Masaya Matamura Position/Title: Postdoctoral Scholar email: mmatamur@uoguelph.ca Phone: Office: ANNU 218 Google Scholar Research Gate LinkedIn

Masaya is currently a Postdoctoral Fellow in the Department of Animal Biosciences at the University of Guelph. His research focuses on precision dairy farming, animal nutrition, automated milking systems, and the application of artificial intelligence and data science to livestock production. His current research involves the use of large-scale longitudinal data from commercial dairy farms to investigate individualized feeding strategies, milk production, cow behaviour, feed efficiency, greenhouse gas emissions, and economic performance in automated milking systems.

Masaya completed his Ph.D. in Animal Science at Mie University, Japan, where his research focused on developing practical approaches for evaluating starch utilization in fattening Japanese Black cattle. His doctoral research integrated animal nutrition, deep learning, machine learning, image analysis, near-infrared spectroscopy (NIRS), and numerical simulation to develop rapid and non-invasive methods for estimating fecal starch concentration and starch digestibility. His work demonstrated the potential of smartphone images and spectroscopy as tools for monitoring nutrient utilization under commercial farm conditions.

His broader research experience includes beef and dairy cattle nutrition, Fourier-transform mid-infrared spectroscopy (FT-MIR), near-infrared spectroscopy, computer vision, machine learning, rumen fermentation, milk fatty acid analysis, and precision livestock farming. He is particularly interested in integrating biological knowledge with large-scale sensor and farm data to develop practical technologies that improve the productive, economic, and environmental sustainability of livestock production.

Education

Ph.D., Animal Science [2023–2026]
Graduate School of Bioresources, Mie University, Japan

Thesis Title: Development of an Integrated Evaluation Approach for Estimating Starch Digestibility in Fattening Japanese Black Cattle

Supervisor: Dr. Makoto Kondo

His doctoral research established an integrated approach combining fecal starch measurements, deep-learning-based image analysis, NIRS, and numerical simulation for evaluating starch utilization in Japanese Black cattle.

M.Sc., Animal Science [2021–2023]
Graduate School of Bioresources, Mie University, Japan

Thesis Title: Estimation of Starch Digestibility and Quantification Method of Fecal Starch Using Deep Learning in Japanese Black Cattle

Supervisor: Dr. Makoto Kondo

B.Sc., Animal Science [2017–2021]
Faculty of Bioresources, Mie University, Japan

Research Theme: Estimation of Urinary Nitrogen Excretion Using Partial Urine in Fattening Japanese Black Cattle

Student Trainee [2016–2017]
Mie Prefectural Livestock Research Institute, Japan

Masaya received practical training in animal management and data collection in beef cattle research settings.

Academic and Research Experience

Postdoctoral Fellow [July 2026–Present]
Department of Animal Biosciences, University of Guelph, Canada

Masaya's postdoctoral research focuses on dairy data science and precision dairy farming, including automated milking systems, precision feeding, feed efficiency, cow behaviour, methane emissions, and machine-learning applications.

Assistant Professor [April 2026–June 2026]
Graduate School of Bioresources, Mie University, Japan

His responsibilities included research and teaching in animal nutrition, livestock science, and data analysis.

Featured Recent Publications

• Matamura, M., Abe, E., Nagahaka, N., Mishima, T., Shibata, T., & Kondo, M. (2026). Influence of sample size and content range on slope–bias correction in Fourier-transform mid-infrared spectroscopy for milk fatty acids. Journal of Dairy Science. Accepted August 5, 2026.
• Matamura, M., & Kondo, M. (2025). Estimation of total-tract starch digestibility using fecal starch concentration in fattening Japanese Black cattle. Scientific Reports, 15.
• Matamura, M., & Kondo, M. (2025). Determination of urinary creatinine excretion and estimation of urinary nitrogen and purine derivatives in Wagyu, high-marbling fattening cattle. Animal, 19.
• Matamura, M., Sekoguchi, T., Nishikawa, Y., Naito, H., Hashimoto, A., & Kondo, M. (2025). Prediction of fecal starch content of fattening cattle using near-infrared spectroscopy and machine learning. Computers and Electronics in Agriculture, 231.
• Matamura, M., Naito, H., Morio, Y., & Kondo, M. (2024). Fecal image-based starch digestibility estimation in fattening cattle using deep learning. Computers and Electronics in Agriculture, 225.

Awards and Honours

• 2025 ADSA Travel Grant Award, American Dairy Science Association
• Japan Science Society Grant, 2024–2025
• Reiwa Environmental Foundation Grant, 2023–2024
• Mie University Fellowship, 2023–2026
• English Presentation Award, Japanese Society of Animal Science, 2022
• Encouragement Award, Meat Cattle Research Society, 2021
• Excellent Student Award, Japanese Society of Animal Science, 2021