Estimation of dairy goat body composition: A direct calibration and comparison of eight methods
The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) auto...
Ausführliche Beschreibung
Autor*in: |
Lerch, Sylvain [verfasserIn] De La Torre, Anne [verfasserIn] Huau, Christophe [verfasserIn] Monziols, Mathieu [verfasserIn] Xavier, Caroline [verfasserIn] Louis, Loïc [verfasserIn] Le Cozler, Yannick [verfasserIn] Faverdin, Philippe [verfasserIn] Lamberton, Philippe [verfasserIn] Chery, Isabelle [verfasserIn] Heimo, Dominique [verfasserIn] Loncke, Christelle [verfasserIn] Schmidely, Philippe [verfasserIn] Pires, José A.A. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Methods - Orlando, Fla. : Academic Press, 1990, 186, Seite 68-78 |
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Übergeordnetes Werk: |
volume:186 ; pages:68-78 |
DOI / URN: |
10.1016/j.ymeth.2020.06.014 |
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Katalog-ID: |
ELV005524873 |
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245 | 1 | 0 | |a Estimation of dairy goat body composition: A direct calibration and comparison of eight methods |
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520 | |a The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. | ||
650 | 4 | |a Ruminant | |
650 | 4 | |a Body chemical composition | |
650 | 4 | |a 3D imaging | |
650 | 4 | |a Computer tomography | |
650 | 4 | |a Adipose cell size | |
650 | 4 | |a Deuterium oxide | |
700 | 1 | |a De La Torre, Anne |e verfasserin |4 aut | |
700 | 1 | |a Huau, Christophe |e verfasserin |4 aut | |
700 | 1 | |a Monziols, Mathieu |e verfasserin |4 aut | |
700 | 1 | |a Xavier, Caroline |e verfasserin |4 aut | |
700 | 1 | |a Louis, Loïc |e verfasserin |4 aut | |
700 | 1 | |a Le Cozler, Yannick |e verfasserin |4 aut | |
700 | 1 | |a Faverdin, Philippe |e verfasserin |4 aut | |
700 | 1 | |a Lamberton, Philippe |e verfasserin |4 aut | |
700 | 1 | |a Chery, Isabelle |e verfasserin |4 aut | |
700 | 1 | |a Heimo, Dominique |e verfasserin |4 aut | |
700 | 1 | |a Loncke, Christelle |e verfasserin |4 aut | |
700 | 1 | |a Schmidely, Philippe |e verfasserin |4 aut | |
700 | 1 | |a Pires, José A.A. |e verfasserin |4 aut | |
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10.1016/j.ymeth.2020.06.014 doi (DE-627)ELV005524873 (ELSEVIER)S1046-2023(20)30140-7 DE-627 ger DE-627 rda eng 540 DE-600 BIODIV DE-30 fid 35.74 bkl Lerch, Sylvain verfasserin aut Estimation of dairy goat body composition: A direct calibration and comparison of eight methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. Ruminant Body chemical composition 3D imaging Computer tomography Adipose cell size Deuterium oxide De La Torre, Anne verfasserin aut Huau, Christophe verfasserin aut Monziols, Mathieu verfasserin aut Xavier, Caroline verfasserin aut Louis, Loïc verfasserin aut Le Cozler, Yannick verfasserin aut Faverdin, Philippe verfasserin aut Lamberton, Philippe verfasserin aut Chery, Isabelle verfasserin aut Heimo, Dominique verfasserin aut Loncke, Christelle verfasserin aut Schmidely, Philippe verfasserin aut Pires, José A.A. verfasserin aut Enthalten in Methods Orlando, Fla. : Academic Press, 1990 186, Seite 68-78 Online-Ressource (DE-627)26784008X (DE-600)1471152-7 (DE-576)104193972 1095-9130 nnns volume:186 pages:68-78 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.74 Enzyme Hormone Vitamine Biochemie AR 186 68-78 |
spelling |
