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      PERFORMANCE ANALYSIS AND CARCASS CHARACTERISTICS OF SANTA INÊS SHEEP USING MULTIVARIATE TECHNICS Translated title: ANÁLISE DE DESEMPENHO E DE CARATERÍSTICAS DE CARCAÇA DE OVINOS SANTA INÊS UTILIZANDO TÉCNICAS MULTIVARIADAS

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          Abstract

          ABSTRACT 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.

          Translated abstract

          RESUMO O objetivo com este estudo foi aplicar técnicas de análise multivariada, sendo elas: Componentes Principais e Discriminante Canônica, em um conjunto de dados de desempenho e carcaça de ovinos da raça Santa Inês. Para identificar as relações e selecionar variáveis que melhor explicam a variação total dos dados, além de quantificar associação entre os recursos de desempenho e carcaça. Os componentes principais gerados foram eficientes em reduzir variação total acumulada de 25 variáveis originais correlacionadas para quatro combinações lineares, que, juntas, tem capacidade de explicar 80% da variação total dos dados. Os dois primeiros componentes principais juntos explicam aproximadamente 65% da variação total das variáveis analisadas. Nessas duas combinações lineares as características com maior coeficiente de ponderação foram PCF (Peso Carcaça Fria), PCQ (Peso Carcaça Quente), PCVZ (Peso Corpo Vazio), Peso Médio, Largura de Garupa, RCF (Rendimento Carcaça Fria) e RCQ (Rendimento Carcaça Quente). As variáveis selecionadas na análise discriminante canônica, em ordem de importância, foram CCHT (Consumo Carboidratos Totais), CNDT (Consumo Nutrientes Digestíveis Totais), CMS (Consumo de Matéria Seca), CCNF (Consumo Carboidrato Não Fibroso) e CFDN (Consumo Fibra Detergente Neutro). A primeira raiz canônica identificada mostra o coeficiente de correlação canônica de aproximadamente 0,82, mostrando alta associação entre as variáveis de desempenho. Os erros de classificação na análise discriminante foram inferiores a 5%, os quais ocorreram provavelmente pela semelhança entre indivíduos quanto as variáveis estudadas. As técnicas multivariadas foram adequadas e eficientes para simplificação do espaço amostral e classificação dos animais em seus grupos de origem.

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          Most cited references34

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          The Application of Electronic Computers to Factor Analysis

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            Direct estimation of genetic principal components: simplified analysis of complex phenotypes.

            Estimating the genetic and environmental variances for multivariate and function-valued phenotypes poses problems for estimation and interpretation. Even when the phenotype of interest has a large number of dimensions, most variation is typically associated with a small number of principal components (eigen-vectors or eigenfunctions). We propose an approach that directly estimates these leading principal components; these then give estimates for the covariance matrices (or functions). Direct estimation of the principal components reduces the number of parameters to be estimated, uses the data efficiently, and provides the basis for new estimation algorithms. We develop these concepts for both multivariate and function-valued phenotypes and illustrate their application in the restricted maximum-likelihood framework.
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              Multivariate Data Analysis

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                Author and article information

                Journal
                rcaat
                Revista Caatinga
                Rev. Caatinga
                Universidade Federal Rural do Semi-Árido (Mossoró, RN, Brazil )
                0100-316X
                1983-2125
                October 2020
                : 33
                : 4
                : 1150-1157
                Affiliations
                [2] Recife Pernambuco orgnameUniversidade Federal Rural de Pernambuco orgdiv1Animal Science Department Brazil tarlanmilanes.dz@ 123456gmail.com
                Article
                S1983-21252020000401150 S1983-2125(20)03300401150
                10.1590/1983-21252020v33n430rc
                a28d38fa-3a7c-4750-8287-fdcbd220890b

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 12 August 2020
                : 25 September 2019
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 34, Pages: 8
                Product

                SciELO Brazil

                Self URI: Full text available only in PDF format (EN)
                Categories
                Zootechnics

                Ovinocultura,Canonical discriminant analysis,Análise discriminante canônica,Sheep production,Principal components,Componentes principais

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