European Journal of Forest Research

, Volume 125, Issue 4, pp 325–333

Stand structure of an uneven-aged fir–beech forest with an irregular diameter structure: modeling the development of the Belevine forest, Croatia

Original Paper

DOI: 10.1007/s10342-006-0120-z

Cite this article as:
Čavlović, J., Božić, M. & Boncina, A. Eur J Forest Res (2006) 125: 325. doi:10.1007/s10342-006-0120-z

Abstract

This study focuses on the problem of irregular diameter structure in a silver fir–beech selection (plenter) forest with a “surplus” of large diameter trees and a lack of natural regeneration and small diameter trees. We sampled 274 plots (900 m2 each) in the Belevine research site (266.24 ha) in the mountain region Gorski Kotar (Croatia), where diameter (dbh) distribution, diameter increment, and natural regeneration were analyzed in detail. A low density of natural regeneration, weak annual recruitment of small (10 cm dbh) diameter trees (only five trees per hectare), delayed diameter growth of trees, and a low annual rate of trees reaching the next dbh class were attributed to the current irregular dbh structure. The stand development prediction for the next 50 years is based on a simulation model, which considers the current diameter structure, increment, recruitment, and future cutting regime. Intensive cutting in the first of five 10-year cutting cycles (intensity higher of 25%) is needed to initiate natural regeneration and to accelerate growth of young silver fir trees. In the next 50 years, the irregular diameter structure will be gradually improved.

Keywords

Uneven-aged silviculture Diameter structure Natural regeneration Simulation model Silver fir European beech 

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Juro Čavlović
    • 1
  • Mario Božić
    • 1
  • Andrej Boncina
    • 2
  1. 1.Faculty of Forestry ZagrebDepartment of Forest ManagementZagrebCroatia
  2. 2.Biotechnical Faculty, Department of Forestry and Renewable Forest ResourcesUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations