Morpho-biometric characterization of indigenous chicken ecotypes in north-western Ethiopia.

Morphological characterization of Animal Genetic Resources is the first step to documenting diversity and designing breed specific breeding programs. The current study characterized the morpho-biometric variation of indigenous chicken ecotypes prevailing in northwestern Ethiopia. A multi-stage purposive, stratified, and random sampling method was employed to select the study areas and chickens. A total of 1200 adult chickens were sampled and characterized for 12 qualitative and 11 quantitative traits. Univariate and multivariate data analysis methods were employed to analyze the data using SAS and R statistical software. Red plumage colour (33.2%), white and red earlobe colour (73.8%) and yellow shank colour (57.0%) were the most predominant colour trait categories. Sex, agro-ecology, location, and the interaction of sex and location had a highly significant (p<0.001) effect on all body measurements. Shank traits were found to have the highest discriminating power in both sexes. The overall classification rates for the female and male sample populations were 57.47% and 69.97%, respectively. The squared Mahalanobis distances between sites were significant (p<0.001) for both sexes. The longest distance was obtained between North Achefer and Banja (19.25) and between North Achefer and Dembecha (16.80) in female and male chickens, respectively. In female chickens, canonical variates 1 (CAN 1) and 2 (CAN 2) explained 82% of total variation and distinctly separated the sample populations of North Achefer and Jawi from others. In male chickens, 90% of the total variance is explained by CAN1, CAN2, and CAN3, which distinctly separate the sample populations of the North Achefer, Sinan, and Jawi, among others. Using cluster analysis, the indigenous chickens found in the study area could be classified into four ecotypes: ecotype 1 (Banja, Dembecha, and Aneded), ecotype 2 (North Achefer), ecotype 3 (Sinan), and ecotype 4 (Jawi).

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.