R.A. Calderon-Ramirez, D. Trujillo-Gutierrez, Ignacio Arturo Dominguez-Vara, J.L. Borquez-Gastelum, E. Morales-Almaraz, J. Mondragon-Ancelmo


Background: Instrumental evaluation of carcass characteristics, meat quality and sheep performance, require specialized equipment, therefore it is necessary to have available technological and economic resources, which sometimes result expensive throughout the meat chain production value of sheep. Prediction of sheep carcass characteristics based on mathematical models, is a good, economic, confident and repeatable method. Objective: To adjust, through two methods of estimation, prediction equations of postmortem variables by means of antemortem productive variables of intensive fattening sheep slaughtered in the valley of Toluca, Estado de México. Methodology: A total of 175 records of fattening sheep, slaughtered in small slaughterhouse of barbecue cookers in Capulhuac Municipality of the Estado de México were used. There were used 8 antemortem variables, in order to estimate prediction equations of 18 variables associated with performance, morphometry, muscular conformation, and grade of sheep carcass greasy. Carcasses were classified according with their similarity and grouped in principal components (PC), then were carried out multiple linear regression (MLR) analysis over original variables and factorial loads with extraction methods of principal components (PC).  Results: The adjusted equations with MLR, showed a R2 ≥ 0.42 for HCW (hot carcass weight), CCW (cold carcass weight), LP (leg perimeter), RW (rump width), TD (thorax depth), and CI (compactness index). The assumptions of MLR were verified and the statistics Tol, VIF, DFBETAS and DFFITS demonstrated multicollinearity between variables. For the regression analysis, the principal components (RPC), were obtained three PC that explained 82.78% of the σ2 (variance), and the adjustment of MLR over factorial loads obtained equations for HCW, CCW, PL, RW and CI with R2 ≥ 0.37, up to 0.73. It should be noted the importance of the adjusted equation for CCW because of its relation with carcass price and its weight as a predictor variable of primary and commercial cuts. Implications: It is useful and necessary that the adjustment of prediction equations for performance variables in animal science, can be accompanied with results of their respective tests of the model assumptions of multiple regression analysis. Our findings, confirm the need to carefully examine the adjustment of prediction equations with the aim of estimating equations with less bias and higher confidence. Conclusions: The multiple regression analysis over original variables and vectors of principal components determined prediction equations with different grades of adjustment for performance variables (HCW, CCW, CI) and carcass quality (LP, RW, TD). In the adjusted equations over original variables, the betas with higher prediction power were for slaughter weight, initial live weight and final live weight. While for the adjustment of prediction equations with factorial loads of PC, the betas with higher power of prediction were for PC1 y PC2, characterized by having higher factorial loads. The values of multicollinearity and autocorrelation bias, determination coefficient and explained variance, showed that practical application of these prediction equations allowed to a real approximation to estimation of postmortem variables; however, these values should be taken considering their reliability.


: fattening sheep; carcass; prediction; linear regression; principal component regression.

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Adenaike, A.S., Akpan, U. and Ikeobi, C.O.N., 2016. Principal components regression of body measurements in five strains of locally adapted chickens in Nigeria. Bulletin of Animal Health and Production in Africa, 64(1), pp. 105-115.

Alaminos, A., Fránces, F. J., Penalva-Verdú, C. and Santacreu, O.A., 2015. Análisis multivariante para las ciencias sociales 1. Índices de distancia, conglomerados y análisis factorial. 1er Edición. Ecuador: PYDLOS ediciones.

Artigue H. and Smith G., 2019. The principal problem with principal components regression. Cogent Mathematics & Statistics, 6(1), pp. 1-11.

Bautista, E.D., Salazar, R.C., Chay, A.J.C., Herrera, R.A.G., Piñeiro, Á.T. V., Monforte, J.G.M. and Gómez, A.V., 2017. Determination of carcass traits in Pelibuey ewes using biometric measurements. Small Ruminant Research, 147, pp. 115-119.

Bello-ibiyemi, A.A., Wheto, M., Adenaike, A.S., Decampos, J.S., Ogunlakin, D.O., Atunnise, M., Shola, S. and Ikeobi, C.O.N., 2016. Principal component regression of the morphostructural traits of West African dwarf sheep. Nigerian Journal of Animal Production, 43(1), pp. 62-71.

Cadavez, V.A.P. and Henningsen, A., 2012. The use of seemingly unrelated regression to predict the carcass composition of lambs. Meat Science, 92(4), pp. 548–553.

Ciliberti, M.G., Santillo, A., Marino, R., Ciani, E., Caroprese, M., Rillo, L. and Albenzio, M., 2021. Lamb meat quality and carcass evaluation of five autochthonous sheep breeds: towards biodiversity protection. Animals, 11;11(11), 3222, pp. 1-10.

