Analysis of the cattle trade network in the state of Mato Grosso, Brazil

• Main Authors: Rísia Lopes Negreiros, José Henrique Hildebrand Grisi-Filho, Ricardo Augusto Dias, Fernando Ferreira, José Soares Ferreira Neto, Raul Ossada, Marcos Amaku
• Format: Article
• Language: English
• Published: Universidade de São Paulo 2020-12-01
• Series: Brazilian Journal of Veterinary Research and Animal Science
• Subjects: Animal movements; Brazil; Cattle; Network analysis; Trade
• Online Access: https://www.revistas.usp.br/bjvras/article/view/171635

We characterized the network of cattle movements in Mato Grosso, Brazil—a state responsible for a significant amount of Brazilian beef exports. In this analysis, we were particularly interested in the importance of slaughterhouses in the network of cattle movements, in the characteristics of the trade for different purposes (especially for reproduction, fattening, and slaughter), and about the importance of the main traders. We found that the median distance of all movements was 79.0 km, with an interquartile range between 37.2 and 163.2 km, which suggests a relatively short-range trade pattern; roughly 20% of the premises purchasing more animals were responsible for 95% of the animals purchased and 20% of the premises selling more animals were responsible for 86% of the animals sold, indicating the importance of the cattle trade hubs. Fitting the degree distribution using a power-law distribution, we obtained a scaling parameter of 2.59, indicating that the cattle movement network of Mato Grosso is less dense than other observed cattle movement networks. The networks for fattening, reproduction, and slaughtering only comprised 73%, 56%, and 25% of all nodes and 52%, 30%, and 17% of all edges, respectively; and slaughterhouses had the highest in-degrees and total degrees, followed by markets and farms, playing the role of main cattle trade hubs. Therefore, slaughterhouses are potentially useful as surveillance points to detect cases, and controlling important trader premises may optimize the use of resources for controlling diseases.