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Perspectives of economic losses due to condemnation of cattle and buffalo carcasses in the northern region of Brazil.

Welligton Conceição da SilvaRaimundo Nonato Colares CamargoÉder Bruno Rebelo da SilvaJamile Andréa Rodrigues da SilvaMárcio Luiz Repolho PicançoMaria Roseane Pereira Dos SantosCláudio Vieira de AraújoAntônio Vinicius Correa BarbosaMarina de Nadai BoninAlbiane Sousa de OliveiraSimone Vieira CastroJosé de Brito Lourenço
Published in: PloS one (2023)
The work aims to study the economical losses of the condemnation of bovine and buffalo carcasses, in order to estimate the losses in animals slaughtered in Santarém-Pará, Brazil, between 2016 and 2018, with data obtained from the Municipal Department of Agriculture and Fisheries. Sex, age, origin, total number of animals slaughtered and causes of condemnation of carcasses were considered. All analyzes were performed in RStudio version 1.1.463. In this study, 71,277 bovine carcasses and 2,016 buffalo carcasses were inspected, of which 300 bovine and 71 buffalo were condemned. The highest prevalence of causes of condemnation in cattle was recorded for brucellosis (0.0020%) and tuberculosis (0.0019%). In buffaloes, tuberculosis (0.0307%) peritonitis (0,0019%) were the main causes of condemnations. Economical losses were more evident in females, for both species. The projection of economical losses related to the condemnation of carcasses showed a sharp growth for the next three years, if the average growth remains constant. The biggest projected loss was for bovine females, with an accumulated projection of $ 5,451.44. The smallest estimated loss was for buffalo males, projected at more than thirty-two thousand reais. The most important causes of condemnation report the diseases brucellosis and tuberculosis, as the ones with the greatest impact. In the buffalo species this was even more accentuated, even though the number of buffaloes slaughtered is more than 35 times smaller than the number of cattle.
Keyphrases
  • mycobacterium tuberculosis
  • climate change
  • hiv aids
  • pulmonary tuberculosis
  • machine learning
  • magnetic resonance imaging
  • image quality