Goat meat production, a widely extended activity in the more arid areas of Tunisia, relies on local breeds. These breeds are well adapted to produce under harsh conditions but have a very small size and low productivity. The aim of this study was to establish the basis for future genetic evaluations to improve growth potential of this local stock. A total of 13,095 body weights and pedigree of 945 kids in the caprine herd of the Arid Areas Institute of Médenine were used. Random regression (RR) and multiple trait (MT) models were analyzed and compared. All models included effects of age and weight of dam, age, sex and type of birth of the kid, and year × month of recording, plus random direct and maternal additive genetic and permanent environmental effects. RR and MT models behave similarly, with RR showing slightly better goodness of fit. Heritability estimates for direct (ranging from 0.15 to 0.4) and maternal (0.05 to 0.3) effects showed that efficient selection for weight is feasible in this population. Estimated correlations between ages were high (> 0.8) for direct effects across all ages and low (down to 0.2) for weights taken at distant ages for maternal effects. Estimated genetic correlations between direct and maternal components revealed an antagonistic relationship, especially at early ages. Recording of at least one weight in the first month of age of the kids to evaluate the maternal capacity and a later weight to evaluate direct effects on weight is recommended for genetic evaluations under field conditions.
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Albuquerque, L.G. and Meyer, K., 2001. Estimation of covariance functions for growth from birth to 630 days of age in Nelore cattle. Animal Science, 79, 2776–2789.
AL Saef, A.M., 2013. Genetic and phenotypic parameters of body weights in Saudi Aradi goat and their crosses with Syrian Damascus goat. Small Ruminants Research, 112, 35–38.
Al-Shorepy, S.A., Alhadranu, G.A. and Abdul Wahab, K., 2002.Genetic and phenotypic parameters for early growth traits in Emirati goat. Small Ruminants Research, 45, 217–223.
Baldi, F., Alencar, M.M. and Albuquerque, L.G., 2010. Estimativas de parâmetros genéticos para características de crescimento em bovinos da raça Canchim utilizando modelos de dimensão finita. Revista Brasileira de Zootecnia, 39, 2409–2417.
Barazandeh, A., Molaei mothball S., Ghavi hossein-zadeh, N and Vatankhah, M., 2012. Genetic évaluation of growth in Raini goat using random regression models. Livestock Production Science, 145,1–6.
Bohlouli, M., Shodja, J., Alijani, S. and Eghbal, A., 2013. The relationship between temperature-humidity index and test-day milk yield of Iranian Holstein dairy cattle using random regression model. Livestock Production Science,157, 414–420.
Boligon, A., Mercadante, M., Forni, S., Lobo, R., and Albuquerque, L.G., 2010. Covariance functions for body weight from birth to maturity in Nellore cows. Journal of Animal Science, 88, 849–859.
Boujenane, I. and Hazzab, A.E., 2008.Genetic parameters for direct and maternal effects on body weights of Draa goats. Small Ruminant Research, 80, 16–21.
Deribe, B. and Taye, M., 2013. Evaluation of Growth Performance of Abergele Goats under Traditional Management Systems in Sekota District, Ethiopia. Pakistan Journal of Biological Science, 16, 692–696.
Deribe, B., Tilahun, M., Lakew, M., Belayneh, N., Zegeye, A., Walle, M., Ayichew, D., Tiruneh Ali, S. and Abriham, S.,2015. On Station Growth Performance of Crossbred Goats (Boer X Central Highland) at Sirinka, Ethiopia. Asian Journal of Animal Science, 9,454–459.
El Faro, L., Cardoso, V.L. and de Albuquerque L.G., 2008. Variance component estimates for test-day milk yield applying random regression models. Genetics and Molecular Biology, 31,665–673.
Fischer, T., Vanderwert, M., Banks, J.H.J., and Ball, G.A.J. 2004. Description of lamb growth using random regression on field data. Livestock Production Science, 89, 175–185.
Ghafouri-Kesbi F., Eskandarinasab MP., Shahir, M.H., 2008 Estimation of direct and maternal effects on body weight in Mehraban sheep using random regression models. Archiv Tierzukht, 51, 235–246.
Gilmour, A.R., Thompson, R. and Cullis, B.R., 1995. Average Information REML : An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models. Biometrics, 51, 1440–1450.
Henderson, C.R., 1975. Best linear unbiased prediction under a selection model, Biometrics 31, 423–447.
