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

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.

Genetics of heat tolerance for milk yield and quality in Holsteins

“…Consequently, selection for higher MY, F% or P% may result in a poor response of the poorly adapted animals to heat stress. A genetic antagonism between general production level and specific ability to respond to heat stress has also been reported in other studies on dairy cattle (Aguilar et al, 2009;Carabaño et al, 2014;Santana et al, 2016). The genetic trends show that, for MY, there was considerable genetic progress in the general production level over the years studied in all three lactations.…”

“…This could have been due to the fact that part of the farms included in the present study had no effective cooling system. In a study involving a large single Brazilian Holstein herd, Santana et al (2016) identified a THI value of 66 as the heat stress threshold for MY in primiparous cows. However, the herd studied used cooling systems.…”

“…Brügemann et al (2011) stated that the effect of heat stress can suppress the full expression of the genetic potential of animals. According to Santana et al (2016), poorly adapted animals raised in tropical environments partially modified may be being selected most of the time within the stress zone. Consequently, there may be less genetic variability of production in this zone.…”

Temperature-Humidity Indices as Indicators to Heat Stress of Climatic Conditions with Relation to Production and Reproduction of Farm Animals

“…However, during recovery phase, milk decline was recorded to be much lesser of about 1.2±0.32 kg [69]. In an experiment on Holstein cows to assess the decrease in milk yields due to heat stress in tropical conditions, a yield loss of 0.23 kg day -1 was observed for unit rise of THI above 66 [70]. In southern Brazil, [71] observed 21% milk yield loss in commercial heard of Holstein cows due to heat stress.…”

Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops

“…The resulting model is a multiple-trait, multiple-environment model with a variety of interactions, in which computational issues may arise due to the increase in the number of parameters being estimated. It has been shown that RRMs can account simultaneously for the additive genetic effect and some degree of GxE in longitudinal traits in animal breeding by allowing for the estimation of genetic (co)variance components and breeding values over the whole trajectory of a time-dependent trait and environmentdependent covariate (Brügemann et al, 2011;Santana et al, 2016;Bohlouli et al, 2019). In plant breeding, therefore, this model may provide considerable biological insights into the mechanisms determining performance in specific environments, making it a worthwhile method for study in future research.…”