Heat stress level as an alternative to fixed regression modeling for fat and protein yield traits in Holstein cattle

Abstract

To date, no study has tested the inclusion of bioclimatic variables or heat stress indicators in the formation of fixed regression for the genetic evaluation of dairy cattle. The objective of the present study was to investigate the inclusion of heat stress level in modeling of fixed regression in random regression models to predict population response and individual genetic components for fat and protein yield in test-day, using data from Holstein cattle in tropical environment. The data comprised 52,012 test-day fat and protein records of 9,858 first-parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (temperature-humidity index – THI, and diurnal temperature variation – DTV) from 18 weather stations. Least square linear regression models were used to determine THI and DTV thresholds for fat and protein yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were analyzed as fixed effects (considering the average of two days before the test-day control), and heat stress level was considered in formation of fixed regression, totaling three models for each trait. THI and DTV thresholds for fat and protein losses was THI = 74 (−0.030 kg/day/ THI) and DTV = 16 (−0.020 kg/day/ DTV), for both traits. The model that included THI and DTV as fixed effects, and used heat stress level in formation of fixed regression (fixed curve), presented a better fit (Akaike's information criterion, Schwarz's Bayesian information criterion, and residual variance). Estimated breeding values (EBV) are improved when using this model, and there is an increase in reliability of estimates. There is an important reranking of sires when heat stress indicators are included in the model to evaluate fat and protein yield in test-day. The increase in reliability of EBV by including heat stress level in the fixed regression shows a better fit of model to the data. It is possible to conclude that the inclusion of heat stress level in the formation of fixed regression is an important factor to be considered in dairy cattle reared in tropical regions to minimize environmental effects and optimize the process of evaluation and genetic selection.