The aim of the study was to demonstrate the use of nonparametric methods in estimating tree-level growth models. In the nonparametric methods the growth of a tree is predicted as a weighted mean of the values of neighboring observations. The selection of the nearest neighbors is based on the similarities between tree- and stand-level characteristics of the target tree and the neighbors. The data for the models were collected from Kuusamo in northeastern Finland. Models for the 5-year diameter growth were constructed for Scots pine (Pinus sylvestris L.) with three different nonparametric methods: the k-nearest neighbor regression, k-most-similar neighbor, and generalized additive model.
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Sironen et al. (2003) studied this question.
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