Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers
Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathe...
Ausführliche Beschreibung
Autor*in: |
M.P. Reis [verfasserIn] R.M. Gous [verfasserIn] L. Hauschild [verfasserIn] N.K. Sakomura [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: |
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Übergeordnetes Werk: |
In: Animal - Elsevier, 2021, 17(2022), Seite 101016- |
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Übergeordnetes Werk: |
volume:17 ; year:2022 ; pages:101016- |
Links: |
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DOI / URN: |
10.1016/j.animal.2023.101016 |
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Katalog-ID: |
DOAJ096249374 |
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520 | |a Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. | ||
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10.1016/j.animal.2023.101016 doi (DE-627)DOAJ096249374 (DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2 DE-627 ger DE-627 rakwb eng SF1-1100 M.P. Reis verfasserin aut Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. Animal simulation Feed intake Profit maximization Protein deposition System thinking Animal culture R.M. Gous verfasserin aut L. Hauschild verfasserin aut N.K. Sakomura verfasserin aut In Animal Elsevier, 2021 17(2022), Seite 101016- (DE-627)534060382 (DE-600)2365209-3 1751732X nnns volume:17 year:2022 pages:101016- https://doi.org/10.1016/j.animal.2023.101016 kostenfrei https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2 kostenfrei http://www.sciencedirect.com/science/article/pii/S1751731123003336 kostenfrei https://doaj.org/toc/1751-7311 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_285 GBV_ILN_293 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 101016- |
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10.1016/j.animal.2023.101016 doi (DE-627)DOAJ096249374 (DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2 DE-627 ger DE-627 rakwb eng SF1-1100 M.P. Reis verfasserin aut Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. Animal simulation Feed intake Profit maximization Protein deposition System thinking Animal culture R.M. Gous verfasserin aut L. Hauschild verfasserin aut N.K. Sakomura verfasserin aut In Animal Elsevier, 2021 17(2022), Seite 101016- (DE-627)534060382 (DE-600)2365209-3 1751732X nnns volume:17 year:2022 pages:101016- https://doi.org/10.1016/j.animal.2023.101016 kostenfrei https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2 kostenfrei http://www.sciencedirect.com/science/article/pii/S1751731123003336 kostenfrei https://doaj.org/toc/1751-7311 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_285 GBV_ILN_293 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 101016- |
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10.1016/j.animal.2023.101016 doi (DE-627)DOAJ096249374 (DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2 DE-627 ger DE-627 rakwb eng SF1-1100 M.P. Reis verfasserin aut Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. Animal simulation Feed intake Profit maximization Protein deposition System thinking Animal culture R.M. Gous verfasserin aut L. Hauschild verfasserin aut N.K. Sakomura verfasserin aut In Animal Elsevier, 2021 17(2022), Seite 101016- (DE-627)534060382 (DE-600)2365209-3 1751732X nnns volume:17 year:2022 pages:101016- https://doi.org/10.1016/j.animal.2023.101016 kostenfrei https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2 kostenfrei http://www.sciencedirect.com/science/article/pii/S1751731123003336 kostenfrei https://doaj.org/toc/1751-7311 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_285 GBV_ILN_293 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 101016- |
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10.1016/j.animal.2023.101016 doi (DE-627)DOAJ096249374 (DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2 DE-627 ger DE-627 rakwb eng SF1-1100 M.P. Reis verfasserin aut Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. Animal simulation Feed intake Profit maximization Protein deposition System thinking Animal culture R.M. Gous verfasserin aut L. Hauschild verfasserin aut N.K. Sakomura verfasserin aut In Animal Elsevier, 2021 17(2022), Seite 101016- (DE-627)534060382 (DE-600)2365209-3 1751732X nnns volume:17 year:2022 pages:101016- https://doi.org/10.1016/j.animal.2023.101016 kostenfrei https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2 kostenfrei http://www.sciencedirect.com/science/article/pii/S1751731123003336 kostenfrei https://doaj.org/toc/1751-7311 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_285 GBV_ILN_293 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 101016- |
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10.1016/j.animal.2023.101016 doi (DE-627)DOAJ096249374 (DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2 DE-627 ger DE-627 rakwb eng SF1-1100 M.P. Reis verfasserin aut Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. Animal simulation Feed intake Profit maximization Protein deposition System thinking Animal culture R.M. Gous verfasserin aut L. Hauschild verfasserin aut N.K. Sakomura verfasserin aut In Animal Elsevier, 2021 17(2022), Seite 101016- (DE-627)534060382 (DE-600)2365209-3 1751732X nnns volume:17 year:2022 pages:101016- https://doi.org/10.1016/j.animal.2023.101016 kostenfrei https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2 kostenfrei http://www.sciencedirect.com/science/article/pii/S1751731123003336 kostenfrei https://doaj.org/toc/1751-7311 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_285 GBV_ILN_293 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 101016- |
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M.P. Reis misc SF1-1100 misc Animal simulation misc Feed intake misc Profit maximization misc Protein deposition misc System thinking misc Animal culture Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers |
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Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers |
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evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers |
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Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers |
abstract |
Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. |
abstractGer |
Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. |
abstract_unstemmed |
Efficient meat production is crucial in addressing global market demands and sustainability goals. Modeling production systems has gained worldwide attention, offering valuable insights for predicting outcomes and optimizing economic returns. In the poultry industry, researchers have developed mathematical models to predict animal performance and maximize profits. These models incorporate theories to explain real-world processes and enable future event predictions. One such model is the Broiler Growth Model (BGM), which serves as a predictive tool for estimating feed intake, growth, and body composition of broilers. The BGM takes into account the genetic potential of the broilers, the feed they are provided, and several constraining factors that may prevent the animal from achieving their genetic potential. To evaluate the BGM, a series of simulations were performed: (i) model behavior was evaluated by simulating the response of males and females from 22 to 35 d to feeds differing in dietary protein content and nutrient density; (ii) model prediction was evaluated using the results of a protein response trial conducted at UNESP in which six dietary protein levels were fed to male and female broilers over a 56 d period; and (iii) model optimization was used to maximize economic returns in the above trial. The model behaved as expected when feeds differing in protein content were fed, with feed intake per kg of BW increasing as protein level was decreased, resulting in lower gains and higher body lipid contents. Increasing nutrient density resulted in higher feed intake in the second level, followed by a reduction in feed intake in the highest nutrient feed. The simulated response to nutrient density resulted in increasing body lipid deposition as the nutrient density increased. In comparing the simulated and actual results of the protein response trial, the overall error of prediction was up to 15% for feed intake, BW, and body protein. The optimization routine allows the simulation of different economic scenarios, helping in decision-making. The Broiler Growth Model emerges as a valuable tool for the poultry industry, offering predictive capabilities and economic optimization potential. While minor discrepancies between simulated and actual results exist, the BGM holds significant promise for enhancing efficiency and profitability in broiler production, contributing to the broader goals of sustainable broiler meat production. |
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title_short |
Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers |
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https://doi.org/10.1016/j.animal.2023.101016 https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2 http://www.sciencedirect.com/science/article/pii/S1751731123003336 https://doaj.org/toc/1751-7311 |
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