This project investigates optimal harvest times or rotation age for Trees in Forestry Management. We employed portfolio management and optimization techniques to investigate the effect and presence of forest disease on economic and ecological outcomes. By integrating risk measures, we have developed a detailed framework that balances economic benefits with risk from external factors (disease outbreaks in this instance). This study accounted for both deterministic and stochastic settings, modeling scenarios of (i) No disease and (ii) Disease presence to establish optimal strategies for both single and n-species cases.

We employed optimization algorithms of Backward Recursion and Forward-Backward Sweep to establish the optimal strategy for the optimization problem, using Lagrangian and Hamiltonian principles from Control theory for control formulation. Monte Carlo simulation was applied to estimate stochastic parameters, while Net Present Value was used to evaluate the economic benefit. Simulated results are presented for a single species model while an extension to the n-species was briefly discussed. Our findings aim to provide actionable insights into disease management and optimized harvesting strategies, demonstrating how the presence of disease alters optimal rotation ages. Future extensions focusing on refining the current model to incorporate broader ecological impacts, policy reflections, and real-world variability are highlighted.