Alternative priority models for forest planning on the landscape level involving multiple ownership

Abstract The study presents four ways to formulate a landscape level forest planning model for group planning using a heuristic optimization method called ‘HERO’. The HERO method is composed of two primary steps: first, forest management goals are defined; then a management plan is sought to fulfill the defined goals. The planning models consider the landscape (whole area) and forest holdings as separate hierarchical levels. Within the planning models, each participant's forest management goals are defined using additive priority functions consisting of weighted sub-utility and/or achievement functions. Maximizing the achievement function minimizes the deviation from the target value for the corresponding goal variable. (i) The integrated top-down model uses achievement functions on the landscape level and sub-utility functions on the individual holding level; while (ii) the integrated bottom-up model uses achievement functions on the holding level and sub-utility functions on the landscape level. (iii) The integrated utility maximization model consists of weighted sub-utility functions on both the landscape and the individual holding levels and (iv) the integrated regret minimization model uses achievement functions on both levels. The use of different priority models was illustrated in a case study, which consisted of four neighboring private land holdings. In general, the priority models worked in a logical way. Large deviations from the targets could be prevented by using achievement functions in the overall priority models. On the other hand, the differences between the models were not very large, and the results of only one case cannot be generalized. It seems that all the alternative priority models might have use in different planning situations. However, interactive use of the models should be preferred.