Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5.
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5; Department of Animal Sciences, Animal Nutrition Group, Wageningen University, Wageningen, The Netherlands 6700 AH.
Poult Sci. 2021 May;100(5):101059. doi: 10.1016/j.psj.2021.101059. Epub 2021 Feb 17.
Growth models describe body weight (BW) changes over time, allowing information from longitudinal measurements to be combined into a few parameters with biological interpretation. Nonlinear mixed models (NLMM) allow for the inclusion of random factors. Random factors can account for a relatively large subset of the total variance explained by bird-specific measurement correlation. The aim of this study was to evaluate different NLMM using birds from 2 heritage chicken lines; New Hampshire (NH) and Brown Leghorn (BL). A total of 32 birds (16 mixed sex birds from each strain) were raised to 17 wk of age. After 12 wk, half were continued on ad libitum (AL) feed intake, and half were pair-fed, using a precision feeding system; they were given 95% of the AL intake of a paired bird closest in BW. Residual feed intake (RFI) of birds, as an indicator of production efficiency, was increased in pair-fed BL birds as a result of minor feed restriction. Growth data of the birds were fit to a mixed Gompertz model with a variety of different bird-specific random coefficients. The model had the form: [Formula: see text] ; where Wm was the mature BW, b was the rate of maturing, t was age (d), t was the inflection point (d). This fixed-effects model was compared with NLMM using model evaluation criteria to evaluate relative model suitability. Random coefficients, Wm ∼ N(0,V) and b ∼ N(0,V) were tested separately and together and their differences, for strains, sex, and feeding treatments, were reported as different where P ≤ 0.05. The model with both random coefficients was determined to be the most parsimonious model, based on an assessment of serial correlation of the residuals. NLMM coefficients allow stochastic prediction of the mean age and its variation that birds need to achieve a certain BW, allowing for unique new decision support modeling applications; these could be used in stochastic modeling to evaluate the economic impact of management decisions.
生长模型描述了体重随时间的变化，使来自纵向测量的数据能够合并为具有生物学解释的几个参数。非线性混合模型（NLMM）允许包含随机因素。随机因素可以解释鸟类特定测量相关性解释的总方差的较大子集。本研究的目的是使用来自 2 个传统鸡系的鸟类（新汉普夏鸡（NH）和褐壳蛋鸡（BL））评估不同的 NLMM。总共饲养了 32 只鸡（每系 16 只雌雄混合鸡），直到 17 周龄。在 12 周后，一半鸡继续自由采食（AL），另一半鸡采用精确饲喂系统进行限饲，给与配对的 BW 最接近的鸟的 95%的 AL 摄入量。限饲导致褐壳蛋鸡的剩余采食量（RFI）增加，这是生产效率的一个指标。鸟类的生长数据符合混合 Gompertz 模型，其中使用了多种不同的鸟类特定随机系数。模型形式为：[公式：见正文]；其中 Wm 为成熟体重，b 为成熟速度，t 为年龄（d），t 为拐点（d）。该固定效应模型与 NLMM 进行了比较，使用模型评估标准来评估相对模型适用性。随机系数 Wm∼N(0,V)和 b∼N(0,V)分别进行了测试，同时也对它们的差异（按系、性别和饲喂处理）进行了测试，当 P≤0.05 时，报告为不同。基于对残差的序列相关性的评估，具有两个随机系数的模型被确定为最简约模型。NLMM 系数允许对达到一定 BW 的鸟类的平均年龄及其变化进行随机预测，从而为独特的新决策支持建模应用提供了可能；这些可以用于随机建模中，以评估管理决策的经济影响。
