Abstract

Simple Summary The European Cooperation in Science and Technology (COST) Action GroupHouseNet aims to provide synergy among scientists to prevent damaging behavior in group-housed pigs and laying hens. One goal of this network is to determine how genetic and genomic tools can be used to breed animals that are less likely to perform damaging behavior on their pen-mates. In this review, the focus is on feather-pecking behavior in laying hens. Reducing feather pecking in large groups of hens is a challenge, because it is difficult to identify and monitor individual birds. However, current developments in sensor technologies and animal breeding have the potential to identify individual animals, monitor individual behavior, and link this information back to the underlying genotype. We describe a combination of sensor technologies and "-omics" approaches that could be used to select against feather-pecking behavior in laying hens. Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of "-omics" approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior.

Keywords

damaging behavior; ultra-wideband; radio frequency identification; computer vision; identification; measuring behavior; -omics; genetic selection

Published in

Animals
2019, Volume: 9, number: 3, article number: 108
Publisher: MDPI

SLU Authors

• Guzhva, Oleksiy

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

UKÄ Subject classification

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


Publication identifier

DOI: https://doi.org/10.3390/ani9030108

Permanent link to this page (URI)

https://res.slu.se/id/publ/99929