The accuracy of body weight estimation in Fat Tailed Sheep based on linear body measurements and tail circumference

Authors

  • sucik maylinda Animal Productin Department, Faculty of Animal Science, University of Brawijaya, Malang Jalan Veteran, Malang (65145) Indonesia
  • Woro Busono Animal Productin Department, Faculty of Animal Science, University of Brawijaya, Malang Jalan Veteran, Malang (65145) Indonesia

DOI:

https://doi.org/10.21776/ub.jiip.2019.029.02.11

Keywords:

fat tailed sheep, body weight estimation, linear measurements, multiple regression

Abstract

The aim of the research was to determine the accuracy of the estimation of DEG body weight based on linear measurements (chest circumference/ CC, tail circumference/ TC, body length/ BL, and body height/ BH). The research is expected to produce a model of DEG body weight estimation based on linear measurements (CC, BL, BH and tail circumference/ TC). Material  used in this  research was male fat tailed sheep which has different age seen from the change of permanent teeth  PI0, PI1, PI2. The total number of animal used in this research was 60 with details of PI0 = 30, PI1 = 24 and PI2 = 6. Data were analysed by Linear and Multiple Regression Analysis between Body weight and BCS, Linear measurements (CC, BL, BH and TC), using Minitab software version 13. 1. All variables of linear measurements of the body have a strong relationship with body weight, the strongest relationship is body weight with chest circumference, and tail circumference. Estimation of body weight using multiple regression functions (BW = -18.7 + 0.408BH + 0.172BL + 0.274CC + 0.178TC) has an accuracy higher than the estimated body weight using the Schoorl formula.

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Published

2019-08-10

How to Cite

maylinda, sucik, & Busono, W. (2019). The accuracy of body weight estimation in Fat Tailed Sheep based on linear body measurements and tail circumference. Jurnal Ilmu-Ilmu Peternakan (Indonesian Journal of Animal Science), 29(2), 193–199. https://doi.org/10.21776/ub.jiip.2019.029.02.11

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