畜牧兽医学报 ›› 2019, Vol. 50 ›› Issue (10): 2032-2040.doi: 10.11843/j.issn.0366-6964.2019.10.009

• 营养与饲料 • 上一篇    下一篇

玉米DDGS常规营养成分和代谢能与鸭酶水解物总能相关性研究

魏杰, 谢明, 唐静, 吴永保, 张琪, 侯水生*   

  1. 中国农业科学院北京畜牧兽医研究所, 动物营养学国家重点实验室, 北京 100193
  • 收稿日期:2019-04-22 出版日期:2019-10-23 发布日期:2019-10-23
  • 通讯作者: 侯水生,主要从事水禽育种与营养研究,E-mail:houss@263.net
  • 作者简介:魏杰(1992-),男,河南人,硕士生,主要从事饲料营养价值评定研究,E-mail:weijiejawin@163.com
  • 基金资助:
    现代水禽产业技术体系建设专项资金(CARS-42)

The Correlation between General Nutritive Components, Metabolizable Energy and Enzyme Hydrolysate Gross Energy of Corn DDGS for Ducks

WEI Jie, XIE Ming, TANG Jing, WU Yongbao, ZHANG Qi, HOU Shuisheng*   

  1. State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
  • Received:2019-04-22 Online:2019-10-23 Published:2019-10-23

摘要: 旨在研究不同来源玉米DDGS的常规营养成分(GNC)和代谢能(ME)与鸭酶水解物总能(EHGE)的相关性。本研究测定了11种不同来源玉米DDGS的GNC和鸭EHGE,以EHGE结果为依据,选出EHGE相差较大、呈一定能量梯度的5个玉米DDGS,将其与玉米淀粉配制成粗蛋白质(CP)含量均为20%的5种混合饲粮。选取84只健康的成年雄性北京鸭,随机分为7组,每组12只鸭,其中2只备用。前5组分别饲喂以上CP含量均为20%的5种混合饲粮、第6组饲喂玉米淀粉以及第七组禁食作为内源能损失组,通过套算法测定不同玉米DDGS的表观代谢能(AME)、真代谢能(TME)和EHGE。结果显示:1)不同玉米DDGS的GNC存在较大变异,尤其是粗脂肪(EE)、粗灰分(Ash)和粗纤维(CF),变异系数均大于15%,仅总能、粗脂肪与EHGE分别呈显著和极显著正相关(r=0.651,P<0.05;r=0.769,P<0.01);2)混合饲粮EHGE与其AME和TME均呈极显著线性正相关(r=0.998,P<0.000 1;r=0.999,P<0.000 1),回归方程分别为AMEmix=0.780×EHGEmix+3.096(R2=0.997,P<0.000 1)、TMEmix=0.778×EHGEmix+4.556(R2=0.997,P<0.000 1);3)选取的5种玉米DDGS的EHGE分别为11.98、12.73、13.17、14.49、15.24 MJ·kg-1,AME分别为12.41、12.93、13.20、14.37、14.68 MJ·kg-1,TME分别13.77、14.27、14.57、15.73、16.05 MJ·kg-1,其EHGE与AME和TME均呈极显著线性正相关(r=0.995,P=0.000 4;r=0.996,P=0.000 3),回归方程分别为AMEDDGS=0.728×EHGEDDGS+3.677(R2=0.991,P=0.000 4)、TMEDDGS=0.732×EHGEDDGS+4.980(R2=0.992,P=0.000 3)。不同来源玉米DDGS的GNC变异较大,EE、Ash、CF变异尤为显著;玉米DDGS和混合饲粮的鸭EHGE与其ME呈显著线性正相关,可采用线性回归模型利用鸭EHGE估测ME。

Abstract: This study was conducted to investigate the correlation between general nutritive components(GNC), metabolizable energy(ME) and enzyme hydrolysate gross energy(EHGE) of different corn DDGS for ducks. The GNC and EHGE of 11 kinds of corn DDGS with different origins were determined. Based on the EHGE results, five corn DDGS with different EHGE and the energy gradient were selected, and they were combined with corn starch to formulate 5 mixed diets with crude protein(CP) 20%. Eighty-four healthy adult male Pekin ducks were randomly divided into 7 groups, 12 ducks for each group and 2 ducks were reserved. Ducks in the first 5 groups were fed the 5 mixed diets with CP 20%, respectively, ducks in the sixth group was fed corn starch, and ducks in the seventh group was kept starving to determine endogenous energy in order to measure the apparent metabolizable energy(AME), true metabolizable energy(TME) and EHGE of different corn DDGS. The results showed that:1) There was remarkable variation in GNC among different corn DDGS, especially ether extract(EE), ash(Ash) and crude fiber(CF), and their coefficient of variation(CV) were all greater than 15%. Furthermore, the significantly positive correlation was observed between gross energy, EE and EHGE(r=0.651, P<0.05; r=0.769, P<0.01); 2) There was significantly linear positive correlation between EHGE and AME or TME of 5 mixed diets(r=0.998, P<0.000 1; r=0.999, P<0.000 1), and the regression equations were AMEmix=0.780×EHGEmix+3.096(R2=0.997, P<0.000 1), TMEmix=0.778×EHGEmix+4.556(R2=0.997, P<0.000 1); 3) The EHGE of 5 corn DDGS were 11.98, 12.73, 13.17, 14.49, 15.24 MJ·kg-1, AME were 12.41, 12.93, 13.20, 14.37, 14.68 MJ·kg-1,and TME were 13.77, 14.27, 14.57, 15.73,16.05 MJ·kg-1, respectively. And there was significantly linear positive correlation between EHGE and AME, TME(r=0.995,P=0.000 4;r=0.996,P=0.000 3), and the regression equations were AMEDDGS=0.728×EHGEDDGS+3.677(R2=0.991, P=0.000 4), TMEDDGS=0.732×EHGEDDGS+4.980(R2=0.992, P=0.000 3). There is large variation in GNC among corn DDGS with different origins, especially EE, Ash and CF; There is significantly linear positive correlation between EHGE and ME of corn DDGS and mixed diets for ducks, and ME can be predicted based on EHGE by linear regression model.

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