1- Sari Agricultural Sciences and Natural Resources University
Abstract: (34 Views)
Introduction
A growth curve describes body weight changes over time or age using mathematical parameters that are capable of biological interpretation. Today, several growth curves, including Logistic, Richards, Gompertz, Von Bertalanffy and Brody curves are used to describe growth in animals and plants. These curves include parameters that can be considered as new traits. Regression coefficients and growth parameters play an important role in decision-making for management, feeding, breeding and genetic improvement programs, however, these growth rates vary depending on the breed, individual and environment. Since the growth of different animals has different growth curves, the process of selecting growth curve models is necessary to determine which one works best under the conditions of the study. The purpose of this study was to investigate and determine the best function that represents the growth pattern of dairy calves from birth to weaning so that this information can be used in the management of dairy calves and commercial purposes.
Materials and Methods
In this research, the weight data of 45 dairy calves were used to compare the performance of non-linear models in growth curve analysis and to identify the best growth pattern. The non-linear models studied included logistic, Richards, Gompertz, von Bertalanffy and Brody models. The fitting of non-linear models was done using the procedure of non-linear least squares (NLIN) of SAS software. In order to select the best model, goodness of fit statistics including coefficient of explanation (R2), root mean of square error (RMSE) and Akaike information criterion (AIC) were used.
Results
Based on the goodness of fit statistics, the highest value of R2 and the lowest values for AIC and RMSE criteria were related to the logistic model. Therefore, the logistic model was selected as the best model to model the growth curve in Holstein calves. Based on, the asymptotic weight was estimated at 85.1876 kg. The highest asymptotic weight (A) (Final weight of the experiment) in this study was estimated according to the Gomperts model (85.3962 kg). The highest and lowest value of parameter B was related to logistic and Gompertz models. The highest and lowest value of parameter K was estimated using Richards and logistic models. The highest and lowest correlations between the observed data and the predicted data were obtained by using the logistic (95.9%) and Richards (94.9%) non-linear functions, respectively.
Conclusion
In total, 5 different non-linear models of the growth curve in Holstein calves were investigated and studied. According to results, the logistic model showed the best description of the growth curve for calves and was selected as the best model. Therefore, this model can be used to determine the management strategies and the optimum weaning age in Holstein dairy calves.
A growth curve describes body weight changes over time or age using mathematical parameters that are capable of biological interpretation. Today, several growth curves, including Logistic, Richards, Gompertz, Von Bertalanffy and Brody curves are used to describe growth in animals and plants. These curves include parameters that can be considered as new traits. Regression coefficients and growth parameters play an important role in decision-making for management, feeding, breeding and genetic improvement programs, however, these growth rates vary depending on the breed, individual and environment. Since the growth of different animals has different growth curves, the process of selecting growth curve models is necessary to determine which one works best under the conditions of the study. The purpose of this study was to investigate and determine the best function that represents the growth pattern of dairy calves from birth to weaning so that this information can be used in the management of dairy calves and commercial purposes.
Materials and Methods
In this research, the weight data of 45 dairy calves were used to compare the performance of non-linear models in growth curve analysis and to identify the best growth pattern. The non-linear models studied included logistic, Richards, Gompertz, von Bertalanffy and Brody models. The fitting of non-linear models was done using the procedure of non-linear least squares (NLIN) of SAS software. In order to select the best model, goodness of fit statistics including coefficient of explanation (R2), root mean of square error (RMSE) and Akaike information criterion (AIC) were used.
Results
Based on the goodness of fit statistics, the highest value of R2 and the lowest values for AIC and RMSE criteria were related to the logistic model. Therefore, the logistic model was selected as the best model to model the growth curve in Holstein calves. Based on, the asymptotic weight was estimated at 85.1876 kg. The highest asymptotic weight (A) (Final weight of the experiment) in this study was estimated according to the Gomperts model (85.3962 kg). The highest and lowest value of parameter B was related to logistic and Gompertz models. The highest and lowest value of parameter K was estimated using Richards and logistic models. The highest and lowest correlations between the observed data and the predicted data were obtained by using the logistic (95.9%) and Richards (94.9%) non-linear functions, respectively.
Conclusion
In total, 5 different non-linear models of the growth curve in Holstein calves were investigated and studied. According to results, the logistic model showed the best description of the growth curve for calves and was selected as the best model. Therefore, this model can be used to determine the management strategies and the optimum weaning age in Holstein dairy calves.
Type of Study: Applicable |
Subject:
ژنتیک و اصلاح نژاد دام
Received: 2023/06/11 | Accepted: 2023/09/13
Received: 2023/06/11 | Accepted: 2023/09/13
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