Abstract

A key aspect when optimizing strategic and long-term forest management policies is the temporal aggregation utilizing time periods of a specific length. As the length of the time periods influence both the problem size and the possible interaction of the management policy with the state of the forest, it implicitly has a major influence on the feasibility of computing the optimal management policy and the quality of the resulting management policy. The objective of this study was twofold: (i) to evaluate the value of considering the risk of wind damage in large-scale strategic forestry management policies, (ii) to investigate the influence of the length of the time periods on the value of considering the risk of wind damage in the management policy. The analysis was executed utilizing a graph-based Markov decision process model capable of considering stochastic wind damage event, and a case study utilizing a forest estate consisting of 1200 ha of forestry, divided into 623 stands. Twenty-, ten-, and five-year-long time periods were utilized to evaluate the influence of the length of the time periods, while the value of considering the risk of wind damage in the management of the estate was evaluated by optimizing and evaluating long-term management policies recognizing and not recognizing the risk of wind damage. Results show that the value of considering the risk of wind damage was small for the whole estate. The expected net present value of the estate increased by ≤2% by managing the estate according to the risk of wind damage. Furthermore, while the length of the time periods had a small influence on the scale of the entire estate, it had a larger influence on the scale of a smaller subset of stands in the estate. For the whole estate, the value of considering the risk of wind damage varied with ≤1.5% depending on the length of the time periods. While for a selected subset of stands, the value of considering the risk of wind damage varied with ≤6.5% depending on the length of the time periods.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call