Biomass equations for birch in Finland
By J. Repola. 2008
255 citations recorded by the source corpus.
Biomass equations were compiled for the aboveand below-ground tree components of birch (Betula pendula Roth and Betula pubescens Ehrh.). The equations were based on 127 sample trees in 24 birch stands located on mineral soil sites. The study material consisted of 20 temporary plots and ten plots from four thinning experiments with different thinning intensities (unthinned, moderately and heavily thinned plots). The equations were estimated for the following individual tree components: stem wood, stem bark, living and dead branches, foliage, stump, and roots. In the data analysis, a multivariate procedure was applied in order to take into account the statistical dependency among the equations. Three multivariate variance component models were constructed for the above-ground biomass components, and one for the below-ground biomass components. The multivariate model (1) was mainly based on tree diameter and height, and in the multivariate models (2) and (3) additional commonly measured tree variables were used. The equations provided logical biomass predictions for a number of tree components, and were comparable with other functions used in Finland and Sweden. The applied statistical method generated equations that gave more reliable biomass estimates than the equations presented earlier. Furthermore, the structure of the multivariate models enables more flexible application of the equations, especially for research purposes.
Subfields: Global and Planetary Change, Mechanical Engineering, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/40139e21ff7dc98caae1953cb5187df1e872a10e
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In Global and Planetary Change
In Mechanical Engineering
In Nature and Landscape Conservation
Citations in the corpus
- Biomass equations for Scots pine and Norway spruce in Finland (2009, methodology): “The hierarchically structured data and contemporaneous correlation have frequently been ignored in the model estimation, which means a loss of precision of the parameter estimates (Parresol 1999, 2001, Repola 2008).”