Animal Bioscience (Apr 2023)

Prediction of total digestible nutrient and crude protein requirements according to daily weight gain, and behavioral measurements of Hanwoo heifers

  • Ju Ri Kim,
  • Jun Sik Woo,
  • Youl Chang Baek,
  • Sun Sik Jang,
  • Keun Kyu Park

DOI
https://doi.org/10.5713/ab.22.0387
Journal volume & issue
Vol. 36, no. 4
pp. 601 – 608

Abstract

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Objective This study was conducted to investigate the effects of energy and protein levels in the diet of Hanwoo heifers on growth response and animal behavior. Methods Forty heifers were randomly allocated into three experimental groups according to the target daily weight gain in 8 pens (T-0.2, 2 replications; T-0.4 and −0.6, 3 replications) based on similar body weight (BW) and age in months. The target average daily gain (ADG) was set at 0.2 (T-0.2), 0.4 (T-0.4), and 0.6 kg/d (T-0.6), and feed was based on National Institute of Animal Science (NIAS, 2017). In order to minimize hunger stress of T-0.2 and −0.4, the feeding ratio of rice straw was set to 55%, 50%, and 45% for T-0.2, −0.4 and T-0.6, respectively, so that the dry matter (DM) intake for all treatment groups was uniform but the energy and protein levels in the diet were adjusted differently. A total of 6 items (lying, standing, eating, rumination, walking and drinking) of animal behavior were analyzed. Results During the whole period of the experiment, the ADG of the T-0.2, −0.4 and −0.6 treatments were 0.48, 0.56, and 0.65 kg/d (p<0.05), respectively, showing higher gain than the predicted value, especially for the low target ADG group. Based on these results, regression equations for the total digestible nutrient (TDN) and crude protein (CP) requirements were derived. No behavioral differences were found according to the energy and protein levels in the diet because the DM intake was kept constant by adjusting the roughage and concentration ratio. However, eating time was longer (p<0.05) at T-0.2 than T-0.6 during the whole day. Conclusion Through this study, it was possible to derive regression equations for predicting TDN and CP requirements according to the target ADG and BW.

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