Shigeru Shimotori. (1986). Private and municipal forests and the forestry planning system in Japan - trends and problems after World War II. Silva Fennica vol. 20 no. 4 article id 5299. https://doi.org/10.14214/sf.a27755

Keywords: forest management; forest owners; forest resources; private forestry; Japan; forestry policy; forestry planning system

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In this paper, the author explains the characteristics of the Japanese forestry planning system and points out some of the problems found therein from the viewpoint of the management of privately-owned forests relating to the economic background and governmental policy.

The forestry planning system is a centralizes type of planning, the planning begins at the top and flows downward and outward the periphery. In order to make this planning system an effective instrument, the district forestry planning founded under the system must approach the problem of how to combine the resources of the forest with the district’s inhabitants and the forest owners; and further, the extent of the effective union of the district and the local timber manufacturing must be examined.

Shimotori, E-mail: ss@mm.unknown

Category : Research article

article id 496, category Research article

Hampus Holmström, Hans Kallur, Göran Ståhl. (2003). Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fennica vol. 37 no. 3 article id 496. https://doi.org/10.14214/sf.496

Keywords: uncertainty; data acquisition; imputation; forestry planning

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The usefulness of kNN (k Nearest Neighbour)-assigned reference sample plot data as a basis for forest management planning was studied. Cost-plus-loss analysis was applied, whereby the inventory cost for a specific method is added to the expected loss due to non-optimal forestry activities caused by erroneous descriptions of the forest state. Four different strategies for data acquisition were evaluated: 1) kNN imputation of sample plots based on traditional stand record information, 2) imputation based on plot-wise aerial photograph interpretation in combination with stand record information, 3) sample plot inventory in the field with 5 plots per stand, and 4) sample plot inventory with 10 plots per stand. Expected losses were derived as mean values of differences between the maximum net present value and the corresponding value obtained when the treatment schedule believed to be optimal (based on data simulated according to method 1–4) was selected. The optimal choice of method was found to depend on factors such as stand maturity, stand area, and time to next treatment (thinning or clearcutting). In general, the field sample plot methods were competitive in large mature stands, especially when the time to the next (optimal) treatment was short. By in each stand (within an estate) employing the method with the lowest cost-plus-loss rather than choosing the method that performed best on average for the entire estate, the total cost for inventory at the estate level could be decreased by 15–50%. However, it was found difficult to identify what method should optimally be employed in a stand based on general stand descriptions.

Holmström, Regional Board of Forestry of Västra Götaland, P.O. Box 20008, SE-50420 Borås, Sweden E-mail: hampus.holmstrom@svsvg.svo.se
Kallur, ÖKA Skogsplan, Kopparvägen 45 O, SE-90750 Umeå, Sweden E-mail: hk@nn.se
Ståhl, Swedish Univ. of Agricultural Sciences, Dept. of Forest Resource Management and Geomatics, SE-90183 Umeå, Sweden E-mail: gs@nn.se

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