Coarse woody debris management with ambiguous chance constrained robust optimization

Fabian Härtl, Thomas Knoke

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Coarse woody debris (deadwood) serves as a dwelling space for many rare species, and is therefore a most important factor to ensure diversity in forest ecosystems. However, wood from forest ecosystems is also needed for construction and heating. Therefore, a forest enterprise has to simultaneously incorporate the provision of suitable habitats, as well as the production of wood into their long-term management plans. If the owner wants to fulfil such multiple objectives in an effective way, the providing of ecosystem services can be included in economic planning. Applying computer aided robust optimization techniques, we optimized the provision of deadwood for two exemplary enterprises in East Bavaria, Germany. The results show that high amounts of deadwood provision can cause severe opportunity costs for the forest owner. These costs highly depend on the tree species, the sorting strategy and the time horizon, in which the deadwood objective is reached. Low deadwood targets up to 5m3 ha-1 can be provided most cost-effectively with crown material, while higher targets (20m3 ha-1 and more) are better achieved with heavy timber grades or the provision of total trees. The novelty of our research is the inclusion of deadwood targets in a risk-considering optimization tool on enterprise level. Instead of calculating the economic loss of commercially not-used timber assortments we show a way of deriving the impact of such decisions at stand level on the economic performance of the whole forest enterprise. We were able to derive optimized opportunity costs. These costs can be used as guidelines for necessary incentives to encourage forest owners to incorporate the provision of deadwood into their management.

Original languageEnglish
Article number504
JournalForests
Volume10
Issue number6
DOIs
StatePublished - 1 Jun 2019

Keywords

  • Deadwood management
  • Expected uncertainty
  • Exponential decay functions
  • Risk integration
  • Robust optimization
  • Stochastic optimization

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