisation. It was calculated for each of the groups of four or five animals across 24 (IS-60 min) and 12 (IS-120 min) time points within a 24-h day to increase the independency of the data points. The Fleiss' kappa coefficient was calculated in two ways: combining all three behavioural classes of each observation day (K FO) as well as separately for lying (K FL), eating (K FE), and standing (K FS). The results were analysed using a selected multilevel linear mixed model. The IS-60 min (presented in the abstract) and IS-120 min results were essentially identical. PAS bulls displayed more synchronised behaviour than the PEN bulls (P < 0.05), the K FO s being 0.618 vs. 0.328 (SEM = 0.035), respectively. The Kappa coefficients calculated separately for the three behaviour classes showed that the differences in the combined kappa coefficients were due to parallel differences in all these three: 0.776 vs. 0.376 (SEM = 0.047) for lying, 0.464 vs. 0.211 (SEM = 0.069) for eating, and 0.518 vs. 0.316 (SEM = 0.048) for standing. Behaviour synchronization is an essential feature of species-specific behaviour in cattle, and our results might point towards a welfare benefit for keeping the bulls in a pasture instead of in a more restricted housing environment. However, there are few other studies to support this welfare conclusion. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, increasing interest in using the pig (Sus scrofa) for biomedical research has become evident. Today, the pig is considered an advantageous alternative animal model for various human diseases and conditions. However, even though a considerable amount of biomedical research has been done on pigs, hardly any studies include systematic welfare assessment. Still, it is essential to assess welfare of laboratory pigs, both domestic pig breeds and smaller purpose-bred breeds, as (1) scientific obligations entail responsibility to ensure and document a fair welfare standard for animals used for experimental purposes; and (2) the scientific outcome can be dependent upon the welfare state of the animals. In order to be able to quantify and control laboratory pig welfare, a practical tool is needed. The purpose of the present paper is to provide an overview of the current status of the extent of welfare assessment in pigs used in biomedical research and to suggest a welfare assessment standard for research facilities based on an exposition of ethological considerations relevant for the welfare of pigs in biomedical research. The tools for porcine welfare assessment presented suggest a method for monitoring the welfare status of individual laboratory pigs, intended to relieve the practical scoring of the welfare of individual pigs as well as the interpretation of the findings. [Copyright &y& Elsevier]

Abstract: With research projects that use animals on the increase worldwide and in Africa in particular, animal research ethics should continue to be reviewed to improve the welfare of animals used in research. The welfare of animals used in research has gained attention globally that has led to the development of guidelines and in some instances national laws governing animal experimentation. Although there may not be empirical data on the existence or adequacy of national and/or institutional policies and guidelines on the use of animals in research in Africa, most African countries are not yet at the same level as developed countries. Consequently, some researchers based at institutions in developed countries may be tempted to ‘export’ their research activities to collaborating African institutions where ethical and legal frameworks on use of animals may be less stringent than in the developed countries. An appreciation of the intrinsic value of animals should be enough driving force for human beings to strive to promote humane treatment of animals. This paper gives an overview of uses and potential abuses of animals in research for the benefit of stakeholders such as researchers, research institutions, Ethics Review Committees (ERCs) and policy makers and ends with suggestions on possible ways of ensuring humane treatment of animals used in research in Africa in particular and globally in general. [Copyright &y& Elsevier]

An abstract of the paper "Validation of a Gait Scoring System for Field Assessment of Walking Ability of Commercial Broilers," by A. B. Webster and colleagues is presented.