Chinese Journal of Animal Nutrition >

2025 , Vol. 37 >Issue 6: 3816 - 3827

DOI: https://doi.org/10.12418/CJAN2025.313

RESEARCH PAPER

Analysis of Nutritional Components of Distiller’s Grains from Different Regions and Establishment of Metabolizable Energy Prediction Model for Meat Ducks

  • LU Zhentao , 1, 2 ,
  • WANG Ning 1 ,
  • WU Zhanyue 1 ,
  • GUO Yanhong 1 ,
  • REN Wenwen 1 ,
  • WANG Qimeng 1 ,
  • XU Tong 1 ,
  • WANG Huihui 1, 2 ,
  • FENG Yulong 3 ,
  • WU Yongbao 1 ,
  • CAO Junting 1 ,
  • WU Xuezhuang , 2, * ,
  • WEN Zhiguo , 1, *
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  • 1 Key Laboratory of Feed Biotechnology of Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • 2 College of Animal Science, Anhui University of Science and Technology, Bengbu 233100, China
  • 3 Guizhou Institute of Animal Husbandry and Veterinary Science, Guiyang 550000, China
* WEN Zhiguo, professor, E-mail: ;
WU Xuezhuang, professor, E-mail:

Received date: 2024-12-04

  Online published: 2025-06-12

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Abstract

The aim of this experiment was to evaluate the effects of distiller’s grains from different regions on metabolizable energy and the apparent utilization ratios of nutrients in meat ducks and establish a predictive model of metabolizable energy in meat ducks based on the nutritional component content of distiller’s grains. Distiller’s grains were collected from Sichuan, Henan, Jiangsu, Guizhou, Anhui and other places, and the nutritional component contents were determined by national standard methods. Twenty-four 48-day-old Z-type male Peking ducks with uniform body weight [(3.00±0.02) kg] were selected and randomly divided into 2 groups (3 replicates in each group and 4 ducks in each replicate), and each group was allocated with 1 raw material of distiller’s grains. The 8 kinds of distiller’s grains were divided into 4 batches for metabolic experiment. The metabolic experiment was conducted by emptying and force-feeding method, and the force-feeding amount of the meat ducks was 50 g. The results showed as follows: 1) the average contents of dry matter (DM), gross energy (GE), crude protein (CP), ether extract (EE), crude ash (Ash), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), calcium (Ca), total phosphorus (TP) and nitrogen free extract (NFE) in 8 kinds of distiller’s grains from different regions were 93.74%, 17.90 MJ/kg, 20.54%, 5.39%, 11.25%, 19.35%, 62.75%, 45.39%, 0.14%, 0.51%, 37.21%, respectively; the nutritional component contents of distiller’s grains from different regions varied greatly, and the coefficients of variation of Ca, EE, Ash, CF and CP contents all exceeded 20%. 2) The variation coefficient of DM apparent utilization ratio for distiller’s grain in meat ducks was 31.77%, the variation coefficient of CP apparent utilization ratio was 43.71%, and the variation coefficient of GE apparent utilization ratio was 22.31%. 3) The variation coefficient of apparent metabolizable energy (AME) and true metabolizable energy (TME) were 26.49% and 19.28%, respectively, with great difference. 4) The multiple regression prediction equation for metabolic energy of meat ducks was established according to the nutritional component contents of distiller’s grains: AME=-0.097NDF-0.211Ash+0.184EE+14.021 (R2=0.876 7, P=0.027 3); TME=-0.137NDF-0.179Ash+0.091EE+18.287 (R2=0.915 7, P=0.013 0). In conclusion, the nutritional component contents of distiller’s grains from different regions are greatly different, and the metabolizable energy and apparent utilization ratios of nutrients for distiller’s grains in meat ducks are significantly different. The nutritional component contents of distiller’s grains are correlated with the metabolizable energy of meat ducks, and the prediction model of metabolizable energy for meat ducks based on the nutritional component contents of distiller’s grains has certain reference value.

Key words： distiller’s grains; meat ducks; nutritional components; metabolic energy; prediction model

Cite this article

LU Zhentao , WANG Ning , WU Zhanyue , GUO Yanhong , REN Wenwen , WANG Qimeng , XU Tong , WANG Huihui , FENG Yulong , WU Yongbao , CAO Junting , WU Xuezhuang , WEN Zhiguo . Analysis of Nutritional Components of Distiller’s Grains from Different Regions and Establishment of Metabolizable Energy Prediction Model for Meat Ducks[J]. Chinese Journal of Animal Nutrition, 2025 , 37(6) : 3816 -3827 . DOI: 10.12418/CJAN2025.313

