Predicting feed intake and liveweight gain of Ongole (Bos indicus) cattle in IndonesiaD. E. Mayberry A E , T. M. Syahniar B , R. Antari B C , G. P. Ningrum B ,
A CSIRO Ecosystem Sciences, 41 Boggo Road, Dutton Park, Qld 4102, Australia.
B Beef Cattle Research Institute, Grati, East Java 67184, Indonesia.
C School of Agriculture and Food Sciences, The University of Queensland, Gatton Campus, Warrego Hwy, Lawes, Qld 4343, Australia.
D Department of Animal Sciences, Tadulako University, Palu, Central Sulawesi 94118, Indonesia.
E Corresponding author. Email: email@example.com
Animal Production Science 54(12) 2089-2096 https://doi.org/10.1071/AN14538
Submitted: 1 May 2014 Accepted: 21 July 2014 Published: 3 September 2014
We evaluated the precision and accuracy of equations from the Australian Ruminant Feeding Standards (ARFS) and the Large Ruminant Nutrition System (LRNS) in predicting the performance of Ongole (Bos indicus) cattle under Indonesian conditions. A database was constructed using information from 121 cattle in five different pen-feeding experiments. Cattle included mature cows and growing bulls, and they were fed a range of diets commonly used by Indonesian farmers. We compared observed and predicted dry matter intake and daily liveweight gain. Model predictions were evaluated for precision and accuracy using mean bias, mean square prediction error and regression of observed against predicted values. Across all experiments, the LRNS provided the better estimates of intake and growth. While both models included animal age, sex, weight and body condition score, the LRNS provided better estimates of metabolisable energy requirements for maintenance of liveweight, feed quality and efficiency of energy utilisation. The LRNS model also better accounted for environmental conditions by including correction factors for minimum night temperature and relative humidity, in addition to average daily temperatures. Based on our results, the LRNS model appears suitable for use in Indonesian beef-production systems.
Additional keywords: metabolisable energy, Nellore, Pennisetum purpureum, rice bran, rice straw, tree legumes.
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