Sharif Numan, McGovern Fiona M, McHugh Noirin, Pabiou Thierry, Berry Donagh P
School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, D04 V1W8, Ireland.
Teagasc, Animal & Grassland Research and Innovation Centre, Mellows Campus, Athenry, Co. Galway, H65 R718, Ireland.
Transl Anim Sci. 2025 Mar 22;9:txaf036. doi: 10.1093/tas/txaf036. eCollection 2025.
Modelling the growth profiles of sheep not only provides information about how body weight changes with age but also distills these serial measures into biologically important parameters which can be used in genetic evaluation programs. The objective of the present study was to evaluate a series of different functions applied to serial body weight measures of lowland sheep and, in doing so, also explore the relationship between the parameters within and across functions. The evaluated functions were Brody, Gompertz, Logistic, negative exponential, Richards and von Bertalanffy. The data set used consisted of 158,463 body weight records (range of 6 to 38 records per animal) from 13,090 female sheep. The functions were fitted to each animal separately. The coefficient of determination (R) and root mean square error (RMSE) per animal, along with the ease of model convergence, were used to evaluate model fit. The percentage of animals that achieved convergence per function ranged from 82.39% (Richards) to 100.00% (negative exponential and Logistic). The mean R value for the Logistic function was the lowest (0.94), while that for the Richards function was the highest (0.98). A weak negative correlation (r = -0.23 to -0.13) was estimated between the A (asymptotic weight) and B (integrated constant related to initial weight) parameters for all the functions except for Richards. The A and K (maturity rate) parameters of all the functions were negatively correlated and ranged from -0.55 (Brody) to -0.41 (Logistic). The values of the A parameter across all the functions were strongly positively correlated. The estimates for the B parameter of Logistic and Richards functions exhibited a very weak correlation (r = -0.04). A weak to strong correlation between the K parameter values across all functions was observed. Results suggested that all the evaluated functions, except the Richards function, can be applied to model the growth of lowland sheep. The Gompertz and von Bertalanffy functions were considered as the best fitting functions to describe the body weight profiles of Irish female lowland sheep based on their fit to the data, the ease of convergence, and the biological sensibility of the estimated function parameters.
对绵羊生长曲线进行建模不仅能提供体重随年龄变化的信息，还能将这些系列测量值提炼为具有生物学重要意义的参数，可用于遗传评估程序。本研究的目的是评估一系列应用于低地绵羊系列体重测量值的不同函数，并在此过程中探讨函数内部和函数之间参数的关系。评估的函数有布罗迪函数、冈珀茨函数、逻辑斯蒂函数、负指数函数、理查兹函数和冯·贝塔朗菲函数。所使用的数据集包括来自13090只母羊的158463条体重记录（每只动物6至38条记录）。这些函数分别拟合到每只动物身上。每只动物的决定系数（R）和均方根误差（RMSE）以及模型收敛的难易程度用于评估模型拟合度。每个函数达到收敛的动物百分比范围从82.39%（理查兹函数）到100.00%（负指数函数和逻辑斯蒂函数）。逻辑斯蒂函数的平均R值最低（0.94），而理查兹函数的平均R值最高（0.98）。除理查兹函数外，所有函数的A（渐近体重）和B（与初始体重相关的积分常数）参数之间估计存在弱负相关（r = -0.23至-0.13）。所有函数的A和K（成熟率）参数呈负相关，范围从-0.55（布罗迪函数）到-0.41（逻辑斯蒂函数）。所有函数的A参数值呈强正相关。逻辑斯蒂函数和理查兹函数的B参数估计值表现出非常弱的相关性（r = -0.04）。观察到所有函数的K参数值之间存在弱到强的相关性。结果表明，除理查兹函数外，所有评估函数均可用于对低地绵羊的生长进行建模。基于对数据的拟合、收敛的难易程度以及估计函数参数的生物学合理性，冈珀茨函数和冯·贝塔朗菲函数被认为是描述爱尔兰雌性低地绵羊体重曲线的最佳拟合函数。