在畜禽生产中,饲料成本占生产总成本的60%,其中能量饲料成本又占饲料总成本的70%[1]。由于家禽特殊的生理结构,家禽饲料能量利用研究一般采用代谢能(metabolizable energy,ME)。白酒糟(distiller’s grains,DGS)是白酒生产过程中产生的工业副产物,每年产生大约1亿t DGS[2-3],且由于传统蒸馏技术和固态发酵的局限性和低效率,DGS中留含有大量的蛋白质、淀粉和膳食纤维等成分[4],这些特性使得DGS具有开发为家禽饲料原料的潜力。目前通过化学成分预测ME的研究主要集中在乙醇生产的工业副产物玉米干酒糟及其可溶物(distiller’s dried grains,DDGS)上,如Cromwell等[5]评估了来自9个不同来源的DDGS的物理、化学和营养特性,发现DDGS样品之间的营养成分含量存在相当大的差异,这些营养成分含量的变异系数(coefficient of variation,CV)在5.3%~27.7%。Spiehs等[6]评估了来自不同工厂的共118个DDGS样品中的营养成分含量和变异性,发现粗蛋白质(crude protein,CP)的CV为6.4%,酸性洗涤纤维(acid detergent fiber,ADF)的CV为28.4%。Anderson等[7]将来自不同工厂的20种DDGS饲喂育肥猪,以确定其消化能(digestible energy,DE),并根据化学成分预测DE,得到最佳拟合方程为DE(kcal/kg DM,1 kcal=4.19 kJ)=1.39总能(gross energy,GE)-20.70中性洗涤纤维(neutral detergent fiber,NDF)-40.30粗脂肪(ether extract,EE)-2 161[决定系数(R2)=0.77]。DDGS与DGS在原料、生产工艺、加工工艺中存在较大差异[8],因此目前研究得到的DDGS预测ME的回归方程不能套用到DGS上。为此,本试验选取不同产地的8种DGS,探讨不同来源DGS营养成分及其在肉鸭上的ME的差异,并基于DGS营养成分建立肉鸭ME的预测模型,为DGS在肉鸭生产中的应用提供参考。

1 材料与方法

1.1 伦理声明

本试验所有程序均经中国农业科学院饲料研究所动物伦理委员会批准,批准编号IFR-CAAS20240718。

1.2 样品采集与处理

DGS样品采集自江苏、河南、安徽、四川等地,具体见表1。白酒酿造原料为玉米、高粱、小麦、大米、甘薯残留物,各地DGS均添加稻壳,比例不明,以粗纤维(crude fibre,CF)含量为准。每个DGS样品采集10 kg,通过四分法取样150 g,粉碎过筛保存待测。
表1 DGS信息

Table 1 DGS information

样品编号
Number of
samples		 外观
Appearance		 产地
Production
place		 粗纤维含量
CF content/%		 样品编号
Number of
samples		 外观
Appearance		 产地
Production
place		 粗纤维含量
CF content/%
1 浅褐色粉状 四川宜宾 24.57 5 浅褐色粉状 河南永城 18.58
2 深褐色粉状 江苏洋河 18.46 6 褐色粉状 四川泸州 20.70
3 浅褐色粉状 河南路河 17.02 7 褐色粉状 安徽宿州 29.79
4 深褐色粉状 贵州遵义 11.35 8 深褐色粉状 贵州贵阳 14.34

1.3 试验方法

选用体重[(3.00±0.02) kg]均一的48日龄Z型雄性北京鸭24只,随机分为2组,每组3个重复,每个重复4只。代谢试验综合参考Adeola等[9]和Huang等[10]的方法进行,试验分为预试期和正试期,共21 d,采用排空强饲法测定每种DGS的ME,每只肉鸭强饲50 g DGS。预试期将24只肉鸭放入代谢笼,单笼饲养,自由饮水,每天08:00和16:00饲喂基础饲粮,自然光照;DGS样品全部采用冷制粒并风干,预试期最后1天饲喂待测DGS样品,正式试验开始后禁食36 h,36 h后精确强饲待测DGS样品50 g,记录强饲时间;肉鸭强饲后收集排泄物36 h,之后继续收集36 h排泄物以计算肠道内源损失,收粪时喷洒10%的盐酸溶液固氮;排泄物收集后待进一步测定。8种DGS共进行4批代谢试验,4批代谢试验分期进行,每期试验完成后肉鸭休息7 d,休息期间饲喂基础饲粮,待肉鸭体重恢复后再进行下一批次试验。基础饲粮组成及营养水平见表2。
表2 基础饲粮组成及营养水平(风干基础)

