Carré B, Méda B
Institut National de la Recherche Agronomique (INRA), Unité de Recherche 83, Recherches Avicoles, F-37380 Nouzilly, France
Institut National de la Recherche Agronomique (INRA), Unité de Recherche 83, Recherches Avicoles, F-37380 Nouzilly, France.
Poult Sci. 2015 Sep;94(9):2191-201. doi: 10.3382/ps/pev214. Epub 2015 Jul 27.
The study presented here used experimental data obtained from 42 articles to calculate the cross relationships between daily feed intake (DFI), feed composition (AMEn and CP), BW, daily weight gain (DG), mean age (A), and composition (FCG and PCG; fat and protein, respectively) of growth in broilers. All of the experiments selected were conducted at standard temperature and with ad libitum feeding. The articles in this database were published from 1980 to 2015, and represented a total of 12,277 broilers, 57 trials, and 384 basic treatments. Data ranged from 3 to 66, 0.077 to 3.322, 7.10 to 15.37, and 7.3 to 35.0 for A (d), mean BW (kg), AMEn (MJ/kg) and CP (%), respectively. Equations were established from regression calculations to calculate daily heat production as a function of BW, daily AME intake (MEI) as a function of BW, DG, FCG and PCG, PCG as a function of A and FCG, and FCG as a function of BW, protein efficiency (PE), AMEn, and CP. The combination of these equations expressing MEI, PCG, or FCG with equations expressing the definitions of PE, AME (from AMEn), and DFI (from MEI) constituted a system of 6 equations which could be used to determine DFI values from the values of AMEn, CP, A, BW, and DG. Using the values of AMEn, CP, A, BW, and DG from the database, the DFI values calculated with this system of equations showed a reasonable correlation with the DFI values measured (R2=0.880). This system of 6 equations yielded values for DFI, PE, PCG, and FCG that were in agreement with the classical effects produced by increased CP values, or by reduced fat deposition associated with genetic selection for leanness. This system of 6 equations might thus be considered an interesting framework for future nutritional modelling systems. Regressions predicting feed efficiency as a function of AMEn, CP, DG, and BW, or as a function of AMEn, FCG, DG, and BW showed R2 values of 0.767 and 0.747, respectively.
本研究利用从42篇文章中获取的实验数据,计算了肉鸡日采食量(DFI)、饲料组成(AME n和粗蛋白)、体重、日增重(DG)、平均年龄(A)以及生长组成(FCG和PCG,分别为脂肪和蛋白质)之间的交叉关系。所有选定的实验均在标准温度下自由采食条件下进行。该数据库中的文章发表于1980年至2015年,共涉及12277只肉鸡、57项试验和384种基本处理。A(日龄)、平均体重(kg)、AME n(兆焦/千克)和粗蛋白(%)的数据范围分别为3至66、0.077至3.322、7.10至15.37和7.3至35.0。通过回归计算建立了方程,以计算日产热量与体重的函数关系、每日AME摄入量(MEI)与体重、DG、FCG和PCG的函数关系、PCG与A和FCG的函数关系以及FCG与体重、蛋白质效率(PE)、AME n和粗蛋白的函数关系。将这些表示MEI、PCG或FCG的方程与表示PE、AME(来自AME n)和DFI(来自MEI)定义的方程相结合,构成了一个由6个方程组成的系统,可用于根据AME n、粗蛋白、A、体重和DG的值确定DFI值。利用数据库中AME n、粗蛋白、A、体重和DG的值,用该方程组计算出的DFI值与实测DFI值显示出合理的相关性(R2 = 0.880)。这个由6个方程组成的系统得出的DFI、PE、PCG和FCG值与粗蛋白值增加或因遗传选择瘦度导致脂肪沉积减少所产生的经典效应一致。因此,这个由6个方程组成的系统可能被认为是未来营养模型系统的一个有趣框架。预测饲料效率与AME n、粗蛋白、DG和体重的函数关系,或与AME n、FCG、DG和体重的函数关系的回归分析显示,R2值分别为0.767和0.747。