肉鸡皮大麦表观代谢能预测模型研究

doi:10.11843/j.issn.0366-6964.2018.10.014

摘要/Abstract

摘要：

旨在通过实验室分析和肉鸡代谢试验，建立皮大麦常规理化指标与代谢能的回归方程，为生产提供便捷的皮大麦代谢能估算方法。试验1：对澳大利亚进口皮大麦及皮大麦的磨粉分离物进行理化指标的检测。依据中华人民共和国麦类分类标准，以粗蛋白质、粗纤维、粗灰分为主要分级指标，以皮大麦磨粉分离的各部分为原料，配制成5种不同营养梯度的人工皮大麦。试验2：选取健康爱拔益加（AA⁺）雄性肉鸡720只（360只用于11~13 d试验，360只用于25~27 d试验），随机分成5个处理，每处理12个重复，每重复6只鸡，分别饲喂A、B、C、D、E 5种人工皮大麦。分别在11~13 d及25~27 d，用全收粪法测定人工皮大麦的表观代谢能（AME）、氮校正表观代谢能（AMEn）和养分消化率，并采用逐步回归法建立11~13 d与25~27 d时AME、AMEn回归方程。结果表明：1）大麦种类对AME、AMEn、粗蛋白（CP）消化率、粗脂肪（EE）消化率、总淀粉（STC）消化率、钙（Ca）消化率、总磷（P）消化率、肉鸡体增重（BWG）影响显著（P<0.05）；肉鸡日龄对AME、AMEn、粗脂肪消化率、总淀粉消化率、总磷消化率、肉鸡体增重影响显著（P<0.05）。25~27 d时，粗脂肪（EE）与总淀粉（STC）的消化率比11~13 d时分别高6.52%、8.30%（P<0.05）；AME、AMEn比11~13 d分别高0.88与0.52 MJ·kg^-1（P<0.05）。2）建立的肉鸡皮大麦AME（MJ·kg^-1 DM）、AMEn（MJ·kg^-1 DM）的预测方程：11~13 d，AME=7.814+10.166TP+0.106NDF（R²=0.996，P=0.004），AMEn=10.336+9.740TP-0.121CP（R²=0.983，P=0.017）；25~27 d时，AME=10.436+5.974NaCl+0.131EE+0.019NDF（R²=0.999，P=0.002），AMEn=15.284-0.312CP+0.258EE-0.081NDF（R²=0.998，P=0.014）。通过预测方程计算所得人工皮大麦代谢饲粮AME的预测值与实测值最大相差0.06 MJ·kg^-1。回归模型以粗蛋白（CP）、总磷（TP）、氯化钠（NaCl）、粗脂肪（EE）、中性洗涤纤维（NDF）等常规营养指标为主效影响因子，较适合在生产实践中推广应用。

Abstract:

This study aimed to establish the regression equations of metabolizable energy of hulled barley based on conventional physiochemical parameters by the methods of laboratory analysis and broilers metabolic test, so as to provide a convenient method for estimating the metabolizable energy values of hulled barley in practice. Trial 1:the physical and chemical parameters were determined for hulled barley imported from Australia and its milling isolates. Five artificial hulled barleys with different nutrient gradients were formulated with milling isolates from hulled barley based on the main classification indexes, namely crude protein, crude fiber and crude ash according to the classification standard of the People's Republic of China. Trial 2:seven hundred twenty healthy male AA⁺ broilers were used (360 for 11-13 d trial, 360 for 25-27 d trial). The birds were randomly allocated into 5 groups with 12 replicates of 6 birds. The birds of each treatment group were fed one of the 5 artificial hulled barley diets, namely A, B, C, D and E. The apparent metabolizable energy (AME), nitrogen corrected apparent metabolizable energy (AMEn) and nutrient apparent digestibility of the artificial hulled barleys were measured by total collection method for broilers of 11 to 13 and 25 to 27 days. And the stepwise regression method was used to establish regression equations for AME and AMEn. The results showed that:1) Barley types had significant influence on AME, AMEn, digestibility of CP, EE, STC, Ca, P and BWG(P<0.05); Days of age of broilers had significant influence on AME, AMEn, digestibility of EE, STC, P and BWG in broilers (P<0.05). The digestibility of EE and STC were 6.52% and 8.30% higher in birds on d 25 to 27 than these on d 11 to 13 (P<0.05). The AME and AMEn were 0.47 and 0.50 MJ·kg^-1 higher in birds on d 25 to 27 than these on d 11 to 13(P<0.05). 2) The prediction equations for AME (MJ·kg^-1 DM) and AMEn(MJ·kg^-1 DM)of hulled barleys for broilers were as follows:AME=7.814+10.166TP+0.106NDF (R²=0.996,P=0.004), AMEn=10.336+9.740TP-0.121CP (R²=0.983,P=0.017) for 11-13 d; AME=10.436+5.974NaCl+0.131EE+0.019NDF(R²=0.999,P=0.002), AMEn=15.284 -0.312CP+0.258EE-0.081NDF(R²=0.998,P=0.014) for 25-27 d. The maximum difference between the calculated AME values for artificial hulled barley diets using prediction equations and the measured values was 0.06 MJ·kg^-1.In conclusion, the main influencing factors in the regression model are conventional nutritional parameters, namely crude protein, total phosphorus, sodium chloride, ether extract, neutral detergent fiber(NDF), which make it suitable for wide application in production practice.

中图分类号:

杜保华, 陈思, 张相德, 何宁, 杨欣, 呙于明, 姚军虎, 杨小军. 肉鸡皮大麦表观代谢能预测模型研究[J]. 畜牧兽医学报, 2018, 49(10): 2180-2190.

DU Bao-hua, CHEN Si, ZHANG Xiang-de, HE Ning, YANG Xin, GUO Yu-ming, YAO Jun-hu, YANG Xiao-jun. Prediction Model of Apparent Metabolizable Energy of Hulled Barley in Broiler[J]. ACTA VETERINARIA ET ZOOTECHNICA SINICA, 2018, 49(10): 2180-2190.

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