Caracteriza\c{c}\~ao de circuitos pecu\'arios com base em redes de movimenta\c{c}\~ao de animais

TL;DR

This paper uses network analysis to identify livestock communities based on animal movement patterns within Mato Grosso, revealing geographical and commercial structures useful for veterinary and trade applications.

Contribution

It introduces a community detection algorithm tailored for livestock movement networks, linking network structure to production zones and trade pathways.

Findings

Identified clear geographical and commercial patterns in livestock communities.

Demonstrated the algorithm's usefulness for disease prevention and trade analysis.

Analyzed over 87,000 premises and 15 million animal movements.

Abstract

A network is a set of nodes that are linked together by a set of edges. Networks can represent any set of objects that have relations among themselves. Communities are sets of nodes that are related in an important way, probably sharing common properties and/or playing similar roles within a network. When network analysis is applied to study the livestock movement patterns, the epidemiological units of interest (farm premises, counties, states, countries, etc.) are represented as nodes, and animal movements between the nodes are represented as the edges of a network. Unraveling a network structure, and hence the trade preferences and pathways, could be very useful to a researcher or a decision-maker. We implemented a community detection algorithm to find livestock communities that is consistent with the definition of a livestock production zone, assuming that a community is a group of farm premises in which an animal is more likely to stay during its life time than expected by chance. We applied this algorithm to the network of within animal movements made inside the State of Mato Grosso, for the year of 2007. This database holds information about 87,899 premises and 521,431 movements throughout the year, totalizing 15,844,779 animals moved. The community detection algorithm achieved a network partition that shows a clear geographical and commercial pattern, two crucial features to preventive veterinary medicine applications, and also has a meaningful interpretation in trade networks where links emerge from the choice of trader nodes.