معرفی
Gabriel Dallago serves as Assistant Professor in the Department of Animal Science within the Faculty of Agricultural and Food Sciences at the University of Manitoba. His research integrates advanced data analytics with livestock production systems to enhance animal welfare and farm decision-making processes.
His educational background includes:
- PhD from McGill University, Canada
- MSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil
- BSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil
Dallago's research program centers on machine learning and deep learning applications for livestock management. Key initiatives include developing predictive models for animal bio-responses, integrating multimodal data streams to analyze complex farm systems, and optimizing dairy cow longevity through data-driven interventions. His work bridges computational science with practical agricultural challenges, focusing on tangible improvements in production efficiency and animal well-being.
- Precision monitoring of dairy cow behavior and welfare
- Early-life management impacts on herd productivity
- Computer vision applications for livestock assessment
- Economic modeling of livestock production systems
Analysis of his 15 most recent publications (2022-2025) reveals strong thematic concentration in dairy science (47%), swine production (20%), and animal nutrition (20%), with consistent application of computational methods. Notable trends include increasing use of machine learning for welfare assessment (evident in 60% of recent works), growing emphasis on economic sustainability metrics, and innovative sensor integration for real-time livestock monitoring.
Dallago teaches ANSC 7500 (Methodology in Agricultural and Food Sciences) and AGRI 4100 (Current Issues in Agricultural Systems), though specific advising relationships and grant details remain unreported in available materials.
His research appears to operate through interdisciplinary collaborations leveraging computational tools and on-farm data collection systems, though dedicated laboratory descriptions are absent from current documentation.
