Prediction of slaughter weight with body components and liner body measurement of cattle at Haramaya University abattoir, Ethiopia.

The aim of the study was to determine the relationship between slaughter weight (SW) with body components and liner body measurements and investigate the coefficient of correlation between slaughter weight with body component and liner body measurements to select the best regression equation. Data on liner body measurements (height at wither and at hips, heart girth, body length, height and width of hump, height at fall and hind legs, body sheath height, height at hooks, barrel circumference, width of face, length of face and tail circumference) and slaughter weight of body components (Hot Carcass Weight (HCW), Empty Body Weight (ESW), Internal Offal (IO) and External Offal (EO)) were collected from 62 Hararghe cattle at Haramaya University abattoir. ESW was calculated as SW with less gut contents. Each carcass was split into halves and weighed to estimate HCW. Simple (linear, quadratic) and multiple (linear, quadratic) regression models were used to explore the relationships between SW, and four linear body measurements. Data were analyzed using the procedure of GLM of SAS, 2018, and JMP version 16 of the SAS software. The result of the study revealed that the average SW, HCW, and dressing percentage of Hararghe cattle were 264 ± 3.37 kg, 113 ± 2.16 kg, and 42.87 ± 0.66 %, respectively. There was a significant (P < 0.01) and low to high correlation were observed between SW and liner body measurements with a correlation coefficient (r) ranging from 0.32 to 0.98. Moreover, the SW had significantly (P < 0.001) and positively correlated with HCW (r = 0.60), EBW (r = 0.96), IO (r = 0.75) and EO (r = 0.85). Of all linear body measurements, the heart girth (HG) gave the highest correlation coefficient with SW and fitted best Hararge Cattle SW predicting model in quadratic regression. Addition of a range of other live animal variables (Width of hook (WH), barrel circumference (BC) and Body length (BL)) to heart girth influenced the relationships between morphometric measurements and SW. However, their inclusion in the regression equations did not improve the predictive power of the models. Likewise, SW significantly (p < 0.001) predicted the body components. The accuracy of prediction (R) for body components (Hump, Bone, HCW, EBW, IO and EO) with the SW were 22 %,46 %,34 %,92 %,72 % and 55 %, respectively. Those equations were evaluated through internal validation, by developing a range of similar new equations from two thirds of the present data and then validating these new equations with the remaining one third of data. The validation revealed higher prediction accuracy for SW. In conclusion, the measurement of HG only proved sufficient for the estimation of SW in quadratic regression model. Furthermore, it could be easily and accurately measured by everyone. Hence, this model could be used by farmers and researchers to efficiently predict and monitor SW, and optimize productivity of the Hararge cattle herds in smallholder farming systems. To achieve wider application of this model, it would be desirable to validate the equation with large population, different breed and age compositions which have been more accurately determined under different management systems.