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Precision Fertility and Resiliency Phenotyping in Dairy Cattle

Regional Priorities Partnership Program (RP3)
Agriculture and Agri Food
Genome Centre(s):
Ontario Genomics
Project Leader(s):
Christine Baes (University of Guelph)
Fiscal Year Project Launched: 
Project Description: 

As the world’s fifth largest exporter of agricultural products, Canadian farmers are poised to play a decisive role in meeting the 70 per cent increase in world food demand expected by 2050. In 2017, $14.3 billion worth of manufactured shipments of milk and dairy products were made, with approximately 33 percent of those coming from Ontario farms (, site accessed December, 2018). Canadian dairy genetics exports are also a growing source of revenue, rising 45 per cent during the last decade to a total value of $155.2 million in 2017; in particular, semen sales rose by 80 per cent ($127 million in 2017) (, site accessed December, 2018). The Semex Alliance, which is owned by three well-established Canadian genetics organizations, delivers innovative genetic solutions both within Canada and globally. With its headquarters in Guelph Ontario, Semex is well aware of the importance of addressing the anticipated environmental changes associated with climate change. Dairy cow performance (such as growth, milk production, and reproduction), as well as animal welfare and health, can be strongly influenced by air temperature, humidity, and other climatic factors. Genetic selection plays a key role in breeding livestock that can better cope with changing climate, and more specifically, tolerate extreme temperatures and humidity and changes thereof. This project will provide novel and innovative methods for genomically selecting robust dairy animals which are resilient to environmental stressors, such as extreme hot/cold temperatures, while maintaining health, production, and reproductive efficiency. This proof-of-concept project integrates phenotypic data collected using automated sensor technologies with high-throughput genotypes of dairy cows through application of machine-learning algorithms to identify the underlying associations therein. In this way, a sustainable and economically profitable genomics-derived process for identifying healthy, fertile, resilient animals for use in genomic selection programs will be demonstrated and ready for large-scale realization, thus strengthening Ontario’s leadership in this field.