A Determination of the Change in Variance Components due to Heat Stress in Dairy Cattle Using a Random Regression Model

Year 2024, Volume: 30 Issue: 1, 108 - 117, 09.01.2024

Ayşe Pınarbaşı , Kemal Yazgan

https://doi.org/10.15832/ankutbd.1298051

Abstract

The aim of this study is to evaluate changes in variance components for dairy cows under heat stress conditions using a random regression model. The daily milk yield and pedigree records used in the research were obtained from a dairy farm in Sanliurfa, Türkiye. The records were from Holstein dairy cows registered between 2017 and 2019 at the farm. A total of 690 lactations from 690 healthy dairy cows were used in the study and the total number of cow-days was 207,003. In order to evaluate heat stress on animals meteorological data were used and collected from a public weather station located 15.04 km away from the farm. In the study, variance components were separately estimated for the comfort period (CP) and the heat stress period (HSP) using a random regression test-day model and six-knot linear spline function was used. In the study, it was observed that heat stress resulted in an increase in additive genetic,
permanent environmental, and consequently, phenotypic variance. During the lactation period, the average heritability was determined to be 0.13±0.007 for CP, while it was found to be 0.18±0.010 for HSP. According to the findings obtained from the study, it was concluded that the time periods for selection should coincide with the peak milk yield under heat stress conditions, while for the period without heat stress, it should be around the 120th day of lactation. These results indicate that climatic factors such as temperature and humidity should be included in the models used for genetic parameter and breeding value estimation. Thus, it may be possible to identify dairy cattle that are genetically more tolerant to hot conditions. In this way, more successful outcomes can be achieved in selection studies.

Keywords

Dairy cattle, Genetic analyses, Temperature-humidity index, Eigenfuction, Weather station

