Optimising the selection of welfare indicators in farm animals.

ABSTRACT:

Introduction

Risk assessment (RA) frameworks are increasingly being applied to improve the welfare of farmed animals. These frameworks have at their core, a logic chain linking welfare hazards (risks) with one or more welfare consequences which, in turn, are each measured by one or more welfare indicators. Effective and efficient monitoring of animal welfare often involves the selection of a subset of indicators from a large pool. Selecting 'iceberg indicators' could be advantageous due to their association with multiple welfare consequences. However, no standardised, data-driven method exists to select optimal combinations under practical constraints. This study addresses this gap by creating an algorithmic approach to optimise indicator selection.

Methods

The work was cond

SUBMITTER: Day J 

PROVIDER: S-EPMC12604357 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature