The Poultry Podcast Show - #12 - Genetics, machine learning, and technology: integrating them for poultry progress

Collecting poultry data is essential to improve production. That’s even more important when discussing quantitative genetics and genomics of complex traits. This research allows us to apply and develop efficient statistical learning methods for analyzing genomic data. We can integrate this and sensor-generated data into machine-learning algorithms for precision poultry farming, such as feeding behavior genetics. In this episode, I talk to Dr. Anderson Alves about his recent genomics, technology, and machine learning research to improve poultry production. We also discuss how poultry can progress toward more efficient and sustainable production in the next few years.

What you’ll learn:

1. Genomics of complex traits
2. Broilers genetics of feeding behavior
3. Precision broiler farming: adoption and perspectives
4. Real-time monitoring of mortality risk
5. New technologies applied to poultry production 

Meet the guest:

Dr. Anderson Alves is a statistical geneticist working as a Research Associate at the University of Wisconsin-Madison. He has been leading different research projects at the Rosa Lab in partnership with Cobb Vantress for dissecting the genetic basis of feeding efficiency and feeding behavior in broilers. Dr. Alves is originally from Brazil, receiving a B.S. and an M.S. degree in Animal Science and a Ph.D. in Animal Breeding and Genetics. Dr. Alves is broadly interested in the application and development of efficient statistical learning methods for the analysis of livestock data, with a focus on the genetics and genomics of complex traits in different domestic species, such as dairy goats and sheep, beef cattle, and broiler chickens. He also integrates machine learning algorithms and other sensor-generated data in precision livestock farming projects, such as for high-throughput phenotyping, early prediction of individual performance, and real-time monitoring of mortality risk.