Costa, T.A., 1998. Ovino de carne: Calidad de la canal ovina. Madrid: Editorial Mundi- Prensa, pp. 373-400.

Delfa, R.L. and Gonzales, C.F., 1995. Modelos de calificación de canales de ovinos en la unión europea. Euro carne, 37, pp. 37-44.

Dias, L.G., Silva, S.R. and Teixeira, A., 2020. Simultaneously prediction of sheep and goat carcass composition and body fat depots using in vivo ultrasound measurements and live weight. Research in Veterinary Science, 133, pp. 180-187.

do Prado Paim, T., Da Silva, A.F., Martins, R.F.S., Borges, B.O., Lima, P.D.M.T., Cardoso, C.C. and McManus, C., 2013. Performance, survivability and carcass traits of crossbred lambs from five paternal breeds with local hair breed Santa Inês ewes. Small Ruminant Research, 112(1-3), pp. 28-34.

Dorantes, E.J.C., Torres, G.H., Hernández, O.M. and Rojo, R.R., 2015. Zoometric measures and their utilization in prediction of live weight of local goats in southern México. Springer Plus, 4(1), pp. 1-8.

FAO (Food and Agriculture Organization of the United Nations), 2021. FAOSTAT Livestock Primary. Disponible en:

FAO (Food and Agriculture Organization of the United Nations)., 2014. Producción y Sanidad Animal, Consumo de carne. Consultado el 23-11-2020. Disponible en:

Freund, R., Wilson, W. and Sa P., 2006, Regression Analysis, Statistical Modeling of a response variable. 2da Edición. United States: Elsevier Inc.

Gagaoua, M. and Picard, B., 2020. Current advances in meat nutritional, sensory and physical quality improvement. Foods. Basel, Switzerland: MDPI.

Garson, D., 2014, Multiple regression. Edición 2014. NC, United States: Statistical Publishing Associates.

Garson, D., 2018, Factor analysis. Edición 2013. NC, United States: Statistical Publishing Associates.

Garson, G.D, 2023, Factor analysis and dimension reduction in R: A social scientist's toolkit. Edición 2023. NY, United States: Taylor & Francis.

Guedes, D.G.P., Ribeiro, M.N. and Carvalho, F.F.R.D., 2018. Multivariate techniques in the analysis of carcass traits of Morada Nova breed sheep. Ciencia Rural, 48(9), pp. 1-7.

Hernández, B.J., Aquino, L.J.L. and Ríos, R.F.G., 2013. Efecto del manejo pre-mortem en la calidad de la carne. Nacameh, 7(2), pp. 41?64.

Hernández, D.F.E., Oliva, J.H., Pascual, A.C. and Hinojosa, J.A.C., 2012. Descripción de medidas corporales y composición de la canal en corderas Pelibuey: estudio preliminar. Nota Técnica. Revista Científica, 22(1), pp. 24-31.

Hopkins, D.L., 2008. An industry applicable model for predicting lean meat yield in lamb carcasses. Australian Journal of Experimental Agriculture, 48(7), pp. 757-761.

Iqbal, F., Ali, M., Huma, Z.E. and Raziq, A., 2019. Predicting live body weight of Harnai sheep through penalized regression models. JAPS: Journal of Animal & Plant Science, 29(6), pp. 1541-1548.

Jerez-Timaure, N., Huerta-Leidenz, N., Ortega, J. and Rodas-González, A., 2013. Prediction equations for Warner–Bratzler shear force using principal component regression analysis in Brahman-influenced Venezuelan cattle. Meat science, 93(3), 771-775.

Jones, B.K. and Tatum, J.D., 1994. Predictors of beef tenderness among carcasses produced under commercial conditions. Journal of Animal Science, 72(6), pp. 1492-1501.

Lê, S., Josse, J. and Husson, F., 2008. FactoMineR: An R package for multivariate analysis. Journal of Statistical Software, 25(1), pp. 1-18.

Liland, K., Mevik, B. and Wehrens, R, 2022. pls: Partial least squares and principal component regression. R package version 2.8-1.

López-Velázquez, M.M., de la Cruz-Colín, L., Partida de la Peña, J.A., Torres-Hernández, G., Becerril-Pérez, C.M., Buendía R.G., Jiménez, B.M.D.R., Alfaro, R.R.H., Martínez-Rojero, R.D. and Hinojosa-Cuéllar, J.A., 2016. Efecto de la raza paterna en características de la canal de corderos para carne en Hidalgo, México. Revista Mexicana de Ciencias Pecuarias, 7(4), pp. 441-453.

