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Multivariate modelling to estimate carcase characteristics and commercial cuts of Boer goats

Published online by Cambridge University Press:  10 May 2022

Elizabete Cristina Batista da Costa Macena ,
Roberto Germano Costa ,
Wandrick Hauss de Sousa ,
Felipe Queiroga Cartaxo ,
Neila Lidiany Ribeiro Open the ORCID record for Neila Lidiany Ribeiro [Opens in a new window] ,
Janaina Kelli Gomes Arandas Open the ORCID record for Janaina Kelli Gomes Arandas [Opens in a new window]  and
Maria Norma Ribeiro
Elizabete Cristina Batista da Costa Macena
Affiliation:
Animal Science Ph.D. Student, Federal University of Paraiba, Areia, Brazil
Roberto Germano Costa
Affiliation:
Professor at Animal Science Department, Federal University of Paraiba, Areia, Brazil
Wandrick Hauss de Sousa
Affiliation:
Researcher at Paraiba State Agriculture and Livestock Research Agency S.A., EMEPA, João Pessoa, Brazil
Felipe Queiroga Cartaxo
Affiliation:
Researcher at Paraiba State Agriculture and Livestock Research Agency S.A., EMEPA, João Pessoa, Brazil
Neila Lidiany Ribeiro*
Affiliation:
Researcher at National Semiarid Institute, Campina Grande, Brazil
Janaina Kelli Gomes Arandas
Affiliation:
Postdoctoral researcher at Animal Science Department, Federal Rural University of Pernambuco, Recife, Brazil
Maria Norma Ribeiro
Affiliation:
Professor at Animal Science Department, Federal Rural University of Pernambuco, Recife, Brazil
*
Author for correspondence: Neila Lidiany Ribeiro, E-mail: neilalr@hotmail.com
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Abstract

The objective was to establish a multivariate model using two complementary multivariate statistical techniques, factor analysis and multiple stepwise regression to predict carcase characteristics, carcase cuts, internal fat, viscera and loin eye area from body measurements of goats Boer mestizos. Thirty-two goats were used, with initial average weights of 3.3 ± 0.61 kg and final average weights of 16 ± 2.5 kg. Before slaughter and after 16 h of fasting, body weight was measured along with the biometric measurements (BMs) of each animal: body length, withers height, croup height, chest width, croup width, croup perimeter, thoracic perimeter, leg length and thigh circumference. The half carcases were sectioned in six anatomical regions that made up the commercial cuts: neck, palette, rib, handsaw, loin and ham. BMs showed a high correlation with a few exceptions; most of the correlations are above 50%. What also happens with the Carcass weight and cuts were also correlated above 50% with BMs. The data presented an index for the Kaiser–Meyer–Olkin test of 0.80, demonstrating the adequacy of the factor analysis. Through factor analysis, it was possible to observe that the first two factors extracted accumulated 75.47% of the total variance of the studied characteristics. Moderate to high and positive correlations of morphological characteristics with body weight, carcase characteristics and primary carcase cuts suggested the adequacy of morphological characteristics as criteria for early selection of crossbred Boer goats for their body weight and carcase characteristics without slaughter.

