A hybrid method for tree-level optimization in continuous cover forest management

Abstract

Abstract A current trend in forestry is the increased use of continuous cover management (CCF). Another trend is the increased availability of tree-level forest inventory data. Accordingly, recent literature suggests methodologies for optimizing the harvest decisions at the tree level. Using tree-level optimization for all trees of the stand is computationally demanding. This study proposed a flexible two-level optimization method for CCF where the harvest prescriptions are optimized at the tree level only for a part of the trees, or only for the first cuttings. The higher-level algorithm optimizes the cutting years and the harvest rates of those diameter classes for which tree-level optimization is not used. The lower-level algorithm allocates the individually optimized trees to different cutting events. The most detailed problem formulations, employing much tree-level optimization, always resulted in the highest net present value and longest time consumption of the optimization run. However, reducing the use of tree-level optimization to the largest trees and first cuttings did not alter the time, intensity, or type of the first cutting significantly, which means that simplified problem formulations may be used when decision support is needed only for the next cutting. The method suggested here can accommodate diversity-related management objectives and makes it possible to analyze the trade-offs between economic profit and diversity objectives. The case study analyses suggested that significant improvements in diversity can be obtained with moderate reductions in economic profitability.