Mavule, B.S., Muchenje, V., Bezuidenhout, C.C. and Kunene, N.W., 2013. Morphological structure of Zulu sheep based on principal component analysis of body measurements. Small Ruminant Research, 111(1-3), pp. 23-30.

Mondragón, J.A., Hernández, J.M., Rebollar, S.R., Salem, A.Z.M., Rojo, R.R., Domínguez, I.A.V. and García, A.M., 2014. Marketing of meat sheep with intensive finishing in southern state of Mexico. Trop. Animal Health Production, 46(8), pp. 1427-1433.

NCSS., 2020, Statistical Software. 2020. NCSS, LLC. Kaysville, Utah, USA

Ngo, L., Ho, H., Hunter, P., Quinn, K., Thomson, A. and Pearson, G., 2015. Post-mortem prediction of primal and selected retail cut weights of New Zealand lamb from carcass and animal characteristics. Meat science, 112, pp. 39-45.

Okpeku, M., Yakubu, A., Peters, S.O., Ozoje, M.O., Ikeobi, C.O.N., Adebambo, O.A. and Imumorin, I.G., 2011. Application of multivariate principal component analysis to morphological traits of goats in southern Nigeria. Acta agriculturae Slovenica. 98(2), pp. 101-109.

Parlamento Europeo., 2013. Reglamento (UE) No 1308/2013 del Parlamento Europeo y del Consejo de 17 de diciembre de 2013 por el que se crea la organización común de mercados de los productos agrarios y por el que se derogan los Reglamentos (CEE) n ° 922/72, (CEE) n ° 234/79, (CE) n ° 1037/2001 y (CE) n ° 1234/2007 Diario Oficial de la Unión Europea. p.p. 347-361

Partida de la Peña, J.A., Ríos, R.F.G., Cruz, C.L.D.L., Domínguez, V.I.A. and Buendía, R.G., 2017. Caracterización de las canales ovinas producidas en México. Revista Mexicana de Ciencias Pecuarias, 8(3), pp. 269-277.

Partida, P.J.A. 2016. Producción y calidad de la carne ovina en México. 1er Edición. Qro, México: INIFAP.

Pethick, D.W., Banks, R.G., Hales, J. and Ross, I.R., 2006. Australian prime lamb–a vision for 2020. International Journal of Sheep and Wool Science, 54, pp. 66-73.

Putra, W.P.B. and Ilham, F., 2019. Principal component analysis of body measurements and body indices and their correlation with body weight in Katjang does of Indonesia. Journal of Dairy Veterinary & Animal Research, 8(3), pp.124-134.

R Core Team. 2021. R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. URL:

Sabharwal, C.L. and Anjum, B., 2016. Data reduction and regression using principal component analysis in qualitative spatial reasoning and health informatics. Polibits, 53, pp. 31-42.

Sankhyan, V., Thakur, Y.P., Katoch, S., Dogra, P.K. and Thakur, R., 2018. Morphological structuring using principal component analysis of Rampur-Bushair sheep under transhumance production in western Himalayan region, India. Indian Journal of Animal Research, 52(6), pp. 917-922.

Sañudo, C., Campo, M.M., Muela, E., Olleta, J.L., Delfa, R., Jiménez, R.B. and Cilla, I., 2012. Carcass characteristics and instrumental meat quality of suckling kids and lambs. Spanish Journal of Agricultural Research, 10(3(, pp. 690-700.

SAS Institute Inc., 2004, SAS/STAT® 9.1 User’s Guide. Cary, NC: SAS Institute Inc.

Scrucca L., Fop M., Murphy T.B. and Raftery A.E., 2016. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), pp. 289-317.

Servicio de información Agroalimentaria y Pesquera. 2020. Población ganadera. México. Consultado: 08-05-2019. Disponible en:

Silva, S.R., Afonso, J., Monteiro, A., Morais, R., Cabo, A., Batista, A. C. and Teixeira, A. 2018. Application of bioelectrical impedance analysis in prediction of light kid carcass and muscle chemical composition. Animal, 12(6), pp. 1324-1330.

Statistics, I., 2013. IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. IBM Corp

USDA. 1992. United States Standards for Grades of Lamb, Yearling Mutton, and Mutton Carcasses. Disponible en:

Wehrens, R. and Mevik, B.H., 2007. The pls package: principal component and partial least squares regression in R. Journal of Statistical Software, 8(2), pp. 1-23.

Yu, C. H. 2011. Principal component regression as a countermeasure against colinearity. In Proc. Western Users of SAS Software Conf., San Francisco, CA (pp. 1-8). SAS Institute Inc., Cary, NC.



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