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Study of broiler chicken responses to dietary protein and lysine using neural network and response surface models

1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were... Full description

Journal Title: British poultry science 2013, Vol.54(4), pp.524-530
Main Author: Faridi , A.
Other Authors: Golian , A. , France , J. , Heravi Mousavi , A.
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1466-1799
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recordid: faoagrisUS201400122550
title: Study of broiler chicken responses to dietary protein and lysine using neural network and response surface models
format: Article
creator:
  • Faridi , A.
  • Golian , A.
  • France , J.
  • Heravi Mousavi , A.
subjects:
  • Models
  • Broiler Chickens
  • Lysine
  • Average Daily Gain
  • Feed Conversion
  • Dietary Protein
ispartof: British poultry science, 2013, Vol.54(4), pp.524-530
description: 1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed.2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R² values, the NN models developed for both ADG (R² = 0.923) and FE (R² = 0.904) were far superior to the RS models (R² for ADG = 0.511; R² for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models.3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG. ; p. 524-530.
language: eng
source:
identifier: ISSN: 1466-1799
fulltext: fulltext
issn:
  • 14661799
  • 1466-1799
url: Link


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titleStudy of broiler chicken responses to dietary protein and lysine using neural network and response surface models
creatorFaridi , A. ; Golian , A. ; France , J. ; Heravi Mousavi , A.
ispartofBritish poultry science, 2013, Vol.54(4), pp.524-530
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subjectModels ; Broiler Chickens ; Lysine ; Average Daily Gain ; Feed Conversion ; Dietary Protein
description1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed.2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R² values, the NN models developed for both ADG (R² = 0.923) and FE (R² = 0.904) were far superior to the RS models (R² for ADG = 0.511; R² for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models.3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG. ; p. 524-530.
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titleStudy of broiler chicken responses to dietary protein and lysine using neural network and response surface models
description1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed.2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R² values, the NN models developed for both ADG (R² = 0.923) and FE (R² = 0.904) were far superior to the RS models (R² for ADG = 0.511; R² for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models.3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG. ; p. 524-530.
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abstract1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed.2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R² values, the NN models developed for both ADG (R² = 0.923) and FE (R² = 0.904) were far superior to the RS models (R² for ADG = 0.511; R² for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models.3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG.
pubTaylor & Francis
doi10.1080/00071668.2013.803517