Site index prediction from site and climate variables for Norway spruce and Scots pine in Norway

Abstract

Abstract Site index prediction models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) were developed using Norwegian National Forest Inventory data. A number of multiple linear regression models with different combinations of site and climate variables were developed in order to facilitate their application to a range of situations where the accessibility of various explanatory data differs. The best models used year of stand origin, temperature sum, vegetation type groups, soil depth, aspect, slope and latitude to predict site index. These models explained a large part of the total variation ( = 0.86 and 0.72 for spruce and pine, respectively) and had little residual variation (RMSE = 2.04 and 1.95 m for spruce and pine, respectively). Alternative models using only year of stand origin, temperature sum and vegetation type groups, or soil depth in addition, had slightly lower but still useful predictive power. All the developed models exhibited a strong non-linear effect of the year of stand origin on site indices, which varied when temperature sum was included. The increase in site indices along with increasing year of stand origin was significantly faster after about 1940 for both species. Similar time trends were observed for mean temperature and precipitation sums for the periods of stand growth, but only exhibited a faster increase after about 1960. Even though increased temperature and precipitation after 1990 seem to contribute to increased site indices, increased nitrogen availability and atmospheric CO2 levels may also be important factors. Keywords: Picea abies Pinus sylvestris site index prediction modelsite index trends Acknowledgements This article is a part of the first author's PhD thesis works supported by the Norwegian State Educational Loan Fund. We wish to thank Rune Eriksen at Norwegian Forest and Landscape Institute for help in data preparation and Ole Martin Bollandsås at Norwegian University of Life Sciences for help in the description of NFI procedures and data properties. We thank the three referees and particularly one referee who helped to improve the presentation of our results substantially by supplying specific comments.