Comparison on accuracy of different nonlinear models in predicting growth of Podolica bulls

Abstract

Animal growth does not follow a linear pattern, being explained mathematically by functions that have parameters with biological meaning. These parameters are used to estimate the expected weight of animals at specific ages. Several nonlinear models have been used to describe growth. This study was carried out to estimate the parameters of logistic, Gompertz, Richards and von Bertalanffy growth curve models in a sample of Podolica young bulls to determine the goodness of fit. Animals were weighed every 3 months from birth to 810 days of age. The results indicate that all the growth models used were easily fitted to the observed data with Gompertz and logistic functions presenting less computational difficulty in terms of number of iterations to achieve convergence. Moreover, logistic and Richards equations provided the best overall fit being useful to describe the growth of Podolica bulls. Considering that the literature lacks information on growth curves in Podolica breed, the study of a mathematical model for growth describing the developmental pattern of a specific population within a peculiar environment is a useful tool to improve Podolica breed production.

Comparison of Three Nonlinear Models to Describe the Growth Curve of Holstein-Friesian Bulls Raised Under Egyptian Conditions

“…According to the studied growth functions, Gompertz reached the highest numerical estimated value for the asymptotic weight (A) and the Logistic function had the lowest value. Although the Gompertz model had the highest asymptotic weight value, the Richards model was the best to fit the data depending on the goodness of fit criteria and asimilar trend was found by Selvaggi et al, (2017). The estimated maturity rate parameter (K) by the Gompertz model was similar to that obtained by the Richards model; both values were lower than those attained through the Logistic model (0.008).…”

“…Several studies reported that Richards model was the best for describing growth pattern in cattle (Tjørve and Tjørve, 2010;Velásquez et al, 2013;Goldberg and Ravagnolo, 2015;Gano et al, 2016 andTutkun, 2019). The logistic function was found to be the least accurate based on AIC and BIC (Forni et al, 2009, Gano et al, 2016and Selvaggi et al, 2017. The estimates of AIC and BIC showed wide range reported by many authors (42.05 to 201857 and 41.43 to 201889, respectively) for the Gompertz model (Velásquezet al,2013, Goldberg and Ravagnolo, 2015and Gano et al, 2016 ranging from 183379 to 189150 and 183560 to 189189, respectively for Richard model and ranging from 184623 to 206277 and 184749 to 206309, respectively for Logistic model (Goldberg andRavagnolo, 2015 andGano et al, 2016).…”

“…Also, by investigating the relationship between parameter K and the asymptotic weight (A), the heaviest animals can be identified at younger ages (Freitas, 2005). Many studies conducted on nonlinear equations indicated that the Richards model was the best to describe the growth curve of cattle (Selvaggi et al, 2017 andYin andKönig,2020). Although Richards has more computational difficulties as it is a four-parameter model, it was found to be the most suitable one due to its accuracy in predicting mature weight, which is considered an important selection goal (Selvaggi et al, 2017).The Gompertz and logistic mathematical models were found among the most frequently used functions for describing cattle growth (Duarte, 1975 andWada et al, 1983).…”

“…Many studies conducted on nonlinear equations indicated that the Richards model was the best to describe the growth curve of cattle (Selvaggi et al, 2017 andYin andKönig,2020). Although Richards has more computational difficulties as it is a four-parameter model, it was found to be the most suitable one due to its accuracy in predicting mature weight, which is considered an important selection goal (Selvaggi et al, 2017).The Gompertz and logistic mathematical models were found among the most frequently used functions for describing cattle growth (Duarte, 1975 andWada et al, 1983). So, the present study aimed to compare the potential of three non-linear models (Logistic, Gompertz, and Richards) to detect which model best fits the data of Holstein bulls.…”

Predicting the Digestive Tract Development and Growth Performance of Goat Kids Using Sigmoidal Models

“…Moreover, Malhado, et al [43] showed that both Gompertz and Logistic models provided the best fit of the growth curve in sheep. In addition, the logistic and Gompertz showed the best fit for the growth of young bulls with fewer iterations needed to achieve convergence [44].…”