Improving Growth and Yield Predictions Using Combined Tree Based Models

Growth and yield predictions are the basis of all forest management activities. The intern will conduct advanced research with the intention of improving growth and yield predictions by combining traditional models with a process-based stand development model. Typically, process-based models are more complicated and require special data as input and are difficult to use. Traditional growth and yield models are simple to use but often rely on extensive field measurement over long-periods of time. For the partner, a manufacturer of newsprint and super-calendered papers, the intern will attempt to take advantage of both approaches by using model predictions from process-based models to improve prediction accuracy of traditional growth and yield models. He will work closely with forest woodland managers to process existing data accumulated by the company to improve the model. He will also be responsible for transferring the technology.

Faculty Supervisor:

Dr. Charles Bourque


Christopher Clowater


Stora Enso Port Hawkesbury Ltd.






Université de Moncton



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