Phenotypic study of egg production curve in commercial broiler breeders using Compartmental function

Abstract

The objective of this study was to analyze the effect of environmental factors on egg production curve traits in broiler breeders. The weekly egg production records of birds collected from four different flocks were used to estimate the egg production curve parameters using the Compartmental function (y = A(1 − exp −b(t − d))exp −ct). The coefficient of determination (R 2) obtained by the model was 0.98. Least squares analysis of variance indicated that the environmental factors such as hatchability and flock had significant effects on egg production curve traits. The highest correlation was observed between the weekly potential maximum yield and yield at the beginning of laying, whereas the lowest correlation was found between the weekly potential maximum production and production after peak yield. Associated factors with the increasing slope of egg production curves had a negative correlation with the decrease after peak. The correlation analysis showed that peak production had a negative and significant relationship with production towards peak and time to reach peak production, while there was a positive and significant correlation with the slope decrease after peak yield. The Compartmental function can be used as an alternative model to predict egg production traits in broiler breeders.

Evaluation of Four Nonlinear Models Describing Egg Production Curve of Fayoumi Layers

“…The four studied models have considerably high with similar R 2 values (close to 1) which ranged from 0.9666 to 0.9939 indicating that all models had good performance (fitting) in describing age-related changes in egg production. Similar high R 2 values have been reported in several studies (Cason and Britton, 1988, Narinc et al, 2014, Otwinowska-Mindur et al, 2016and Safari-Aliqiarloo et al, 2018 (Miyoshi et al, 1996, Savegnago et al 2012and Narinc et al 2014) ranging from -224548 to 393 for compartmental model (Miyoshi et al, 1996, Savegnago et al 2012, Otwinowska-Mindur et al, 2016 and ranging from (-223588to -195.2) for McNally model (Savegnago et al 2012, Narinc et al2013and Narinc et al2014 and ranging from -301.43 to 242 for Wood model (Miyoshi et al, 1996, Narinc et al2013, Narinc et al, 2014and Otwinowska-Mindur et al, 2016. Estimates of BIC were -171.02, -188.27 and -346.76 for Wood, McNally and modified compartmental models (Narinc et al, 2014), respectively.…”

Investigation of egg production curve in ostrich using nonlinear functions

“…The time of peak production in four functions of the Wood, the McNally, the Nelder, and the Lokhorst was similar to the real-time (second month) in the real dataset, although, the time of the peak of the egg production in the Yang function was estimated in the third month. Therefore, all the studied functions except the Yang could estimate the time of the peak in the ostrich egg production curve properly which is in contrast with the results reported by in layers ( Oni, et al, 2007 ; Safari-Aliqiarloo et al . , 2018 ; Emam, 2021 ).…”

“…, 2013 ). Two distinct experiments studying egg production modeling suggested that the Compartmental function was applicable to predict egg production traits in broiler breeders and laying hens ( Safari-Aliqiarloo et al . , 2018 ; Wolc et al .…”