Date of Award

Fall 12-16-2022

Level of Access Assigned by Author

Open-Access Thesis

Language

English

Degree Name

Master of Science (MS)

Department

Forest Resources

Advisor

Aaron Weiskittel

Second Committee Member

Ian Prior

Third Committee Member

Laura Kenefic

Additional Committee Members

Anil Kizhakkepurakkal

Abstract

Estimating product ratios in hardwood forests is difficult due to the highly complex stem forms and diverse growth and regeneration strategies found in hardwood species. This is of particular concern for landowners with large ownerships requiring robust product potential estimates as part of both short-term tactical harvest planning and long-term growth and yield projections. Hardwood-dominated forests are generally managed for high-value sawlog and veneer products. This makes it increasingly important to identify strategies for improving hardwood management to meet economic (maximizing sawlog recovery) and ecological (maintaining healthy and diverse forests) goals.

This study seeks to achieve two primary goals. The first is to further develop predictive models for sawlog potential utilizing a stem form and risk framework that can be easily added to existing field inventory protocols. The second is to compare commonly used field merchandising techniques and a commonly used regional growth model with the form and risk approach to understand how each method affects plot-level predictions of sawlog volume, stand value, and the variability of sawlog volume compared to merchantable volume in forest inventories.

Results from this study show that sawlog potential in hardwood forests can be effectively modeled using easily collected data on stem form and risk in a way that both improves short-term estimates of available sawlog volume and long-term projections of sawlog potential in future stand conditions. In past studies, stem form and risk have been shown to be important predictors of diameter growth and mortality in growth and yield modeling. This study further demonstrates the importance of this data by showing that they can be effective predictors of sawlog presence and volume in commercially important hardwood and softwood species.

Robust models predicting sawlog presence and volume in 14 commercial species were developed in this study. The results from these model predictions using stem form and risk were compared to estimates of sawlog volume using four common field inventory methods for predicting product ratios and predictions from a commonly used regional growth model, the Forest Vegetation Simulator (FVS), in ten northern hardwood stands. Statistically significant differences were found among methods, and all field methods demonstrated reasonable estimates of available sawlog volume and stand value while FVS did not. We also found that there was significantly more variability among sample plots when measuring sawlog volume than when measuring merchantable volume. This showed the importance of utilizing an efficient field methodology that can be applied with the intensity required to lower error rates in forest inventories.

Form and risk observations are a potentially effective alternative to commonly used field methodologies. They provide reasonable estimates of sawlog potential, an easy framework to integrate into existing inventories, and a tool that may be used to predict sawlog potential in future stand conditions, thus improving the performance of existing forest growth and yield models and capturing an accurate picture of current and future hardwood forest conditions for both short-term tactical and long term harvest planning.

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