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Use of multivariate analysis as a tool in the morphological characterization of the main indigenous bovine ecotypes in northeastern Algeria

Aissam Bousbia, , Yassine Gueroui, Kamel Hadded, Abdelkader Bouzaoui, Dounia Kiboub, George Symeon
Published: 26 July 2021

Abstract: Sustainability in livestock farming requires monitoring of autochthonous breeds which are well adapted to the local environment. The morphometric measurements seem to be the first approach which can provide useful information on the suitability of animal genetic resources for selection. In this work, thirteen morphometric variables were used for the phenotypic characterization of 130 adult autochthones cattle randomly selected from 30 local farms in Guelma. There were cases from four commonly accepted and traditional ecotypes: Guelmois, Cheurfa, Sétifien and Fawn. The results showed several and significant positive correlations between the different variables. Correlations were analyzed using Varimax orthogonal rotation PCA and three factors were extracted, which explain more than 75% of the total variation in the four ecotypes. Stepwise discriminant analysis showed that 6 of the 13 variables had discriminatory power to define the phenotypic profile of the ecotypes. Canonical discriminant analysis indicated that the Sétifien ecotype is separate from the other three ecotypes. Mahalanobis distances were significant between the different ecotypes except for the distance between the Guelmois and Fawn ecotypes. The cross-validation procedure assigned 91.42% of the Sétifien animals to their genetic group, while the percentages of animals assigned to the Cheurfa, Guelmois and Fawn ecotypes were 80.00%, 65.71% and 53.33% respectively. The multivariate approach has proven to be effective in differentiating the four ecotypes, with clear morphological differences from the Sétifien ecotype that may benefit from a genetic improvement program for more sustainable genetic resources preservation.
Keywords: Cattle / Morphometry / Livestock / Genetics / Principal component analysis / Sustainability science / Animal genetics / Population genetics

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