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

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