动物营养学报 >

2015 , Vol. 27 >Issue 5: 1450 - 1460

DOI: https://doi.org/10.3969/j.issn.1006-267x.2015.05.015

实验方法 Experimental Methods

猪饲料有效能值预测模型的构建

展开
  • 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;
    2. 延庆县动物卫生监督管理局, 北京 102100
潘晓花(1988—),女,河南商丘人,博士研究生,从事反刍动物营养研究。E-mail:panxiaohuacaas@163.com

收稿日期: 2014-11-12

  网络出版日期: 2015-05-13

基金资助

基本业务费课题(2013ywf-zd-3);动物科学与动物医学数据共享平台课题;国家科技支撑计划课题(2014BAD08B05)

收起

Construction of Prediction Models of Feedstuffs Effective Energy Values for Swine

Expand
  • 1. Institute of Animal Science, Chinese Academy of Agricultural Sciences, State Key Laboratory of Animal Nutrition, Beijing 100193, China;
    2. Yanqing Animal Health Supervision and Administration, Beijing 102100, China

Received date: 2014-11-12

  Online published: 2015-05-13

Fold

摘要

为探索饲料常规成分及碳水化合物组分与饲料有效能值之间的关系方程,本研究以NRC第11版《猪营养需要量》中发布的122套饲料营养成分表为基础,将饲料中11种基础成分[6项常规成分:干物质、粗蛋白质(CP)、粗纤维(CF)、粗脂肪(EE)、酸性醚提取物、粗灰分(ash);5项碳水化合物组分:淀粉(ST)、中性洗涤纤维(NDF)、酸性洗涤纤维(ADF)、半纤维素、酸性洗涤木质素]作为自变量,将饲料中的消化能(DE)、代谢能(ME)及净能(NE)作为因变量,采用SAS软件中的REG过程,分别建立不同性质饲料、自变量的不同组合与DE、ME及NE之间的回归关系方程,并以相关系数(R2)及变异系数(CV)作为评价回归模型的优劣。研究表明,有效能值与CP、ST及纤维类指标显著或极显著相关(P<0.05或P<0.01)。将所有饲料作为研究对象时,饲料的DE、ME及NE与上述11种基础成分之间建立的普适性回归模型预测效果较差。当将14种玉米及其加工产品形成子集时,建立饲料基础营养成分与DE、ME及NE的关系方程分别为7、6和7套(P<0.05),且3组回归模型R2分别为0.632 8~0.772 3、0.646 9~0.684 9和0.670 5~0.822 1,CV分别为6.61%~8.40%、6.58%~7.34%和6.21%~8.27%;当将13种大豆及其加工产品形成子集时,共建立饲料基础成分与DE、ME关系方程分别有3和4套,回归模型R2分别为0.907 1~0.926 9、0.890 7~0.922 3,CV分别为5.40%~6.09%、5.79%~6.78%,NE与基础营养成分指标之间无法建立具有营养学意义的有效回归方程。对于同类饲料中具有相同自变量组合的DE及ME预测模型而言,两者之间的差异主要是自变量CP的系数上,且CP部分对ME的正效应低于DE,这保证模型预测的ME低于DE。同时选用本研究构建的适宜模型,补充了NRC第11版成分表中第97(去皮大豆粕,低寡糖,浸提)、101(全脂大豆,高蛋白质)及102号(全脂大豆,低寡糖)饲料的DE值分别为15.99、17.35、17.27 MJ/kg,ME值分别为14.53、16.15和16.14 MJ/kg。综上,以NRC(2012)饲料营养成分表为基础,建立的普适性有效能值回归模型预测效果较差。按照玉米类和大豆类进行分类,可建立DE、ME和NE与饲料化学成分之间的多元回归方程,其中最优的预测因子为CP、EE、ST、ash、NDF、ADF。具有相同自变量的同类饲料DE和ME预测模型之间的差异是CP系数,CP影响DE转化为ME的效率。

关键词： NRC饲料成分表; 消化能; 代谢能; 净能; 预测模型

本文引用格式

潘晓花, 杨亮, 庞之洪, 王建芬, 熊本海 . 猪饲料有效能值预测模型的构建[J]. 动物营养学报, 2015 , 27(5) : 1450 -1460 . DOI: 10.3969/j.issn.1006-267x.2015.05.015

