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Predicting carcass energy content and composition in broilers using the group method of data handling-type neural networks

Published online by Cambridge University Press:  17 December 2010

A. FARIDI ,
M. MOTTAGHITALAB ,
H. DARMANI-KUHI ,
J. FRANCE  and
H. AHMADI
A. FARIDI*
Affiliation:
Young Researchers Club, Islamic Azad University, Rasht branch, Rasht, Iran
M. MOTTAGHITALAB
Affiliation:
Department of Animal Science, Faculty of Agricultural Science, University of Guilan, PO Box 41635-1314, Rasht, Iran
H. DARMANI-KUHI
Affiliation:
Department of Animal Science, Faculty of Agricultural Science, University of Guilan, PO Box 41635-1314, Rasht, Iran
J. FRANCE
Affiliation:
Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph, Canada
H. AHMADI
Affiliation:
Center of Excellence in Animal Sciences Department, Ferdowsi University of Mashhad, Mashhad 91775-1163, Iran
*
*To whom all correspondence should be addressed. Email: Ako_Faridi@yahoo.com
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Summary

The success of poultry meat production has been strongly related to improvements in growth and carcass yield, mainly by increasing breast proportion and reducing carcass fat. Conventional laboratory techniques for determining carcass composition are expensive, cumbersome and time consuming. These disadvantages have prompted a search for alternative methods. In this respect, the potential benefits from modelling growth are considerable. Neural networks (NNs) are a relatively new option for modelling growth in animal production systems. One self-organizing sub-model of artificial NN is the group method of data handling-type NN (GMDH-type NN). The present study aimed at applying the GMDH-type NNs to data from two studies with broilers in order to predict carcass energy (CEn, MJ/g) content and relative growth (g/g of body weight) of carcass components (carcass protein, breast muscle, leg and thigh muscles, carcass fat, abdominal fat, skin fat and visceral fat). The effective input variables involved in the prediction of CEn and carcass fat content using data from the first study were dietary metabolizable energy (ME, kJ/kg), crude protein (CP, g/kg of diet), fat (g/kg of diet) and crude fibre (CF, g/kg of diet). For data from the second study, the effective input variables involved in the prediction of carcass components were dietary ME (MJ/kg), CP (g/kg of diet), methionine (g/kg of diet), lysine (g/kg of diet) and body weight (kg). Quantitative examination of the goodness of fit, using R2 and error measurement indices, for the predictive models proposed by the GMDH-type NN revealed close agreement between observed and predicted values of CEn and carcass components.

Type
Animals
Information
The Journal of Agricultural Science , Volume 149 , Issue 2 , April 2011 , pp. 249 - 254
Copyright
Copyright © Cambridge University Press 2010

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