Discrepancies between predictions of mainstream empirical growth models and observed forest growth of Pinus radiata (D. Don) plantations in New Zealand

Pinus radiata (D. Don) dominates New Zealand’s forestry industry, constituting 91% of plantations, and is among the world’s most important plantation species. Given the socio-economic and environmental importance of this species, it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects, especially for permanent carbon forests. Future projections of any natural resource systems rely on modeling; however, the acceleration of climate change makes future projections of yield less certain. These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement. Using a large national-scale set of contemporary ground-measured data (2013–2023), this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers: 1) the Pumice Plateau Model 1988 (PPM88) and 2) the 300-index (including a model variant including regional drift). Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time. Basal area (BA, m 2 ·ha −1 ) and volume (m 3 ·ha −1 ) were simulated, and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years. Model residuals were then separated and analyzed for the main plantation growing regions. The models overpredicted observed growth by between 6.8% and 16.2%, but model predictions and errors varied significantly between regions. The results of this study provided clear evidence of divergence between the outputs of both models and the measured data. Finally, this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.