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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	M.P. Reis [verfasserIn] R.M. Gous [verfasserIn] L. Hauschild [verfasserIn] N.K. Sakomura [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Animal simulation Feed intake Profit maximization Protein deposition System thinking

Übergeordnetes Werk:	In: Animal - Elsevier, 2021, 17(2022), Seite 101016-
Übergeordnetes Werk:	volume:17 ; year:2022 ; pages:101016-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.animal.2023.101016

Katalog-ID:	DOAJ096249374

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ096249374
003	DE-627
005	20240413144928.0
007	cr uuu---uuuuu
008	240413s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.animal.2023.101016 \|2 doi
035			\|a (DE-627)DOAJ096249374
035			\|a (DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a SF1-1100
100	0		\|a M.P. Reis \|e verfasserin \|4 aut
245	1	0	\|a Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
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.
650		4	\|a Animal simulation
650		4	\|a Feed intake
650		4	\|a Profit maximization
650		4	\|a Protein deposition
650		4	\|a System thinking
653		0	\|a Animal culture
700	0		\|a R.M. Gous \|e verfasserin \|4 aut
700	0		\|a L. Hauschild \|e verfasserin \|4 aut
700	0		\|a N.K. Sakomura \|e verfasserin \|4 aut
773	0	8	\|i In \|t Animal \|d Elsevier, 2021 \|g 17(2022), Seite 101016- \|w (DE-627)534060382 \|w (DE-600)2365209-3 \|x 1751732X \|7 nnns
773	1	8	\|g volume:17 \|g year:2022 \|g pages:101016-
856	4	0	\|u https://doi.org/10.1016/j.animal.2023.101016 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2 \|z kostenfrei
856	4	0	\|u http://www.sciencedirect.com/science/article/pii/S1751731123003336 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1751-7311 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_121
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_165
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_252
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_374
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 17 \|j 2022 \|h 101016-

Indexfelder

author_variant	m r mr r g rg l h lh n s ns
matchkey_str	article:1751732X:2022----::vlainfmcaitcoetaetmtsednaerwhnbdcmoiinureteurmnsn
hierarchy_sort_str	2022
callnumber-subject-code	SF
publishDate	2022
allfields	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-
spelling	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-
allfields_unstemmed	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-
allfieldsGer	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-
allfieldsSound	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-
language	English
source	In Animal 17(2022), Seite 101016- volume:17 year:2022 pages:101016-
sourceStr	In Animal 17(2022), Seite 101016- volume:17 year:2022 pages:101016-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Animal simulation Feed intake Profit maximization Protein deposition System thinking Animal culture
isfreeaccess_bool	true
container_title	Animal
authorswithroles_txt_mv	M.P. Reis @@aut@@ R.M. Gous @@aut@@ L. Hauschild @@aut@@ N.K. Sakomura @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	534060382
id	DOAJ096249374
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ096249374</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413144928.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.animal.2023.101016</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ096249374</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">SF1-1100</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">M.P. Reis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Animal simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feed intake</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Profit maximization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Protein deposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">System thinking</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Animal culture</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">R.M. Gous</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">L. Hauschild</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">N.K. Sakomura</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Animal</subfield><subfield code="d">Elsevier, 2021</subfield><subfield code="g">17(2022), Seite 101016-</subfield><subfield code="w">(DE-627)534060382</subfield><subfield code="w">(DE-600)2365209-3</subfield><subfield code="x">1751732X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:17</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:101016-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.animal.2023.101016</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S1751731123003336</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1751-7311</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_121</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_252</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">17</subfield><subfield code="j">2022</subfield><subfield code="h">101016-</subfield></datafield></record></collection>
callnumber-first	S - Agriculture
author	M.P. Reis
spellingShingle	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
authorStr	M.P. Reis
ppnlink_with_tag_str_mv	@@773@@(DE-627)534060382
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	SF1-1100
illustrated	Not Illustrated
issn	1751732X
topic_title	SF1-1100 Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers Animal simulation Feed intake Profit maximization Protein deposition System thinking
topic	misc SF1-1100 misc Animal simulation misc Feed intake misc Profit maximization misc Protein deposition misc System thinking misc Animal culture
topic_unstemmed	misc SF1-1100 misc Animal simulation misc Feed intake misc Profit maximization misc Protein deposition misc System thinking misc Animal culture
topic_browse	misc SF1-1100 misc Animal simulation misc Feed intake misc Profit maximization misc Protein deposition misc System thinking misc Animal culture
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Animal
hierarchy_parent_id	534060382
hierarchy_top_title	Animal
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)534060382 (DE-600)2365209-3
title	Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers
ctrlnum	(DE-627)DOAJ096249374 (DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2
title_full	Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers
author_sort	M.P. Reis
journal	Animal
journalStr	Animal
callnumber-first-code	S
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
container_start_page	101016
author_browse	M.P. Reis R.M. Gous L. Hauschild N.K. Sakomura
container_volume	17
class	SF1-1100
format_se	Elektronische Aufsätze
author-letter	M.P. Reis
doi_str_mv	10.1016/j.animal.2023.101016
author2-role	verfasserin
title_sort	evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers
callnumber	SF1-1100
title_auth	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.
collection_details	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
title_short	Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers
url	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
remote_bool	true
author2	R.M. Gous L. Hauschild N.K. Sakomura
author2Str	R.M. Gous L. Hauschild N.K. Sakomura
ppnlink	534060382
callnumber-subject	SF - Animal Culture
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1016/j.animal.2023.101016
callnumber-a	SF1-1100
up_date	2024-07-03T19:06:34.182Z
_version_	1803585946055081984
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ096249374</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413144928.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.animal.2023.101016</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ096249374</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ1d69293aad9d40f0b2d52ffc1e5877d2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">SF1-1100</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">M.P. Reis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Animal simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feed intake</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Profit maximization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Protein deposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">System thinking</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Animal culture</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">R.M. Gous</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">L. Hauschild</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">N.K. Sakomura</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Animal</subfield><subfield code="d">Elsevier, 2021</subfield><subfield code="g">17(2022), Seite 101016-</subfield><subfield code="w">(DE-627)534060382</subfield><subfield code="w">(DE-600)2365209-3</subfield><subfield code="x">1751732X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:17</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:101016-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.animal.2023.101016</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/1d69293aad9d40f0b2d52ffc1e5877d2</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S1751731123003336</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1751-7311</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_121</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_252</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">17</subfield><subfield code="j">2022</subfield><subfield code="h">101016-</subfield></datafield></record></collection>
score	7.399351

Nicht das Richtige dabei?

Schreiben Sie uns!

Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements, and optimum economic response of broilers

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?