Keywords

Factor analysisgenotypemorphological traitmultiple regressionprediction
Type
Animal Research Paper
Information
The Journal of Agricultural Science , Volume 160 , Issue 5 , October 2022 , pp. 371 - 379
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Abdel-Mageed, I and Ghanem, N (2013) Predicting body weight and longissimus muscle area using body measurements in subtropical goat kids. Egyptian Journal of Sheep and Goat Sciences 8, 95–100.10.12816/0005029CrossRefGoogle Scholar
Agamy, R, Abdel-Moneim, AY, Abd-Alla, MS, Abdel-Mageed, II and Ashmawi, GM (2015) Using linear body measurements to predict body weight and carcass characteristics of three Egyptian fat-tailed sheep breeds. Asian Journal of Animal and Veterinary Advances 10, 335–344.10.3923/ajava.2015.335.344CrossRefGoogle Scholar
Arandas, JKG, Silva, NV, Nascimento, RB, Pimenta-Filho, EC, Brasil, LHA and Ribeiro, MN (2016) Multivariate analysis as a tool for phenotypic characterization of an endangered breed. Journal of Applied Animal Research 45, 152–158.10.1080/09712119.2015.1125353CrossRefGoogle Scholar
Assan, N (2013) Bioprediction of body weight and carcass parameters from morphometric measurements in livestock and poultry. Scientific Journal Review 2, 140–150.Google Scholar
Bautista-Diaz, E, Salazar-Cuytun, R, Chay-Canul, AJ, Herrera, RAG, Piñeiro-Vázquez, AT, Monforte, JGM, Tedeschi, LO, Cruz-Hernández, A and Gómez-Vázquez, A (2017) Determination of carcass traits in Pelibuey ewes using biometric measurements. Small Ruminant Research 147, 115–119.CrossRefGoogle Scholar
Bingol, M, Gokdal, O, Aygun, T, Yilmaz, A and Daskiran, I (2011) Some productive characteristics and body measurements of Norduz goats of Turkey. Tropical Animal Health and Production 44, 545–550.CrossRefGoogle ScholarPubMed
Bonny, SPF, Hocquette, JF, Pethick, DW, Legrand, I, Wierzbicki, J, Allen, P, Farmer, LJ, Polkinghorme, RJ and Gardner, GE (2018) Review: The variability of the eating quality of beef can be reduced by predicting consumer satisfaction. Animal: An International Journal of Animal Bioscience 12, 2434–2442.10.1017/S1751731118000605CrossRefGoogle ScholarPubMed
Brasil (2000) Ministério da Agricultura. Instrução Normativa no. 3, de 07 de janeiro de 2000. Regulamento técnico de métodos de insensibilização para o abate humanitário de animais de açougue. Diário Oficial da União, Brasília, pp. 14–16, 24 de janeiro de 2000, Seção I.Google Scholar
Cézar, MF and Sousa, WH (2007) Carcaças Ovinas e Caprinas: Obtenção-avaliação-classificação. Uberaba: Agropecuária Tropical.Google Scholar
Costa, RG, Lima, AGVO, Ribeiro, NL, Medeiros, AN, Medeiros, GR, Gonzaga Neto, S and Oliveira, RL (2020) Predicting the carcass characteristics of Moada Nova lambs using biometric measurements. Revista Brasileira de Zootecnia 49, e20190179.10.37496/rbz4920190179CrossRefGoogle Scholar
De Paula, NF, Tedeschi, LO, Paulino, MF, Fernandes, HJ and Fonseca, MA (2013) Predicting carcass and body fat composition using biometric measurements of grazing beef cattle. Journal of Animal Science 91, 3341–3351.10.2527/jas.2012-5233CrossRefGoogle ScholarPubMed
Ellies-Oury, MP, Chavent, M, Conanec, A, Bonnet, M, Picard, B and Saracco, J (2019) Statistical model choice including variable selection based on variable importance: a relevant way for biomarkers selection to predict meat tenderness. Scientific Reports 9, 10014.10.1038/s41598-019-46202-yCrossRefGoogle ScholarPubMed
Fernandes, HJ, Tedeschi, LO, Paulino, MF and Paiva, LM (2010) Determination of carcass and body fat compositions of grazing crossbred bulls using body measurements. Journal of Animal Science 88, 1442–1453.CrossRefGoogle ScholarPubMed
Festing, MFW and Altman, DG (2002) Guidelines for the design and statistical analysis of experiments using laboratory animals. ILAR Journal 43, 244–258.10.1093/ilar.43.4.244CrossRefGoogle Scholar
