Forecasting egg production curve with neural networks

ABSTRACT: The comparison between the real egg production curve and the graph proposed by management guidelines, aims towards continuous performance evaluation. The objectives of this study was to compare the capacity of curve fitting daily egg production of Lokhorst (LM), neural network multilayer p...

Full description

Autores:
Galeano Vasco, Luis Fernando
Cerón Muñoz, Mario Fernando
Galvan, I.M.
Aler, R.
Tipo de recurso:
Article of investigation
Fecha de publicación:
2018
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/32693
Acceso en línea:
https://hdl.handle.net/10495/32693
Palabra clave:
Modelos Teóricos
Models, Theoretical
Curvas de frecuencia
Frequency curves
Polinomios
Polynomials
Funciones
Functions
Avicultura
Aviculture
Producción de huevos
Egg production
http://aims.fao.org/aos/agrovoc/c_2498
Rights
openAccess
License
http://creativecommons.org/licenses/by-sa/2.5/co/
Description
Summary:ABSTRACT: The comparison between the real egg production curve and the graph proposed by management guidelines, aims towards continuous performance evaluation. The objectives of this study was to compare the capacity of curve fitting daily egg production of Lokhorst (LM), neural network multilayer perceptron (MP) and Jordan and Elman recurrent neural network (RNNJ and RNNE, respectively) for the prediction of the daily egg production in commercial laying hens. The models were fitted using 4650 data from 12 selected batches. The MP and LM models gave good fitting to the data, with correlation values greater than 0.95 and accounting for more than 95% of the variability in daily egg production. For the production forecast, MP was a technique with acceptable accuracy and less variation. The MP model can be recommended as a tool for fit and forecast of daily egg production curve in commercial hens.