Evaluation of non‐linear growth curve models in the Vietnamese indigenous Mia chicken

Abstract

Growth is an economically important trait for farm animals and can be defined as any change in body size such as body weight or length per time unit (Narinç et al. 2017). This trait is influenced by genetic factors, environmental factors such as feeding condition, and the interaction between them. The knowledge of animal growth is important for the improvement of management and feeding practices in order to maximize the profit of livestock industry (Narinç et al. 2017; Do & Miar 2020). Mathematical models have been successfully characterized growth patterns and visualized the shape of growth over time. Among these models, non-linear models are the most applied models as they allow the interpretation and understanding of growth patterns underlying growth periods (Schnute 1981). Several non-linear models such as the Gompertz (Gompertz 1825), Logistic (Pearl 1977), Bridges (Bridges et al. 1992), and Richards (Richards & Kavanagh 1945) are widely used to describe the growth curve (Kaplan & Gürcan 2018). The growth parameters derived from mathematical models have shown that these parameters are heritable and are responsive to the selection programs (Mignon-Grasteau et al. 2000; Manjula et al. 2018).In Vietnam, chicken is one of the most common farm animals.The species is not only important for the food resources for people but also for the cultures of the country. Vietnam owns about

Mathematical models to describe the growth curves of Vietnamese Ri chicken

“…In addition, we also observed inconsistency in the ranking of the goodness of fit in Ri chicken in the current study compared to our previous research in the Mia chicken (Nguyen Hoang et al, 2021). Previously, the Gompertz model and Bridges models were the best models in males and females Mia chicken, respectively (Nguyen Hoang et al, 2021), but were not in the current study. This inconsistency is possibly due to the sample size, genetics, and management.…”

“…A similar trend was also observed for the weight at the inflection. The result of a higher BW in males than females is consistent with our previous reports for Mia chicken (Nguyen Hoang et al, 2021) and other earlier studies for other breed chicken, such as Athens-Canadian population chickens (Aggrey, 2002) and indigenous Venda chickens (Norris et al, 2007) and local Italian chicken (Rizzi et al, 2013) and Shaobo, Huaixiang and Youxi Chicken (Zhao et al, 2015). The asymmetric weights (α) for Ri chicken were varied among models but generally lower than values reported for other breeds (Rizzi et al, 2013;Mata-Estrada et al, 2020).…”

“…The k values also varied among the models, with very low values of k were obtained for Brody, Bridges, and Janoschek models. The estimated k values from the Gompertz model is 0.33 in males, and 0.30 in females were higher than the values of k =0.15 (g/week) for both sexes in Mia chicken in another study (Nguyen Hoang et al, 2021). Yang (2006) reported the values of 0.13 (g/week) for males and 0.14 (g/week) for females, which was lower than k values from the current studies.…”

“…This information is important for improving the local chicken breeds' genetic and feeding strategies (Selvaggi et al, 2015). In Vietnam, the Gompertz model recently reported a best model to describe the growth curve in Mia chicken compared to the Richards, Logistic, and Bridges models (Nguyen Hoang et al, 2021). Therefore, this study compared six different growth models (Gompertz, Brody, Logistic, Richards, Bridges, and Janoschek) for describing growth pattern of Ri chicken in Vietnam.…”

“…A total of six different growth models, which included three three-parameters models (Gompertz, Brody, and Logistic) and three four-parameter models (Richards, Bridges, and Janoschek), were used for modeling of growth curves in males and females separately (Table 2). Similar methods were applied for fitting models, as described by Nguyen Hoang et al (2021). In brief, the body weight (BW) was fitted as a function of time (week) using nlsLM() command in minpack.lm packages (implementing the Levenberg-Marquardt algorithm) (Elzhov et al, 2016) in R software ( R Development Core Team, 2011).…”