Modeling the growth curve of Iranian Shall sheep using non-linear growth models

Journal of Applied Animal Research

2017

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In order to describe the growth curves in Iranian Mehraban sheep, five non-linear mathematical equations (Brody, Negative exponential, Logistic, Gompertz and Von Bertalanffy) were used. The data set used in this study was obtained from the Agricultural Organization of Hamedan province and comprised 35,414 weight records of lambs which were collected from birth to 365 days of age during 1991-2011. Each model was fitted separately to body weight records of all lambs, male and female lambs and single and twin lambs using the NLIN and MODEL procedures in SAS. The models were tested for goodness of fit using adjusted coefficient of determination (R 2 adj), root mean square error (RMSE), Durbin-Watson statistic (DW), Akaike's information criterion (AIC) and Bayesian information criterion (BIC). The Brody model provided the best fit of growth curve in all lambs, male and female lambs and single and twin lambs due to the lower values of AIC and BIC than other models. The Logistic model provided the worst fit of growth curve for all lambs, male and female lambs and single and twin lambs. Evaluation of different growth equations used in this study indicated the potential of the non-linear functions for fitting body weight records of Mehraban sheep.

Section: Discussionsupporting

confidence: 85%

Comparison of non-linear growth models to describe the growth curve of Mehraban sheep

Hojjati

Journal of Applied Animal Research

2017

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“…Akbas et al (1999) studied the fitting performance of Brody, Negative exponential, Gompertz, Logistic and von Bertalanffy models to data on weight-age of Kivircik and Daglic male lambs and found that the Brody model was the best equation for describing the growth of lambs. Similar to the current results, Goliomytis et al (2006) and Ghavi Hossein-Zadeh (2015) reported the excellent fit of the Richards function to the weight-age data According to the low DW values obtained in the current study from fitting the non-linear models of the growth curve, it was concluded that there was positive autocorrelation between residuals for all models. The Negative exponential model provided the most positive autocorrelation among different models.…”

Section: Discussionsupporting

confidence: 89%

Comparison of non-linear models to describe growth of Iranian Guilan sheep

Golshani²

2016

Rev Colomb Cienc Pecu

SummaryBackground: non-linear mathematical models, empirically developed by plotting body weight against age, are used to describe the growth curve in different animals. Objective: to describe the growth pattern in Guilan sheep using non-linear models. Methods: six non-linear mathematical equations (Brody, Negative exponential, Logistic, Gompertz, von Bertalanffy and Richards) were used to describe the growth curves in Guilan sheep. The Agricultural Organization of Guilan province (Rasht, Iran) provided the dataset used in this study. The dataset included 42,257 weight records of lambs from birth to 240 days of age during years 1994 to 2014. Each model was separately fitted to body weight records of all lambs, males and females, using the NLIN and MODEL procedures of SAS. The models were tested for goodness of fit using adjusted coefficient of determination, root means square error (RMSE), Durbin-Watson statistic, akaike's information criterion (AIC) and bayesian information criterion (BIC). Results: the Richards model provided the best fit to the growth curve in females and all lambs, with the lowest RMSE, AIC, and BIC values compared to the other models. The Brody model provided the best fit of growth in male lambs due to the lower values of AIC and BIC compared to the other models. The negative exponential model provided the worst fit of growth for males, females and all lambs. Conclusion: the evaluation of the growth equations used in this study indicates the potential of non-linear functions for fitting body weight records of Guilan sheep.

“…Carneiro J unior et al (2007) found that the Bayesian methodology is well justifiable for analysing small populations or data set when great historical information is attainable. The Gibbs Sampling algorithm produces posterior distributions of parameters to permit random sample estimation of parameter estimates based on a specific data set (Magnabosco et al 2000;Ghavi Hossein-Zadeh 2015;Ghavi Hossein-Zadeh 2017b). The aims of current study were to estimate variance components, heritabilities and genetic trends for morphometric traits and to estimate genetic correlations between these traits and yearling weight in Moghani sheep using Bayesian algorithm via Gibbs sampling.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimates of genetic parameters and genetic trends for morphometric traits and their relationship with yearling weight in Moghani sheep

Italian Journal of Animal Science

Ghahremani

2017

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This study was conducted to estimate variance components, genetic parameters and genetic trends for morphometric traits and their relationship with yearling weight in Moghani sheep. The data set and pedigree information of Moghani sheep were obtained from the breeding station of Moghani sheep (in Ardebil province, Iran) and comprised 2387 lamb records for morphometric traits during 1994 to 2012. Traits considered were height at withers (HW), height at rump (HR), body length (BL), heart girth (HG), leg circumference (LC) and yearling weight (YW). Four different animal models were fitted, differentiated by including or excluding maternal effects, with covariance between maternal and direct genetic effects were implemented by Gibbs sampling methodology. Selection of the best model of analysis for each trait was based on Akaike's information criterion (AIC). Posterior mean estimates of direct heritabilities for BL, HW, HR, LC, HG and YW were 0.34, 0.037, 0.47, 0.073, 0.065 and 0.17, respectively. Estimates of direct genetic correlation between morphometric traits varied from À0.21 (between BL-LC) to 0.67 (between LC-HG). Estimates of direct genetic correlation between morphometric traits and YW were positive and varied from 0.08 (between HW-YW) to 0.52 (between HG-YW). Estimates of direct genetic trends for BL, HW, HR, LC and HG were significant (p < .0001), and their corresponding values were À0.054 ± 0.009, À0.005 ± 0.001, 0.032 ± 0.006, À0.009 ± 0.002 and À0.014 ± 0.003, respectively. The results implied that applicable genetic variations observed for under study traits could be applied in designing future genetic selection plans for Moghani sheep.