Quail growth curve model identity

Abstract

In this study, we sought to apply the model identity technique to compare the influence of eight treatments on growth parameters for three broiler quail lines, estimated using a logistic nonlinear regression model. For the analysis, we used the weight and age data obtained for three lines of European broiler quails (Coturnix coturnix coturnix) in a completely randomized 2×4 factorial scheme, with two levels of metabolizable energy (2900 and 3100 kcal of ME kg-1 of diet), four levels of raw protein (22%, 24%, 26% and 28% crude protein), and six repetitions. Results obtained for model identity tests indicated that although there were no significant differences among the parameters of the model between the treatments evaluated in each strain, there were, with the exception of Treatment 5 (3100 kcal of ME kg-1 and 22% crude protein), significant differences with respect to the adult weight parameter between lines within each treatment.

Growth curves of different free-range chicken lineages by the Gompertz model

“…Many studies report differences in the growth of free-range chicken genotypes regardless of the model used (Santos et al, 2005;Rizzi et al, 2013;Eleroğlu et al, 2014;Tavares et al, 2015). This shows that knowing the differences in growth speed between hardy lineages makes it possible to adopt management techniques that can influence bird feeding performance, the rearing system, and the choice of the genetic group to be used (Ribeiro et al, 2020). In doing so, producers can reduce slaughter age and, consequently, production costs.…”

“…The likelihood ratio test with approximations given by F statistics (Regazzi and Silva, 2004;Ribeiro et al, 2020) was applied, using the PROC NLIN procedure of SAS software (version 9.0; SAS Institute, Inc Cary, NC, 2001). The unrestricted NLIN procedure, called full model (Ω), and the model with restrictions (specified by ῳ1 or H01; ῳ2 or H02; ῳ3 or H03; ῳ4 or H04; ῳ5 or H05; ῳ6 or H06; ῳ7 or H07) were run to obtain the necessary estimates to execute the statistical test of the evaluated hypotheses, for all evaluated lineages (Table 1).…”

“…This trend has been accompanied by an evolution in the field of genetics, with the emergence of several free-range chicken lineages encompassing characteristics like hardiness and elevated production indices (Del Castilho et al, 2013). However, varying information can be found regarding body growth between these different lineages (Cruz et al, 2018;Ribeiro et al, 2020). Therefore, estimating the growth curve of the chicken lineages used in this production system may allow for the adoption of management practices that optimize meat production, in which the nutritional requirements of each growth phase can be prioritized.…”