Bailey, an assistant professor in the Department of Plant Sciences, specializes in computational modeling of crop and plant systems. He completed his Ph.D. in mechanical engineering at the University of Utah. Bailey joined the UC Davis faculty in 2016 after working at the USDA-ARS Horticultural Crops Research Unit in Corvallis, Oregon.
Crop modeling, transport processes in the soil-plant-atmosphere continuum, high-performance computing
Population growth, climate change, and diminishing resources are all factors driving the agricultural industry to adapt at an unprecedented rate. Traditionally, adoption of new practices or technologies is slow, particularly in perennial crops, as many seasons of trials may be needed before efficacy has been adequately demonstrated. Computer models are widely used in other industries to accelerate the design process and better understand current designs, but they have been underutilized in the agricultural industry.
I am developing the next generation of computational models to design and understand cropping systems. The models seek to provide growers with a virtual environment to simulate potential design and management choices, without the financial risks associated with field trials. For example, growers can use models to test how various management decisions (e.g., irrigation scheduling, pruning, fertilization) would affect their crops.
The computer models are also valuable scientific tools that fill in the gaps between measurements to provide a more complete, three-dimensional representation of important processes such as photosynthesis, water use, or CO2 exchange. The work is transdisciplinary, and relies on collaborations between experts in the fields of plant physiology, epidemiology, engineering, and computer science.
- Developing the next generation of crop and plant simulation tools
- Developing a three-dimensional data visualization system for vineyards
- Understanding the mechanisms influencing airborne dispersion in plant canopies
- Mapping the three-dimensional structure of plants using ground-based LiDAR scanning