Table 2 Composition and nutrient levels of the basal diet (air-dry basis) %

项目Items 含量Content
原料Ingredients
玉米Corn 67.42
豆粕Soybean meal 26.50
大豆油Soybean oil 2.30
磷酸氢钙CaHPO4 1.60
石粉Limestone 0.75
食盐NaCl 0.30
DL-蛋氨酸DL-Met 0.13
预混料Premix1) 1.00
合计Total 100.00
营养水平Nutrient levels2)
代谢能ME/(MJ/kg) 12.56
粗蛋白质CP 17.51
赖氨酸Lys 0.87
蛋氨酸Met 0.41
蛋氨酸+胱氨酸Met+Cys 0.71
色氨酸Try 0.20
精氨酸Arg 1.16
苏氨酸Thr 0.66
缬氨酸Val 0.81
异亮氨酸Iso 0.70
亮氨酸Leu 1.51
钙Ca 0.85
总磷TP 0.71

1)预混料为每千克饲粮提供 The premix provided the following per kg of the diet:VA 10 000 IU,VD3 3 000 IU,VE 20 IU,VK3 2 mg,VB6 4 mg,VB10 0.6 mg,VB1 2 mg,核黄素 riboflavin 8 mg,D-泛酸 D-pantothenic acid 20 mg,烟酸 niacin 50 mg,叶酸 folic acid 1 mg,生物素 biotin 0.2 mg,Cu (as copper sulfate) 10 mg,Fe (as ferrous sulfate) 60 mg,Zn (as zinc sulfate) 60 mg,Mn (as manganese sulfate) 80 mg,Se (as sodium selenite) 200 mg,I (as potassium iodide) 0.2 mg,氯化胆碱 choline chloride 1 000 mg。
2)粗蛋白质、钙、总磷为实测值,代谢能和氨基酸依据《中国饲料成分及营养价值表(2022年第33版)》[11]中各原料对应数值进行计算。CP, Ca and TP were measured values, while ME and amino acids were calculated according to the corresponding values of each raw material in the Table of Feed Composition and Nutritional Value of China (33rd edition, 2022)[11].

1.4 样品处理及指标测定

肉鸭排泄物收集完成后,在65 ℃烘箱烘72 h,室温下回潮24 h后称重并记录,实验室粉碎过1 mm筛,-20 ℃冷冻保存。饲粮、DGS与排泄物中常规营养成分含量测定方法如下: GE含量测定参照SN/T 4942—2017中方法,水分或干物质(dry matter,DM)含量测定参照GB/T 6435—2014中方法,CP或氮含量测定参照GB/T 6432—2018中方法,EE含量测定参照GB/T 6433—2006中方法,粗灰分(crude ash,Ash)含量测定参照GB/T 6438—2007中方法,CF含量测定参照GB/T 6434—2022中方法,NDF含量测定参照GB/T 20806—2022中方法,酸性洗涤纤维(acid detergent fiber,ADF)含量测定参照NY/T 1459—2022中方法,钙(calcium,Ca)含量测定参照GB/T 6436—2018中方法,总磷(total phosphorus,TP)含量测定参照GB/T 6437—2018中方法。DGS与排泄物中无氮浸出物(nitrogen-free extract,NFE)含量利用公式NFE(%)=100-[水分(%)+CP(%)+EE(%)+CF(%)+Ash(%)]计算得出。

1.5 计算公式

ME及营养物质表观利用率具体计算方法如下:

表观代谢能(apparent metabolizable energy,AME,

MJ/kg)=[DGS强饲量(g)×DGS

的GE含量(MJ/kg)-排泄物重量(g)×排泄物

的GE含量(MJ/kg)]/DGS强饲量(g);

真代谢能(true metabolizable energy,TME,

MJ/kg)=AME(MJ/kg)+[内源排泄物

重量(g)×排泄物的GE含量(MJ/kg)];

营养物质表观利用率(%)=[DGS营养物质

含量(%)×DGS强饲量(g)-排泄物营养

物质含量(%)×排泄物重量(g)]/[DGS营养

物质含量(%)×DGS强饲量(g)];

沉积氮(RN,g/kg)=[DGS强饲量(g)×

DGS氮含量(%)-排泄物重量(g)×排泄物氮

含量(%)]/DGS强饲量(kg);

氮校正表观代谢能(nitrogen corrects apparent

metabolizable energy,AMEn,MJ/kg)=

AME(MJ/kg)-RN(g/kg)×34.39(MJ/kg);

氮校正真代谢能(nitrogen-corrected true

metabolizable energy,TMEn,MJ/kg)=

TME(MJ/kg)-RN(g/kg)×34.39(MJ/kg)。

式中:34.39为氮校正因子(MJ/kg)。

1.6 统计分析

数据使用SAS 9.3的MIXED程序通过一般线性模型进行分析。Tukey多范围检验用于检验主效应之间差异的显著性,如果P<0.05,认为差异显著。使用8个DGS样品在SAS 9.3中运行PROC CORR以获得ME与营养成分含量之间的关系。使用SAS 9.3的PROC REG开发8个DGS样品中ME的预测方程。首先,使用因变量对每个考虑的解释变量进行简单回归,然后使用对因变量贡献最大的变量相对应的回归方程作为基础,其余变量逐渐引入此过程;逐步回归后,模型中保留的变量既显著又不具有严重的多重共线性。R2、均方根误差(RMSE)和贝叶斯信息准则用作最佳拟合方程的选择标准,R2最大和相对标准偏差(RSD)最小被认为是最佳拟合方程。

