Researchers Aim to Decode Cow Communications at Dalhousie University

Researchers at Dalhousie University in Truro, Nova Scotia, are embarking on a groundbreaking project to decipher cow vocalizations. Led by Dr. Ghader Manafiazar, the team is investigating whether different cow sounds convey distinct meanings, similar to how humans use variations in speech to express different emotions or intents.

The research specifically focuses on the vocal patterns of cows during critical periods, such as the week before and after calving. Manafiazar, a professor in the Faculty of Agriculture, explains the significance of these vocalizations: “When they say ‘moo,’ is it different from saying ‘moooooo?’” To explore this question, Manafiazar and graduate student Hassan Miraei have recorded over 8,400 hours of audio from cows.

Utilizing an artificial intelligence algorithm, the team aims to correlate the recorded sounds with other data, such as time of day and the cows’ behaviors. For example, if a cow produces a specific type of moo followed by giving birth the next day, this information would be integrated into the AI model. Manafiazar notes that identifying patterns in vocalizations could lead to insights about the cows’ emotional states, such as hunger or discomfort.

Advancing Understanding of Animal Behavior

Manafiazar likens the process to how parents learn to interpret a baby’s cries. Over time, parents can recognize specific cries indicating hunger, fatigue, or distress. In contrast, the challenge for researchers is akin to interpreting the sounds of a large herd, which can produce thousands of vocalizations simultaneously. “The challenge is having 10,000 babies at a time,” Manafiazar states.

As the project progresses, Miraei aims to transition from a supervised algorithm, which requires manual input and verification, to an unsupervised model. This would enable a recording device within the barn to transmit audio directly to the AI system. The goal is to provide real-time interpretations of cow vocalizations, allowing farmers to respond promptly to their animals’ needs.

Early results are promising, and both Manafiazar and Miraei express optimism about the potential applications of their research. They believe that a deeper understanding of cow communications could significantly enhance farmers’ ability to monitor and improve their livestock’s welfare, thereby potentially transforming agricultural practices in the region.

This innovative approach to animal behavior not only highlights the intersection of technology and agriculture but also underscores the importance of understanding animal emotions and needs in farming contexts. As the team continues their research, they hope to pave the way for a future where farmers can anticipate changes in their cows’ moods and health effectively.