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Article

Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty

1
School of Environmental and Forest Sciences, University of Washington, Box 352100, Seattle, WA 98195, USA
2
Industrial and Systems Engineering, University of Washington, Seattle, WA 98195, USA
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Author to whom correspondence should be addressed.
Forests 2020, 11(11), 1230; https://doi.org/10.3390/f11111230
Received: 12 October 2020 / Revised: 1 November 2020 / Accepted: 21 November 2020 / Published: 23 November 2020
(This article belongs to the Section Forest Ecology and Management)
Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. In this paper, we propose to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different growth scenarios compared to the approach using the expected growth and yield. The SAA method requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability. View Full-Text
Keywords: data-driven optimization; climate change; harvest planning; optimality gap; forest data-driven optimization; climate change; harvest planning; optimality gap; forest
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MDPI and ACS Style

Bagaram, M.B.; Tóth, S.F. Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty. Forests 2020, 11, 1230. https://doi.org/10.3390/f11111230

AMA Style

Bagaram MB, Tóth SF. Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty. Forests. 2020; 11(11):1230. https://doi.org/10.3390/f11111230

Chicago/Turabian Style

Bagaram, Martin B., and Sándor F. Tóth 2020. "Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty" Forests 11, no. 11: 1230. https://doi.org/10.3390/f11111230

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