Behavioural and physiological responses of shelter dogs to long-term confinement

In Italy, National Law (281/1991) prohibits euthanasia of shelter dogs if they are not dangerous or suffering seriously. Adoption rates in rescue shelters are often lower than entrance rates, leading inevitably to overcrowded facilities where animals are likely to spend the rest of their lives in kennels. In this situation, housing conditions (i.e. space provided, environmental, and social stimulation) may have an impact on canine welfare. In this research project, the effects of two different forms of housing (group- and pair housing) on long-term shelter dogs were compared using behavioural and physiological parameters. Observational data and saliva samples were collected from dogs exposed to both experimental settings; behaviour and cortisol concentration levels were used as welfare indicators. Pair housing offered fewer social and environmental stimuli and behavioural analysis showed a significant decrease in locomotor, exploratory, and social behaviour. Cortisol levels show that this parameter varied independently of housing conditions. Although this study found no evidence suggesting that one form of confinement reduced animal welfare more than the other (e.g. in terms of abnormal behaviour, or higher cortisol concentrations), the type of confinement did affect the expression of a variety of behaviours and these variations should not be ignored with respect to housing decisions for long-term shelter dogs.

Unloading or not unloading? Sheep welfare implication of rest stop at control post after a 29h transport

In Europe, maximum journey time for transported sheep is set at 29. h (EC Regulation 1/2005), after which animals must be unloaded, fed and watered in control posts stopping for 24. h, as all other species, before continuing their journey. The industry considers these resting times too general, not taking into account the peculiar differences between species or age classes. Also, loading and unloading have been reported to be detrimental for the animals. Therefore, the industry pushes to reduce the times at control post and avoid unloading the animals from the truck. Since there is little information concerning the effect of resting in a stationary truck after long journeys, the present study aims to evaluate the effect of an 8. h rest stop on the truck for transported ewes compared to being unloaded for resting in a control post for the same amount of time, considering physiological and behavioural measures. Two groups of ewes were transported for 29. h, after which one was unloaded and housed in a pen (P) at the control post while the other was left inside the truck (T). After 8. h stop, a further 6. h travel was headed to the farm of origin. A third group (C) stayed at the farm as control. During the stop, standing, resting, moving and eating behaviour of all groups was recorded. Blood parameters, salivary and faecal cortisol were assessed at different stages. The behaviour of P animals during the resting period was more similar to C than to T ones, where feeding and lying behaviours were restricted by the limited space allowance on the truck. After returning to the farm of origin, both T and P animals showed different parameters' levels as compared to C. P ewes showed a mean loss weight of 2. kg not recorded in group T and showed higher signs of muscular damage compared to C group. It was concluded that, with so short resting times as 8. h, there is no clear advantages in terms of animal welfare for avoiding the unloading and loading of the animals in the control post after long journeys.

A pilot study to develop an assessment tool for sheep welfare after long journey transport

Sheep transport within Europe involves 9.5 million animals yearly, 63% of which travel over longjourneys (> 8 h). Livestock transport, particularly over long journeys, gives rise to concern about the welfare of transported animals. The European Commission stimulates the development of market-oriented animal welfare standards for all phases of livestock production, providing an alternative to the 'regulatory approach'. This study aimed to develop and test a new sheep welfare assessment protocol to be used following transport, irrespective of the journey purpose. The protocol included outcome (animal-based measures) and input variables (resource-based and management-based measures), being welfare-relevant aspects of both transport and unloading procedures. Weighted Cohen's Kappa and Heiss' Kappa index of agreement were calculated to evaluate the raters accuracy and the inter-observer reliability.Overall, good agreement levels were found. The protocol was tested on 40 commercial transports arriving at previously selected assembly centres and slaughterhouses in Italy and Greece. The protocol was found to be feasible when applied to commercial transports, allowing for a comprehensive and quick sheep welfare assessment during unloading, without impairing stockman work. Univariate analysis was carried out to evaluate associations between outcome and input variables. In this study, significant association between outcome measures and risk factors were identified when associated to unloading procedures but not to travel conditions. In collaboration with the relevant stakeholders, this protocol might be developed into a tool for routine checks for certification purposes and could provide direct feedback to all professionals involved in animal transportation on the weaknesses and strengths of their work.

Kinect and Episodic Reasoning for Human Action Recognition

This paper presents a method for rational behaviour recognition that combines vision-based pose estimation with knowledge modeling and reasoning. The proposed method consists of two stages. First, RGB-D images are used in the estimation of the body postures. Then, estimated actions are evaluated to verify that they make sense. This method requires rational behaviour to be exhibited. To comply with this requirement, this work proposes a rational RGB-D dataset with two types of sequences, some for training and some for testing. Preliminary results show the addition of knowledge modeling and reasoning leads to a significant increase of recognition accuracy when compared to a system based only on computer vision.