References
- Zavadilova L, Stipkova M, Svitakova A, Krupova Z, Kasna E. Genetic parameters for clinical mastitis, fertility and somatic cell score in czech holstein cattle. Ann Anim Sci 2017;17:1007-18. https://doi.org/10.1515/aoas-2017-0006
- Taubert H, Rensing S, Stock KF, Reinhardt F. Development of a breeding value for mastitis based on SCS-results. Interbull Bull 2013:161-5.
- Bondan C, Folchini JA, Noro M, Quadros DL, Machado KM, Gonzalez FHD. Milk composition of Holstein cows: a retrospective study. Cienc Rural 2018;48:e20180123. https://doi.org/10.1590/0103-8478cr20180123
- Weigel KA, Shook GE. Genetic selection for mastitis resistance. Vet Clin North Am Food Anim Pract 2018;34:457-72. https://doi.org/10.1016/j.cvfa.2018.07.001
- Duran Aguilar M, Roman Ponce SI, Ruiz Lopez FJ, et al. Genome-wide association study for milk somatic cell score in holstein cattle using copy number variation as markers. J Anim Breed Genet 2017;134:49-59. https://doi.org/10.1111/jbg.12238
- Govignon-Gion A, Dassonneville R, Baloche G, Ducrocq V. Multiple trait genetic evaluation of clinical mastitis in three dairy cattle breeds. Animal 2016;10:558-65. https://doi.org/10.1017/S1751731115002529
- Streit M, Reinhardt F, Thaller G, Bennewitz J. Reaction norms and genotype-by-environment interaction in the German Holstein dairy cattle. J Anim Breed Genet 2012;129:380-9. https://doi.org/10.1111/j.1439-0388.2012.00999.x
- Falconer DS. Introduction to quantitative genetics. 3rd ed. Harlow, UK: Longman Scientific & Technical; 1989.
- Tiezzi F, de los Campos G, Parker Gaddis KL, Maltecca C. Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle. J Dairy Sci 2017;100:2042-56. https://doi.org/10.3168/jds.2016-11543
- Dingemanse NJ, Wolf M. Between-individual differences in behavioural plasticity within populations: causes and consequences. Anim Behav 2013;85:1031-9. https://doi.org/10.1016/j.anbehav.2012.12.032
- Alvares CA, Stape JL, Sentelhas PC, de Moraes Goncalves JL, Sparovek G. Koppen's climate classification map for Brazil. Meteorol Z 2013;22:711-28. https://doi.org/10.1127/0941-2948/2013/0507
- SAS Institute Inc. SAS 9.1.3 Help and documentation. Cary, NC, USA: SAS Institute Inc; 2013.
- Schaeffer LR. Application of random regression models in animal breeding. Livest Prod Sci 2004;86:35-45. https://doi.org/10.1016/S0301-6226(03)00151-9
- Meyer K. WOMBAT-a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J Zhejiang Univ Sci B 2007;8:815-21. https://doi.org/10.1631/jzus.2007.B0815
- Cardoso FF, Tempelman RJ. Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction. J Anim Sci 2012;90:2130-41. https://doi.org/10.2527/jas.2011-4333
- MdAPeA. Instrucao normativa Nº 31, de 29 de junho de 2018.
- United States Department of Agriculture. Determining U.S. milk quality using bulk-tank somatic cell counts, 2018. Fort Collins, CO, USA: USDA; 2018.
- de Paula MC, Martins EN, da Silva LOC, de Oliveira CAL, Valotto AA, Ribas NP. Interacao genotipo × ambiente para producao de leite de bovinos da raca Holandesa entre bacias leiteiras no estado do Parana. Rev Bras Zootec 2009;38:467-73. https://doi.org/10.1590/S1516-35982009000300010
- Haiduck Padilha A, Alfonzo EPM, Daltro DS, Torres HAL, Braccini Neto J, Cobuci JA. Genetic trends and genetic correlations between 305-day milk yield, persistency and somatic cell score of Holstein cows in Brazil using random regression model. Anim Prod Sci 2019;59:207-15. https://doi.org/10.1071/AN16835
- Alam M, Cho CI, Choi TJ, et al. Estimation of genetic parameters for somatic cell scores of Holsteins using multi-trait lactation models in Korea. Asian-Australas J Anim Sci 2015;28:303-10. https://doi.org/10.5713/ajas.13.0627
- Kheirabadi K, Razmkabir M. Genetic parameters for daily milk somatic cell score and relationships with yield traits of primiparous Holstein cattle in Iran. J Anim Sci Technol 2016;58:38. https://doi.org/10.1186/s40781-016-0121-5
- Bohlouli M, Alijani S, Naderi S, Yin T, Konig S. Prediction accuracies and genetic parameters for test-day traits from genomic and pedigree-based random regression models with or without heat stress interactions. J Dairy Sci 2019;102:488-502. https://doi.org/10.3168/jds.2018-15329
- Meyer K. Random regression analyses using B-splines to model growth of Australian Angus cattle. Genet Sel Evol 2005;37:473. https://doi.org/10.1186/1297-9686-37-6-473
- Pegolo NT, Albuquerque LG, Lobo RB, de Oliveira HN. Effects of sex and age on genotype × environment interaction for beef cattle body weight studied using reaction norm models1. J Anim Sci 2011;89:3410-25. https://doi.org/10.2527/jas.2010-3520
- Carvalheiro R, Costilla R, Neves HHR, Albuquerque LG, Moore S, Hayes BJ. Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions. Genet Sel Evol 2019;51:29. https://doi.org/10.1186/s12711-019-0470-x
- Morrissey MB, Liefting M. Variation in reaction norms: statistical considerations and biological interpretation. Evolution 2016;70:1944-59. https://doi.org/10.1111/evo.13003
- Robertson A. Experimental design on the measurement of heritabilities and genetic correlations: biometrical genetics. New York, USA: Pergamon; 1959. pp. 219-26.
- Kolmodin R, Strandberg E, Danell B, Jorjani H. Reaction norms for protein yield and days open in Swedish red and white dairy cattle in relation to various environmental variables. Acta Agric Scand A Anim Sci 2004;54:139-51. https://doi.org/10.1080/09064700410032040
- van der Veen AA, ten Napel J, Oosting SJ, Bontsema J, van der Zijpp AJ, Groot Koerkamp PWG. Robust performance: principles and potential applications in livestock production systems. In: Proceedings of the Joint International Agricultural Conference 2009; 2009 Jul 6-8: Netherlands. pp.173-80.
- Bohlouli M, Shodja J, Alijani S, Pirany N. Interaction between genotype and geographical region for milk production traits of Iranian Holstein dairy cattle. Livest Sci 2014;169:1-9. https://doi.org/10.1016/j.livsci.2014.08.010
- Aubin-Horth N, Renn SCP. Genomic reaction norms: using integrative biology to understand molecular mechanisms of phenotypic plasticity. Mol Ecol 2009;18:3763-80. https://doi.org/10.1111/j.1365-294X.2009.04313.x