Evaluation of predictive models of carcass tissue composition in Black Belly sheep
DOI:
https://doi.org/10.19136/era.a13n1.4725Palabras clave:
Canal, Ovinos de pelo, Modelos matemáticos, Predicción, Corte de costillasResumen
This study aimed to evaluate the adequacy of predictive models of carcass tissue composition in Black Belly sheep using an independent database of sixteen male sheep aged six months and weighing 31.99±2.38 kg. The animals were slaughtered, and the carcasses were refrigerated at 1°C for a 24-hour period. The carcass was then split along the dorsal midline and 9–11 rib section was removed from the left half. The remainder of the left half of the carcass was dissected into muscle (TCM), fat (TFC) and bone (TCB), and the weight of each component was adjusted to account for the total carcass weight. Regression analysis showed that the intercept (β₀) differed from 0 in all models (P > 0.05). However, the slope (β1) did not differ from 1 (P > 0.05). All models showed moderate to high precision (0.59 ≤ r² ≤ 0.82). The TMC and TCB models were highly accurate (bias correction factor: 0.94 ≤ Cb ≤ 0.99) and moderately to highly reproducible (0.76 ≤ CCC ≤ 0.90). In contrast, the TCF model had low Cb and CCC values. These results suggest that the TCM and TCB models could be used to predict muscle and bone content in Black Belly sheep with moderate precision and very high accuracy.
Descargas
Referencias
AFRC (1993). Technical Committee on Responses to Nutrients. Energy and protein requirements of ruminants. CAB International, Wallingford, UK.
Escalante-Clemente S, Vázquez-Jiménez S, López-Durán S K, Arcos-Alvarez DN, Arbez-Abnal TA, Piñeiro-Vazquez ÁT, Muñoz-Benítez AL, Vargas-Bello-Pérez E, Chay-Canul AJ (2022) Using the 9th–11th rib section to predict carcase tissue composition in Blackbelly sheep. Italian Journal of Animal Science, 21(1), 161-167.
Fonseca MA, Tedeschi LO, Valadares Filho SC, De Paula NF, Silva LD, Sathler DFT (2017). Evaluation of equations to estimate body composition in beef cattle using live, linear and standing-rib cut measurements. Animal Production Science, 57(2), 378-390. http://dx.doi.org/10.1071/AN15312
Fonseca MA, Valadares Filho SC, Tedeschi LO, Chizzotti ML, Machado MG, Abreu DC (2015) Evaluation of predictive equations developed to assess body composition of F1 Nellore× Angus bulls and steers. Animal Production Science, 55(8), 978-987. http://dx.doi.org/10.1071/AN13439
Lin LI (1989) A concordance correlation-coefficient to evaluate reproducibility. Biometrics 45, 255–268.
Marcondes MI, Tedeschi LO, Valadares Filho SC, Chizzotti ML (2012) Prediction of physical and chemical body compositions of purebred and crossbred Nellore cattle using the composition of a rib section. Journal of Animal Science, 90(4), 1280-1290. https://doi.org/10.2527/jas.2011-3839
Mehaba N, Schrade S, Eggerschwiler L, Dohme-Meier F, Schlegel P (2025) Accuracy and precision in DM intake prediction models for lactating dairy cows. Animal, 101535. https://doi.org/10.1016/j.animal.2025.101535
Neves MLMW, Souza EJO, Véras RML, Valadares Filho SC, Marcondes MI, Silva GS, Barreto LMG, Ferreira MA, Véras ASC (2018) Can the body composition of crossbred dairy cattle be predicted by equations for beef cattle?. Asian-Australasian Journal of Animal Science, 31, 10:1604-1610. https://doi.org/10.5713/ajas.17.0876
Oliveira AS, Abreu DC, Fonseca MA, Antoniassi PMB (2013) Short communication: Development and evaluation of predictive models of body weight for crossbred Holstein-Zebu dairy heifers. Journal of Dairy Science. 96 :6697–6702. https://doi.org/10.3168/jds.2013-6988
Quijano-Gallegos EJ, Vázquez-Martínez I, Meza-Villalvazo VM, Gastelum-Delgado MÁ, Orzuna-Orzuna JF, Muñoz-Osorio GA, Marcondes MI, Chay-Canul AJ (2025) Can equations for Black Belly lambs be used to predict the carcass tissue composition of Dorper lambs? Agro Productividad. https://doi.org/10.32854/1653s096
Silva AL, DeVries TJ, Tedeschi LO, Marcondes MI (2019) Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves. Animal, 13(1), 83-89. https://doi.org/10.1017/S1751731118000666
Silva LC, Valadares Filho SC, Detmann E, Marcondes M I, Rotta PP, Prados LF, Zanetti D (2013) Evaluation of equations to predict body composition in Nellore bulls. Livestock Science, 151(1), 46-57. https://doi.org/10.1016/j.livsci.2012.09.014
Souza VL, Drackley JK, Almeida R, Bittar CMM, Albertini TZ, Morrison SY, Lanna DPD (2016) Evaluation of nutrition models to estimate performance of young dairy calves: a meta-analytical study under tropical conditions. Animal 10, 1965–1974. https://doi.org/10.1017/S1751731116000975.
Tedeschi LO, Fox DG, Kononoff PJ (2013) A dynamic model to predict fat and protein fluxes associated with body reserve changes in cattle. Journal of Dairy Science 96, 2448–2463. https://doi.org/10.3168/jds.2012-6070
Tedeschi, L.O., 2006. Assessment of the adequacy of mathematical models. Agricultural Systems. 89, 225–247. https://doi.org/10.1016/j.agsy.2005.11.004
Vargas J, Swenson M, Schilling-Hazlett AK, Reis IA, Velasquez C, Martins EC, Sitorski L, Campos LM, Carvalho PHV, Stackhouse-Lawson KR, Place SE (2025) Evaluation of models of enteric methane emissions in finishing steers. Animal, 101536. https://doi.org/10.1016/j.animal.2025.101536
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Ecosistemas y Recursos Agropecuarios
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
1. Política propuesta para revistas de acceso abierto
Los autores/as que publiquen en esta revista aceptan las siguientes condiciones:
1. Los autores/as conservan los derechos de autor y ceden a la revista el derecho de la primera publicación, con el trabajo registrado con la Licencia CC BY-NC-ND 4.0 Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional de Creative Commons, que permite a terceros utilizar lo publicado siempre que mencionen la autoría del trabajo y a la primera publicación en esta revista.
2. Los autores/as pueden realizar otros acuerdos contractuales independientes y adicionales para la distribución no exclusiva de la versión del artículo publicado en esta revista (p. ej., incluirlo en un repositorio institucional o publicarlo en un libro) siempre que indiquen claramente que el trabajo se publicó por primera vez en esta revista.
3. Se permite y recomienda a los autores/as a publicar su trabajo en Internet (por ejemplo en páginas institucionales o personales) antes y durante el proceso de revisión y publicación, ya que puede conducir a intercambios productivos y a una mayor y más rápida difusión del trabajo publicado (vea The Effect of Open Access).
This work is licensed under CC BY-NC-ND 4.0 
