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Matières premières

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Définitions

Espèces animales

Maigre

Viandes séparées mécaniquement (VSM)

Tissu conjonctif

Gras

Abats

Approvisionnement en viandes

1999

In vivo estimation of body composition: comparison of eight methods in dairy goats

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Anne de la Torre (Inrae) et al., 71st. Annual Meeting of the European Federation of Animal Science (EAAP), décembre 2020, Porto, Portugal. p.202

The objective was to test 8 methods for estimation of empty body lipid (EBL) and protein (EBP) mass in dairy goats. The methods tested on 20 Alpine goats (3±0.6 years old; 226±9 DIM; 47 to 72 kg of BW) were: adipose cell size; deuterium oxide dilution space (D2OS); 3-dimension (3D) imaging: whole body 3D-scan and automatic 3D assessment of body condition score (3D-BCS); manual sternal and lumbar BCS and ultrasound imagery; computer tomography and bioelectrical impedance spectroscopy (BIS). Simple and multiple regressions (Proc GLM, SAS) were tested between different variables and EBL and EBP mass measured by chemical analyses after slaughter. Perirenal adipose tissue mass and cell diameter combined with BW provided the most accurate predictive equations for EBL (R2=0.95, residual coefficient of variation, rCV=12%). Nonetheless, such predictors can only be measured postmortem. The best equations for EBL derived from in vivo variables included BW combined with 1/ the volume of fatty tissues measured by computer tomography (R2=0.92, rCV=17%), 2/ the D2OS (R2=0.91, rCV=19%), and 3/ the BIS (R2=0.87, rCV=23%). D2OS combined with BW provided the best equation for EBP (R2=0.97, rCV=3%), whereas BW alone provided a fair EBP estimate (R2=0.92, rCV=4%). Manual BCS combined with BW provided good EBL and EBP estimations (R2=0.80 and 0.94, rCV=28 and 4%, respectively). BCS is a non-invasive technique and does not require particular equipment, but it is subjective and prone to operator bias. Compared to manual BCS, 3D-BCS combined with BW slightly decreased the accuracy of the predictive equation for EBL (R2=0.74, rCV=32%), and did not improve the estimation of EBP compared with BW alone. Ultrasound and whole body 3D imaging techniques were not satisfactory estimators of EBL and EBP (R2≤0.40).

2020

Estimation of dairy goat body composition: A direct calibration and comparison of eight methods

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Sylvain Lerch (Agroscope, Suisse) et al., Methods, 2021, volume 186, février, p. 68-78

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.

source : https://reader.elsevier.com/reader/sd/pii/S1046202320301407?token=7AFF32A92CED9F97FEF83FD31ACBF07C617A519A96EBC2508702057978936FBA06C14FD12CE2088CB276DB560551A4A7

2021