Reduction of uncertainty in projection of growth and yield of Hyrcanian trees in Jabowa-4 model by applying artificial neural network (Case study: kheyroud forest- Nowshahr)

Document Type : Research paper


1 Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Department of Agricultural Machinery Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran


Gap models have a long history in assessing the potential effects of climate change on forest structure and composition. In Hyrcanian forests, there is a lack of efficient models for predicting growth and yield. Therefore, to fill this gap in this study, we tried to use the Jabowa-4 model, which has the ability to predict forest dynamics by considering climatic factors. To apply the model in Hyrcanian forests, the main species selected and parameterized in different modes using experimental models. After the simulation process over 90 years, the values ​​related to the observed and predicted BA, correlation coefficient and RMSE calculated and the species response to climate change evaluated. The results of this simulation show that climate change can have a negative impact on growth and yield by reducing rainfall and creating drought conditions in the studied forests. Due to these changes, the percentage of more resistant speciessuch as Oak increases.On the other hand, the results of this study showed that Jabowa-4 is effective in providing forest performance predictions. However, it has a weak ability to explain the amount and height of trees in Hyrcanian forests.

Keywords: Climate change, Forest dynamics, Forest management, Growth and yield model,

Graphical Abstract

Reduction of uncertainty in projection of growth and yield of Hyrcanian trees in Jabowa-4 model by applying artificial neural network (Case study: kheyroud forest- Nowshahr)


  • The present study conducted due to the lack of efficient growth and yield models in important forests of northern Iran.
  • The effect of environmental factors such as climate change, which until now were considered static, was examined.
  • The use of gap models for the first time in these areas in order to depict the dynamics and succession of forest stands examined.
  • The use of artificial networks to reduce growth and yield prediction uncertainty was investigated.


Main Subjects

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