AI Summary & Key Takeaways
AI InsightResults showed the best growth and yield model had relative RMSE and bias values, respectively, that were 31.9% and 6.3% for the diameter growth model, 11.3% and 0.17% for the height model, and 22% and 0.14% for the ingrowth model.
📌 3 Key Takeaways
Abstract
Predicting future forest growth and yield is a key element of sustainable forest management. Hyrcanian forests are the most valuable forests in the north of Iran, and industrial harvesting occurs only in this area of the country. While uneven-aged Hyrcanian forests are one of the most important vegetated areas, and the only commercial forests in Iran, there is a lack of growth and yield models for management and planning purposes. The aim of this study is to develop distance-independent individual tree growth and yield models for uneven-aged forests in northern Iran managed under selection systems.A distance-independent diameter growth model, a static height model, an ingrowth model, and a survival model for uneven-aged stands of Fagus orientalis Lipsky were developed using measurements from Sangdeh, within the Mazandaran providence in Iran. The models are based on 130 permanent sample plots established in 2009 and remeasured in 2014. For modeling diameter and height growth, we employed mixed effect regression. For modeling survival, we used binary logistic regression analysis. Ingrowth was modeled using multilinear regression. Results showed the best growth and yield model had relative RMSE and bias values, respectively, that were 31.9% and 6.3% for the diameter growth model, 11.3% and 0.17% for the height model, and 22% and 0.14% for the ingrowth model. Wald tests and other model evolution parameters showed that the parameter estimates for tree mortality were statistically significant. Overall results indicated that growth and yield model performance was consistent with expectations, and that the general fit to the validation data was acceptable