Heritability and additive breeding value in sheep obtained through industrial crossing
Abstract
This study aimed to estimate the heritabilities, the most assertive selection gain for each characteristic of sheep genetic improvement, together with the reference additive genetic value for industrial crosses in the Southern Region of Brazil. The study was carried out in the municipality of Boa Vista do Cadeado - RS, with data collection from 2020 to 2023. The animals were stratified by gender and separated into stalls, with free access to water. The measurements inherent to the parents (50 dams and 5 rams) were carried out at the time of crossing. For the 100 progenies, the height and weight of male and female lambs at birth were measured. The average daily gain of the progenies was obtained, and a standard weighing and height measurement at 80 days after birth. The final weight and carcass yield of the lambs were obtained at the time of animal slaughter. High genetic variability and narrow-sense heritability were obtained in the characteristics weight at birth of male lambs and height at birth of lambs in both genders. Average daily weight gain revealed high heritability with restricted meaning, this attribute being unrelated to the expressed meteorological variables, identifying effective potential for selection. Pressures of 10% and selection intensities of 1.76 can be employed for most traits to be improved through industrial sheep crossbreeding. Industrial crossing is effective not only due to heterotic effects but also due to additive genetic effects expressed in heritability.
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Abasi-Mousa, S., Varkoohi, S., Joezy, S., Salary, N., & Khansefid, M. (2023). Meta-analysis of genetic parameters for growth traits in meat, wool and dual-purpose sheep breeds in the world using a random-effects model. Veterinary Medicine and Science, 9, 380-390. https://doi.org/10.1002/vms3.1038
ANUALPEC. Anuário da Pecuária Brasileira. (2020). São Paulo, SP: S&P Global.
Barbosa, L. T., Santos, G. B., Muniz, E. N., Azevedo, H. C., & Fagundes, J. L. (2015). Genetic parameters for growth traits of Santa Ines sheep using Gibbs Sampling. Revista Caatinga, 28(4), 211-216. https://doi.org/10.1590/1983-21252015v28n423rc
Carvalho, G. C., Barbosa, L. T., de Oliveira, T. M., Fonseca, F. E. P., Muniz, E. N., & Azevedo, H. C. (2014). Estimation of genetic parameters Santa Inês Sheep breed using single and two-traits models. Ciência Rural, 44, 111-116. https://doi.org/10.1590/S0103-84782014000100018
Falconer, D. S. (1987). Introdução a genética quantitativa. Viçosa, MG: Editora UFV.
Garcia, I. F. F., Perez, J. R. O., & Oliveira, M. V. (2000). Características de carcaça de cordeiros Texel x Bergamácia, Texel x Santa Inês e Santa Inês Puros, terminados em confinamento, com casca de café como parte da dieta. Revista Brasileira de Zootecnia, 29, 253-260. https://doi.org/10.1590/S1516-35982000000100033
Giannotti, J. D. G., Packer, I. U., & Mercadante, M. E. Z. (2005). Meta-analysis for heritability of estimates growth traits in beef cattle. Revista Brasileira de Zootecnia, 34, 1173-1180. https://doi.org/10.1590/S1516-35982005000400011
Habtegiorgis, K., Haile, A., Getachew, T., Kirmani, M. A., & Gemiyo, D. (2022). Analysis of genetic parameters and genetic trends for early growth and reproductive traits of Doyogena sheep managed under community-based breeding program. Heliyon, 8, 1-10. https://doi.org/10.1016/j.heliyon.2022.e09749
Hadfield, J. D. (2010). MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R Package. Journal of Statistical Software, 33, 1-22. https://doi.org/10.18637/jss.v033.i02
Landim, A. V., Costa, H. H. A., Carvalho, F. C., Costa, A. C., Alencar, R. T., Silva, L. N. C., Gomes, J. S., Batista, A. S. M., Miyagi, E. S., & Lima, L. D. (2017). Productive performance and carcass characteristics of pure Rabo Largo lambs and those crossed with Santa Inês. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 69(5), 1267-1274. https://doi.org/10.1590/S1516-35982010000600021
Lira, A. B., Gonzaga Neto, S., Sousa, W. H., Ramos, J. P. de F., Cartaxo, F. Q., Santos, E. M., Cézar, M. F., & Freitas, F. F. (2017). Desempenho e características de carcaça de dois biótipos de ovinos da raça Santa Inês terminados a pasto suplementados com blocos multinutricionais. Revista Brasileira de Saúde e Produção Animal, 18(2), 313-326. https://doi.org/10.1590/S1519-99402017000200010
Mohammadi, Y., Mokhtari, M. S., Saghi, D. A., & Shahdadi, A. R. (2019). Modeling the growth curve in Kordi sheep: the comparison of non-linear models and estimation of genetic parameters for the growth curve traits. Small Ruminant Research, 177, 117-123. https://doi.org/10.1016/j.smallrumres.2019.06.012
Monteiro, M. G., Brisola, M. V., & Vieira Filho, J. E. R. (2021). Diagnóstico da cadeia produtiva de caprinos e ovinos no Brasil. Rio de Janeiro, RJ: Instituto de Pesquisa Econômica Aplicada.
