Morphological characteristics and linear body measurements of Doyogena sheep in Doyogena district of SNNPR, Ethiopia

Mengistie Taye, M. Yilma, Barbara Rischkowsky, Tadelle Dessie, Ally Okeyo Mwai, Getinet Mekuriaw, Aynalem Haile. (1/12/2016). Morphological characteristics and linear body measurements of Doyogena sheep in Doyogena district of SNNPR, Ethiopia. African Journal of Agricultural Research, 11(48), pp. 4873-4885.

Description of the physical characteristics of livestock breeds is very important for developing a breeding strategy in a particular production system. Doyogena sheep are among the potential breeds of Ethiopia reared in the mixed perennial crop and livestock production system of Southern Ethiopia. This research was conducted to characterise the morphological features of Doyogena sheep in an attempt to develop a breeding strategy that suits the production system of the area. A total of 512 sheep were characterized for different morphological features. Most (74.6%) of the sheep in the flock were females and 34.4% were old sheep of four and above dentition classes. Light red and red coat colours were abundant (71.5%) and 74% of the colour pattern was plain. Doyogena sheep are long fat-tailed (100%) and short haired (93.4%). The overall least square mean body weight was 31.64±0.43 kg and was affected by sex and age of sheep. The mean heart girth (74.08±0.39 cm), body length (58.84±0.30 cm), and height at rump (69.71±0.29 cm) were also affected by sex and dentition. Positive and significant correlations were obtained between body weight and other linear body measurements. The highest correlation coefficient was between body weight and heart girth. The positive and significant correlation of weight with linear body measurements indicate that linear body measurements can be used as a marker to estimate weight for different purposes. Different models can be used for different purposes. For simplicity, models with one variable can be used for marketing and by farmers. For breeding and selection purposes, since there is a need to be more precise, use of models involving more number of variables is important