Comparing Functional Data Analysis and Hysteresis Loops When Testing Treatments for Reducing Heat Stress in Dairy Cows

Abstract

Various techniques are commonly used to reduce heat stress, including sprayers and misters, shading, and changes in feed. Oftentimes studies are performed where researchers do not control the times when animals use shading or other means available to reduce heat stress, making it hard to test differences between treatments. Two methods are used on data from a study where Holstein cows were given free access to weight activated "cow showers." Functional data analysis can be used to model body temperature as a function of time and environmental variables such as the Heat Load Index. Differences between treatment groups can be tested using a Functional Bayesian MCMC model. Alternatively hysteresis loops, such as the ellipse, formed by a plot of air temperature or the Heat Load Index against body temperature over the course of a day can be estimated and their parameters used to test differences between cows with access to showers and cows without. Results from an R package hysteresis, which can estimate these loops and their parameters are illustrated. Functional data analysis allows for looser assumptions regarding the body temperature curve and the ability to look for differences between groups at specific time points, while hysteresis loops give the ability to look at heat stress over the course of a day holistically in terms of parameters such as amplitude, lag, internal heat load and central values.