Canaza-Cayo Ali William, Mamani-Cato Rubén Herberth, Churata-Huacani Roxana, Rodríguez-Huanca Francisco Halley, Calsin-Cari Maribel, Huacani-Pacori Ferdynand Marcos, Cardenas Minaya Oscar Efrain, Bueno Filho Júlio Sílvio de Sousa
Escuela Profesional de Ingeniería Agronómica, Facultad de Ciencias Agrarias, Universidad Nacional del Altiplano. Av. Floral 1153, Código postal 21001, Puno, Perú.
Departamento de Estatística, Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Lavras, Código postal 3037, CEP 37200-900 Lavras, MG, Brasil.
Vet Anim Sci. 2025 Mar 20;28:100447. doi: 10.1016/j.vas.2025.100447. eCollection 2025 Jun.
The objective of this study was to fit four nonlinear models (Brody, von Bertalanffy, Gompertz and Logistic) to realizations of llama weight, using frequentist and Bayesian approaches. Animals from both sexes and types (K'ara and Ch'accu) were observed. Data consisted of 43,332 monthly body weight records, taken from birth to 12 months of age from 3611 llamas, collected from 1998 to 2017 in the Quimsachata Experimental Station of the Instituto Nacional de Innovación Agraria (INIA) in Peru. Parameters for Non-linear models for growth curves were estimated by frequentist and Bayesian procedures. The MCMC method using the Metropolis-Hastings algorithm with noninformative prior distributions was applied in the Bayesian approach. All non-linear functions closely fitted actual body weight measurements, while the Brody function provided the best fit in both frequentist and Bayesian approaches in describing the growth data of llamas. The analysis revealed that female llamas reached higher asymptotic weights than males, and K'ara-type llamas exhibited higher asymptotic weights compared to Ch'accu-type animals. The asymptotic body weight, estimated for all data using the Brody model, was 42 kg at 12 months of age in llamas from Peru. The results of this research highlight the potential of applying nonlinear functions to model the weight-age relationship in llamas using a Bayesian approach. However, limitations include the use of historical data, which may not fully represent current growth patterns, and the reliance on non-informative priors, which could be improved with prior knowledge. Future studies should refine these aspects.
本研究的目的是使用频率学派和贝叶斯方法，将四个非线性模型（布罗迪模型、冯·贝塔朗菲模型、冈珀茨模型和逻辑斯蒂模型）拟合到羊驼体重数据上。观察了不同性别和类型（卡拉型和查库型）的动物。数据包括从1998年到2017年在秘鲁国家农业创新研究所（INIA）的金萨查塔实验站收集的3611只羊驼从出生到12个月大的43332条月度体重记录。通过频率学派和贝叶斯程序估计生长曲线非线性模型的参数。在贝叶斯方法中应用了使用具有非信息先验分布的梅特罗波利斯-黑斯廷斯算法的MCMC方法。所有非线性函数都与实际体重测量值密切拟合，而布罗迪函数在频率学派和贝叶斯方法中对描述羊驼生长数据的拟合效果最佳。分析表明，雌性羊驼达到的渐近体重高于雄性，并且卡拉型羊驼与查库型动物相比表现出更高的渐近体重。使用布罗迪模型对所有数据估计的12个月大的秘鲁羊驼的渐近体重为42千克。本研究结果突出了使用贝叶斯方法应用非线性函数来模拟羊驼体重与年龄关系的潜力。然而，局限性包括使用历史数据，可能无法完全代表当前的生长模式，以及依赖非信息先验，这可以通过先验知识加以改进。未来的研究应完善这些方面。
