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Fitting Nonlinear Growth Models on Weight in Mengali Sheep through Bayesian Inference

Fitting Nonlinear Growth Models on Weight in Mengali Sheep through Bayesian Inference

Farhat Iqbal1, Mohammad Masood Tariq2,*, Ecevit Eyduran3, Zil-e-Huma4, Abdul Waheed5, Farhat Abbas2, Muhammad Ali2, Nadeem Rashid2, Majed Rafeeq2, Asadullah2 and Zahid Mustafa2

1Department of Statistics, University of Balochistan, Quetta, Pakistan
2Centre of Advanced Studies in Vaccinology and Biotechnology, University of Balochistan, Quetta, Pakistan
3Igdir University, Faculty of Agriculture, Department of Animal Science, Igdir, Turkey
4Department of Zoology, Sardar Bahadur Khan Women’s University, Quetta, Pakistan
5Faculity of Veterinary Sciences, Bahauddin Zakariya University, Multan, Pakistan




*      Corresponding author:


This article deals with the Bayesian analysis as an alternative to the classical approach for estimating the body growth of Mengali sheep breed of Balochistan, Pakistan. The parameters mature weight, integration constant, maturity rate and their credible intervals of four widely-used nonlinear sigmoidal growth models were estimated through Bayesian inference. Gompertz, Logistic, Brody and Von Bertalanffy models were fitted to average monthly body weight data of (n = 412) Mengali sheep from birth to 24 months of age for both sexes (male and female) and type of births (single and twin). The overall goodness of fit was checked by calculating Deviance Information Criteria (DIC) and the square of correlation (R2) between observed body weight and the predicted value’s marginal density means. The DIC and R2 values of the models ranged from 31.2 to 59.3 and 0.9702 to 0.9977, respectively. Our results revealed the superior performance of the Brody model in terms of lower DIC and higher R2 values for male, female, single and twin birth sheep data, thus providing the overall best fit than the competing nonlinear growth models. The findings of this study indicate the potential of fitting complex nonlinear functions to weight-age relationship of animal data via Bayesian approach.

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Pakistan Journal of Zoology


Vol. 53, Iss. 3, Pages 801-1200


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