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Integrating natural risks into silvicultural decision models: A survival function approach

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  • Staupendahl, Kai
  • Möhring, Bernhard

Abstract

In the context of climate change, the frequency and intensity of natural disturbances of silvicultural production, such as storms and insects, are expected to increase. Hence, now more than ever before such factors must be considered in forest management. As a contribution to this topic, this article presents a calculation model implemented in Excel frames, which supports decisions in forest production under changing conditions. Risk is integrated into the model by the Weibull function, which serves as an age-dependent survival function. In order to facilitate an intuitive interpretation of its coefficients, it was used in a reparametrised form. Furthermore, salvage price reductions and cost additions caused by calamities are considered. The target variable is the 'annuity under risk'. We demonstrate exemplarily how different parameters of the survival function influence the probability distribution and thus the expected value of the annuity of a spruce stand. The differences between the annuities with and without a consideration of risk are interpreted as current, annual risk costs. It can be shown that risk lowers the annuity, whereas scenarios with high risks in the young stand stages have a higher impact than those with high risks in mature stands. In the latter case, adaptation is possible by shortening the rotation period. This does not hold in the case of early risks, which cannot be avoided. For this case, an extension of the rotation length is recommended. By changing the parameters of the survival function, this scheme allows forest managers to incorporate changing risks into their management planning.

Suggested Citation

  • Staupendahl, Kai & Möhring, Bernhard, 2011. "Integrating natural risks into silvicultural decision models: A survival function approach," Forest Policy and Economics, Elsevier, vol. 13(6), pages 496-502, July.
  • Handle: RePEc:eee:forpol:v:13:y:2011:i:6:p:496-502
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    References listed on IDEAS

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    1. Dieter, Matthias, 2001. "Land expectation values for spruce and beech calculated with Monte Carlo modelling techniques," Forest Policy and Economics, Elsevier, vol. 2(2), pages 157-166, June.
    2. Newman, D.H., 2002. "Forestry's golden rule and the development of the optimal forest rotation literature," Journal of Forest Economics, Elsevier, vol. 8(1), pages 5-27.
    3. Holecy, Jan & Hanewinkel, Marc, 2006. "A forest management risk insurance model and its application to coniferous stands in southwest Germany," Forest Policy and Economics, Elsevier, vol. 8(2), pages 161-174, March.
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    Citations

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    Cited by:

    1. Thiele, Jan C. & Nuske, Robert S. & Ahrends, Bernd & Panferov, Oleg & Albert, Matthias & Staupendahl, Kai & Junghans, Udo & Jansen, Martin & Saborowski, Joachim, 2017. "Climate change impact assessment—A simulation experiment with Norway spruce for a forest district in Central Europe," Ecological Modelling, Elsevier, vol. 346(C), pages 30-47.
    2. Deegen, Peter & Matolepszy, Kai, 2015. "Economic balancing of forest management under storm risk, the case of the Ore Mountains (Germany)," Journal of Forest Economics, Elsevier, vol. 21(1), pages 1-13.
    3. Loisel, Patrice, 2014. "Impact of storm risk on Faustmann rotation," Forest Policy and Economics, Elsevier, vol. 38(C), pages 191-198.
    4. Anna Jönsson & Fredrik Lagergren & Benjamin Smith, 2015. "Forest management facing climate change - an ecosystem model analysis of adaptation strategies," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(2), pages 201-220, February.
    5. repec:eee:ecomod:v:255:y:2013:i:c:p:58-69 is not listed on IDEAS
    6. Härtl, Fabian & Knoke, Thomas, 2014. "The influence of the oil price on timber supply," Forest Policy and Economics, Elsevier, vol. 39(C), pages 32-42.

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