The Welfare Aggregation and Guidance (WAG) Tool: A New Method to Summarize Global Welfare Assessment Data for Equids.

Simple Summary Animal welfare can be considered in terms of health, the nutrition animals receive, the behavior of animals, the humans they interact with, their living environment, or working conditions. Each of these indicators can be measured in myriad ways. Consequently, welfare assessments tend to be lengthy, resulting in a wealth of data about each animal. There is, however, a need to report animal welfare concisely in order to compare and measure change. We propose a method to aggregate an existing questionnaire-based welfare assessment into five 'grades' to reflect the main components of animal welfare. We aim to provide a succinct way for stakeholders such as animal welfare charities to measure and report on welfare, aiding resource allocation, and enabling monitoring of the efficacy of intervention measures aimed at improving welfare conditions. In an assessment of the health and behavior of over 6000 equids across Europe and Asia, equids in Pakistan and India were found to have the poorest welfare levels. We recommend detailed assessments in these areas to identify the specific causes of the identified issues in order to guide the development of appropriate intervention schemes and, ultimately, improve equid welfare. Abstract Animal welfare can be represented by an array of indicators. There is, however, increasing demand for concise welfare assessments that can be easily communicated and compared. Previous methods to aggregate welfare assessments have focused on livestock systems and produced a single welfare score, which may not represent all aspects of welfare. We propose an aggregation method for the recently developed Equid Assessment Research and Scoping (EARS) welfare assessment tool that results in grades for five welfare categories: housing conditions, working conditions, health, nutrition, and behavior. We overcome the problems associated with existing approaches by using a single aggregation method (decision trees) that incorporates the most important welfare indicators in a single step. The process aims to identify equids with the poorest welfare and aid decision-making when allocating resources. We demonstrate its application using a case study of over 6000 equids across Europe and Asia, where equids in India and Pakistan had the poorest welfare status in terms of health (respiratory disease and open wounds) and behavior (signs of fear and distress, and limb tethering practices). We recommend identification of the specific causes of these issues, using either existing detailed welfare data or through issue-specific assessments by an appropriate professional, to guide the development of appropriate interventions and, ultimately, improve equid welfare.