Predicting performance of broiler chickens from dietary nutrients using group method of data handling-type neural networks

Abstract

1. Successful artificial neural network (ANN) applications have been found for many areas. One sub-model of ANNs is the group method of data handling-type neural networks (GMDH-type NNs). The use of self-organising networks leads to successful application in a broad range of areas. However, the use of such methods is not common in poultry science.

2. Broiler chicken nutrition is recognised as a biological system consisting of a complex set of interconnected variables. The adequate information on nutrients (variables), such as metabolisable energy (ME) and amino acid requirements, can help to establish specific feeding programmes, defining optimal performance and reducing production costs.

3. This study addressed the question of whether GMDH-type NNs can be used to estimate the performance of broiler chickens (output) based on specified variables—inputs (dietary crude protein (CP), ME, ME/CP, methionine (Met), lysine (Lys), ME/Met and ME/Lys)—for a commercial broiler chicken farm. The recorded data from 10 broiler chicken flocks were obtained, from March 2003 to April 2005, corresponding to 52 data lines.

4. The results suggested that the GMDH-type NNs may provide an effective means of recognising the patterns in data and accurately predicting the performance of broiler chickens based on investigating inputs. In addition the polynomial equations obtained can be used to optimise the performance of broilers.