2 结果与分析

2.1 不同产地DGS营养成分含量

由表3可知,本试验中的8种DGS的DM、GE、CP、EE、Ash、CF、NDF、ADF、Ca、TP、NFE含量的平均值分别为93.74%、17.90 MJ/kg、20.54%、5.39%、11.25%、19.35%、62.75%、45.39%、0.14%、0.51%、37.21%;各个营养成分含量的CV均较大,最大的是Ca含量,CV达到49.31%,EE含量的CV为47.73%,Ash的CV为39.48%,CF的CV为29.87%。
表3 不同产地DGS营养成分含量(风干基础)

Table 3 Nutritional component contents of DGS from different regions (air-dry basis) %

项目
Items		 干物质
DM/%		 总能
GE/
(MJ/kg)		 粗蛋
白质
CP/%		 粗脂肪
EE/%		 粗灰分
Ash/%		 粗纤维
CF/%		 中性洗
涤纤维
NDF/%		 酸性洗
涤纤维
ADF/%		 钙
Ca/%		 总磷
TP/%		 无氮
浸出物
NFE/%
样品编号
Number of
samples		 1 94.46 16.41 16.13 4.00 16.15 24.57 67.24 53.85 0.10 0.44 33.61
2 94.27 17.67 23.92 4.17 11.86 18.46 64.07 50.72 0.23 0.50 35.85
3 93.55 17.52 18.77 5.40 10.09 17.02 59.51 35.75 0.12 0.48 42.26
4 90.68 18.87 24.03 6.09 6.02 11.35 51.42 29.64 0.14 0.73 43.19
5 94.81 16.00 17.47 3.65 19.13 18.58 64.83 47.19 0.23 0.54 35.99
6 94.81 18.35 17.70 4.47 9.52 20.70 66.36 49.07 0.11 0.47 42.42
7 94.80 18.13 16.56 3.90 10.38 29.79 67.20 51.77 0.04 0.41 34.17
8 92.56 20.28 29.74 11.42 6.86 14.34 61.34 45.11 0.10 0.54 30.20
平均值Mean 93.74 17.90 20.54 5.39 11.25 19.35 62.75 45.39 0.14 0.51 37.21
标准差SD 1.47 1.36 4.84 2.57 4.44 5.78 5.34 8.44 0.07 0.10 4.83
变异系数CV/% 1.56 7.58 23.57 47.73 39.48 29.87 8.52 18.60 49.31 18.79 12.98
最大值Maximum 94.81 20.28 29.74 11.42 16.15 29.79 67.24 53.85 0.23 0.73 43.19
最小值Minimum 90.68 16.00 16.13 3.65 6.02 11.35 51.42 35.75 0.04 0.41 30.20

2.2 DGS在肉鸭上的ME和主要营养成分表观利用率

由表4可知,在肉鸭上,8种DGS的AME平均值为6.59 MJ/kg(4.78~9.24 MJ/kg),CV为26.49%;AMEn平均值为6.33 MJ/kg(4.63~9.11 MJ/kg),CV为27.54%;TME平均值为8.17 MJ/kg(6.56~11.01 MJ/kg),CV为19.28%;TMEn平均值为7.89 MJ/kg(6.34~10.97 MJ/kg),CV为19.66%。肉鸭对8种DGS的DM、GE、CP表观利用率平均值分别为26.44%、59.79%、27.20%;肉鸭对不同产地DGS的DM、CP、GE表观利用率存在较大差异,DM、CP、GE表观利用率CV分别为31.77%、43.71%、22.31%。
表4 DGS在肉鸭上的代谢能和主要营养成分表观利用率

Table 4 ME and apparent utilization ratios of major nutritional components in DGS by meat ducks

