article id 321,
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Research article
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This research aims to combine two different data sets with Bayesian statistics in order to predict the diameter distribution of trees at harvest. The parameters of prior distribution are derived from the forest management plans supplemented by additional ocular information. We derive the parameters for the sample data from the first trees harvested, and then create the posterior distribution within the Bayesian framework. We apply the standard normal distribution to construct diameter (dbh) distributions, although many other theoretical distributions have been proved better with dbh data available. The methodology developed is then tested on nine mature spruce (Picea abies) dominated stands, on which the normal distribution seems to work well in mature spruce stands. The tests indicate that prediction of diameter distribution for the whole stand based on the first trees harvested is not wise, since it tends to give inaccurate predictions. Combining the first trees harvested with prior information seems to increase the reliability of predictions.
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Uusitalo,
The Finnish Forest Research Institute, Parkano unit, FI-39700 Parkano, Finland
E-mail:
jori.uusitalo@metla.fi
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Puustelli,
University of Tampere, Department of Mathematics, Statistics and Philosophy, FI-33014 University of Tampere, Finland
E-mail:
ap@nn.fi
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Kivinen,
University of Helsinki, Department of Forest Resource Management, Box 27, FI-00014 University of Helsinki, Finland
E-mail:
vpk@nn.fi
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Nummi,
University of Tampere, Department of Mathematics, Statistics and Philosophy, FI-33014 University of Tampere, Finland
E-mail:
tn@nn.fi
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Sinha,
Stat-Math Division, Indian Statistical Institute, 203 B.T. Road, Kolkata - 700 108, India
E-mail:
bks@nn.in