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Longleaf pine (Pinus palustris Mill.) is an important tree species of the southeast U.S. Currently there is no comprehensive stand-level growth and yield model for the species. The model system described here estimates site index (SI) if dominant height (H dom ) and stand age are known (inversely, the model can project H dom at any given age if SI is known). The survival (N) equation was dependent on stand age and H dom , predicting greater mortality on stands with larger H dom . The function that predicts stand basal area (BA) for unthinned stands was dependent on N and H dom . For thinned stands BA was predicted with a competition index that was dependent on stand age. The function that best predicted stand stem volume (outside or inside bark) was dependent on BA and H dom . All functions performed well for a wide range of stand ages and productivity, with coefficients of determination ranging between 0.946 (BA) and 0.998 (N). We also developed equations to estimate merchantable volume yield consisting of different combinations of threshold diameter at breast height and top diameter for longleaf pine stands. The equations presented in this study performed similarly or slightly better than other reported models to estimate future N, H dom and BA. The system presented here provides important new tools for supporting future longleaf pine management and research.

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There are many ways of estimating the parameters of an equation to represent the relationship between two variables. While least-squares regression is generally acknowledged to be the best method to use when estimating the conditional mean of one variable given a fixed value for another, it is not usually an appropriate method to use when your primary interest is in the values of the equation parameters themselves (functional relations). In this case there are many other techniques (Bartlett's three-group method, Schnute's trend line, the general structural relationship, major axis regression, and reduced major axis) that may provide better estimates of these values. When all of the above techniques are compared, it is found that reduced major axis is often the most applicable because of its desirable properties and ease of estimation.