Growth curves in meat-type and laying quail: a Bayesian perspective

Rossi, Robson Marcelo; Grieser, Daiane de Oliveira; Conselvan, Vagner de Almeida; Marcato, Simara Márcia

doi:10.5433/1679-0359.2017v38n4supl1p2743

Cited by 6 publications

(10 citation statements)

References 14 publications

(28 reference statements)

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“…Corroborating the results obtained in the current study for the two lines, the Gompertz model has been applied in other studies to fit growth curves for quail (Narinç et al ., 2017; Rossi et al ., 2017). The longitudinal nature of the data, where variance with age is not constant, led to the selection of a heterogeneous residual structure (Craig and Schinckel, 2001; Schinckel and Craig, 2002).…”

Section: Discussionmentioning

confidence: 99%

Application of non-linear mixed models for modelling the quail growth curve for meat and laying

et al. 2018

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The objective of the current paper was to apply mixed models to adjust the growth curve of quail lines for meat and laying hens and present the rates of instantaneous, relative and absolute growth. A database was used with birth weight records up to the 148th day of female quail of the lines for meat and posture. The models evaluated were Brody, Von Bertalanffy, Logistic and Gompertz and the types of residues were constant, combined, proportional and exponential. The Gompertz model with the combined residue presented the best fit. Both strains present a high correlation between the parameters asymptotic weight (A) and average growth rate (k). The two strains presented a different growth profile. However, growth rates allow greater discernment of growth profiles. The meat line presented a higher growth rate (6.95 g/day) than the lineage for laying (3.65 g/day). The relative growth rate was higher for lineage for laying (0.15%) in relation to the lineage for meat (0.13%). The inflection point of both lines is on the first third of the growth curve (up to 15 days). All results suggest that changes in management or nutrition could optimize quail production.

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Section: Discussionmentioning

confidence: 99%

Application of non-linear mixed models for modelling the quail growth curve for meat and laying

et al. 2018

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“…Gompertz-Laird is one of the more commonly used because it presents a better adjustment for models [24]. The Gompertz-Laird model is given in equation 1:…”

Section: Theoretical Conceptmentioning

confidence: 99%

System Dynamics Model of the Kinetics of Biogas Production from Vegetal Matter

Momodu¹,

Adepoju²

2020

IJEPE

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The use of renewable energy sources including biomass for energy generation, to achieve diversification in energy production, has been found to be sustainable economically, financially and environmentally. Various energy production technologies exist by which biomass can be converted for energy generation. Such technologies include anaerobic digestion, gasification, thermal depolymerization, pyrolysis, fermentation, anaerobic digestion, amongst others. The focus of this study is on the use of anaerobic digestion technology. Anaerobic digestion is recognized as one of the best options for treating biomass as it helps to avoid CO 2 emissions and run off of biomass. It is a natural process in which bacteria convert organic materials into biogas and fertilizer production in an environmentally friendly way. Anaerobic digestion is a series of sequential process including hydrolysis, acidogenesis, acetogenesis and methanogenesis. Different models have been applied to capture the characteristics of the anaerobic digestion process such as first-order model, Gompertz model and logistic model. However, Gompertz model is considered as the best model in describing the growth of animals and plants as well as the volume of bacteria. It is also used to describe the cumulative biogas production curve in batch digestion assuming that substrate levels limit growth in a logarithmic relationship. This study developed a System Dynamics model (SDM) for predicting biogas production (BP) in an anaerobic condition, based on Gompertz-Laird model. The objective is to describe the process of a System Dynamic (SD) model of two stage kinetics of BP. Primary data used were obtained from a laboratory experiment of BP using vegetal wastes, while secondary data were obtained from literature on studies using similar substrates. The Causal loop diagram generated, describes the anaerobic digestion (AD) process usually undergone by a substrate, while the Stock Flow diagram depicts the building blocks of the dynamic behavior of the same process. The developed SD model consists of two-level variables which depict the equations driving the AD process represented as hydrolysis-acidogenesis and acetogenesis-methanogenesis. The model results showed a significant lag phase between methanogenesis and fermentation stage, which was found to be linked to the inoculum-substrate ratio. The study conclusion includes: inoculum to substrate ratio affects BP; inconsistency of the experimental data caused by inhibition explains the variation observed between the empirical and simulated results.

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“…However, the Gompertz model was the best fit for growth data of female quails in both lines. Several studies (Akbaş and Oğuz, 1998;Narinç et al, 2010a;Rossi et al, 2017) reported that the Gompertz model best fits the growth data of Japanese quail. On the other hand, Beiki et al (2013) found that the Richards model was better than other models in fitting growth data of Japanese quail in both sexes.…”

Section: Goodness Of Fitmentioning

confidence: 99%

“…The most common growth models used in the studies on Japanese quail are Gompertz and Richards. Both models are considered the best to fit data in terms of goodness-of-fit criteria and their biologically interpretable model parameters (Karadavut et al ., 2017; Rossi et al ., 2017; Kaplan and Gürcan, 2018). Inflection point weight in these models is mostly identified as 35–40% of the asymptotic weight (Teleken et al ., 2017; Kaplan and Gürcan, 2018).…”

Section: Introductionmentioning

confidence: 99%

Modelling of growth alteration in Japanese quail after a selection experiment for body weight at 4 weeks of age

2018

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The current study investigated the influence of selection for body weight (BW) on growth curve parameters in two lines of Japanese quail through a mixed model approach. Live BWs of 1400 Japanese quail were recorded at 3-day intervals from hatching to 42 days of age. Birds were distributed equally across lines (selected and control) and sexes (male and female). The asymptotic weight parameter (β0) values were always higher in Gompertz than Richards models in both lines. The values of β0 were higher in the selected than control lines and in females than males across models. Differences were found in the inflection point for age and weight across lines and sexes. Values of the growth rate parameter (β2) ranged from 0.06 to 0.10 in both models, favouring males over females in both lines. Lower weights at the inflection point of both models were observed in the control line. Determination coefficient (R2) of both models in different genetic groups and sexes was similar. Mean square error (MSE) values of the Gompertz model were lower for females in selected v. control lines. In contrast, MSE values of the Richards model were lower for selected males v. control males. According to the criteria of choice (highest R2 and lowest MSE, Akaike information criterion and Bayesian information criterion), the Richards model was considered the best fitting model for the growth data of males, while the Gompertz model was the best for growth data of female quails in both lines.

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Growth curves in meat-type and laying quail: a Bayesian perspective

Cited by 6 publications

References 14 publications

Application of non-linear mixed models for modelling the quail growth curve for meat and laying

Application of non-linear mixed models for modelling the quail growth curve for meat and laying

System Dynamics Model of the Kinetics of Biogas Production from Vegetal Matter

Modelling of growth alteration in Japanese quail after a selection experiment for body weight at 4 weeks of age

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