Gomes, HFB, Gonçalves, HC, Polizel Neto, A, Cañizares, GIL, Roça, RO, Marques, RO, Oliveira, JM and Queiroz, EO (2013) Common factors method to predict the carcass composition tissue in kid goats. Revista Brasileira de Zootecnia 42, 193–203.10.1590/S1516-35982013000300007CrossRefGoogle Scholar
Hair Júnior, JF, Tatham, RL, Anderson, RE and Black, WC (2009) Análise Multivariada de Dados, 6th Edn. Porto Alegre: Bookman.Google Scholar
Hair Júnior, JF, Black, WC, Babin, BJ and Anderson, RE (2014) Multivariate Data Analysis. 7th Edn. New York City, USA: Pearson.Google Scholar
Hernandez-Espinoza, DF, Oliva-Hernández, J, Pascual-Córdova, A and Hinojosa-Cuéllar, JA (2012) Descripción de medidas corporales y composiciónde la canal en corderas Pelibuey: estudio preliminar. Revista Científica 22, 24–31.Google Scholar
Kaiser, HF (1974) An index to factorial simplicity. Psychometrika 39, 31–36.CrossRefGoogle Scholar
Laville, E, Martin, V and Bastien, O (1996) Prediction of composition traits of young Charolais bull carcasses using a morphometric method. Meat Science 44, 93–104.CrossRefGoogle ScholarPubMed
MacNeil, MD (1983) Choice of a prediction equation and the use of the selected equation in subsequent experimentation. Journal of Animal Science 57, 1328–1336.CrossRefGoogle Scholar
Mahieu, M, Naves, M and Arquet, R (2011) Predicting the body mass of goats from body measurements. Livestock Research for Rural Development 23, 192. Retrieved May 24, 2022, from http://www.lrrd.org/lrrd23/9/mahi23192.htmGoogle Scholar
Mallows, CL (1973) Some comments on C p. Technometrics 15, 661–675.Google Scholar
Morrison, DF (1976) Multivariate Statistical Methods, 2nd Edn. New York: McGraw-Hill Company.Google Scholar
National Research Council – NRC (2007) Nutrient Requirements of Small Ruminants, 7th Edn. Washington: National Academic Press.Google Scholar
Ogah, DM, Yusuf, ND and Ari, MM (2011) Path coefficient model for assessment of weight using linear traits at birth and at weaning in Nigeria indigenous pig. In Proceedings of the 34th conference of Tanzania Society of Animal Production. Belgrade-Zemun: Institute for Animal Husbandry.Google Scholar
Ricardo, HL, Roça, RO, Lambre, NR, Seno, LO, Fuzikawa, IH and Fernandes, ARM (2016) Prediction of weight and percentage of salable meat from Brazilian market lambs by subjective conformation and fatness scores. Revista Brasileira de Zootecnia 45, 639–644.CrossRefGoogle Scholar
Souza, DS, Silva, HP, Carvalho, JMP, Melo, WO, Monteiro, BM and Oliveira, DR (2014) Growth of Santa Inês lambs until weaning and relationship between biometric measurements and body weight, when raised in the Amazonia climate. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 66, 1787–1794.CrossRefGoogle Scholar
Tesema, Z, Tilahumn, M, Zegeye, A and Yizengaw, L (2019) Statistical modeling of carcass traits, primal carcass cuts, body weight, and morphological traits of pure Central Highland and crossbred Boer goats. Journal of Applied Animal Science 12, 39–55.Google Scholar
Woods, CM and Edwards, MC (2011) Factor analysis and related methods. In Essential Statistical Methods for Medical Statistic, pp. 174–201.10.1016/B978-0-444-53737-9.50009-8CrossRefGoogle Scholar
Yakubu, A and Mohammed, GL (2012) Application of path analysis methodology in assessing the relationship between body weight and biometric traits of red Sokoto goat in Northern Nigeria. Biotechnology in Animal Husbandry 28, 101–117.CrossRefGoogle Scholar
Yakubu, A, Idahor, KO and Agade, YI (2009) Using factor scores in a multiple linear regression model for predicting the carcass weight of broiler chickens using body measurements. Revista Cientifica UDO Agricola 9, 963–967.Google Scholar
Yakubu, A, Salako, AE and Abdullah, AR (2011) Varimax rotated principal component analysis of the zoometrical traits of Uda sheep. Archivos de Zootecnia 60, 813–816.Google Scholar
Younas, UM, Abdullah, JA, Bhatti, TN, Pasha, N, Ahmad, MN and Hussain, A (2013) Inter-relationship of body weight with linear body measurements in hissardale sheep at different stages of life. Journal of Animal Plant Science 23, 40–44.Google Scholar