An empirical comparison of two subject-specific approaches to dominant heights modeling: The dummy variable method and the mixed model method

The varying (local) parameter(s) in site index models can be treated as fixed or random. Two primary subject-specific approaches to height modeling, the dummy variable method (fixed individual effects) and the mixed model method (random individual effects), were compared using Chapman–Richards type models fitted to second-rotation loblolly pine (Pinus taeda L.) data from a designed experiment. For height prediction of new growth series, tested on our validation subset data, the mixed model provides a new (local) parameter prediction method (termed as mixed predictor), which generally performed better than the traditional method of recovering local parameters (the least squares (LS) predictor we used). However, using the LS predictor, both the dummy variable estimation method and mixed model estimation showed almost identical prediction results. With multiple pairs of height–age measurements, no big difference was found in empirical site index prediction between the LS and mixed predictor. Theoretically, one main advantage of the mixed model approach is the ability of its mixed predictor to predict several local parameters using a single height–age pair. However, our empirical results failed to support this point.