<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>612</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>89</pageFirst>
    <pageLast>97</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>69</volume>
    <type>article</type>
    <publisherName>Sciendo</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-05-18</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Estimation of Scots pine bark biomass delivered to the wood industry in Northern Germany</title>
    <abstract language="eng">Scots pine (Pinus sylvestris L.) is the most widely distributed pine species in the world. In Germany, as in many other European countries, it is a very important species both culturally and economically. Few studies have focused on bark volumes being delivered to the wood industry together with the roundwood, being potentially a valuable resource for material or energetic utilization. Therefore, logs from six different forest sites were collected and bark variables including double bark thickness (DBT) in three different categories, diameter, and bark damage (as a degree of miss-DBT) in three different categories, diameter, and bark damage (as a degree of miss-ing bark) were measured and analyzed in order to model bark volume (Vbark) and bark mass (Mbark). The correlation analysis using Pearson’s method showed that the highest correlation coefficients were observed from the correla-tion between DBT and Vbark, as well as between DBT and Mbark. Also, results demonstrated that with DBT greater than 20 mm, the percentage of Vbark exceeded 20%. Finally, different linear regression models were recommended to predict Vbark and Mbark based on the other variables. The results of this study can be used in different wood industries in order to predict bark volume and bark mass of e.g. truckloads or roundwood stacks.</abstract>
    <parentTitle language="eng">Central European Forestry Journal</parentTitle>
    <identifier type="doi">10.2478/forj-2022-0019</identifier>
    <identifier type="urn">urn:nbn:de:kobv:eb1-opus-6126</identifier>
    <identifier type="issn">2454-0358</identifier>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Ferréol Berendt</author>
    <author>Iman Bajalan</author>
    <author>Charlett Wenig</author>
    <author>Charlotte Hinds</author>
    <author>Ľubomír Blaško</author>
    <author>Tobias Cremer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bark thickness; bark volume; bark mass; Pinus sylvestris; correlation</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Fachbereich Wald und Umwelt</collection>
    <collection role="Hochschulbibliographie" number=""/>
    <collection role="Hochschulbibliographie" number="">Zweitveröffentlichung</collection>
    <collection role="Hochschulbibliographie" number="">Referiert</collection>
    <thesisPublisher>Hochschule für nachhaltige Entwicklung Eberswalde</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-hnee/files/612/10.2478_forj-2022-0019.pdf</file>
  </doc>
</export-example>
