Discriminant analysis based on sheep carcass conformation and finishing scores

Author:

Maciel Marilene dos S.1ORCID,Arandas Janaína K. G.1ORCID,Carvalho Francisco F. R. de1ORCID,Cruz George R. B. da2ORCID,Ribeiro Neila L.3ORCID,Ribeiro Maria N.1ORCID

Affiliation:

1. Universidade Federal Rural de Pernambuco, Brazil

2. Universidade Federal da Paraíba, Brazil

3. Instituto Nacional do Semiárido, Brazil

Abstract

ABSTRACT Carcass classification consists of grouping animals with similar carcass characteristics. When the groups are defined a priori, as in the case of conformation and finishing scores, the interest is to identify the contribution of each variable used in separating the groups. Therefore, discriminant analysis was used to discriminate Santa Inês animals according to the conformation and carcass finishing scores (score 2 = regular, score 3 = good) and to identify the variables that most contribute to the differentiation. The conformation and carcass finishing scores vary from 1 to 5. This study used scores 2 and 3, considering that the evaluated animals ranged between these two respective scales. The database consisted of information from 122 uncastrated Santa Inês sheep submitted to the confinement regime, of which 24 variables related to the carcass of the animals were recorded. Data were submitted to the Mardia test to verify multivariate normality, followed by the nonparametric k-nearest neighbor (k-NN) test. The stepwise procedure selected a particular subset of variables, and the Mahalanobis Distance (D2) was used to assess the separation of groups (p-value ˂ 0.05). The variables with the highest discriminatory power for the carcass conformation scores were cold carcass weight (CCW), external carcass length (ECL), and neck (NEC), for carcass finishing were live weight at slaughter (LWS), ECL, and thoracic perimeter (TP). The multivariate discriminant analysis proved efficient in allocating the animals in their groups of origin.

Publisher

FapUNIFESP (SciELO)

Subject

General Agricultural and Biological Sciences

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