Predicting the internal quality and value of Norway spruce trees by using two non-parametric nearest neighbor methods

End-use oriented sawing, which has become more and more popular for Norway spruce, demands more accurate evaluation of the inner quality of trees to be logged. The needed description includes characteristics such as wood density, decay, amount of heartwood, and knottiness. The emphasis of this study was to test possibilities to predict the yield and internal quality of spruce trees, and value of lumber and sideproducts by stand and tree factors for traditionally oriented sawing with two non-parametric nearest neighbor methods: k-nearest-neighbor (k-nn), most similar neighbor (MSN) and locally adaptable neighborhood (LAN) MSN method. According to the results, the non-parametric nearest neighbor methods seem to provide one interesting option to estimate the internal quality and value of Norway spruce trees. The (LAN) MSN method was found to be slightly better than the k-nn MSN method in most cases when using relative root mean square error as the criteria to compare results. Treewise information improved the accuracy of the methods compared to the results of standwise input information.