Comparison of Nonlinear Growth Curve Models in Broiler Chickens

Topal, Mehmet; ., S. Canan Bolukbasi

doi:10.1080/09712119.2008.9706960

Cited by 39 publications

(34 citation statements)

References 8 publications

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“…Most anatomical structures follow an asymptotic growth trajectory; hence, data from both sexes were fitted to different nonlinear models to find the best-fit model. We used growth functions commonly used in the literature, namely the logistic, Weibull, Gompertz, and monomolecular functions (Koppe, Klauke, Lee, & Schumacher, 2000;Koya & Goshu, 2013;Topal & Bolukbasi, 2008) and a modified logistic equation (Prossinger, 2001(Prossinger, , 2008. Models were calculated with InfoStat software.…”

Section: A Ter I a Ls A N D M Eth Od Smentioning

confidence: 99%

Frontal sinus ontogeny and covariation with bone structures in a modern human population

Sardi

Joosten

Pandiani

et al. 2018

Journal of Morphology

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In humans, the frontal sinus (FS) is located in the medial part of the supraorbital region, sometimes expanded throughout the frontal squama. It exhibits high morphological variability, but its general form appears to be constrained by surrounding structures. The goal of this study is to analyze FS growth and test for covariation between FS volume and the glabellar region, upper nasal region, bone thickness and endocranial size in a human sample from Argentina. The sample comprises 149 reconstructions derived from computed tomography images of individuals aged 0–31 years. Volume of the FS and measurements of the surrounding structures were recorded. The FS growth trajectory was assessed by parametric and nonparametric methods, and covariation was determined using correlations and partial correlations. The FS volume could be measured at an age of about 6 years and older; adults had no aplasia but hyperplasia was found in some cases. Since the most conspicuous characteristic found was variation among individuals, the nonparametric smoothing spline produced very poor fitting. The modified logistic function was the only parametric method providing significant parameters. Sexes differed in the age at which FS growth began and ended, with FS developing earlier but at a slower rate in females than in males. The FS volume did not correlate with either upper nasal width or endocranial volume, but it correlated with bone thickness measurements (mainly from the glabellar region), even when age was held constant. Expansion of the FS at the frontal poles also correlated with frontal bone thickness. Despite the difficulty in modeling and predicting the trajectory and morphology of FS, our results suggest that it is affected by its surrounding bony environment.

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Section: A Ter I a Ls A N D M Eth Od Smentioning

confidence: 99%

Frontal sinus ontogeny and covariation with bone structures in a modern human population

Sardi

Joosten

Pandiani

et al. 2018

Journal of Morphology

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“…Growth in an animal is a whole of complex physiological and morphological processes from hatching to maturity which is defined as the increases in the weight and volume measurements of the organs or body for a given time (Topal et al 2003;2004;Topal & Bölükbaşı 2008). Numerous growth models have been used considering the growth of poultry species.…”

Section: Introductionmentioning

confidence: 99%

Comparison of growth curves using non-linear regression function in Japanese quail

Kaplan

Gürcan

2016

Journal of Applied Animal Research

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This study was conducted to determine the goodness of fit of Gompertz, Logistic, Von Bertalanffy, Richards, Levakovich and Janoschek growth models in Japanese quail. Therefore, weekly live-weight data obtained from 372 females and 339 males were fitted. Females' live weights were found to be higher than that of males, and the first divergence in the growth of female and male birds occurred in 21-28 days, and it survived until the experiment (P < .001). The coefficient of determination (R 2), adjusted coefficient of determination (adj. R 2), mean square error (MSE), Akaike's information criteria (AIC) and Bayesian information criterion (BIC) were used to determine the best growth model. R 2 and adjusted R 2 values of the growth models were similar and close to 1, indicating that all models perform well in describing age-related changes in live weight in quail. Based on the MSE, AIC and BIC values, Richards model was determined to be the best fitting model to the growth data of both sexes. Consequently, it has been demonstrated that Richards function which has a flexible structure in terms of inflection point is the most appropriate growth function for both female and male birds.

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“…In this model, the growth curve is asymmetric around the point of maximum growth rate. However, the point of inflection in Gompertz model is fixed [26,27] compared the Logistic, Gompertz, Von Bertalanfi, Morgan-Mercer-Flodin (MMF) and Weibull growth models and suggested that MMF, Weibull and Gompertz models can be useful for explaining broiler chickens growth performance. Compared the Richards, logistic, Gompertz and spline linear regression models for describing chicken growth curves.…”

mentioning

confidence: 99%

Comparison of Nonlinear Models Describing Growth Curves of Broiler Chickens Fed on Different Levels of Corn Bran

Masoudi¹

2017

IJAWB

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This study compared four nonlinear models to describe growth parameters of broiler chicken fed on different levels of corn bran. Two experiments were designed for this purpose. In the first experiment, 80 chickens (308 Ross strain) that had been fed on the same diet and weighed separately were used to determine the best model. Indicators of R 2 , ACI and the number of circulation of the model were used to confirm the best model. In the second experiment, 300 one-day-old Ross 308 broiler chickens were used in a completely randomized design with four treatments and five replicates. The treatments included control and diets contained 2.5, 5 and 7.5% corn bran. Results showed that the Gompertz function had the highest R 2 and the lowest AIC and number of iterations. So, the Gompertz model best described the broiler growth curves. R 2 , AIC values and number of iterations of the Gompertz model were 0.9970, 648 and 5, respectively. Different levels of corn bran significantly affected mature body weight (W f ) and body weight at the inflection point (W i ) (P<0.05), but did not affect the initial body weight (W 0 ), inflection point (T i ) and coefficient of relative growth (b) (P>0.05). Additionally, corn bran significantly decreased the growth rate on days 21, 28, 35 and 42 (P<0.05), but had no significant effect on the growth rate on days 7 and 14 (P>0.05). Overall, the results showed that the Gompertz model described the biological curves of broiler fed on corn bran better than other models. Also, growth parameters were affected by corn bran.

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Comparison of Nonlinear Growth Curve Models in Broiler Chickens

Cited by 39 publications

References 8 publications

Frontal sinus ontogeny and covariation with bone structures in a modern human population

Frontal sinus ontogeny and covariation with bone structures in a modern human population

Comparison of growth curves using non-linear regression function in Japanese quail

Comparison of Nonlinear Models Describing Growth Curves of Broiler Chickens Fed on Different Levels of Corn Bran

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