Predicting caloric and feed efficiency in turkeys using the group method of data handling-type neural networks

Abstract

Neural networks (NN) are a relatively new option to model growth in animal production systems. One self-organizing submodel of artificial NN is the group method of data handling (GMDH)-type NN. The use of such self-organizing networks has led to successful application of the GMDH algorithm over a broad range of areas in engineering, science, and economics. The present study aimed to apply the GMDH-type NN to predict caloric efficiency (CE, g of gain/kcal of caloric intake) and feed efficiency (FE, kg of gain/kg of feed intake) in tom and hen turkeys fed diets containing different energy and amino acid levels. Involved effective input parameters in prediction of CE and FE were age, dietary ME, CP, Met, and Lys. Quantitative examination of the goodness of fit for the predictive models was made using R2 and error measurement indices commonly used to evaluate forecasting models. Statistical performance of the developed GMDH-type NN models revealed close agreement between observed and predicted values of CE and FE. In conclusion, using such powerful models can enhance our ability to predict economic traits, make precise prediction of nutrition requirements, and achieve optimal performance in poultry production.

Predicting methionine and lysine contents in soybean meal and fish meal using a group method of data handling-type neural network

“…The use of such self-organizing networks has led to their successful application in a broad range of areas in engineering, science, and economics (Seginer et al, 1994;Vallejo-Cordoba et al, 1995;Roush et al, 1996). The GMDH-type NNs have been used in poultry science for the prediction of broiler performance (Faridi et al, 2011;2013b), turkey performance (Mottaghitalab et al, 2010), egg production of broiler breeder hens (Faridi et al, 2012;2013a) and true metabolizable energy content in feather meal and poultry offal meal (Ahmadi et al, 2008).…”

“…The high CCC values and low contribution of ER to MSPE provide measures of a close a R 2 =proportion of variance accounted for by the model; CCC=concordance correlation coefficient; MSPE=mean square prediction error; EB=error attributable to bias, as a percentage of total MSPE; ER=error attributable to regression, as a percentage of total MSPE; ED=Error attributable to disturbance (random), as a percentage of total MSPE; RMSPE=root mean square prediction error, expressed as a percentage (%) of the observed mean; Bias=average bias; MAPE=mean absolute percentage error (%); µ=location shift relative to the scale. Neural network models to predict amino acid content of feeds systems has been demonstrated in relation to poultry production (Faridi et al, 2013a,b;Mottaghitalab et al, 2010). In this study, the validity of this type of ANN model was examined using a genetic algorithm method to predict Met and Lys content of SBM and FM based on their proximate analysis.…”

Artificial neural networks (ANNs) in food science – A review

“…The use of such self-organizing networks has led to successful application of the GMDH algorithm over a broad range of areas in engineering, science, and economics. Mottaghitalab et al [45] aimed to apply the GMDH-type ANN to predict caloric efficiency (CE, g of gain/kcal of caloric intake) and feed efficiency (FE, kg of gain/kg of feed intake) in tom and hen turkeys fed diets containing different energy and amino acid levels. Involved effective input parameters in prediction of CE and FE were age, dietary ME, CP, Met, and Lys.…”

Response surface and neural network models for performance of broiler chicks fed diets varying in digestible protein and critical amino acids from 11 to 17 days of age

“…This methodology is quite fiexible in regard to the number and form of experimental data, which makes it possible to use more informal experimental designs than with statistical approaches. In recent years, the ANN has been applied in the field of poultry nutrition for different purposes such as prediction of poultry performance given dietary nutrients (Ahmadi et al, 2007(Ahmadi et al, , 2008bMottaghitalab et al, 2010), modeling true ME of feedstuffs (Ahmadi et al, 2008a;Perai et al, 2010), growth and body composition analysis of chickens (Ahmadi and Golian, 2010a), and analyses of chicken threonine responses (Ahmadi and Golian, 2010b), The usual optimization methods may not be used for optimizing the input space of an ANN model. Thus, CA as an artificial intelligence-based stochastic optimization method is often used to optimize the input space of an ANN model.…”