Stochastic Optimization for Scheduling Thinning Operations in Mediterranean Pine Forest Stands Under Fire Damage Risk

M Acuna (), Varo-Martínez Ma (), V Lerma-Arce (), G Palacios-Rodríguez () and R Navarro-Cerrillo ()
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M Acuna: Natural Resources Institute of Finland (Luke)
Varo-Martínez Ma: University of Cordoba, Dpto. Forest Engineering
V Lerma-Arce: Polytechnic University of Valencia, Institute of Information and Communication Technologies
G Palacios-Rodríguez: University of Cordoba, Dpto. Forest Engineering
R Navarro-Cerrillo: University of Cordoba, Dpto. Forest Engineering

A chapter in Advances in Optimization and Wildfire, 2026, pp 1-20 from Springer

Abstract: Abstract Forest planning in Mediterranean pine forests requires balancing the economic benefits of commercial thinning with the risks associated with wildfire suppression. This study applies two-stage stochastic programming (SP) to optimize a thinning program across a 45,000-hectare area over a 10-year horizon (Special Plan). The approach focuses on maximizing Net Present Value (NPV) by incorporating revenues from biomass, timber sales, and carbon credits, while minimizing fire risk, represented by the fire suppression difficulty index. The findings reveal that thinning operations and timber supply are significantly influenced by carbon prices and fire risk. The optimized thinning schedule prioritizes activities in the early and final years of the Special Plan, with reduced activity during the intermediate years. As fire risk increases, timber demand shapes thinning schedules, but this demand tapers off toward the end of the planning period. The study highlights the critical role of stochastic programming in addressing uncertainty related to fire risk. The low Expected Value of Perfect Information (EVPI) and high Value of the Stochastic Solution (VSS) indicate that while knowing the exact fire risk would not dramatically improve the model’s performance, incorporating uncertainty into the planning process greatly enhances silvicultural strategies. This underscores the need for adaptive forest management that leverages optimization tools and strategic planning to ensure sustainable timber supply, maximize carbon sequestration, and mitigate wildfire risks effectively.

Keywords: Stochastic optimization; Thinning scheduling; Adaptive silviculture; Planning under uncertainty; Risk management (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-03108-2_1

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