Current issue: 53(4)
In this study, logistic regression and neural networks were used to predict non-industrial private forests (NIPF) landowners’ choice of forest taxation basis. The main frame of reference of the study was the Finnish capital taxation reform of 1993. As a consequence of the reform, landowners were required to choose whether to be taxed according to site-productivity or realized-income during the coming transition period of thirteen years.
The most important factor affecting the landowners’ choice of taxation basis was the harvest rate during the transition period, i.e. the chosen timber management strategy. Furthermore, the estimated personal marginal tax rate and the intention to cut timber during next three years affected the choice. The descriptive landowner variables did not have any marked effect on the choice of forest taxation basis.
On average, logistic regression predicted 71% of the choices correctly; the corresponding figure for neural networks was 63%. In both methods, the choice of site-productivity taxation was predicted more accurately than the choice of realized-income taxation. An increase in the number of model variable did not significantly improve the results of neural networks and logistic regression.
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.
In the study, the potential allowable cut in the district of North-Savo, Eastern Finland was clarified based on the non-industrial private forest landowners’ (NIPF) choices of timber management strategies. Alternative timber management strategies were generated, and the choices and factors affecting the choices of timber management strategies by NIPF landowners were studied. The choices of timber management strategies were solved by maximizing the utility functions of the NIPF landowners. The parameters of the utility functions were estimated using the Analytic Hierarchy Process (AHP).
The level of the potential allowable cut was compared to the cutting budgets based on the 7th and 8th National Forest Inventories (NFI7, NFI8) in Finland, to the combining of private forestry plans, and to the realized drain from non-industrial private forests. The potential allowable cut was calculated using the MELA system that has been used in calculating the national cutting budget.
The data consisted of the NIPF holdings that had been inventoried compartmentwise and had forestry plans made in 1984–92. The NIPF landowners’ choices of timber management strategies were clarified by a mail inquiry.
The most preferred strategy obtained was ”sustainability” (chosen by 62% of landowners). The second was ”finance” (17%) and the third ”savings” (11%). ”No cuttings”, and ”maximum cuttings” were the least preferred (9% and 1%, resp.). The factors promoting the choices of strategies with intensive cuttings were: a) ”farmer as forest owner” and ”owing fields”, b) ”increase in the size of the forest holding”, c) agriculture and forestry orientation in production, d) ”decreasing short-term stumpage earnings expectations”, e) ”increasing intensity of future cuttings”, and f) ”choice of forest taxation system based on site productivity”.
The potential allowable cut defined in the study was 20% higher than the average of the realized drain in 1988–93, which was at the same level as the cutting budget based on the combining of forestry plans in Eastern Finland. The potential allowable cut defined in the study was 12% lower than the NFI8-based greatest sustained allowable cut for the 1990. Using the method, timber management strategies can be clarified for private forest owners.