A hybrid model for intensively managed Douglas-fir plantations in the Pacific Northwest, USA

Recent advances in traditional forest growth models have been achieved by linking growth predictions to key ecophysiological processes in a hybrid approach that combines the strengths of both empirical and process-based models. A hybrid model was constructed for intensively managed Douglas-fir plantations in the Pacific Northwest, USA, by embedding components representing fundamental physiological processes and detailed tree allometrics into an empirical growth model for projecting individual tree and stand development. The simulated processes operated at a variety of scales ranging from individual branches to trees and stands. The canopy structure submodel improved predictions of leaf area index at the stand level when compared to allometric and other empirical approaches (reducing mean square error by 30-42%). In addition, the hybrid model achieved accuracy in short-term volume growth prediction comparable to an empirical model. Biases in 4-year stand growth predictions from the hybrid model were similar to those from the empirical model under thinning, fertilization, and the combination of these treatments; however, volume growth predictions in unmanaged plantations averaged approximately 36% less bias. These improvements were attributed to detailed information on crown structure (i.e. size, location, and foliage mass of primary branches), simple representation of key physiological processes, and improved site characterization. Soil moisture, temperature, and nitrogen mineralization predicted by the hybrid model also agreed closely with observed values from several previous studies. Overall, the model framework will be helpful for future analyses as it can lend insight into the influence of weather and site edaphic factors on growth, help identify mechanisms of response to silvicultural treatments, and facilitate the design of sound management regimes for Douglas-fir plantations across the Pacific Northwest region.