Henderson C.R., 1982. Analysis of covariance in the mixed model: higher-level, non-homogeneous, and random regressions. Biometrics.38, 623–640.
Iwaisaki H., Tsuruta S., Misztal I. and Bertrand J.K.., 2005. Genetic parameters estimated with multitrait and linear spline-random regression models using Gelbvieh early growth data. Journal of Animal Science, 83, 757–763.
Kariuki, C.M., Ilatsia, E.D., Wasike, C.B., Kosgey, I.S. and Kahi, A.K., 2010.Genetic evaluation of growth of Dorper sheep in semi-arid Kenya using random regression models. Small. Ruminants Research, 93, 126–134.
Mandal, A., Neser, F.W.C., Rout, P.K., Roy, R. and Notter, D.R., 2006. Genetic parameters for direct and maternal effects on body weights of Muzaffarnagari sheep. Animal Science, 82, 133–140.
Menezes G.R.O. 2010. Uso de Polinômios Segmentados na Modelagem de Dados Longitudinais de Ponderal em Bovinos da Raça Tabapuã. Doctoral thesis. Universidade Federal de Viçosa, Viçosa.
Meyer, K., 1992. Variance components due to direct and maternal effects for growth traits of Australian beef cattle. Livestock Production Science, 31,179–204.
Meyer, K., 2004. Scope for a random regression model in genetic evaluation of beef cattle for growth. Livestock Production Science, 86, 69–83.
Misztal, I., 2006. Properties of random regression models using linear splines. Journal of Animal Breeding and Genetics, 123, 74–80.
Misztal, I., S. Tsuruta, T. Strabel, B. Auvray, T. Druet, and D. H. Lee. 2002. BLUPF90 and related programs. Commun. No. 28-07 in Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France.
Mugambi, J.N., Wakhungu, J.W., Inyangala, B.O., Muhuyi, W.B. and Muasya, T., 2007. Evaluation of the performance of the Kenya dual purpose goat composites: additive and non-additive genetic parameters. Small Ruminants Research, 72, 149–156.
Najari, S. (2005). Caractérisation zootechnique et génétique d'une population caprine. Cas de la population caprine locale des régions arides tunisiennes. Thèse de doctorat d’Etat, 214 pp.
Najari, S., Gaddour A., Ouni M., Abdennebi M. and Ben Hammouda, M., 2007. Indigenous kids weight variation with respect to non genetic factors under pastoral mode in Tunisian arid region. Journal of Animal Veterinary Advances, 6, 441–450.
Nobre, P. R. C., Misztal, I., Tsuruta, S., Bertrand, J. K, Silva, L.O.C. and Lopes, P.S., 2003. Analyses of growth curves of Nellore cattle by multiple-trait and random regression models. Journal of Animal Science, 81, 918–926
Ouni, M. (2006). Caractérisation morphométrique des ressources génétiques caprines des régions arides tunisiennes, Mastère en Génétique et Bio ressources, 78 pp.
Patterson, H.D., Thompson, R., 1971. Recovery of inter-block information when block sizes are unequal, Biometrika,58, 545–554.
Rashidi, A., Bishop, S. and Matika, O., 2011. Genetic parameter estimates for pre-weaning performance and reproduction traits in Markhoz goats. Small Ruminants Research, 100, 100–106.
Safari, E., Fogarty, N.M. and Gilmour, A.R., 2005. A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livestock Production Science, 92, 271–289.
Schaeffer, L. R., and J.C.M. Dekkers., 1994. Random regressions in animal models for test-day production in dairy cattle. Livestock Production Science, 18,443–453.
Zhang, C., Yang, L. and Shen, Z., 2008. Variance components and genetic parameters for weight and size at birth in the Boer goat. Livestock Production Science, 115, 73–79.
Zhang C.Y., Zhang Y., Xe DQ., Li X., Su J. and Yang L.G., 2009. Genetic and phenotypic parameter estimates for growth traits in Boer goat. Livestock Production Science, 124, 66–71.
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The authors declare that they have no conflict of interest.
This study does not involve any human or animal testing, only routine management practices in animal husbandry.
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Atoui, A., Carabaño, M.J., Díaz, C. et al. Genetic analysis of live weight of local kids to promote genetic evaluations in the arid areas of Tunisia. Trop Anim Health Prod 52, 955–968 (2020). https://doi.org/10.1007/s11250-019-02081-z
- Growth traits
- Genetic evaluations
- Random regression models
- Multitrait models