Prediction and Error of Baldcypress Stem Volume from Stump Diameter

The need to estimate the volume of removals occursformany reasons, such as in trespass cases, severance tax reports, and post-harvest assessments. A logarithmic model is presented for prediction of baldcypress total stem cubic foot volume using stump diameter as the independent variable. Because the error of prediction is as important as the volume estimate, I demonstrate construction and use of simple and joint confidence intervals about the mean and individual predictions. For completeness, I address prediction and error from inventory estimates of removals. South. J. Appl. For. 22(2):69-73. Many circumstances exist today where the volume, and ultimately the value, of trees must be assessed from stump measurements. These situations include: ( 1) assessing timber sales based on stump diameters, (2) checking harvesting practices following tree removals, (3) tracing the history of cutover lands, (4) assessing damage resulting from adverse environmental conditions, (5) determining volume loss resulting from trespass cutting, and (6) calculating growth on cut as part of a forest inventory (Bylin 1982, Wharton 1984). In all these situations only a stump diameter is known. In terms of wetland tree species, baldcypress (Taxodium distichum [L.] Rich) ranks high in importance. Cypress has made a strong comeback since the last virgin stands were cut in the 1940s. Current growing stock is estimated at around 6 billion cubic feet in the southeastern United States (Beltz et al. 1992) and its distribution ranges from East Texas to southern Maryland. Its increasing volume, distribution, and desirability as a species for management, point to the need for the ability to assess removals. Currently no published equations exist for estimating cypress volume from stump diameter. In this paper I present a simple linear model for estimating bole cubic foot volume above stump, and I provide details and examples on the construction and use of simple and joint confidence intervals about the mean and individual predictions. Also, prediction and associated error in the context of forest inventories on removals is discussed.