Ilpo Tikkanen. (1981). Causality as a conceptual frame for forest policy analysis. Silva Fennica vol. 15 no. 1 article id 5100. https://doi.org/10.14214/sf.a15041

Keywords: forest policy; small-scale forestry; causality; empirical policy analysis; public forest policy

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The need for the planning and analysing of public policy has increased in economic and social policy, along with the expansion of the public sector, i.e. as the number of aims of the policy has increased and the objects of it have become more versatile, the objects of allocation have likewise been multiplied. In addition, the significance of planning a public forest policy has been emphasized by many economic and social changes in forestry and the timber economy.

The purpose of the present paper is to outline a general frame of reference for empirical policy analysis, upon which the effectiveness analysis of forest policy is also based. The approach serves as a methodological frame for the empirical analysis, presented in the following paper of this issue of Silva Fennica, in which econometric methods are applied to the examination of the effect of public forest policy.

Tikkanen, E-mail: it@mm.unknown

Category : Research article

article id 1013, category Research article

Nils Fahlvik, Björn Elfving, Peder Wikström. (2014). Evaluation of growth functions used in the Swedish Forest Planning System Heureka. Silva Fennica vol. 48 no. 2 article id 1013. https://doi.org/10.14214/sf.1013

Keywords: basal area; simulation; validation; volume; empirical

Highlights: Growth models based on historical growth data gave reliable growth predictions up to the century shift; Detailed single tree growth models had lower precision for estimation of total growth than one single stand-based model; The prediction error was in average about 15% and did not increase with extended prediction period.

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The performance of growth models implemented in the Swedish Forest Planning System Heureka was evaluated. Four basal area growth models were evaluated by comparing their predictions to data from five-year growth records for 1711 permanent sample plots of the National Forest Inventory (NFI-data). Also, two alternative implementations of Heureka, including a combined stand- and tree-level basal area growth model and a single tree-level model, respectively, were evaluated using data from 57 blocks in a thinning experiment (GG-data) involving Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst) in which the trees were monitored for 30 years after the first thinning. The predicted volume growth was also compared to observed values. Growth models based on data from 1970’s and 1980’s overestimated growth in the NFI test plots from the early 2000’s by about 3%. Stand-level models had larger precision than tree-level models. Basal area growth was underestimated in dense NFI-plots and overestimated in non-thinned GG-plots, illustrating an un-solved modelling problem. Basal area growth was overestimated by 2–5% also in the GG-plots over the whole observation period. Volume growth was however accurately predicted for pine and underestimated by 2% for spruce. The relative prediction error did not increase with increasing length of prediction period. Thinning response models calibrated with GG-data worked well in the total application and produced growth levels for different thinning alternatives in line with observations.

Fahlvik, Department of Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, P.O. Box 49, SE-230 53 Alnarp, Sweden E-mail: nils.fahlvik@slu.se
Elfving, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden E-mail: bjorn.elfving@slu.se
Wikström, Peder Wikström Skogsanalys AB, c/o Peder Wikström, Huldrans väg 1, SE-907 52 Umeå, Sweden E-mail: peder.wikstrom@slu.se

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