References

• Anonymous (2023a). https://www.mgm.gov.tr/eng/forecast-cities.aspx?m=SANLIURFA Access date: 08.05.2023 Anonymous (2023b). http://didgeridoo.une.edu.au/km/wombat.php Access date: 12.02.2023
• Abeni F, Calamari L & Stefanini L (2007). Metabolic conditions of lactating Friesian cows during the hot season in the Po valley. 1. Blood indicators of heat stress. International Journal of Biometeorology 52(2):87-96. https://doi.org/10.1007/s00484-007-0098-3
• Aguilar I, Misztal I & Tsuruta S (2009). Genetic components of heat stress for dairy cattle with multiple lactations. Journal of Dairy Science. 92: 5702–5711. https://doi.org/10.3168/jds.2008-1928
• Armstrong D V (1994). Heat stress interaction with shade and cooling. Journal of Dairy Science 77: 2044–2050. https://doi.org/10.3168/jds.S0022-0302(94)77149-6
• Bernabucci U, Biffani S, Buggiotti L, Vitali A, Lacetera N & Nardone A (2014) The effects of heat stress in Italian Holstein dairy cattle. Journal of Dairy Science 97(1):471-86. https://doi.org/10.3168/jds.2013-6611
• Bohmanova J, Miglior F, Jamrozik J, Misztal I & Sullivan P G (2008). Comparison of random regression models with legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows. Journal of Dairy Science 91: 3627–3638 https://doi.org/10.3168/jds.2007-0945
• Bryant J R, Lopez-Villalobos N, Pryce J E, Holmes C W, Johnson D L & Garrick D J (2007). Environmental sensitivity in New Zealand dairy cattle. Journal of Dairy Science 90: 1538–1547. https://doi.org/10.3168/jds.S0022-0302(07)71639-9
• Das R, Sailo L, Verma N, Bharti P, Saikia, J & Kumar R (2016). Impact of heat stress on health and performance of dairy animals: A review. Veterinary World 9(3): 260-268. https://doi.org/10.14202/vetworld.2016.260-268
• Demir O & Yazgan K (2023). Effects of air temperature and relative humidity on milk yield of Holstein dairy cattle raised in hot-dry Southeastern Anatolia Region of Türkiye. Journal of Agricultural Sciences 29(2): 710-720. https://doi.org/10.15832/ankutbd.1159540
• Dikmen S & Hansen P J (2009). Is the temperature-humidity index best indicator of heat stress in lactating dairy cows in a subtropical environment? Journal of Dairy Science 92(1): 109-116. https://doi.org/10.3168/jds.2008-1370
• Druet T, Jaffrézic F, Boichard D & Ducrocq V (2003). Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. Journal of Dairy Science 86: 2480–2490. https://doi.org/10.3168/jds.S0022-0302(03)73842-9
• Freitas M S, Misztal I, Bohmanova J & West J (2006). Utility of on- and off-farm weather records for studies in genetics of heat tolerance. Livestock Science 105: 223–228. https://doi.org/10.1016/j.livsci.2006.06.011
• Garcia-Ispierto I, Lopez-Gatius F, Bech-Sabat G, Santolaria P, Yaniz J L, Nogareda C, De Rensis F & Lopez-Bejar M (2007). Climate factors affecting conception rate of high producing dairy cows in northeastern Spain. Theriogenology 67: 1379–1385. https://doi.org/10.1016/j.theriogenology.2007.02.009
• Gonzalez-Rivas P A, Sullivan M, Cottrell J J, Leury B J, Gaughan J B & Dunshea F R (2018). Effect of feeding slowly fermentable grains on productive variables and amelioration of heat stress in lactating dairy cows in a sub-tropical summer. Tropical Animal Health and Production 50: 1763–1769. https://doi.org/10.1007/s11250-018-1616-5
• Jordan E R (2003). Effects of heat stress on reproduction. Journal of Dairy Science 86 (E Suppl.):E104–E114. https://doi.org/10.3168/jds.S0022-0302(03)74043-0
• Kadzere C T, Murphy M R, Silanikove N & Maltz E (2002). Heat stress in lactating dairy cows: a review. Livestock Production Science 77(1): 59-91. https://doi.org/10.1016/S0301-6226(01)00330-X
• Kirkpatrick M, Lofsvold D & Bulmer M (1990). Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124: 979–993. https://doi.org/10.1093/genetics/124.4.979
• Meyer (2007). WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B 8(11): 815-821. https://doi.org/10.1631/jzus.2007.B0815
• Misztal I (1999). Model to study genetic component of heat stress in dairy cattle using national data. Journal of Dairy Science 82(Suppl. 1):32.(Abstr.).(Cited by: Aguilar I, Misztal I & Tsuruta S (2009). Genetic components of heat stress for dairy cattle with multiple lactations. Journal of Dairy Science. 92: 5702–5711. https://doi.org/10.3168/jds.2008-1928 )
• Misztal I (2006). Properties of random regression models using linear splines. Journal Animal Breeding and Genetic 123: 74–80. https://doi.org/10.1111/j.1439-0388.2006.00582.x
• Osei-Amponsah R, Dunshea F R, Leury B J, Cheng L, Cullen B, Joy A, Abhijith A, Zhang M H & Chauhan S S (2020). Heat Stress Impacts on lactating cows grazing Australian summer pastures on an automatic robotic dairy. Animals 10(5): 869. https://doi.org/10.3390/ani10050869
• Perera KS, Gwazdauskas FC, Pearson RE, Brumback TB (1986). Effect of Season and Stage of Lactation on Performance of Holsteins. Journal of Dairy Science 69 (1): 228-236. https://doi.org/10.3168/jds.S0022-0302(86)80390-3
• Polsky L, Marina A G & Keyserlingk V (2017). Invited review: Effects of heat stress on dairy cattle welfare. Journal of Dairy Science 100: 8645-8657 https://doi.org/10.3168/jds.2017-12651
• Ravagnolo O & Misztal I (2000). Genetic component of heat stress in dairy cattle, parameter estimation. Journal of Dairy Science 83: 2126–2130. https://doi.org/10.3168/jds.S0022-0302(00)75095-8
• Ravagnolo O, Misztal I & Hoogenboom G (2000). Genetic component of heat stress in dairy cattle, development of heat index function. Journal of Dairy Science 83(9): 2126–2130. https://doi.org/10.3168/jds.s0022-0302(00)75094-6
• Ravagnolo O & Misztal I (2002). Effect of heat stress on nonreturn rate in Holsteins: Fixed-model analyses. Journal of Dairy Science 85: 3101–3106. https://doi.org/10.3168/jds.S0022-0302(02)74397-X
• Rearte R, LeBlanc S J, Corva S G, De Da Sota R L, Lacau-Mengido I M & Giuliodori M J (2018). Effect of milk production on reproductive performance in dairy herds. Journal of Dairy Science 101(8): 7575-7584. https://doi.org/10.3168/jds.2017-13796
• SAS institute Inc. (2000). SAS User’s guide statistics, version ed. SAS Institute, Gary. N.C. http://www2.sas.com/pdfs/s2k/v1_psm.pdf
• Schaeffer L R (2004). Application of random regression models in animal breeding. Livestock Production Science 86: 35–45. https://doi.org/10.1016/S0301-6226(03)00151-9
• Silvestre A M, Petim-Batista F & Colaco J (2005). Genetic parameter estimates of Portuguese dairy cows for milk, fat, and protein using a spline test-day model. Journal of Dairy Science 88: 1225–1230. https://doi.org/10.3168/jds.S0022-0302(05)72789-2
• West J W, Mullinix B G & Bernard J K (2003). Effects of hot, humid weather on milk temperature, dry matter intake and milk yield of lactating dairy cows. Journal of Dairy Science 86(1): 232-242. https://doi.org/10.3168/jds.s0022-0302(03)73602-9
• White I M S, Thompson R & Brotherstone S (1999). Genetic and environmental smoothing of lactation curves with cubic splines. Journal of Dairy Science 82: 632–638. https://doi.org/10.3168/jds.S0022-0302(99)75277-X
• Yazgan K (2017). Determining heat stress effect in Holstein dairy cattle using daily milk yield and meteorological data obtained from public weather station in Sanliurfa province of Turkey. Indian Journal of Animal Research 51(6): 1002-1011. https://doi.org/10.18805/ijar.v0i0f.3806