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肉鸡饲料的净能预测和能量效率。

Net energy prediction and energy efficiency of feed for broiler chickens.

机构信息

School of Environmental and Rural Science, University of New England, Armidale NSW 2351, Australia.

INRA, UMR1348 Pegase, F-35590 Saint Gilles, France.

出版信息

Poult Sci. 2019 Mar 1;98(3):1222-1234. doi: 10.3382/ps/pey442.

Abstract

Global consumption of chicken meat has increased at a faster rate than any other animal protein source, and thus refinements in energy formulation techniques for feed have continued to gain importance. Formulation of animal feed based on net energy (NE) has been implemented in ruminants and pigs but not in poultry. A closed-circuit respiratory calorimetry system was employed on 25- to 28-day-old broilers fed 19 diets formulated with varying nutrient composition to produce equations to predict NE and apparent metabolizable energy (AME) efficiency of feed for broiler chickens. Performance, energy and N balance, respiratory quotient, and energy utilization were measured in the birds. Linear regression analysis was performed to generate prediction equations for dietary energy content and AME efficiency. The NE content was positively related to AME and ether extract, but negatively to crude protein. The study generated equations that can accurately predict NE, and NE/AME using AME value and chemical composition of feeds. The NE prediction equations were further validated on a separate set of diets with high correlation (r = 0.99) and accuracy. The outcomes are an important step for the broiler industry to adapt to an NE system in place of AME systems for the formulation of broiler chicken feeds following robust validation experiments.

摘要

全球鸡肉的消费增长速度超过其他任何动物蛋白来源,因此饲料能量配方技术的改进一直在不断得到重视。反刍动物和猪的饲料配方基于净能 (NE),但家禽则没有。本研究使用闭路呼吸测热系统对 25-28 日龄肉鸡进行了 19 种不同营养成分配方的饲料喂养,以产生预测肉鸡饲料 NE 和表观代谢能(AME)效率的方程。在鸟类中测量了性能、能量和氮平衡、呼吸商和能量利用率。进行线性回归分析以生成用于预测日粮能量含量和 AME 效率的预测方程。NE 含量与 AME 和乙醚提取物呈正相关,但与粗蛋白呈负相关。该研究使用 AME 值和饲料的化学成分生成了可以准确预测 NE 和 NE/AME 的方程。通过使用高相关性(r=0.99)和准确性的单独饲料组对 NE 预测方程进行了进一步验证。这些结果是肉鸡行业的重要一步,它将适应基于净能的系统,而不是基于 AME 系统,为肉鸡饲料配方制定提供了有力的验证实验。

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