Opportunities to Improve Resilience in Animal Breeding Programs

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Livestock Production Systems

Background:

• Resilience, the ability of animals to withstand disturbances, is crucial but lacks defined indicators for breeding.
• Current breeding goals do not incorporate resilience, potentially limiting livestock adaptability to environmental changes.
• Advancements in big data collection offer opportunities to develop new resilience indicators using longitudinal data.

Purpose of the Study:

• To define resilience indicator traits using big data analysis.
• To establish economic values for these resilience indicators.
• To demonstrate the potential of including resilience in livestock breeding goals.

Main Methods:

• Utilizing longitudinal big data to identify deviations from expected production levels.
• Defining resilience indicators such as variance, autocorrelation, skewness of deviations, and reaction norm slope.
• Calculating economic values for resilience indicators based on reduced labor and treatment costs.

Main Results:

• New resilience indicators derived from big data can be incorporated into breeding programs.
• Economic values for resilience can be significant, especially under labor restrictions, rivaling production traits.
• Including resilience indicators in selection indices substantially improves resilience, leading to more manageable livestock.

Conclusions:

• Breeding for resilience is feasible and beneficial, especially with the advent of big data.
• Incorporating resilience indicators into breeding goals enhances livestock adaptability and reduces management intensity.
• Future breeding programs can leverage big data for improved livestock resilience and sustainability.