Quantifying morphometric and adaptive characteristics of indigenous cattle genetic resources in northwest Ethiopia

Ethiopia is the gateway of livestock genetic resources to Africa and has a wide range of altitude. It is endowed with huge diverse cattle genetic resources. The aim of this research was to determine the morphometric and potentioally adaptive characteristics of cattle populations. Multi-stage purposive and random sampling methods were employed to select the study areas, households and animals. A total of 1200 adult cattle were sampled and characterized for 14 qualitative and eight morphometric variables. The comparison of marginal means, chi-square tests, canonical discriminant analysis, and clustering analysis were employed using SAS and SPSS statistical software. The sex of the animal, location and agro-ecology were fitted as fixed effects in the model and had highly significant (p<0.001) effects for most body measurements. The chi-square test values of all categorical variables were significantly different (p<0.001) and potentioally adaptive characteristics such as coat colour type, navel flap, and tail length had higher association (> 0.45) values. White with red, light red, black and dark red were the most predominant coat colour types of cattle. The maximum hit rates were recorded in Enebsie and Sinan cattle. From five extracted canonical variate, (can1 and can2) accounted 75.4% and 78.8% in the female and male cattle populations, respectively. The canonical class has separated cattle populations of Sinan from Banja at can1 and Mecha from Sinan populations at can2. The square Mahalanobis distances between sites were significant (p<0.001) and the largest distance was found between Banja and Sinan locations. Cluster analysis result classified the study populations into four major cattle groups. The cumulative analysis results showed that the cattle populations of the study area can be categorized into four breed types as Jawi Sanga, Gojjam Zenga, Banja cattle, and Sinan cattle. However, this morphology based grouping need to be confirmed by molecular data.