Timo Pukkala. (1989). Predicting diameter growth in even-aged Scots pine stands with a spatial and non-spatial model. Silva Fennica vol. 23 no. 2 article id 5376. https://doi.org/10.14214/sf.a15533

Keywords: Pinus sylvestris; growth prediction; spatial distribution; growth models; tree models

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The single tree growth models presented in this study were based on about 4,000 trees measured in 50 even-aged Scots pine (Pinus sylvestris L.) sample plots with varying density, spatial pattern of trees and stand age. Predictors that used information about tree locations decreased the relative standard error of estimate by 10 percentage points (15%), if past growth was not used as a predictor, and about 15 percentage points (30%) when past growth was one of the predictors. When ranked according to the degree of determination, the best growth models were obtained for the basal area increment, the next best for relative growth, and the poorest for diameter increment. The past growth decreased the relative standard error of estimate by 15–20 percentage points, but did not make the spatial predictors unnecessary. The degree of determination of the spatial basal area growth model was almost 80% if the past growth was unknown and almost 90% if the past growth was known. Variables that described the amount of removed competition did not improve the growth models.

The PDF includes an abstract in Finnish.

Pukkala, E-mail: tp@mm.unknown

Category : Research article

article id 111, category Research article

Ilona Pietilä, Annika Kangas, Antti Mäkinen, Lauri Mehtätalo. (2010). Influence of growth prediction errors on the expected losses from forest decisions. Silva Fennica vol. 44 no. 5 article id 111. https://doi.org/10.14214/sf.111

Keywords: growth prediction; uncertainty; forest information; updating; inoptimality loss

Abstract | View details | Full text in PDF | Author Info

In forest planning, forest inventory information is used for predicting future development of forests under different treatments. Model predictions always include some errors, which can lead to sub-optimal decisions and economic loss. The influence of growth prediction errors on the reliability of projected forest variables and on the treatment propositions have previously been examined in a few studies, but economic losses due to growth prediction errors is an almost unexplored subject. The aim of this study was to examine how the growth prediction errors affected the expected losses caused by incorrect harvest decisions, when the inventory interval increased. The growth models applied in the analysis were stand-level growth models for basal area and dominant height. The focus was entirely on the effects of growth prediction errors, other sources of uncertainty being ignored. The results show that inoptimality losses increased with the inventory interval. Average relative inoptimality loss was 3.3% when the inventory interval was 5 years and 11.6% when it was 60 years. Average absolute inoptimality loss was 230 euro ha^–1 when the inventory interval was 5 years and 860 euro ha^–1 when it was 60 years. The average inoptimality losses varied between development classes, site classes and main tree species.

Pietilä, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ip@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: am@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi

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