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My research involves using operations research/management science methods to improve decision making in forest ecosystem management. The current focus of my research is how to achieve the large potential gains available if hardwood stems could be cut into logs ("log bucking") that more closely approach the maximum value available in the stem. Although this seems to be a process that should be fairly straightforward, and therefore result in limited mistakes, earlier research has shown that most buckers recover only between two-thirds and three-quarters of the potential value. A subsequent research project confirmed these large losses, but showed that a training program including log defect identification, an introduction to log grading and scaling, and optimal bucking strategies could reduce this loss by ½. Current efforts focus on improving the training tools and methods and making this training program widely available. There is currently a collaborative effort involving the Wood Education Resource Center (WERC) of the Forest Service, the Ohio Forestry Association (OFA), and MTU to provide our training program across the eastern hardwood region.

I feel strongly that our students need to develop their problem solving skills, integrating all the relevant information and data to reach sound and defensible solutions. I try to make development of these skills the focus of my classes. The most encouraging student evaluation I ever received was: This guy doesn't care what we think. He cares how we think.

I am the coordinator of the recently initiated Master of Forestry (MF) program, which is a professional graduate degree that allows students who have performed well at the undergraduate level in another field to change to a forestry career. This program was recently accredited by the Society of American Foresters (SAF), and six students have completed this degree during the first two years.

Specific Interests:
Optimal bucking of hardwood logs
Log bucker training to increase value recovery
Approaches for adapting to risk and uncertainty in optimization models

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