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Determination and prediction of digestible and metabolizable energy concentrations in byproduct feed ingredients fed to growing pigs

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. METHODS: Twelve barrows with an initial mean body weight o...

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Detalles Bibliográficos
Autores principales: Son, Ah Reum, Park, Chan Sol, Kim, Beob Gyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Asian-Australasian Association of Animal Production Societies (AAAP) and Korean Society of Animal Science and Technology (KSAST) 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5394841/
https://www.ncbi.nlm.nih.gov/pubmed/27857027
http://dx.doi.org/10.5713/ajas.16.0607
Descripción
Sumario: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. METHODS: 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:SBM 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 on the dry matter [DM] basis) in the test ingredients were: DE = 5,528–(156×ash)–(32.4×neutral detergent fiber [NDF]) with root mean square error = 232, R(2) = 0.958, and p<0.001; ME = 5,243–(153 ash)–(30.7×NDF) with root mean square error = 277, R(2) = 0.936, and p<0.001. All independent variables are in % on the DM basis. 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.