Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/86610
Type of publication: Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Miškotyra / Forestry (A004)
Author(s): Petrauskas, Edmundas;Rupšys, Petras
Title: The Generalised Height-Diameter Equations of Scots Pine (Pinus sylvestris L.) Trees in Lithuania
Is part of: Rural development 2013 : the sixth international scientific conference, 28-29 November, 2013, Akademija : proceedings. Akademija : Aleksandras Stulginskis University, Vol. 6, b. 3 (2013)
Extent: p. 407-411
Date: 2013
Abstract: Scots pine (Pinus Silvestris L.) is a dominant tree species in Lithuania. Due to high commercial importance for wood industry the reliable future trends of sustainable use are needed. Traditionally these trends are based on unbiased yield and growth models. Since 1998 the National Forest Inventory in Lithuania has been carried out. Data collected on a permanent sampling plots is a new source for trustworthy empirical material sets that could and should be used to develop new or calibrate existing yield models. Height-diameter equations has been attracting attention in forestry growth modelling. Several mathematical models have been proposed for tree height model completely based on diameter at breast height and generalised height-diameter model based on tree diameter at breast height and stand variables. The objective of the research was to develop a generalised height–diameter model for Pinus sylvestris L. in Lithuania with diameter at breast height outside the bark larger than 0 cm. Additionally four generalised height-diameter equations were selected as candidate functions to compare the height-diameter predictions. The parameters of all used models were estimated using an estimation data set and were evaluated using a validation data set. Performance statistics for the generalised height equations included four statistical indices: mean percentage of absolute bias, precision, Akaike’s Information Criteria, and an adjusted coefficient of determination
Internet: https://hdl.handle.net/20.500.12259/86610
Affiliation(s): Vytauto Didžiojo universitetas
Žemės ūkio akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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