By; Kristin Lee, Flavio Schenkel, Ángela Cánovas – University of Guelph, Department of Animal Biosciences, Center for Genetic Improvement of Livestock, Guelph, ON, Ricardo Ventura, Universidade de São Paulo, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Brazil , Gordon Vandervoort – AgSights, Elora, ON, Canada
In recent years, the beef cattle industry has seen significant advancements in technology, which have enabled producers to make more informed decisions about improving their herd genetics. These advancements include genomics, sensors for monitoring animal health and performance, and methods for measuring methane emissions and feed efficiency. This information can be used in a genetic evaluation system which analyzes data on cattle, including pedigree or genomic information and physical characteristics, to predict which animal will produce the best offspring. However, the most commonly used genetic evaluation systems were developed in a computer language called Fortran90. Fortran90 can facilitate quick computing of breeding values, but Fortran90 has limitations that hinder the ability of these systems to quickly adapt to the changing needs of the industry. This is because these new technologies generate large and complex datasets that require sophisticated tools for analysis. Researchers are developing new statistical models and algorithms to analyze these datasets, and genetic evaluation systems must be constantly updated to incorporate these advancements and remain relevant and informative over time. Recent research has found that Python, a flexible computer programming language, is a promising option for developing genetic evaluation systems. Python’s flexibility allows it to easily adapt to new data sources and algorithms, which makes it more suitable for genetic evaluation systems than Fortran90. Additionally, Python’s fast and flexible development capabilities make it easier to refine computational algorithms and enhance user interfaces, ensuring seamless integration with industry practices.
To test the efficacy of the genetic evaluation systems, a dataset of over 976,000 Angus beef cattle across 15 generations was used. The Python genetic evaluation system was developed to predict each animal’s breeding value and the results were compared with a widely used commercial genetic evaluation system called MiXBLUP. The results showed that the Python genetic evaluation system performed just as well as MiXBLUP, with a Pearson correlation of 1.0 (maximum possible correlation value) between the breeding values of the two genetic evaluation systems. However, the Python genetic evaluation systems took longer to run, taking 8 minutes compared to less than a minute for the MiXBLUP results.
The study’s results demonstrate that using Python to develop genetic evaluation systems is a promising approach. While Fortran may offer slightly faster computing times for specific tasks such as breeding value calculations, Python provides faster development speed and greater flexibility. This faster development process can be especially beneficial for genetic evaluation systems that require ongoing updates and maintenance. With Python, new technologies like genomic analysis and sensor-based performance metrics can be integrated more quickly into the genetic evaluation systems, which can provide farmers with the latest tools to make informed breeding decisions. This can give farmers a competitive edge in the marketplace by enabling them to take advantage of the most recent technologies to improve their herds and ensure a successful economic future. Future research will focus on refining computational algorithms and exploring parallel processing techniques for faster computing of breeding values. Additionally, user interfaces will be enhanced to ensure seamless integration with industry practices.
Dairy and Beef Cattle Production 