Resilience is a critical trait that reflects an animal’s ability to cope with macro climatic stressors while maintaining optimal production performance. This project aims to explore the interplay between ambient environmental factors, specifically temperature and humidity, and key performance indicators, such as milk yield and other production traits, in dairy cattle.
By integrating in barn sensor-based environmental data with historical and real-time production records, the project will develop predictive models to quantify resilience traits. Advanced statistical methods and machine learning techniques will be employed to assess the genetic and phenotypic variation associated with resilience. The insights gained may contribute to the design of more robust breeding strategies for improving animal welfare and productivity in changing climatic conditions.
To be decided in agreement with the supervisor.
We provide a study place at a student office in QGG Aarhus main campus.
60 ECTS: Experimental theses in which the student is responsible for planning, trial design and collection and analysis of his/her own original data
Programming skills in Python and R is an advantage but not a prerequisite.
