Advances in Animal and Veterinary Sciences

Research Article
Adv. Anim. Vet. Sci. 9(2): 189-193
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Mubashir Ali Rather1*, Imran Bashir1, Ambreen Hamdani2, Nusrat Nabi Khan2, Showkat A. Ahangar1, M. Nazki

1Department of Sheep Husbandry, Kashmir, 2Division of Animal Genetics and Breeding, SKUAST-Kashmir 190006.

Abstract | Data on 349 Kashmir Merino sheep managed at Government Sheep Breeding Farm Kralpathri, Kashmir for body length (BL), hight at withers (BH), heart girth (HG), paunch girth (PG), tail length (TL), ear length (EL), ear width (EW), face length (FL) and adult body weight (BW) were analysed with the Mixed Model Least Squares and Maximum Likelihood algorithms, PC-2 version computer programme (Harvey. 1990) with sex and year of birth as fixed effects. The effect of year of birth was significant on all traits under study whereas effect of sex was significant on BL, BH, HG, PG, TL and BW. Heritability estimates obtained were high for BH, HG and PG, moderate for BL, EL and TL and low for FL and EB in present study. The genetic and phonetic correlations ranged from to -0.61±0.14 to 0.95±0.34 and -0.12 to 0.74, respectively among different traits. The R2 values for different regression equations developed to predict adult body weight varied from 0.00 to 56.96%. the coefficient multiple determination (R2) values increased with the addition of traits (independent variables or linear body measurements) in the equation and the maximum R2 value of 56.96 % was obtained when all the variables were used together and poor R2 value of 0.00 % was obtained when TL alone was used as the independent variable. The study revealed that the height at withers is the best predictor for the estimation of body weight from body measurements in Kashmir Merino sheep under field conditions. This may be a useful finding in conditions when a weighing balance may not be available with farmer. This also indicates a high correlation between traits and therefore can be used for making selection decisions.

Keywords | Kashmir Merino, Measurements, Regression Analysis, Prediction