A Stochastic Dynamic Programming Approach to Optimize Short-Rotation Coppice Systems Management Scheduling: An Application to Eucalypt Plantations under Wildfire Risk in Portugal

Abstract

This article presents and discusses research with the aim of developing a stand-level management scheduling model for short-rotation coppice systems that may take into account the risk of wildfire. The use of the coppice regeneration method requires the definition of both the optimal harvest age in each cycle and the optimal number of coppice cycles within a full rotation. The scheduling of other forest operations such as stool thinning and fuel treatments (e.g., shrub removals) must be further addressed. In this article, a stochastic dynamic programming approach is developed to determine the policy (e.g., fuel treatment, stool thinning, coppice cycles, and rotation length) that maximizes expected net revenues. Stochastic dynamic programming stages are defined by the number of harvests, and state variables correspond to the number of years since the stand was planted. Wildfire occurrence and damage probabilities are introduced in the model to analyze the impact of the wildfire risk on the optimal stand management schedule policy. For that purpose, alternative wildfire occurrence and postfire mortality scenarios were considered at each stage. A typical Eucalyptus globulus Labill. stand in Central Portugal was used as a test case. Results suggest that the proposed approach may help integrate wildfire risk in short-rotation coppice systems management scheduling. They confirm that the maximum expected discounted revenue decreases with and is very sensitive to the discount rate and further suggest that the number of cycles within a full rotation is not sensitive to wildfire risk. Nevertheless, the expected rotation length decreases when wildfire risk is considered. FOR. SCI. 58(4):353-365.

A model of shrub biomass accumulation as a tool to support management of Portuguese forests

“…For instance, a preliminary shrub build-up model (Botequim et al 2009) was integrated with a growth and yield model to accomplish maritime pine stand-level optimizations and determine optimal stand-level treatments (e.g., thinning, fuel treatment), so as to reduce the hazard of fire (Ferreira et al 2014). This information is very valuable as it may effectively support the development of adaptive management strategies (Ferreira et al 2012(Ferreira et al , 2014.…”

“…This information may be used to develop a shrub biomass build-up model, which would contribute to more accurate estimates of fire behavior and may quantify the impact of silvicultural treatments on the probability of wildfire occurrence. It can also improve decision-making in forest management, especially taking into account the risk of forest fires (Ferreira et al 2012(Ferreira et al , 2014.…”

Stand-Level Forest Management Planning Approaches

“…We would also like to direct interested readers to Yousefpour et al [9] who reviewed techniques for modeling climate change under the scheme of adaptive forest management. Other papers that addressed stochasticity or risk and uncertainty, in a DP framework include the following: Gunn [18], Díaz-balteiro and Rodriguez [19], Zhou and Buongiorno [20]; Ferreira et al [21] and Ferreira et al [22]; Yoshimoto and Shoji [23]; Yoshimoto [24]. In this study, we use deterministic solution techniques to describe a situation where the future state of a forest stand can be predicted exactly from knowledge of the present and all inputs and events are assumed to be known with certainty.…”

Optimisation in Forest Management

“…The Pontryagin minimum principle is used to ensure that the necessary conditions for an optimal management regime have been located after examination of the various paths within the network. In Ferreira et al [56], dynamic programming was used to assess the management options for short-rotation coppice stands of Eucalyptus globulus in Portugal while taking into account the risk of loss due to wildfire. Probabilities of complete and partial damage from wildfire and their effects on regeneration are factored into the states and transitions as bare land value is optimised.…”