ISSN 2285-5750, ISSN CD-ROM 2285-5769, ISSN-L 2285-5750, ISSN Online: 2393 – 2260
 

PREDICTION OF CARCASS WEIGHT OF HOLSTEIN AND BROWN SWISS CATTLE GROWN IN A 12-MONTHS INTENSIVE BEEF PRODUCTION SYSTEM BY USING REAL-TIME CARCASS MEASUREMENTS

Published in Scientific Papers. Series D. Animal Science, Vol. LX
Written by Yalçın BOZKURT, Stepan VARBAN, Nazire MIKAIL, Cihan DOGAN

In this study, it was aimed to evaluate the use of some morphometric carcass measurements to predict carcass weight of Holstein and Brown Swiss cattle grown in a 12-months intensive beef production system. Associations between carcass weights (CW) and some carcass measurements such as carcass heart girth (CHG), carcass length (CL) and carcass depth (CD) were examined for prediction ability, using the data with 134 observations for each traits. The linear, quadratic and cubic regression models were performed to predict CW for both breeds and since there were no statistically significant (P >0.05) differences in carcass measurements between breeds. The data of these breeds were combined and found that CL and CHG would be the best possible traits in predicting CW (R2 =57.9 and 50.7% respectively) among the other measurements. The highest R2 values were obtained from both the equation contained all carcass traits (R2 =65.5%) and the equation that included only CHG and CL (R2 =65.4%). All type of regressions showed that addition of quadratic and cubic terms contributed little benefit in predicting CW. Therefore, all linear terms of all carcass measurements were considered for analysis and they were significant (P ˂0.05) and the R2 value for other carcass measurement CD was approximately 20.8%. It can be concluded that in management situations where CW cannot be measured it can be predicted accurately by measuring CL and CHG alone and different models may be needed to predict CW in different feeding and environmental conditions and for other breeds.

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© 2019 SCIENTIFIC PAPERS. SERIES D. ANIMAL SCIENCE. To be cited: SCIENTIFIC PAPERS. SERIES D. ANIMAL SCIENCE.

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