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Random regression models to account for the effect of genotype by environment interaction due to heat stress on the milk yield of Holstein cows under tropical conditions

  • Animal Genetics • Original Paper
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Abstract

The present study had the following objectives: to compare random regression models (RRM) considering the time-dependent (days in milk, DIM) and/or temperature × humidity-dependent (THI) covariate for genetic evaluation; to identify the effect of genotype by environment interaction (G×E) due to heat stress on milk yield; and to quantify the loss of milk yield due to heat stress across lactation of cows under tropical conditions. A total of 937,771 test-day records from 3603 first lactations of Brazilian Holstein cows obtained between 2007 and 2013 were analyzed. An important reduction in milk yield due to heat stress was observed for THI values above 66 (−0.23 kg/day/THI). Three phases of milk yield loss were identified during lactation, the most damaging one at the end of lactation (−0.27 kg/day/THI). Using the most complex RRM, the additive genetic variance could be altered simultaneously as a function of both DIM and THI values. This model could be recommended for the genetic evaluation taking into account the effect of G×E. The response to selection in the comfort zone (THI ≤ 66) is expected to be higher than that obtained in the heat stress zone (THI > 66) of the animals. The genetic correlations between milk yield in the comfort and heat stress zones were less than unity at opposite extremes of the environmental gradient. Thus, the best animals for milk yield in the comfort zone are not necessarily the best in the zone of heat stress and, therefore, G×E due to heat stress should not be neglected in the genetic evaluation.

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References

  • Aguilar I, Misztal I, Tsuruta S (2009) Genetic components of heat stress for dairy cattle with multiple lactations. J Dairy Sci 92:5702–5711

    Article  PubMed  CAS  Google Scholar 

  • Aguilar I, Misztal I, Tsuruta S (2010) Short communication: genetic trends of milk yield under heat stress for US Holsteins. J Dairy Sci 93:1754–1758

    Article  PubMed  CAS  Google Scholar 

  • Ali TE, Schaeffer LR (1987) Accounting for covariances among test day milk yields in dairy cows. Can J Anim Sci 67:637–644

    Article  Google Scholar 

  • Berman A (2005) Estimates of heat stress relief needs for Holstein dairy cows. J Anim Sci 83:1377–1384

    PubMed  CAS  Google Scholar 

  • Bernabucci U, Biffani S, Buggiotti L, Vitali A, Lacetera N, Nardone A (2014) The effects of heat stress in Italian Holstein dairy cattle. J Dairy Sci 97:471–486

    Article  PubMed  CAS  Google Scholar 

  • Bignardi AB, El Faro L, Cardoso VL, Machado PF, de Albuquerque LG (2009) Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil. Livest Sci 123:1–7

    Article  Google Scholar 

  • Bohlouli M, Shodja J, Alijani S, Eghbal A (2013) The relationship between temperature–humidity index and test-day milk yield of Iranian Holstein dairy cattle using random regression model. Livest Sci 157:414–420

    Article  Google Scholar 

  • Bohmanova J, Misztal I, Tsuruta S, Norman HD, Lawlor TJ (2008) Short communication: genotype by environment interaction due to heat stress. J Dairy Sci 91:840–846

    Article  PubMed  CAS  Google Scholar 

  • Brügemann K, Gernand E, von Borstel UU, König S (2011) Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature × humidity-dependent covariates. J Dairy Sci 94:4129–4139

    Article  PubMed  Google Scholar 

  • Carabaño MJ, Bachagha K, Ramón M, Díaz C (2014) Modeling heat stress effect on Holstein cows under hot and dry conditions: selection tools. J Dairy Sci 97:7889–7904. doi:10.3168/jds.2014-8023

    Article  PubMed  Google Scholar 

  • Menéndez-Buxadera A, Serradilla JM, Molina A (2014) Genetic variability for heat stress sensitivity in Merino de Grazalema sheep. Small Rumin Res 121:207–214

    Article  Google Scholar 

  • Misztal I (1999) Model to study genetic component of heat stress in dairy cattle using national data. J Dairy Sci 82(Suppl 1):32

    Google Scholar 

  • Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH (2002) Blupf90 and related programs. In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 19–23 August 2002

  • National Research Council (NRC) (1971) A guide to environmental research on animals. National Academy of Sciences, Washington, DC

    Google Scholar 

  • Porter JR, Xie L, Challinor AJ, Cochrane K, Howden SM, Iqbal MM, Lobell DB, Travasso MI (2014) Food security and food production systems. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, United Kingdom and New York, pp 485–533

    Google Scholar 

  • Ravagnolo O, Misztal I (2000) Genetic component of heat stress in dairy cattle, parameter estimation. J Dairy Sci 83:2126–2130

    Article  PubMed  CAS  Google Scholar 

  • Smith BJ (2005) Bayesian output analysis program (BOA) for MCMC. Home page at: http://www.public-health.uiowa.edu/boa/. Accessed April 26 2014

  • Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B 64:583–639

    Article  Google Scholar 

  • Togashi K, Lin CY (2007) Genetic modification of the lactation curve by bending the eigenvectors of the additive genetic random regression coefficient matrix. J Dairy Sci 90:5753–5758

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We are grateful to Agropecuária Agrindus S.A. for providing the production data set and to the National Institute for Space Research (INPE) for providing the climate data.

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Correspondence to Mário L. Santana Jr.

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Communicated by: Maciej Szydlowski

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Santana, M.L., Bignardi, A.B., Pereira, R.J. et al. Random regression models to account for the effect of genotype by environment interaction due to heat stress on the milk yield of Holstein cows under tropical conditions. J Appl Genetics 57, 119–127 (2016). https://doi.org/10.1007/s13353-015-0301-x

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  • DOI: https://doi.org/10.1007/s13353-015-0301-x

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