Computational modelling of resilience to heat stress in dairy cattle

Background

Heat stress is a state of physiological stress caused by excessive heat, and it has a negative effect on productivity and profitability in dairy cattle. Seasonal losses (deviations) in milk yield due to heat stress during heat weaves are recurring challenges for dairy producers.

Quantifying the deviation between an animal’s expected and perturbed production trajectories offers an indirect measure of resilience. Resilience is defined as the capacity of an animal to remain minimally affected by disturbances or to rapidly return to its pre-disturbance state (Berghof et al., 2019). Accordingly, characterizing individual variation in both the response to and recovery from heat stress is of particular interest.

Although previous studies have assessed the impact of heat stress using deflections in performance, no standardized methodology currently exists to quantify the recovery phase following a heat stress event. Rapid recovery is a key component of resilience and may serve as a valuable trait for selection in breeding programs.

Goal

The aim of this project is to develop methods for quantifying the genetic basis of resilience to heat stress in dairy cattle. This includes several possible goals that a student may pursue in the project:

• Develop and evaluate a methodology for detecting and quantifying recovery following heat stress perturbation, thereby establishing recovery as a resilience indicator in dairy cattle.
• Develop and evaluate a methodology for calculating resilience indicators specific to heat stress, and investigate their usefulness for estimating genetic parameters for resilience to heat stress.
• Evaluate the usefulness of high-resolution daily milk yield data compared to sparse test data for estimating genetic parameters for resilience to heat stress.

Project Description

This project will use simulation to generate lactation curves and introduce perturbations during heat waves. It will build on general mathematical models to quantify cow’s response to heat stress. These simulations will be based on established parametric milk curve equations and plausible parameter values based on literature and real data. Additionally, using the quantitative genetic simulator AlphaSimR, the project will simulate genetic values for the cows, allowing us to estimate the genetic component of the resilience indicator.

The project is suitable for students in Animal Science and Bioinformatics, who are interested in computational work and dairy cattle.

References

Berghof TVL, Poppe M and Mulder HA (2019) Opportunities to Improve Resilience in Animal Breeding Programs. Front. Genet. 9:692. https://doi.org/10.3389/fgene.2018.00692