项目
Items		 样品编号Number of samples 平均值
Mean		 标准差
SD		 变异系数
CV/%
1 2 3 4 5 6 7 8
表观代谢能AME/(MJ/kg) 4.78±0.46 5.12±0.79 6.64±0.62 9.24±1.06 4.87±0.35 7.45±0.78 5.86±0.72 8.74±0.99 6.59 1.75 26.49
氮校正表观代谢能
AMEn/(MJ/kg)		 4.63±0.34 4.80±0.37 6.22±0.27 9.11±0.35 4.63±0.07 7.34±0.08 5.63±0.43 8.31±0.53 6.33 1.74 27.54
真代谢能TME/(MJ/kg) 6.56±0.55 6.90±0.92 8.41±0.60 11.01±1.24 6.64±0.68 8.49±0.55 7.64±0.82 9.69±0.67 8.17 1.57 19.28
氮校正真代谢能TMEn/(MJ/kg) 6.37±0.34 6.60±0.44 8.07±0.26 10.97±0.25 6.47±0.19 8.32±0.16 7.43±0.49 8.85±0.39 7.89 1.55 19.66
干物质表观利用率
DM apparent utilization ratio/%		 25.35±4.09 19.70±0.05 21.19±2.22 18.30±3.00 19.38±4.97 38.92±6.18 37.79±2.27 30.87±1.49 26.44 8.40 31.77
粗蛋白质表观利用率
CP apparent utilization ratio/%		 21.38±4.49 19.91±16.07 15.74±8.10 14.64±5.84 24.57±7.63 35.19±14.13 47.50±6.13 38.63±0.71 27.20 11.89 43.71
总能表观利用率
GE apparent utilization ratio/%		 66.33±3.78 67.67±3.96 70.84±2.78 69.56±2.21 70.99±4.47 51.03±5.63 43.26±1.91 38.63±0.71 59.79 13.34 22.31

2.3 DGS营养成分含量与ME的相关性分析及回归方程的建立

由表5可知,DGS的AME与GE、EE含量呈显著或极显著正相关(P<0.05或P<0.01),与DM、Ash、NDF、ADF含量呈显著或极显著负相关(P<0.05或P<0.01);DGS的AMEn与GE、EE、CP含量呈显著或极显著正相关(P<0.05或P<0.01);DGS的TME与GE、EE、CP含量呈显著或极显著正相关(P<0.05或P<0.01),与Ash、NDF、ADF含量呈显著或极显著负相关(P<0.05或P<0.01);DGS的TMEn与GE、TP含量呈显著正相关(P<0.05),与Ash、NDF、ADF含量呈显著或极显著负相关(P<0.05或P<0.01)。
表5 DGS营养成分含量与代谢能的相关系数

Table 5 Correlation coefficients between nutritional component contents and ME of DGS

项目
Items		 干物质
DM		 总能
GE		 粗蛋
白质
CP		 粗脂肪
EE		 粗灰分
Ash		 粗纤维
CF		 中性洗
涤纤维
NDF		 酸性洗
涤纤维
ADF		 钙
Ca		 总磷
TP		 无氮
浸出物
NFE		 表观
代谢能
AME		 氮校正表
观代谢能
AMEn		 真代
谢能
TME		 氮校正
真代谢能
TMEn
干物质DM 1.000
总能GE -0.588 1.000
粗蛋白质CP -0.667 0.769* 1.000
粗脂肪EE -0.581 0.815* 0.846** 1.000
粗灰分Ash 0.678 -0.912** -0.610 -0.606 1.000
粗纤维CF 0.773* -0.399 -0.709* -0.552 0.412 1.000
中性洗涤纤维NDF 0.944** -0.422 -0.532 -0.394 0.591 0.820* 1.000
酸洗洗涤纤维ADF 0.832* -0.329 -0.354 -0.309 0.540 0.760* 0.952** 1.000
钙Ca 0.072 -0.423 0.154 -0.234 0.469 -0.417 -0.087 -0.021 1.000
总磷TP 0.871** 0.342 0.543 0.341 -0.402 -0.832* -0.892** -0.782* 0.290 1.000
无氮浸出物NFE -0.268 -0.067 -0.246 -0.339 -0.273 -0.337 -0.495 -0.635 0.059 0.375 1.000
表观代谢能AME -0.806* 0.857** 0.638 0.713* -0.876** -0.642 -0.720* -0.738* -0.352 0.658 0.311 1.000
氮校正表观代谢能AMEn -0.635 0.905** 0.714* 0.881** -0.865** -0.619 -0.643 -0.205 0.359 0.333 0.337 0.976** 1.000
真代谢能TME -0.575 0.881** 0.762* 0.833* -0.976** -0.667 -0.790* -0.738* -0.084 0.467 0.405 0.981** 0.976** 1.000
氮校正真代谢能TMEn -0.675 0.717* 0.506 0.516 -0.836** -0.635 -0.842** -0.717* -0.310 0.765* 0.481 0.952** 0.960** 0.988** 1.000

*:相关性显著 significant correlation (P<0.05);**:相关性极显著 extremely significant difference (P<0.01)。

