Abstract
The data were taken from the “Enhancing of Mutton Production through goat breeding” a Project run at Livestock Production Research Institute Bahadurnagar (Okara); the growth performance of Beetal goats were compared by statistical non-linear models, like Exponential, Gompertz and Logistic models. These models were based to obtain the best fitted model by following the criteria of mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Run test were used to check the independence and normality of the models and residuals were checked by Shapiro - Wilk test. Errors were found to be normal and independently distributed. Body weights of Beetal goats and Bucks were examined by shape of the curves and it revealed that the values of α, β and γ of Gompertz model were higher than that of exponential and logistics in both the district of Okara and Sahiwal. Also, values of MAE, RMSE and MAPE of Gompertz model were lower than the logistic and exponential. Key words: Bodyweight of Beetal, Gompertz, least value.
Highlights
The knowledge of growth curves in Beetal goats was important for determining the biological, genetic and economical efficiencies (Mehta et al, 1997)
In Sahiwal District of Pakistan, 49 male bucks and 38 female Beetal goats for period of 32 weeks were under observations in this way total 68 weeks (16 months) time period Beetal of certain Farms are under observation and data collected from them
The results obtained from Okara District for male bucks indicated that the values α, β and γ for Gompertz model were 27.62, 2.54, 0.10, the values for exponential and logistic were 4.61, 0.06, 0.00 and 7.67, 0.17, 24.74, respectively
Summary
The data were taken from the “Enhancing of Mutton Production through goat breeding” a Project run at Livestock Production Research Institute Bahadurnagar (Okara); the growth performance of Beetal goats were compared by statistical non-linear models, like Exponential, Gompertz and Logistic models. These models were based to obtain the best fitted model by following the criteria of mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Run test were used to check the independence and normality of the models and residuals were checked by Shapiro Wilk test.
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