When I look at a map, I think about how water moves, how pollutants travel from farms into streams and lakes, and how our choices shape food production and the environment. I didn’t always think this way. My perspective shifted during my PhD at the University of Guelph, where I spent four years working with maps, data, and real challenges in farming areas.
It started with a simple question: How do you tackle water pollution when it’s hard to know where it comes from? For example, fertilizer helps crops grow, but some of it washes away with rain and causes algae blooms in lakes. I wondered if we could match the right pollution control measures to the right places so farmers can grow food, and water stays clean in lakes. The questions were simple, but the answers were not.
To find real solutions, I tried precision conservation. This means matching land management practices, including fertilizer use, cover crops, and pollution control measures like riparian buffers, water and sediment control basins, wetlands, and drainage water management, to the unique features of each field and watershed. I found that farmers and watershed managers have many options built up over years of research and funding, which can be overwhelming for
precise decision-making. They need clear recommendations for practices that reduce water pollution from farms. To work on this, I experimented with tile-drained fields in southern Ontario. These fields drain water quickly so farmers can plant early, but this also lets nutrients move more easily into streams. The main challenge is focusing on the critical areas and identifying the conservation practices that will make the biggest difference, based on how water and nutrients actually move from soil to water.
To tackle this, I collected as much data, tools, and ideas as possible. After reviewing various studies, I chose to integrate geospatial analysis, watershed modeling, and machine learning into a decision-support framework. It was not easy but often messy. Some days were full of error messages, confusing maps, and computer code that didn’t work. But I wanted to prepare something practical that could show a pathway to better understanding our watersheds, how farmers work, and what an effective conservation strategy looks like.
Over time, I saw precision conservation as a cycle that keeps changing, adapting, and learning. The key questions became: Where is the problem? What solution fits that critical place? What would be the scale of the solution that can fix the problem? When will it work? And what can we learn after trying it? Collectively, I focused on making precision conservation practical for people in farming areas by connecting mapping, computer modeling, real-world action, and ongoing learning.
What kept me motivated on hard days was knowing this work matters for better decisions and a healthier ecosystem. Watersheds are more than shapes on a map. They are home to families, farms, and communities. Improving water quality means listening to and respecting the people who live on and care for the land. Through field visits, talking with practitioners, and many rounds of trial and error in computer modeling, I learned that good science needs to be both careful and practical.
It wasn’t always easy. Some days, my models gave only errors, my results were confusing, or my manuscripts came back with more questions than answers. But there were successes too: finding real patterns in messy data, getting helpful advice from my advisor, Dr. Wanhong Yang, and realizing every setback taught me something new.
Now, as I finish my PhD, I see my work as just one part of a bigger effort to make conservation planning more precise, flexible, and useful. My main contribution isn’t just about tools or computer models. It’s about showing that good data, better science, practical solutions, and local knowledge all need to work together.
My PhD taught me to see environmental problems in a new way. I realized science only matters when it leads to real decisions and actions. Going forward, I want to be a researcher who connects complex data and information to real solutions for better decision-making. You can follow my research on my Google Scholar Profile.