Tree basal area (ba) or diameter at breast height (dbh) are universally used to represent tree secondary growth in individual tree based growth models. However, the long-term implications of using either ba or dbh for predictions are rarely fully assessed. In this analysis, Δba and Δdbh increment equations were fit to identical datasets gathered from six conifer and four hardwood species grown in central Maine. The performance of Δba and Δdbh predictions from nonlinear mixed-effects models were then compared with observed growth measurements of up to 29 years via a Monte Carlo simulation. Two evaluation statistics indicated substantial improvement in forecasting dbh using Δdbh rather than Δba. Root mean squared error (RMSE) and percentage mean absolute deviation (MAD%) were reduced by 14% and 15% on average, respectively, across all projection length intervals (5-29 years) when Δdbh was used over Δba. Differences were especially noted as projection lengths increased. RMSE and MAD% were reduced by 24% when Δdbh was employed over Δba at longer projection lengths (up to 29 years). Simulations found that simulating random effects rather than using local estimates for random effects performed as well or better at longer interval lengths. These results highlight the implications that selecting a growth model dependent variable can have and the importance of incorporating model uncertainty into the growth projections of individual tree based models.