Müller, V., Moraes, B. S. S., Carvalho, I. R., Wendt, C. G., Patten, R. D., & Nogueira, C. E. W. (2021). Genetic parameters of morphometric measurements in Criollo horses. Animal Breeding and Genetics, 138, 174-178. https://doi.org/10.1111/jbg.12503
NASA POWER. (2023). Prediction of Worldwide Energy Resource Applied Science Program. Accessed: July, 2023. Available at: https://power.larc.nasa.gov/docs/.
Oliveira, H. R., Silva, F. F., Silva, M. V. G. B., Siqueira, O. H. G. B. D., Machado, M. A., Panetto, J. C. C., Glória, L. S., & Brito, L. F. (2017). Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactation in Gyr cattle. Livestock Science, 201, 78-81. https://doi.org/10.1016/j.livsci.2017.05.007
Petrovic, M. P., Petrovic, V. C., Muslic, D. R., Maksimovic, N., Pavlovic, I., Cekic, B., & Costic, I. (2019). The phenomenon of heterosis and experience in crossing different breeds of sheep in Siberia. Biotechnology in Animal Husbandry, 34(4), 311-321. https://doi.org/10.2298/BAH1904311P
Pires, M. P., Farah, M. M., Carreño, L. O. D., Utsunomiya, A. T. H., Ono, R. K., Bertipaglia, T. S., & Fonseca, R. (2015). Estimates of genetic parameters for growth characteristics in Suffolk sheep in Brazil. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 67, 1119-1124. https://doi.org/10.1590/1678-4162-6949
R CORE TEAM. (2023). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Rodrigues, F. N., Sarmento, J. L. R., Leal, T. M., Araújo, A. M., & Figueiredo Filho, L. A. S. (2021). Genetic parameters for worm resistance in Santa Inês sheep using Bayesian animal model. Animal Bioscience, 34(2), 185-191. https://doi.org/10.5713/ajas.19.0634
Shiotsuki, L., Cardoso, F. F., & Albuquerque, L. G. (2018). Method for estimation of genetic merit of animals with uncertain paternity under Bayesian inference. Journal of Animal Breeding and Genetics, 132(2), 116-123. https://doi.org/10.1111/jbg.12322
Tesema, Z., Deribe, B., Lakew, M., Getachew, T., Tilahun, M., Belayneh, N., Kefale, A., Shibesh, M., Zegeye, A., Yizengaw, L., Alebachew, G. W., Tiruneh, S., Kiros, S., Asfaw, M., & Bishaw, M. (2022). Genetic and non-genetic parameter estimates for growth traits and Kleiber ratios in Dorper x indigenous sheep. Animal, 12, 1-10. https://doi.org/10.1016/j.animal.2022.100533
Villemereuil, P. (2012). Estimation of a biological trait heritability using the animal model and MCMCGLMM. Vienna: R Foundation for Statistical Computing.
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