由表6可知,AME以NDF、Ash、EE含量为预测因子建立的预测方程为AME=-0.097NDF-0.211Ash+0.184EE+14.021(R2=0.876 7,RSD=0.810 3,P<0.05);AMEn以Ash含量为预测因子建立的预测方程为AMEn=-0.340Ash+10.156(R2=0.748 9,RSD=0.891 2,P<0.05);TME以NDF、Ash、EE含量为预测因子建立的预测方程为TME=-0.137NDF-0.179Ash+0.091EE+18.287(R2=0.915 7,RSD=0.605 7,P<0.05);TMEn以Ash、NDF含量为预测因子建立的预测方程为TMEn=-0.181Ash-0.155NDF+19.652(R2=0.883 8,RSD=0.391 0,P<0.05)。
表6 DGS的代谢能预测方程

Table 6 Prediction equations of ME for DGS

预测方程
Prediction equations		 决定系数
R2		 相对标准偏差
RSD		 P值
P-value
AME=-0.097NDF-0.211Ash+0.184EE+14.021 0.876 7 0.810 3 0.027 3
AMEn=-0.340Ash+10.156 0.748 9 0.891 2 0.005 5
TME=-0.137NDF-0.179Ash+0.091EE+18.287 0.915 7 0.605 7 0.013 0
TMEn=-0.181Ash-0.155NDF+19.652 0.883 8 0.391 0 0.004 6

AME:表观代谢能 apparent metabolizable energy;AMEn:氮校正表观代谢能 nitrogen corrects apparent metabolizable energy;TME:真代谢能 true metabolizable energy;TMEn:氮校正真代谢能 nitrogen-corrected true metabolizable energy;NDF:中性洗涤纤维 neutral detergent fiber;Ash:粗灰分 crude protein;EE:粗脂肪 ether extract。

2.4 DGS的ME预测方程验证

将营养成分含量代入预测方程AME=-0.097NDF-0.211Ash+0.184EE+14.021、AMEn=-0.340Ash+10.156、TME=-0.137NDF-0.179Ash+0.091EE+18.287、TMEn=-0.181Ash-0.155NDF+19.652,将得到的结果与代谢试验结果与进行比较验证,以确定预测方程的准确性和普适性。由表7可知,AME实测值与预测值差值平均值为0.44 MJ/kg,AMEn实测值与预测值差值平均值为0.72 MJ/kg,TME实测值与预测值差值平均值为0.32 MJ/kg,TMEn实测值与预测值差值平均值为0.38 MJ/kg。
表7 DGS代谢能预测值与实测值对比

Table 7 Comparison of predicted and measured ME values of DGS

项目
Items		 样品编号Number of samples 平均值
Mean
1 2 3 4 5 6 7 8
AME实测值
Measured value of AME/(MJ/kg)		 4.78 5.12 6.64 9.24 4.87 7.45 5.86 8.74 6.59
AME预测值
Predicted value of AME/(MJ/kg)		 4.83 6.07 7.11 8.88 4.37 6.40 6.03 8.72 6.55
差值Difference value/(MJ/kg) 0.04 0.95 0.48 0.36 0.50 1.05 0.17 0.02 0.44
差值占比
Percentage of difference value/%		 0.93 18.48 7.16 3.84 10.29 14.11 2.88 0.19 7.23
AMEn实测值
Measured value of AMEn/(MJ/kg)		 4.63 4.80 6.22 9.11 4.63 7.34 5.63 8.31 6.33
AMEn预测值
Predicted value of AMEn/(MJ/kg)		 4.67 6.12 6.73 8.11 3.65 6.92 6.63 7.82 6.33
差值Difference value/(MJ/kg) 0.04 1.32 0.51 1.00 0.98 0.42 1.00 0.49 0.72
差值占比
Percentage of difference value/%		 0.79 27.55 8.15 10.99 21.11 5.72 17.68 5.87 12.23
TME实测值
Measured value of TME/(MJ/kg)		 6.56 6.90 8.41 11.01 6.64 8.49 7.64 9.69 8.17
TME预测值
Predicted value of TME/(MJ/kg)		 6.55 7.77 8.82 10.72 6.31 7.90 7.58 9.69 8.17
差值Difference value/(MJ/kg) 0.01 0.87 0.41 0.29 0.33 0.59 0.06 0.00 0.32
差值占比
Percentage of difference value/%		 0.14 12.57 4.84 2.67 4.96 6.96 0.84 0.04 4.13
TMEn实测值
Measured value of TMEn/(MJ/kg)		 6.37 6.60 8.07 10.97 6.47 8.32 7.43 8.85 7.89
TMEn预测值
Predicted value of TMEn/(MJ/kg)		 6.31 7.57 8.60 10.59 6.14 7.64 7.36 8.90 7.89
差值Difference value/(MJ/kg) 0.06 0.97 0.53 0.38 0.33 0.68 0.07 0.05 0.38
差值占比
Percentage of difference value/%		 1.01 14.75 6.55 3.45 5.13 8.18 0.96 0.55 5.07

