三种描述埃及条件下荷斯坦-弗里西亚公牛生长曲线的非线性模型的比较

R.A.M. Somida
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引用次数: 0

摘要

本研究旨在通过三种非线性模型(Logistic, Gompertz和Richards)估计生长曲线参数,以确定哪种模型最适合数据。2017-2019年期间从牲畜发展协会(ELLahhamy农场)的荷尔斯坦-弗里西亚牛群中收集的102头荷尔斯坦-弗里西亚公牛的活重记录,该协会位于法尤姆省以西30公里处。本文研究的模型参数为渐近权值(A)、积分常数(b)和成熟率(K),取值范围分别为626.15 ~ 879.82 kg、2.708 ~ 11.08和0.0035 ~ 0.008。根据所研究的生长函数参数,(A)的数值估估值Gompertz最高,Logistic函数的数值估估值最低。Gompertz模型估计的参数(K)与Richards模型近似;这两个值都低于Logistic模型的结果(0.008)。拐点性状、拐点时间(IPT)和拐点体重(IPW)估计值分别为300.64 kg ~ 314.59 kg和323.92 d ~ 336.14 d。与其他模型相比,Richards模型的IPW和IPT估计值最高,并且在模型拟合优度标准上具有最好的调整,其中赤池信息准则(AIC)、施瓦茨贝叶斯信息准则(BIC)、均方误差(MSE)和决定系数最高(R2, 14489.18、14510.6、317.37和0.9983),其次是Gompertz和logistic函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COMPARISON OF THREE NONLINEAR MODELS TO DESCRIBE THE GROWTH CURVE OF HOLSTEIN-FRIESIAN BULLS RAISED UNDER EGYPTIAN CONDITIONS
The current study aimed to estimate the growth curve parameters through three non-linear models (Logistic, Gompertz and Richards) to determine which model best fits the data. Live weight records of 102 HolsteinFriesian bulls collected between 2017-2019 from a Holstein-Friesian herd that belongs to the Association of Livestock Development (ELLahhamy farm), located thirty kilometers west of Fayoum Governorate. In this work, the parameters of the studied models, asymptotic weight (A), constant of integration (b) and the maturation rate (K) ranged from 626.15 kg to 879.82 kg, 2.708 to 11.08, and 0.0035 to 0.008, respectively. According to the studied parameters of growth functions, Gompertz reached the highest numerical estimated value for (A) and the Logistic function had the lowest value. Parameter (K) estimate by the Gompertz model was similar to that obtained by the Richards model; both values were lower than those attained through the Logistic model (0.008). The inflectionpoint traits, time at Point of inflection (IPT) and weight at point of inflection (IPW) estimates ranged from 300.64 kg to 314.59 kg and 323.92 days to 336.14 days, respectively. The Richards model has the highest estimates of IPW and IPT comparedto the other models, also it had the best adjustment according to model goodness of fitcriteria, by having the lowest values for Akaike information criterion (AIC), Schwarz Bayesian information criter ion (BIC), Mean square error (MSE) and highest coefficient of determination (R2, ,14489.18, 14510.6, 317.37 and 0.9983) followed by the Gompertz, and logistic functions.
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