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 R^{2} 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.