Abstract

This study was conducted to establish the relation equations between feedstuffs' chemical compositions, carbohydrate fractions and effective energy value. Based on intensively analysis of the indexes changed in the NRC 11th ed. swine feedstuff composition table, and the table was selected as data source to predict DE, ME and NE indirectly by basic chemical compositions and their different combinations, which were 6 kinds proximate nutrients [dry metter (DM), crude protein (CP), crude fiber (CF), ether extract (EE), acid hydrolysis ether extract (AEE), ash] and 5 kinds of carbohydrate (CHO) composition [starch (ST), neutral detergent fiber (NDF), acid detergent fiber (ADF), hemicellulose (HC), acid detergent lignin (ADL)] were considered as independent variables, while digestible energy (DE), metabolizable energy (ME) and net energy (NE) were treated as dependent variables, using REG process of SAS to set up these relationship equations for different feedstuff groups and different independent variable combinations. The correlation coefficient (R2) and coefficient of variation (CV) were used to evaluate the fitness of models. The results showed as follows: when considering all feedstuff as a group, universally applicable prediction models couldn't be established between DE, ME, NE and feedstuffs' chemical compositions. Further research found that when considering corn and it's by-products as a subset, 7, 6 and 7 models were built for DE, ME and NE, respectively, and their R2 were 0.632 8 to 0.772 3 (CV was 6.61% to 8.40%), 0.646 9 to 0.684 9 (CV was 6.91% to 7.34%) and 0.670 5 to 0.822 1 (CV was 6.22% to 8.28%). Three and four models were built for DE and ME respectively when considering soybean and it's by-products as a subset, and their R2 were 0.907 1 to 0.926 9 (CV was 5.40% to 6.09%), 0.890 7 to 0.922 3 (CV was 5.79% to 6.78%), no linear regression existed between NE and basic chemical composition. The R2 and CV values above indicated that soybean group's DE and ME prediction models have higher goodness of fit than corn subsets, and for the same kind feedstuffs, the difference between DE and ME models with the same independent variables was mainly the coefficient of CP, and CP shows a greater impact on DE than ME, which guarantee ME predicted value is lower than DE. NE models showed that the main factor influenced NE values were starch. Also we complement the DE and ME values of No. 97 (soybean meal, low oligosaccharide, dehulled, solvent extracted), No. 101 (soybeans, high protein, full fat) and No. 102 (soybeans, low oligosaccharide, full fat) feedstuffs in 11th version of NRC by applying the appropriate models in this study, and their DE and ME value were 15.99, 17.35, 17.27 MJ/kg and 14.53, 16.15, 16.14 MJ/kg, respectively. In conclusion, universally applicable prediction models for effective energy values cann't obtain based on feedstuffs' ingredients composition in NRC (2012), but DE, ME and NE prediction models for corn and soybeans are established by appropriate classification of feedstuffs and different independent variables combinations. The most important factors in predicting effective energy are CP, EE, ST, ash, NDF and ADF. For the same feedstuff, the differences between DE and ME model are the coefficients of CP, and CP played an important role in the conversion of DE to ME.

Key words： NRC feedstuff composition table; digestible energy; metabolizable energy; net energy; prediction model

参考文献

[1] NRC.Nutrient requirements of swine[M].11th ed.Washington,D.C.:National Academy Press,2012.

[2] NRC.Nutrient requirements of swine[M].10th ed.Washington,D.C.:National Academy Press,1998.

[3] EWAN R C.Predicting the energy utilization of diets and feed ingredients by pigs[C]//VAN DER HONING Y,CLOSE W H.Energy Metabolism of Farm Animals[P].Pudoc,Wageningen,the Netherlands: EAAP Publication,1989,43:215-218.

[4] NOBLET J,PEREZ J M.Prediction of digestibility of nutrients and energy values of pig diets from chemical analysis [J].Journal of Animal Science,1993,71(12):3389-3398.