10.1016/j.ymeth.2020.06.014 doi (DE-627)ELV005524873 (ELSEVIER)S1046-2023(20)30140-7 DE-627 ger DE-627 rda eng 540 DE-600 BIODIV DE-30 fid 35.74 bkl Lerch, Sylvain verfasserin aut Estimation of dairy goat body composition: A direct calibration and comparison of eight methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. Ruminant Body chemical composition 3D imaging Computer tomography Adipose cell size Deuterium oxide De La Torre, Anne verfasserin aut Huau, Christophe verfasserin aut Monziols, Mathieu verfasserin aut Xavier, Caroline verfasserin aut Louis, Loïc verfasserin aut Le Cozler, Yannick verfasserin aut Faverdin, Philippe verfasserin aut Lamberton, Philippe verfasserin aut Chery, Isabelle verfasserin aut Heimo, Dominique verfasserin aut Loncke, Christelle verfasserin aut Schmidely, Philippe verfasserin aut Pires, José A.A. verfasserin aut Enthalten in Methods Orlando, Fla. : Academic Press, 1990 186, Seite 68-78 Online-Ressource (DE-627)26784008X (DE-600)1471152-7 (DE-576)104193972 1095-9130 nnns volume:186 pages:68-78 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.74 Enzyme Hormone Vitamine Biochemie AR 186 68-78 |
allfields_unstemmed |
10.1016/j.ymeth.2020.06.014 doi (DE-627)ELV005524873 (ELSEVIER)S1046-2023(20)30140-7 DE-627 ger DE-627 rda eng 540 DE-600 BIODIV DE-30 fid 35.74 bkl Lerch, Sylvain verfasserin aut Estimation of dairy goat body composition: A direct calibration and comparison of eight methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. Ruminant Body chemical composition 3D imaging Computer tomography Adipose cell size Deuterium oxide De La Torre, Anne verfasserin aut Huau, Christophe verfasserin aut Monziols, Mathieu verfasserin aut Xavier, Caroline verfasserin aut Louis, Loïc verfasserin aut Le Cozler, Yannick verfasserin aut Faverdin, Philippe verfasserin aut Lamberton, Philippe verfasserin aut Chery, Isabelle verfasserin aut Heimo, Dominique verfasserin aut Loncke, Christelle verfasserin aut Schmidely, Philippe verfasserin aut Pires, José A.A. verfasserin aut Enthalten in Methods Orlando, Fla. : Academic Press, 1990 186, Seite 68-78 Online-Ressource (DE-627)26784008X (DE-600)1471152-7 (DE-576)104193972 1095-9130 nnns volume:186 pages:68-78 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.74 Enzyme Hormone Vitamine Biochemie AR 186 68-78 |
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10.1016/j.ymeth.2020.06.014 doi (DE-627)ELV005524873 (ELSEVIER)S1046-2023(20)30140-7 DE-627 ger DE-627 rda eng 540 DE-600 BIODIV DE-30 fid 35.74 bkl Lerch, Sylvain verfasserin aut Estimation of dairy goat body composition: A direct calibration and comparison of eight methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. Ruminant Body chemical composition 3D imaging Computer tomography Adipose cell size Deuterium oxide De La Torre, Anne verfasserin aut Huau, Christophe verfasserin aut Monziols, Mathieu verfasserin aut Xavier, Caroline verfasserin aut Louis, Loïc verfasserin aut Le Cozler, Yannick verfasserin aut Faverdin, Philippe verfasserin aut Lamberton, Philippe verfasserin aut Chery, Isabelle verfasserin aut Heimo, Dominique verfasserin aut Loncke, Christelle verfasserin aut Schmidely, Philippe verfasserin aut Pires, José A.A. verfasserin aut Enthalten in Methods Orlando, Fla. : Academic Press, 1990 186, Seite 68-78 Online-Ressource (DE-627)26784008X (DE-600)1471152-7 (DE-576)104193972 1095-9130 nnns volume:186 pages:68-78 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.74 Enzyme Hormone Vitamine Biochemie AR 186 68-78 |
allfieldsSound |
