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Asian-Australas J Anim Sci > Accepted
doi: https://doi.org/10.5713/ajas.16.0607    [Accepted]
Determination and prediction of digestible and metabolizable energy concentrations in byproduct feed ingredients fed to growing pigs
Ah Reum Son1,2, Chan Sol Park1, Beob Gyun Kim1,2,*
1Department of Animal Science and Technology, Konkuk University, Seoul, Korea
2Monogastric Animal Feed Research Institute, Konkuk University, Seoul, Korea
* Corresponding Author: Beob Gyun Kim ,Tel: +82-2-2049-6255, Fax: +82-2-455-1044, Email: beobgyun@naver.com
Received: August 12, 2016;  Revised: September 28, 2016.  Accepted: November 14, 2016.  Published online November 17, 2016.

ABSTRACT
Objective:
An experiment was conducted to determine digestible energy (DE) and metabolizable energy (ME) of different byproduct feed ingredients fed to growing pigs, and to generate prediction equations for the DE and ME in feed ingredients.
Method:
Twelve barrows with an initial mean body weight of 31.8 kg were individually housed in metabolism crates that were equipped with a feeder and a nipple drinker. A 12 × 10 incomplete Latin square design was employed with 12 dietary treatments, 10 periods, and 12 animals. A basal diet was prepared to mainly contain the corn and soybean meal (SBM). Eleven additional diets were formulated to contain 30% of each test ingredient. All diets contained the same proportion of corn:soybean meal ratio at 4.14:1. The difference procedure was used to calculate the DE and ME in experimental ingredients. The in vitro dry matter disappearance for each test ingredient was determined.
Results:
The DE and ME values in the SBM sources were greater (p < 0.05) than those in other ingredients except high-protein distillers dried grains. However, DE and ME values in tapioca distillers dried grains (TDDG) were the lowest (p < 0.05). The most suitable regression equations for the DE and ME concentrations (kcal/kg) in the test ingredients were: DE = 5,528 – (156 × ash) – (32.4 × neutral detergent fiber, NDF) with root mean square error = 232, R2 = 0.958, and p < 0.001; ME = 5,243 – (153 × ash) – (30.7 × NDF) with root mean square error = 277, R2 = 0.936, and p < 0.001. All independent variables are in %.
Conclusion:
The energy concentrations were greater in the SBM sources and were the least in the TDDG. The ash and NDF concentrations can be used to estimate the energy concentrations in the byproducts from oil-extraction and distillation processes.
Keywords: Feedstuff; Prediction Models; Protein Supplements; Swine
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