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Automating Inconsistency Discover and Historical Investigation of External Influences on Livestock Population Data Cover

Automating Inconsistency Discover and Historical Investigation of External Influences on Livestock Population Data

Open Access
|Sep 2025

Abstract

Livestock population data is crucial in researching and modeling animal disease (Sibhat et al., 2017), economic health burden (Muraguri et al., 1998), climate change (Fordyce et al., 2023), antimicrobial resistance calculations (Mulchandani et al., 2023), and other topics. The issue is that there are conflicting population data from the World Organization for Animal Health (WOAH), the Food and Agriculture Organization of the United Nations (FAOSTAT), and others for identical populations over the same period. This can pose a challenge in identifying the most accurate source for research. This paper outlines methodologies for comparing FAOSTAT and WOAH data against each other and against other influences to grade and outline data so that researchers can make informed decisions on which data to use and when. The advantages and disadvantages of multiple methods that compare FAOSTAT and WOAH data with other external sources are discussed. External influences such as economic recessions, and government policies are researched and discussed. These practices were utilized for comparing cattle, pig, chicken, and sheep population data across fifteen countries to gauge which sources appeared more probable over which times. A software tool was created to identify outliers in data trends which can be a starting point for researchers to do their investigations in the future. After the points were acquired from the software, research was performed into historical documentation for influences on livestock. It was found during the investigation of the magnitudes of FAOSTAT and WOAH data that government surveys and research paper populations had the most comparable data. Comparing historical records of natural disasters, market pressures, and government policies explained most trends in the data.

Language: English
Submitted on: Jul 9, 2024
Accepted on: Jun 18, 2025
Published on: Sep 19, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Ian McKechnie, Kassy Raymond, Deborah Stacey, Theresa Bernardo, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.