10.1016/j.ymeth.2020.06.014 doi (DE-627)ELV005524873 (ELSEVIER)S1046-2023(20)30140-7 DE-627 ger DE-627 rda eng 540 DE-600 BIODIV DE-30 fid 35.74 bkl Lerch, Sylvain verfasserin aut Estimation of dairy goat body composition: A direct calibration and comparison of eight methods 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. Ruminant Body chemical composition 3D imaging Computer tomography Adipose cell size Deuterium oxide De La Torre, Anne verfasserin aut Huau, Christophe verfasserin aut Monziols, Mathieu verfasserin aut Xavier, Caroline verfasserin aut Louis, Loïc verfasserin aut Le Cozler, Yannick verfasserin aut Faverdin, Philippe verfasserin aut Lamberton, Philippe verfasserin aut Chery, Isabelle verfasserin aut Heimo, Dominique verfasserin aut Loncke, Christelle verfasserin aut Schmidely, Philippe verfasserin aut Pires, José A.A. verfasserin aut Enthalten in Methods Orlando, Fla. : Academic Press, 1990 186, Seite 68-78 Online-Ressource (DE-627)26784008X (DE-600)1471152-7 (DE-576)104193972 1095-9130 nnns volume:186 pages:68-78 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.74 Enzyme Hormone Vitamine Biochemie AR 186 68-78 |
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Lerch, Sylvain @@aut@@ De La Torre, Anne @@aut@@ Huau, Christophe @@aut@@ Monziols, Mathieu @@aut@@ Xavier, Caroline @@aut@@ Louis, Loïc @@aut@@ Le Cozler, Yannick @@aut@@ Faverdin, Philippe @@aut@@ Lamberton, Philippe @@aut@@ Chery, Isabelle @@aut@@ Heimo, Dominique @@aut@@ Loncke, Christelle @@aut@@ Schmidely, Philippe @@aut@@ Pires, José A.A. @@aut@@ |
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Lerch, Sylvain |
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Lerch, Sylvain ddc 540 fid BIODIV bkl 35.74 misc Ruminant misc Body chemical composition misc 3D imaging misc Computer tomography misc Adipose cell size misc Deuterium oxide Estimation of dairy goat body composition: A direct calibration and comparison of eight methods |
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540 DE-600 BIODIV DE-30 fid 35.74 bkl Estimation of dairy goat body composition: A direct calibration and comparison of eight methods Ruminant Body chemical composition 3D imaging Computer tomography Adipose cell size Deuterium oxide |
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Estimation of dairy goat body composition: A direct calibration and comparison of eight methods |
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Lerch, Sylvain De La Torre, Anne Huau, Christophe Monziols, Mathieu Xavier, Caroline Louis, Loïc Le Cozler, Yannick Faverdin, Philippe Lamberton, Philippe Chery, Isabelle Heimo, Dominique Loncke, Christelle Schmidely, Philippe Pires, José A.A. |
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estimation of dairy goat body composition: a direct calibration and comparison of eight methods |
title_auth |
Estimation of dairy goat body composition: A direct calibration and comparison of eight methods |
abstract |
The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. |
abstractGer |
The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. |
abstract_unstemmed |
The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness. |
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Estimation of dairy goat body composition: A direct calibration and comparison of eight methods |
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De La Torre, Anne Huau, Christophe Monziols, Mathieu Xavier, Caroline Louis, Loïc Le Cozler, Yannick Faverdin, Philippe Lamberton, Philippe Chery, Isabelle Heimo, Dominique Loncke, Christelle Schmidely, Philippe Pires, José A.A. |
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Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). 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Philippe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chery, Isabelle</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Heimo, Dominique</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Loncke, Christelle</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schmidely, Philippe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pires, José A.A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" 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