3 讨论

3.1 不同产地DGS营养成分含量分析

DGS作为工业副产物,因其营养丰富,主要作为非常规饲料替代玉米、豆粕饲喂畜禽[12-13],而DGS营养成分含量因不同产地白酒生产原料及生产工艺不同存在较大差异。代国滔等[14]测定了12种不同来源的DGS的营养成分,检测结果显示DGS的DM含量为86.56%~89.29%,CP含量为21.62%~27.71%,EE含量为9.21%~13.34%;董文轩等[15]测定了12种不同来源的DGS,发现12个DGS样品的DM含量为91.97%~95.13%,GE含量为15.27~19.52 MJ/kg,CP含量为13.41%~26.08%,EE含量为0.95%~5.37%,CF含量为16.16%~30.20%。
生产原料如高粱、玉米、小麦、糯米、稻壳的比例不同,使用的酒曲不同,都会引起发酵过程中微生物比例的改变,进而影响副产物DGS化学成分的改变[16]。发酵主产物为小麦时,在60~70 ℃发酵产生的优势菌群主要为芽孢杆菌、乳酸菌[17],发酵主产物为小麦、大麦、豌豆时,在50~60 ℃发酵过程中的优势菌群为地衣芽孢杆菌、总状横梗霉[18-19],在发酵的同时部分白酒还进行糖化[20]。白酒生产的工艺复杂影响了酿造白酒副产物DGS的化学成分。例如,泸州老窖、古井贡等浓香单粮大曲型白酒,酿造原料为高粱,酒曲由小麦辅以豌豆等组成;浓香多粮大曲型白酒如五粮液酿造原料为高粱、大米、糯米及玉米,酒曲由小麦组成,酿酒辅料稻壳添加比例一般为56%~68%;酱香大曲型白酒如茅台、郎酒酿造原料为高粱,酒曲由小麦组成,稻壳添加比例为8%~12%;汾酒、二锅头等为清香型白酒,酿造原料为高粱,酒曲以豌豆、小麦为主,稻壳添加比例为65%~76%[21-22]。研究显示,浓香型DGS的EE含量为4.83%,酱香型DGS的EE含量为7.95%,酱香型DGS的CF含量为17.12%,低于浓香型DGS[23],清香型DGS的CP含量为16.97%,EE含量为6.98%,CF含量为21.02%[24]。
CV是衡量数据库中各观测值变异程度的统计量。当进行多个数据变异程度的比较时,CV可以消除单位和(或)平均值不同对多个数据变异程度比较的影响[25],在进行模型拟合时,为避免模型在“舒适区”过度拟合,需要评价数据的离散程度,通过数据的CV可以反映模型数据的可用性。本试验选择的不同产地的8种DGS营养成分(CP、Ash、ADF)含量的CV较大,表明DGS中的CP、Ash、ADF含量易受到产地的影响,选择它们作为方程的预测因子具有一定代表性[26],后续分析发现DGS的AME与EE含量呈显著正相关,与Ash、ADF含量呈显著负相关。谭高明[27]的研究发现,GE和CF含量是影响干白酒糟ME的主要成分,对白羽肉鸡ME的影响最大,可以解释81.9%的AMEn变化,可作为预测因子。本研究中,不同产地DGS中Ash含量的CV为39.48%,与肉鸭AME、TME、AMEn、TMEn呈显著负相关,且Ash含量可以解释74.89%的AMEn变化,可作为预测AMEn的最佳预测因子。本研究结果与谭高明[27]的研究结果有所不同的原因可能是肉鸭对饲料中纤维含量的敏感度小于肉鸡。Ca、P作为必需矿物元素,在核酸合成、能量代谢、肌肉收缩、酶活性、信号转导和骨矿化方面发挥着重要而广泛的作用[28],Ca、P缺乏会影响畜禽骨骼发育、营养物质利用率及生产性能等[29-30],因此,非常规饲料资源Ca、P含量值得关注。本试验中选取不同产地的8种DGS,测得 Ca、P含量平均值分别为0.14%、0.51%,CV分别为49.31%、18.79%,差异较大。因此,在肉鸭生产中利用DGS时,还需要测定Ca、P含量。不同产地的DGS营养成分含量的差异与各地白酒的酿造工艺、谷物的比例、酿酒酵母等相关[31-32]。

