A statistical analysis of nonlinear regression models for different treatments for layers

Title
A statistical analysis of nonlinear regression models for different treatments for layers
Publication Date
2022-09
Author(s)
Song, Eugine
Oh, Mia S
Billard, Lynne
Moss, Amy
( creator )
OrcID: https://orcid.org/0000-0002-8647-8448
Email: amoss22@une.edu.au
UNE Id une-id:amoss22
Pesti, Gene M
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Elsevier BV
Place of publication
The Netherlands
DOI
10.1016/j.psj.2022.102004
UNE publication id
une:1959.11/60858
Abstract

As the cost of research increases, mathematical models become valuable tools to answer research questions. A major application of mathematical modeling is accurate estimation of production performance, growth, and feed consumption for poultry research and production. There are many ways that a given data set can be analyzed, and different models have been proposed to fit those curves. To explore the models available, data were investigated from a study on the effects of a series of balanced dietary protein levels on egg production and egg quality parameters in lying hens from 18 to 74 wk of age. Forty eight pullets were assigned to each of 3 different protein levels. The results clearly demonstrated that balanced dietary protein level was the limiting factor for body weight (BW), average daily feed intake (ADFI), egg weight, and egg production. To test differences of fitted curves, the sum of squared reduction test is used. Using a unique data set with data from individual hens, 6 commonly used models were fitted to hen performance technical data. The resulting statistical inferences from using individual and pooled data were compared. There are only differences in using individual or grouped data in fitting nonlinear models to laying hen response data. For the most important response variables, hen-day egg production, and feed intake, predicted responses are within 0.12 and 0.65%, respectively, throughout the production cycle.

Link
Citation
Poultry Science, 101(9), p. 1-11
ISSN
1525-3171
0032-5791
Start page
1
End page
11
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International

Files:

NameSizeformatDescriptionLink
openpublished/AStatisticalMossPesti2022JournalArticle.pdf 1144.402 KB application/pdf Published version View document