Masters Theses

Date of Award


Degree Type


Degree Name

Master of Science



Major Professor

Jason Henning

Committee Members

John Coulston, Donald Hodges


The purpose of this study was to determine if control charts are an effective tool to identify trends in forest growth and yield model accuracy. Accurate forest growth and yield models are important for projecting future forest composition. However, environmental factors have the potential to make forest growth models created from historic data inaccurate. Control charts in this study determine if forest growth predictions fall within confidence limits established for historic growth at a number of points in time. Two data sets were used in this study: the first was a Continuous Forest Inventory (CFI) from three tracts at the University of Tennessee Cumberland Research Station and the second data set was Forest Inventory and Analysis data collected by the U.S. Forest Service. The CFI plots represented a stand level data set measured every 5 years from 1962-1977 and revisited for a re-measurement in 2009. The FIA plots were a regional data with subsets of plots measured annually from 1999-2008. The FIA data set was limited to plots of the oak/hickory forest type from Tennessee, Alabama, and Georgia. Two forest growth and yield models were used to predict growth: (1) WinYield and (2) Forest Vegetation Simulator (FVS). The two different data sets were used with both FVS and WinYield to evaluate control charts using different models ad at different spatial and temporal scales. The data sets were also subset by site index, stand age, stocking percent, aspect, and species composition to determine if control charts could identify changes in model accuracy for forests subjected to different growing conditions. The CFI and FIA data had short growth predictions and control charts indicated that there were no trends affecting accuracy. The CFI data also had a long growth prediction of 32 years and the control charts found that the predictions using WinYield and FVS were inaccurate, indicating that there may be a trend causing inaccuracy in the model.

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