Peter Lohmander

Estimation of a basal area growth model for individual trees in uneven-aged Caspian mixed species forests

The aim of this study was to estimate a basal area growth model for individual trees in uneven-aged Caspian forests. A survey was conducted in order to find a natural forest without any harvesting activities, a so called ‘untouched forest’ and an area was selected from the Iranian Caspian forest. Three sample plots in the same aspect and of the same forest type were selected. In each plot, total tree height, diameter at breast height, distance of neighbor trees and azimuth were measured. Thirty trees were selected and drilled with increment borer to determine the increment model. Regression analysis was used to estimate the growth model. Results show that, for individual trees, there is a significant nonlinear relationship between the annual basal area increment, as the dependent variable, and the basal area. The results also show that the basal area of competing trees has a positive influence on growth. That the increment is higher with more competing neighboring trees is possibly because plots with higher volume per hectare and more competition, most likely also have higher site index or better soil or better site productivity than the plot with lower volume per hectare.

Applications and Mathematical Modeling in Operations Research

Theoretical understanding of the relevant problem structure and consistent mathematical modeling are necessary keys to formulating operations research models to be used for optimization of decisions in real applications. The numbers of alternative models, methods and applications of operations research are very large. This paper presents fundamental and general decision and information structures, theories and examples that can be expanded and modified in several directions. The discussed methods and examples are motivated from the points of view of empirical relevance and computability.

Optimal Stochastic Dynamic Control of Spatially Distributed Interdependent Production Units

Stochastic dynamic programming, SDP, is often the optimal method. SDP can be extended to handle very large dimensionality in the decision space, as long as the dimensionality of the state space is not too large, since SDP can be combined with linear or quadratic programming subroutines for every state and stage. When the number of decision variables is large and the optimal decisions are dependent on detailed information in a state space of large dimensionality, SDP cannot be applied. Then, optimal control functions for local decisions may be defined and the parameters can be determined via stochastic full system simulation and multidimensional regression analysis. This paper includes an approach to determination of all local decisions based on locally relevant state space information within stochastic dynamic and spatially explicit production. The expected present value of all harvests, over time and space, in a forest area, is maximized. Each tree is affected by competition from neighbor trees. The harvest decisions, for each tree, are functions of the price in the stochastic market, the dimensions and qualities of the individual trees and the local competition. The expected present value of the forest is an increasing function of the level of price risk.