Skip to main content

Advertisement

SpringerLink
Log in

Analysis and modeling of timber storage accumulation after severe storm events in Germany

  • Original Paper
  • Published:
European Journal of Forest Research Aims and scope Submit manuscript

Abstract

In this study, we explore the determinants of forest enterprises’ timber storage accumulation after severe storm events. The explanatory power of variables reflecting economic, institutional and tree species-related factors is tested via econometric analyses. Timber storage accumulation is assessed conducting a weighted multiple linear regression analysis. In addition, the moderating effect of timber price alterations on timber storage accumulation is tested employing timber price alterations as a moderator variable in a hierarchical regression analysis. The results show a linear positive relation between forest enterprises’ damaged and stored timber quantities. It was found that coniferous timber was stored to a greater extent than non-coniferous timber which can be explained by higher storm damage as well as better suitability for multiyear storage. State forestry stored the highest shares of damaged timber, followed by municipal and private forest enterprises, which can be explained as a countervailing measure. A further central finding of our study is that the timber price drops after storm events act as a moderator variable on the relation between damaged and stored timber quantities. Hence, empirical timber price reactions help to improve estimation accuracy regarding the shares of damaged timber which are stored. Derived from our results, we find timber storage accumulation to be a common practice by forest companies to mitigate revenue losses caused by extreme storm events. As coefficients vary strongly among the analyzed variables’ categories, we consider case-specific storage accumulation estimations to be crucial for improving the accuracy of national wood and timber accounting, but also for a full view on storm-related economic damage in the forest sector. In addition, it is likely that capturing the proportions of timber storage in forests will gain relevance in the future, since forest damage from natural disturbances is expected to increase as a consequence of climate change.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: special analysis at Forest Research Institute of Baden-Württemberg (FVA) based on an in-house management information system (FoFIS)

Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aiken LS, West SG (1991) Multiple regression. Testing and interpreting interactions. Sage Publication, Newbury Park

    Google Scholar 

  • Backhaus K, Erichson B, Plinke W, Weiber R (2016) Multivariate Analysemethoden. Eine anwendungsorientierte Einführung. Lehrbuch, 14. überarbeitete und aktualisierte Auflage edn. Springer Gabler, Berlin

  • Bolte A, Ammer C, Löf M, Madsen P, Nabuurs GJ, Schall P, Spathelf P, Rock J (2009) Adaptive forest management in central Europe: climate change impacts, strategies and integrative concept. Scand J For Res 24:473–482. https://doi.org/10.1080/02827580903418224

    Article  Google Scholar 

  • Brunette M, Holecy J, Sedliak M, Tucek J, Hanewinkel M (2015) An actuarial model of forest insurance against multiple natural hazards in fir (Abies Alba Mill.) stands in Slovakia Forest. Policy Econ 55:46–57. https://doi.org/10.1016/j.forpol.2015.03.001

    Article  Google Scholar 

  • Carroll RJ, Ruppert D (1988) Transformation and weighting in regression. Chapman & Hall Ltd, London

    Book  Google Scholar 

  • Cuddeback G, Wilson E, Orme JG, Combs-Orme T (2004) Detecting and statistically correcting sample selection bias. J Soc Serv Res 30:19–33. https://doi.org/10.1300/J079v30n03_02

    Article  Google Scholar 

  • Dieter M, Rosin A, Thoroe C (2004) Die forstwirtschaftliche Gesamtrechnung der Bundesrepublik Deutschland im Rahmen des ESVG 1995 für die Jahre 1991 bis 2002. Arbeitsbericht des Instituts für Ökonomie; 2004/15. Institut für Ökonomie, Hamburg

    Google Scholar 

  • Englert H, Seintsch B (2014) Aktualisierte Methodenbeschreibung zur Waldgesamtrechnung mit Tabellen für das Berichtsjahr 2012 (endgültig) und 2013 (vorläufig); Projektbericht für die umweltökonomischen Gesamtrechnungen des Statistischen Bundesamtes. Thünen-Institut für Internationale Waldwirtschaft und Forstökonomie, Hamburg, p 122

    Google Scholar 

  • Gujarati DN (2003) Basic econometrics, 4th edn. McGraw-Hill, Boston

    Google Scholar 

  • Hapla F (1992) Holzqualität von Kiefern aus einem Waldschadensgebiet nach fünfjähriger Naßlagerung. Holz als Roh-und Werkstoff 50(7–8):268–274

    Article  Google Scholar 

  • Hillmann M (2007) Kyrill—das Ende der Solidarität. AFZ Wald 62(22):1190–1191

    Google Scholar 

  • Holecy J, Hanewinkel M (2006) A forest management risk insurance model and its application to coniferous stands in southwest Germany. For Policy Econ 8:161–174. https://doi.org/10.1016/j.forpol.2004.05.009

    Article  Google Scholar 

  • Institute SAS (2016) JMP 13 multivariate methods. SAS Institute, Cary

    Google Scholar 

  • JMP (2017). http://www.jmp.com/support/help/Launch_the_Fit_Model_Platform.shtml#213135. Accessed 22 May 2017

