Egg production curve fitting using nonlinear models for selected and nonselected lines of White Leghorn hens
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Data
2012-11-01
Autores
Savegnago, R. P. [UNESP]
Cruz, V. A. R. [UNESP]
Ramos, S. B. [UNESP]
Caetano, S. L. [UNESP]
Schmidt, G. S.
Ledur, M. C.
El Faro, L.
Munari, D. P. [UNESP]
Título da Revista
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Título de Volume
Editor
Poultry Science Assoc Inc
Resumo
Egg production curves describe the laying patterns of hen populations over time. The objectives of this study were to fit the weekly egg production rate of selected and nonselected lines of a White Leghorn hen population, using nonlinear and segmented polynomial models, and to study how the selection process changed the egg-laying patterns between these 2 lines. Weekly egg production rates over 54 wk of egg production (from 17 to 70 wk of age) were measured from 1,693 and 282 laying hens from one selected and one nonselected (control) genetic line, respectively. Six nonlinear and one segmented polynomial models were gathered from the literature to investigate whether they could be used to fit curves for the weekly egg production rate. The goodness of fit of the models was measured using Akaike's information criterion, mean square error, coefficient of determination, graphical analysis of the fitted curves, and the deviations of the fitted curves. The Logistic, Yang, Segmented Polynomial, and Grossman models presented the best goodness of fit. In this population, there were significant differences between the parameter estimates of the curves fitted for the selected and nonselected lines, thus indicating that the effect of selection changed the shape of the egg production curves. The selection for egg production was efficient in modifying the birds' egg production curve in this population, thus resulting in genetic gain from the 5th to the 54th week of egg laying and improved the peak egg production and the persistence of egg laying.
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Palavras-chave
asymptotic egg production, genetic gain, model error, nested model, t-test
Como citar
Poultry Science. Savoy: Poultry Science Assoc Inc, v. 91, n. 11, p. 2977-2987, 2012.