Journal of Animal Health and Production

Research Article
J. Anim. Health Prod. 9(4): 417-424
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Ali William Canaza-Cayo 1*, Rodrigo Reis Mota2, Fernando Amarilho-Silveira3, Darlene Ana Souza Duarte2, Jaime Araujo Cobuci3

1Faculty of Agrarian Sciences, Agronomic Engineering School, National University of Altiplano, (051) 599430 Av. Floral Nº 1153, Puno, Peru; 2CDCB - Council on Dairy Cattle Breeding 4201 Northview Drive, One Town Centre, Suite 302 Bowie, MD 20716, United States of America; 3Animal Science Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.

Abstract | We aimed to verify the relationship between body measurements (BM) and body weight as well as to investigate the prediction of live weight (LW) by using original BM and principal component scores of Corriedale ewes. BM of 100 ewes collected in the Illpa Experimental Centre of the National University of Altiplano in Peru were used. Data were recorded on LW, wither height (WH), rump height (RH), thoracic perimeter (TP), abdominal perimeter (AP), fore-shank length (FSL), fore-shank width (FSW), fore-shank perimeter (FSP), tail width (TW), tail perimeter (TPe), hip width (HW), loin width (LWi), shoulder width (SW), forelimb length (FL) and body length (BL). Pearson correlation and principal component analysis (PCA) were applied to LW and others BM. Additionally, regression equations of LW on BM and on its principal components (PC) were computed. Models were compared by using coefficients of multiple determinations (R2), Akaike information (AIC), Bayesian information (BIC) criteria and root mean squared error (RMSE). Correlations (r) for all BM with LW were positive and significant (r = 0.20 -0.78), except for FSW (r = 0.18). The PCA of BM and LW extracted four components explaining 68.7% of the total variance. The prediction LW model by using four PC had the lowest RMSE, AIC and BIC values as well as the highest R2 compared to models with smaller number of PC or based on original measurements. Our results suggested that this approach is a feasible alternative to predict LW.

Keywords | Correlation, Live weight, Regression equations, Statistical criteria, Ewes