Volume 11, Issue 27 (4-2020)                   rap 2020, 11(27): 88-94 | Back to browse issues page


XML Persian Abstract Print


University of Tabriz
Abstract:   (2455 Views)

     The aim of this research was to assess the growth curve of Makuie lambs using four growth functions including Logistic, Gompertz, Von Bertalanffy, and Verhulst as well as estimation of the parameters of these functions. 14454 live body weight records (LBW) that were collected in Makuie Sheep Breeding Center during 1990 -2016 were used. The NLMIXED and NLIN procedures of SAS (version 9.4) were used for fitting and estimation of parameters. Different indices were considered for selection of the most appropriate model. The asymptotic mature weight was 25.93 to 36.8 kg for all animal, 32.46 to 25.23 kg for male lambs and 29.29 to 31.15 kg for female lambs, respectively. The highest and lowest growth rate was observed in Verhulst and Von Bertalanffy (0.021 and 0.011, respectively). The logistics function showed the high growth rate for males than females (M: 0.014, F: 0.013), whereas Gompertz function showed a high growth rate for females (M: 0.011, F: 0.013). In order to compare different models, correlation coefficient (R), coefficient of determination (R2), Bayesian information criterion (BIC), Akaike information criterion (AIC), the mean absolute deviation (MAD), residual variance index (Se2) and RMSE were used. According to these indices, the Von Bertalanffy curve was the appropriate model because it achieved the lowest values for AIC, BIC, RMSE, MAD and S2e indices and the highest values for R and R2. When data were analyzed based on sexuality, the accuracy of assessment increased and the models better fitted to the data. The results of this study demonstrated that Von Bertalanffy model could accurately describe the growth pattern of Makuie sheep, especially, when males and females were evaluated separately.
 

Full-Text [PDF 334 kb]   (1147 Downloads)    
Type of Study: Research | Subject: ژنتیک و اصلاح نژاد دام
Received: 2016/11/6 | Revised: 2021/02/6 | Accepted: 2020/02/29 | Published: 2020/05/12

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.