Abstract

Resilience could be referred to as the animal's ability to successfully adapt to a challenge. This is typically displayed by a quick return to initial metabolic or activity levels and behaviors. Pigs have distinct diurnal activity patterns. Deviations from these patterns could potentially be utilized to quantify resilience. However, human observations of activity are labor intensive and not feasible in practice on a large scale. In this study, we show the use of a computer vision tracking algorithm to quantify resilience based on activity individual patterns following a lipopolysaccharide (LPS) challenge, which induced a sickness response. We followed 121 individual pigs housed in barren or enriched housing systems, as previous work suggests an impact of housing on resilience, for eight days. The enriched housing consisted of delayed weaning in a group farrowing system and extra space compared with the barren pens and environmental enrichment. Enriched housed pigs were more active pre-injection of LPS, especially during peak activity times, than barren housed pigs (49.4 +/- 9.9 vs. 39.1 +/- 5.0 meter/hour). Four pigs per pen received an LPS injection and two pigs a saline injection. LPS injected animals were more likely to show a dip in activity than controls (86% vs 17%). Duration and Area Under the Curve (AUC) of the dip were not affected by housing. However, pigs with the same AUC could have a long and shallow dip or a steep and short dip. Therefore the AUC:duration ratio was calculated, and enriched housed pigs had a higher AUC:duration ratio compared to barren housed pigs (9244.1 +/- 5429.8 vs 5919.6 +/- 4566.1). Enriched housed pigs might therefore have a different strategy to cope with an LPS sickness challenge. However, more research on this strategy and the use of activity to quantify resilience and its relationship to physiological parameters is therefore needed.

Keywords

resilience; activity; computer vision; LPS; pig; lipopolysaccharide; behavior

Published in

Frontiers in animal science
2022, Volume: 3, article number: 883940
Publisher: FRONTIERS MEDIA SA

SLU Authors

• Guzhva, Oleksiy

  • Department of Biosystems and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Computer Vision and Robotics (Autonomous Systems)
Animal and Dairy Science


Publication identifier

DOI: https://doi.org/10.3389/fanim.2022.883940

Permanent link to this page (URI)

https://res.slu.se/id/publ/122835