Show simple item record

dc.contributor.authorFont-i-Furnols, M.
dc.contributor.authorTerré, M.
dc.contributor.authorBrun, A.
dc.contributor.authorVidal, M.
dc.contributor.authorBach, A.
dc.contributor.otherProducció Animalca
dc.contributor.otherIndústries Alimentàriesca
dc.date.accessioned2020-12-24T09:04:51Z
dc.date.available2020-12-24T09:04:51Z
dc.date.issued2020-12-13
dc.identifier.citationFont-i-Furnols, M., M. Terré, A. Brun, M. Vidal, and A. Bach. 2021. "Prediction Of Tissue Composition Of Live Dairy Calves And Carcasses By Computed Tomography". Livestock Science 243: 104371. doi:10.1016/j.livsci.2020.104371.ca
dc.identifier.issn1871-1413ca
dc.identifier.urihttp://hdl.handle.net/20.500.12327/1036
dc.description.abstractComputed tomography (CT) is a non-destructive technique, based on X-rays, that has been used in several livestock species to evaluate carcass composition. The objective of this study was to construct predictive equations to estimate carcass and viscera composition for preweaning calves using CT. For this purpose, 24 Holstein male calves (4 ± 0.9 d of age; 40 ± 2.2 kg of body weight) were fed a milk replacer (MR; 23% CP; 15% fat) either 4 L/d or 8 L/d of MR at the rate of 125 g/L of water to ensure different levels of fat and protein accretion and generate sufficient variation to obtain the equations of calibration. Then, at 30 ± 2.4 d of age, 3 calves from each feeding program, and at 50 ± 1.9 d of age, 9 calves from each feeding program were CT-scanned, and humanly sacrificed. Carcasses were also CT scanned 24 h post mortem. Images from CT were analysed and used to predict content of protein and fat of carcasses, red and white viscera. The models rendered a residual predictive deviation between 1.1 (protein red viscera) and 2.6 (fat white viscera) in live animal images and between 1.1 (carcass moisture) and 4.5 (fat white viscera) in carcass images. The root mean square error of prediction relative to the mean ranged between 1.32 (carcass moisture) and 17.3% (fat white viscera) in live animal images and between 1.38 (carcass moisture) and 17.3 (fat red viscera) in carcass images. The coefficient of determination ranged between 0.19 (protein red viscera) and 0.88 (fat white viscera) in images from live calves and between 0.26 (carcass protein) and 0.98 (fat white viscera) in carcass images. In conclusion, it is possible to predict body composition of calves using a non-destructive technique by means of computed tomography images and this prediction could be used in studies were the estimation of this content would be relevant.ca
dc.format.extent7ca
dc.language.isoengca
dc.publisherElsevierca
dc.relation.ispartofLivestock Scienceca
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titlePrediction of tissue composition of live dairy calves and carcasses by computed tomographyca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.relation.projectIDMINECO/Programa Estatal de fomento de la investigación científica y técnica de excelencia/AGL2015-68463-C2-1-P/ES/DETERMINACION DE LOS AMINOACIDOS LIMITANTES PARA EL CRECIMIENTO DE LOS TERNEROS AMAMANTADOS Y SU FUNCIONALIDAD/ca
dc.relation.projectIDMINECO/Programa Estatal de fomento de la investigación científica y técnica de excelencia/AGL2015-68463-C2-2-P/ES/ESTUDIO MOLECULAR DE LOS EFECTOS DE LA SUPLEMENTACION DE AMINOACIDOS EN TERNEROS AMAMANTADOS MEDIANTE UNA APROXIMACION BIOQUIMICA Y PROTEOMICA/ca
dc.subject.udc663/664ca
dc.identifier.doihttps://doi.org/10.1016/j.livsci.2020.104371ca
dc.contributor.groupProducció de Remugantsca
dc.contributor.groupQualitat i Tecnologia Alimentàriaca


Files in this item

 

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/