Document Type : Research Paper
Authors
- Noorolahe SHahroodi 1
- Mohammad Rokouei 2
- Hadi Faraji- Arough 3
- Ali Maghsoudi 4
- Morteza Kykha Saber 5
1 Former M.Sc. Student, Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
2 Associate Professor, Department of Animal Science and Bioinformatics, Agriculture Faculty, University of Zabol, Zabol, Iran. ;Associate Professor, Department of Animal and Poultry Science, College of Aburaihan, University of Tehran Pakdasht, Tehran, Iran.
3 Assistant Professor, Research Center of Special Domestic Animals, University of Zabol, Zabol, Iran
4 Associate Professor, Department of Animal Science and Bioinformatics, Agriculture Faculty, University of Zabol, Zabol, Iran
5 Assistant Professor, Agricultural Research, Education and Extension Organization, Zabol, Iran
Abstract
The purpose of this study was to fit different nonlinear models to describe growth curve and selection the best model to describe a growth curve for calves of Sistani calves. Body weight records of 241 calves (118 males and 123 females) collected by the Sistani Dairy Cattle Research Station of Zahak from year 2010 to 2017 were used. Four nonlinear models (Gompertz, Logistic, Richards, and Weibull) were fitted to the body weight records and the best model was selected by the goodness-of-fit criteria (Mean square error, Bayesian information criterion, Akaike information criterion and corrected coefficient of determination). According to goodness-of-fit criteria, Richards model was the most appropriate model to describe the growth curve in male and female calves. The effect of sex on curve parameters was significant in many functions (P <0.05). Logistic and Richards models had the highest and the lowest initial weight parameter, respectively. Male calves reached to the inflection point in a higher age and weight compared to female calves. According to the results of this study, a proper model can be used to study the growth pattern of this breed in order to better nutritional management and selection for rapid growth with high accuracy.
Keywords
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