Body temperature and motion: Evaluation of an online monitoring system in pigs challenged with Porcine Reproductive & Respiratory Syndrome Virus

A critical review on experimental Streptococcus suis infection in pigs with a focus on clinical monitoring and refinement strategies

Streptococcus suis (S. suis) is a major pig pathogen worldwide with zoonotic potential. Though different research groups have contributed to a better understanding of the pathogenesis of S. suis infections in recent years, there are still numerous neglected research topics requiring animal infection trials. Of note, animal experiments are crucial to develop a cross-protective vaccine which is highly needed in the field. Due to the severe clinical signs associated with S. suis pathologies such as meningitis and arthritis, implementation of refinement is very important to reduce pain and distress of experimentally infected pigs. This review highlights the great diversity of clinical signs and courses of disease after experimental S. suis pig infections. We review clinical read out parameters and refinement strategies in experimental S. suis pig infections published between 2000 and 2021. Currently, substantial differences exist in describing clinical monitoring and humane endpoints. Most of the reviewed studies set the body temperature threshold of fever as high as 40.5°C. Monitoring intervals vary mainly between daily, twice a day and three times a day. Only a few studies apply scoring systems. Published scoring systems are inconsistent in their inclusion of parameters such as body temperature, feeding behavior, and respiratory signs. Locomotion and central nervous system signs are more common clinical scoring parameters in different studies by various research groups. As the heterogenicity in clinical monitoring limits the comparability between studies we hope to initiate a discussion with this review leading to an agreement on clinical read out parameters and monitoring intervals among S. suis research groups.

Accelerometer systems as tools for health and welfare assessment in cattle and pigs - A review

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.

How many pigs within a group need to be sick to lead to a diagnostic change in the group's behavior?1

Disease is a leading cause of diminished welfare and productivity in pig systems, but its spread among pigs within commercial herds can be limited through early detection. Identifying specific behavioral changes at the onset of disease can have a substantial diagnostic value by improving treatment success through timely intervention. Our study aimed to identify key behaviors that visibly change at the group level when only a few individuals are acutely sick. First, we quantified the behavioral changes seen during an acute health challenge in groups of pigs, using total pen vaccination as an artificial sickness model. Then we investigated the minimum proportion of sick pigs needed to detect group level behavioral changes using three treatments: a control (Con; 0% pigs), low (±20% pigs), or a high (±50% pigs) number of pigs vaccinated in the pens. Total pen vaccination in Trial 1 produced group level behavioral changes, including reduced feeding (P < 0.001), non-nutritive visits to the feeder (P < 0.01), drinking (P < 0.001), standing (P < 0.001), and interaction with pen enrichment (P < 0.001), accompanied by increased lying rates (P < 0.01) and elevated body temperatures (P < 0.001), confirming that vaccination is an appropriate model to study effects of acute sickness. In Trial 2, group level declines in interaction with the enrichment device (P < 0.001) and standing rates (P = 0.064), along with an increase in pen lying rates (P < 0.001), were apparent in the Low treatment when compared to the Con rates, which suggests these key behaviors could serve an important diagnostic value for early disease detection in groups. These changes lasted for up to 3 h post vaccination. In contrast, feeding rates (treatment × time of day: P < 0.01) only showed a decrease from the Con in the High treatment after vaccination, with pen drinking showing a similar trend (treatment: P = 0.07), suggesting that these behaviors would be more appropriate for confirming the spread of disease within a herd. Identifying key behaviors that alert to the presence of disease is critical to further refine automated early warning systems using pen level sensors for commercial pig operations.