Current issue: 54(2)
Many kinds of planning systems have been labelled decision support systems (DSS), but few meet the most important features of real DSSs in planning and control of wood procurement. It has been concluded that many reasons exist to develop DSSs for wood procurement. The purchasing of timber seems to be one of the most promising areas for DSS, because there is no formal structure for these operations and decisions deal with human behaviour. Relations between DSSs and different features of the new approaches in wood procurement are also discussed, and hypotheses for future studies suggested.
The two introductory books written by emeritus professor William Duerr provide an opportunity to scope the research progress in the forest economic discipline during almost a quarter century. This paper gives a presentation of the books (Fundamentals of Forest economics, 1960, and Forestry Economics as Problem Solving, 1984), and the development of forest economics during the period.
The PDF includes a summary in English.
The factors affecting the non-industrial, private forest owners’ (NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the forest owners’ choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models.
The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, forest owner’s positive price expectations for the next eight years, forest owner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40.
In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R2 of the optimal regression equation was 0.3 and the relative root mean square error from the test data 0.38.
In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.