Applying proximity sensors to monitor beef cattle social behaviour as an indicator of animal welfare

There are currently no approved monitoring programs in the beef industry that use paddock based behaviour as an indicator of animal welfare. Current animal welfare assessments are conducted at a single point in time, such as supplying food and water and treating illnesses as these needs arise. These aspects comply with the five freedoms that animals should have when addressing animal welfare, however, the assessments are infrequent. Of the five freedoms, the freedom to express normal behaviour can be a subjective measure, due to differences in the way individual animals express certain behaviours. There is a need for continual monitoring of welfare indicators in modern animal assessment methods to objectively measure behaviour and address public concerns about the welfare state of animals. The experiment commenced in June 2017 to assess changes in cattle social interaction patterns in response to social stress created by regrouping four groups of eight heifers. Previous research with cattle has provided evidence that social contact and spatial behaviour differ when novel individuals are introduced (Patison et al., 2010b), and re-grouped animals continue to experience stress until the social hierarchy is re-established after regrouping (Kondo and Hurnik, 1990). Proximity sensors that record the frequency and duration of close proximity contacts (<4 m) will be used to remotely collect animal association data, while blood cortisol concentrations will be used as an independent measure of stress. Responses to stress will be compared with a group of heifers where re-grouping does not occur. This paper outlines the background and methodology to explore the potential for proximity sensors as a continual welfare monitoring device, related to an animal’s freedom to express normal behaviour. Preliminary results of the project will be presented at The International Tri-Conference for Precision Agriculture held in New Zealand in October, 2017

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).