[5] SAUVANT D,PEREZ J -M,TRAN G.Tables of Composition and nutritional value of feed materials[M].2nd ed.Wageninge:Wageningen Academic Publishers,2004.

[6] KIM J C,SIMMINS P H,MULLAN B P,et al.The digestible energy value of wheat for pigs,with special reference to the post-weaned animal [J].Animal Feed Science and Technology,2005,122(3/4):257-287.  

[7] WAN H F,CHEN W,QI Z L,et al.Prediction of true metabolizable energy from chemical composition of wheat milling by-products for ducks[J].Poultry Science,2009,88(1):92-97.  

[8] HUANG Q,PIAO X S,REN P,et al.Prediction of digestible and metabolizable energy content and standardized ileal amino acid digestibility in wheat shorts and red dog for growing pigs [J].Asian-Australasian Journal of Animal Sciences,2012,25(12):1748-1758.  

[9] HUANG Q,SHI C X,SU Y B,et al.Prediction of the digestible and metabolizable energy content of wheat milling by-products for growing pigs from chemical composition[J].Animal Feed Science and Technology,2014,196:107-116.  

[10] NORUSIS M.SPSS 16.0 Statistical Procedures Companion [M].Prentice Hall Press,2008.

[11] SAS Institute.The SAS System for Windows,Software,Release 8.01[Z].Cary,NC:SAS Inst.Inc.,2000.

[12] 许振英.生长肥育猪的营养[J].养猪,1991(4):37-43.

[13] DIGGS B G,BEEKER D E,TERRILL S W,et al.The energy value of various feedstuffs for the young pig [J].Journal of Animal Science,1959,18:1482-1486.

[14] DIGGS B G,BEEKER D E,JENSEN A H,et al.Energy value of various feeds for the young pig [J].Journal of Animal Science,1965,24(2):555-558.

[15] AFZ,CEREOPA.Economic and technical feed data[DB/OL][2013-11-04].http://www.feedbase.com/index.php?Lang=E.

[16] 中国农业科学院北京畜牧兽医研究所,中国饲料数据库情报网中心.饲料数据库[DB/OL][2014-09-07].http://www.chinafeeddata.org.cn/.

[17] 林建云,宋春生.膨化饲料中粗脂肪总量的测定[J].台湾海峡,2009(增刊):159-163.

[18] MORGAN D J,COLE D J A,LEWI S D.Energy values in pig nutrition.Ⅱ the prediction of energy values from dietary chemical analysis [J].The Journal of Agricultural Science,1975,84(1):19-27.

[19] 何英.糠麸糟渣、饼粕类饲料猪有效能预测模型的研究[D].硕士学位论文.雅安:四川农业大学,2004.

[20] WISEMAN I,COLE D J A.Predicting the energy content of pig feeds[M]//HARESIGN W.Recent Advances in Animal Nutrition-1983.London,1983:59-70.

[21] MAY R M,BELL J M.Digestible and metabolizable energy values of some feeds for the growing pig[J].Canadian Journal of Animal Science,1971,51(2):271-278.  

[22] NOBLET J,FORTUNE H,DUBOIS S,et al.Nouvelles bases d'estimations des teneurs energie digestible,métabolisable et nette des aliments pour le porc[M].Paris:Institut National de La Recherche Agrimonigue,1989.

[23] NOBLET J,FORTUNE H,SHI X S,et al.Prediction of net energy values for growing pigs [J].Journal of Animal Science,1994,72(2):344-354.

[24] BLOK M C.Development of a new NE formula by CVB using the database by INRA[C]//Presystems Workshop,Net Energy Systems for Growing and Fattening Pigs.Vejle,Denmark,2006.

[25] 孙献忠,熊本海.用饲料化学成分预测猪饲料能值的研究进展[J].中国畜牧兽医,2006,33(11):19-23.
Options
文章导航

/

〈 〉

    联系我们

  • 地址：北京市海淀区圆明园西路2号
    　　　中国农业大学动科动医大楼153室
  • 邮编：100193
  • 电话：010－62817823
  • 网址：http://www.chinajan.com
  • E-mail：cjan@chinajan.com
京公网安备 11010802041926号    京ICP备17050989号-2