Equations for modeling the height-age pattern of forest trees or stands are typically developed for a given species in a specified region. In order to adequately model height-age patterns, the resulting equations are often quite complex. This study addresses the question of how to increase the accuracy and precision of the prediction of height-age relationships through the use of localized equations.

Although the pattern of height-age relationships of loblolly pine plantations was studied, the methodology should prove valuable for other species as well. The Schumacher logarithm of height-reciprocal of age model was fitted to data from loblolly pine plantations to attain an average guide curve. Various methods of localizing this equation to a particular stand were examined. The methods are based on empirical Bayesian, maximum likelihood, and Kalman filter theory. All of these methods employ the general concept of feedback in localizing the simple equation. The best of these various models is compared with the unadjusted model and a more complex polymorphic equation. The adjusted model compares favorably with these other two models.