3.2 不同产地DGS对肉鸭的ME及主要营养成分表观利用率的影响

家禽能量代谢评价采用ME体系,ME是评定营养物质在家禽体内能量利用的重要指标。本试验选用48日龄北京鸭测定了8种不同产地DGS的AME、TME,AME、TME平均值分别为6.59、8.17 MJ/kg,CV分别为26.49%、19.28%。田璐[33]测定的樱桃谷鸭对DGS的AME、TME分别为10.42、11.29 MJ/kg,较本试验测定结果高,总结原因可能是与试验动物、DGS产地、试验条件不同有关。本试验条件下测得肉鸭对DGS的DM、GE、CP表观利用率平均值分别为26.44%、59.79%、27.20%,肉鸭对DGS的GE表观利用率较高,田璐[33]研究表明,肉鸭对DGS的GE表观利用率为55.01%,与本试验结果基本一致;肉鸭对DGS的DM和CP表观利用率较低,且不同产蛋DGS的营养成分表观利用率存在明显差异,这主要是因为不同产地DGS的CF含量不同影响了肉鸭对DGS中营养物质的利用[34]。吴占月等[35]研究表明,酱油渣CF含量为17.46%,糖渣CF含量为3.23%,肉鸭对酱油渣的DM、CP表观利用率分别为44.61%、45.08%,肉鸭对糖渣的DM、CP表观利用率分别为57.46%、53.13%,说明肉鸭对不同CF含量饲料的营养成分表观利用率存在较大差异,Singh等[36]的研究结果与此一致。不同产地DGS营养成分含量差异较大,营养成分表观利用率也存在较大差异,证明DGS中营养成分含量与ME存在相关性。

3.3 DGS营养成分含量与ME回归方程的选择

饲料成本占家禽生产总成本的60%以上,其中能量是最昂贵的组成部分,约占饲料成本的70%[37],而饲料能量水平的高低对家禽生产性能具有显著影响[38]。这种经济重要性及对家禽生产性能的影响导致能量的精准饲养成为家禽养殖行业的重要课题。由于家禽粪便和尿液一起排泄,因此在家禽饲料能量评价时通常采用ME体系。家禽饲料ME评价可选用的方法众多,包括指示剂法、排空强饲法、全收粪法和排空诱饲法,这些传统的方法基于定量饲喂和排泄物不丢失、无污染的原则,虽然采用动物试验得到的结果较为准确,但试验费时、费钱、费力,且对试验场地、试验人员技术水平有较高的要求[39],因此成本较高。随着家禽有效能评价体系的发展,各种有效能评价的新方法被逐渐利用,回归法是根据饲料原料的化学组成与有效能值建立预测方程,测定同类其他原料化学组成后通过方程预测其有效能,此方法虽方便、成本低,但建立预测方程需要较多饲料原料,且饲料原料具有一定代表性。
本试验选择的DGS营养成分含量的CV适当,数据覆盖范围广,具有一定的代表性。通过分析DGS营养成分含量与ME的相关性发现,GE含量与AME、TME显著正相关,是一元线性回归方程预测饲料原料ME的最佳预测因子[40]。DGS的Ash、NDF、ADF含量与ME呈显著负相关,Ash与ADF、NDF提供的能量较少,且饲料中的纤维通过增加食糜黏度延长消化时间,损害胆汁酸肠肝循环,降低肉鸭对营养物质的利用率[41],这与Cozannet等[42]的研究结果一致,他们认为CF含量可作为预测家禽ME的最佳预测因子,从一元方程到多元方程,可以通过补充Ash含量数据可进一步提高模型的准确性。因此,目前的结果表明,成年肉鸭DGS校准样品的ME可能在很大程度上取决于NDF、GE、Ash含量,以GE、NDF、Ash含量作为预测因子建立的多元回归预测方程具有较高的可信度,在肉鸭对玉米的ME上也有类似的观察结果[43]。本试验对预测方程的验证结果虽然显示其预测偏差较小,但是本试验采集的DGS样本较少,未使用预测方程数据库以外的数据验证方程的可信性,后续试验可采集更多不同产地的DGS以丰富预测方程的数据库,增强其准确性。

4 结论

① 不同产地DGS的营养成分含量存在差异,Ca、EE、Ash、CF与CP含量的CV均在20%左右。
② 肉鸭对8种DGS的AME、AMEn、TME、TMEn平均值分别为6.59、6.33、8.17、7.89 MJ/kg,DM、GE、CP表观利用率平均值分别为26.44%、59.79%、27.20%。
③ 肉鸭对DGS的AME、AMEn、TME、TMEn的多元回归预测方程为:AME=-0.097NDF-0.211Ash+ 0.184EE+14.021(R2=0.876 7,RSD=0.810 3,P=0.027 3);AMEn=-0.340Ash+10.156(R2=0.748 9,RSD=0.891 2,P=0.005 5);TME=-0.137NDF-0.179Ash+0.091EE+ 18.287(R2=0.915 7,RSD=0.605 7,P=0.013 0);TMEn=-0.181Ash-0.155NDF+ 19.652(R2=0.883 8,RSD=0.391 0,P=0.004 6)。

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