article id 579,
                            category
                        Research article
                    
        
                                    
                                    
                            Abstract |
                        
                                    View details
                             |
                            
Full text in PDF |
                        
Author Info
            
                            The aim of the study was to develop a yield prediction model using the  non-parametric Most Similar Neighbour (MSN) reference method. The model  is constructed on stand level but it contains information also on tree  level. A 10-year projection period was used for the analysis of stand  growth. First, the canonical correlation matrix was calculated for the  whole study material using stand volumes at the beginning and at the end  of the growth period as independent variables and stand characteristics  as dependent variable. Secondly, similar neighbour estimates were  searched from the data categories reclassified according to thinnings.  Due to this, it was possible to search for growth and yield series which  is as accurate as possible both at the beginning and at the end of the  growth period. The reliability of the MSN volume predictions was  compared to the volumes predicted with the simultaneous yield model. The  MSN approach was observed to be more reliable volume predictor than the  traditional stand level yield prediction model both in thinned and  unthinned stands.
                        
                
                                            - 
                            Maltamo,
                            Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland
                                                        E-mail:
                                                            matti.maltamo@forest.joensuu.fi
                                                                                          
- 
                            Eerikäinen,
                            Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland
                                                        E-mail:
                                                            ke@nn.fi