PERFORMANCE ANALYSIS AND CARCASS CHARACTERISTICS OF SANTA INÊS SHEEP USING MULTIVARIATE TECHNICS

• Autor(a) principal: Milanês, Tarlan Oliveira
• Data de Publicação: 2020
• Outros Autores: Soares, Luciana Felizardo Pereira, Ribeiro, Maria Norma, Carvalho, Francisco Fernando Ramos de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Caatinga
• Texto Completo: https://periodicos.ufersa.edu.br/caatinga/article/view/8946
• topic: Análise discriminante canônica. Componentes principais. Ovinocultura.; Canonical discriminant analysis. Principal components. Sheep production.

The objective of this study was to apply multivariate analysis techniques such as principal component and canonical discriminant analyses to a set of performance and carcass data of Santa Inês sheep, to identify the relationships and select the variables that best explain the total variation of the data, in addition to quantifying an association between performance and carcass characteristics. The main components generated were efficient in reducing a cumulative total variation of 25 original variables correlated to four linear combinations, which together explained 80% of the total variation of the data. The first two principal components together explained approximately 65% of the total variation of the variables analyzed. In the first two linear combinations, the characteristics with the highest factor loading coefficients were cold carcass weight (CCW), hot carcass weight (HCW), empty body weight (EBW), average weight (AW), croup width (CW), cold carcass yield (CCY), and hot carcass yield (HCY). The variables selected in the canonical discriminant analysis, in order of importance, were total carbohydrate intake (TCI), total digestible nitrogen intake (TDNI), dry matter intake (DMI), non-fibrous carbohydrate intake (NFI), and fiber detergent neutral intake (NDFI). The first canonical root shows a correlation coefficient of approximately 0.82, showing a high association between the performance variables. The classification errors in the discriminant analysis were less than 5%, which were probably due to the similarity between individuals for the studied traits. The multivariate techniques were adequate and efficient in simplifying the sample space and classifying the animals in their original groups.