Sinusoidal function to describe the growth curve of dairy heifers
H. Darmani Kuhi A E , N. Ghavi Hossein-Zadeh A , S. López B , S. Falahi C and J. France D
+ Author Affiliations
- Author Affiliations
A Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, 41635-1314, Iran.
B Instituto de Ganadería de Montaña (IGM), Universidad de León-Consejo Superior de Investigaciones Científicas, Departamento de Producción Animal, Universidad de León, E-24007 León, Spain.
C Department of Mathematics, Salman Farsi University of Kazerun, Kazerun, Iran.
D Centre for Nutrition Modelling, Department of Animal Biosciences, University of Guelph, Guelph ON, N1G 2W1, Canada.
E Corresponding author. Email: darmani_22000@yahoo.com
Animal Production Science 59(6) 1039-1047 https://doi.org/10.1071/AN18104
Submitted: 8 February 2018 Accepted: 29 May 2018 Published: 1 August 2018
Abstract
The objective of the present study is to introduce a sinusoidal function into dairy research and production by applying it to bodyweight records (from 1 to 24 months) from six dairy cow breeds reported by the Dairy Heifer Evaluation Project of Penn State Extension (USA) from 1991 to 1992. The function was evaluated with regard to its ability to describe the relationship between bodyweight and age in dairy heifers, and then compared with seven standard growth functions, namely monomolecular, logistic, Gompertz, von Bertalanffy, Richards, Schumacher and Morgan. The models were fitted to monthly bodyweight records of dairy heifers using non-linear regression to derive estimates of the parameters of each function. The models were tested for goodness of fit by using adjusted coefficient of determination, root mean square error, Akaike’s information criterion and Bayesian information criterion. Values of adjusted coefficient of determination were generally high for all models, suggesting the generally appropriate fit of the models to the data. The sinusoidal function provided the best fit of the growth curves for Brown Swiss, Guernsey and Milking Shorthorn breeds due to the lowest values of root mean square error, Akaike’s information criterion and Bayesian information criterion. According to the chosen statistical criteria, the Richards function provided the best fit for Ayrshire heifers, and the monomolecular the best for Holstein and Jersey. The least accurate estimates were obtained with the logistic. In conclusion, the sinusoidal function introduced here can be considered as an appropriate alternative to standard growth functions when modelling growth patterns in dairy heifers.
Additional keywords: bodyweight, genetic assessments, management, modelling.
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