  • Jochem D, Weimar H, Bösch M, Mantau U, Dieter M (2015) Estimation of wood removals and fellings in Germany: a calculation approach based on the amount of used roundwood. Eur J Forest Res 134:869–888. https://doi.org/10.1007/s10342-015-0896-9

    Article  Google Scholar 

  • Kinnucan HW (2016) Timber price dynamics after a natural disaster: Hurricane Hugo revisited. J For Econ 25:115–129. https://doi.org/10.1016/j.jfe.2016.09.002

    Article  Google Scholar 

  • Kish L (1965) Survey sampling. Wiley, New York

    Google Scholar 

  • Lohner P, Appel V, Dieter M, Seintsch B (2016) Das TBN-Forst: Ein Datenschatz für die deutsche Forstwirtschaft, vol 17/2016

  • Mantau U (2015) Wood flow analysis: quantification of resource potentials, cascades and carbon effects. Biomass Bioenerg 79:28–38. https://doi.org/10.1016/j.biombioe.2014.08.013

    Article  CAS  Google Scholar 

  • McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) (2001) Climate change 2001: impacts, adaption and vulnerability. Contribution of working group II to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 1008

  • Neter J, Wasserman W, Kutner MH (1985) Applied linear statistical models. Regression, analysis of variance, and experimental designs, 2nd edn. Irwin, Homewood

    Google Scholar 

  • NFI3 (2014) Der Wald in Deutschland. Ausgewählte Ergebnisse der dritten Bundeswaldinventur. Bundesministerium für Ernährung und Landwirtschaft, Berlin

    Google Scholar 

  • Odenthal-Kahabka J (2004) Orkan “Lothar” - Bewältigung der Sturmschäden in den Wäldern Baden-Württembergs : Dokumentation, Analyse, Konsequenzen. Landesforstverwaltung, Stuttgart

    Google Scholar 

  • Polley H (2006) The Second National Forest Inventory—NFI 2. Results; Covering the National Forest Inventory Surveys of 2001–2002 and 1986–1988. Federal Ministry of Food, Agriculture and Consumer Protection, Berlin

    Google Scholar 

  • Riguelle S, Hébert J, Jourez B (2015) WIND-STORM: a decision support system for the strategic management of windthrow crises by the forest community. Forests 6:3412

    Article  Google Scholar 

  • Robinson C, Schumacker RE (2009) Interaction effects: centering, variance inflation factor, and interpretation issues. Mult Linear Regres Viewp 35:6–11

    Google Scholar 

  • Schelhaas M-J, Nabuurs G-J, Schuck A (2003) Natural disturbances in the European forests in the 19th and 20th centuries. Glob Change Biol 9:1620–1633. https://doi.org/10.1046/j.1365-2486.2003.00684.x

    Article  Google Scholar 

  • Statistisches Bundesamt (2001) Agrarstrukturerhebung/Landwirtschaftszählung 1999. Metzler-Poeschel, Stuttgart

    Google Scholar 

  • Thorn S, Bässler C, Svoboda M, Müller J (2017) Effects of natural disturbances and salvage logging on biodiversity–Lessons from the Bohemian Forest. For Ecol Manag 388:113–119. https://doi.org/10.1016/j.foreco.2016.06.006

    Article  Google Scholar 

  • ZMP-Marktbilanz Forst und Holz (2002) ZMP-Marktbilanz. Zentrale Markt- und Preisberichtstelle für Erzeugnisse der Land-, Forst- und Ernährungswirtschaft 2002

  • ZMP-Marktbilanz Forst und Holz (2008) ZMP-Marktbilanz. Zentrale Markt- und Preisberichtstelle für Erzeugnisse der Land-, Forst- und Ernährungswirtschaft 2008

Download references

Acknowledgements

Financial support by the Agency of Renewable Resources (FNR) under the auspices of the Federal Ministry of Food and Agriculture (BMEL) within the project Wood Resource Monitoring (“Rohstoffmonitoring Holz”) is gratefully acknowledged. We would like to thank Matthias Dieter for his continuous support, Niels Janzen for productive discussion, and Nils Ermisch, Emanuel Meier, and Wolfgang Hercher for the provision of data.

Funding

This study was funded by the Agency of Renewable Resources (FNR) within the project Wood Resource Monitoring (“Rohstoffmonitoring Holz”) (Grant Number 22021614).

Author information

Authors and Affiliations

  1. Thünen Institute of International Forestry and Forest Economics, Leuschnerstraße 91, 21031, Hamburg, Germany

    Klaus Zimmermann & Holger Weimar

  2. Hydrology Department, FBVI Regional and Environmental Sciences, Trier University, Behringstraße 21, 54296, Trier, Germany

    Tobias Schuetz

Authors
  1. Klaus Zimmermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tobias Schuetz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Holger Weimar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Klaus Zimmermann.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by Arne Nothdurft.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zimmermann, K., Schuetz, T. & Weimar, H. Analysis and modeling of timber storage accumulation after severe storm events in Germany. Eur J Forest Res 137, 463–475 (2018). https://doi.org/10.1007/s10342-018-1116-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10342-018-1116-1

Keywords

Search

Navigation