Articles containing the keyword 'forest planning'

Ten trails, one kilometre each, were evaluated by 15 persons for scenic beauty, recreational value and variety. All trails passed through commercially managed forests dominated by conifers. The trails were first evaluated by viewing computer simulations based on a series of graphical illustrations of forest landscapes, then from a slide show, and finally in the field. In the computer simulation and slide show, landscape pictures along the trail at an interval of 35–40 m were presented for 3–4 seconds. The ranks between slide show and field were slightly more similar than those between simulation and field. The mean correlation of 12 persons between the field ranking and assessment of either computer simulations or slide shows or graphics than scenic beauty or recreational value. Spearman’s rank correlations computed from median scores of a group of 12 peers were clearly better than the average of individual persons varying from 0.6 to 0.9.

The PDF includes an abstract in Finnish

• Nousiainen, E-mail: in@mm.unknown
• Pukkala, E-mail: tp@mm.unknown

An alternative approach to formulating a forestry goal programming problem is presented. First, single objective optima levels are solved. The Analytical Hierarchy Process is applied in the estimation of a priori weights of deviations from the goal target levels. The ratios of the weights can be interpreted as relative importance of the goals, respectively. The sum of the weighted deviations from all single optima levels associated with the management goals is minimized. Instead of absolute deviations, relative ones are used. A case study problem of forest management planning with several objectives, measured in different units, is analysed.

The PDF includes an abstract in Finnish.

• Kangas, E-mail: jk@mm.unknown
• Pukkala, E-mail: tp@mm.unknown

Length of the regeneration period is a criterion commonly used for comparing different reforestation methods. The time factor should be evaluated using a realistic system for long-term planning. In this paper the preliminary evaluation is made by simplified calculations based on the development series. The slow regeneration method is assumed to be otherwise equal to the rapid one but it has a 5- or 10-years delay at the beginning, and the rotation is thus the final cutting age plus 5- or 10-years delay. Cost of the time delay is taken to be the difference in reforestation costs that makes the rapid and the slow methods equivalent. Calculations are made using zero costs for the slow method; but if the cost of the slow method increases, the critical cost difference decreases very slowly. The final cutting age and the regeneration method must be decided simultaneously. Therefore, the cost of the time delay is presented as a function of final cutting age. By maximizing the average annual revenue, rotation can be even increased if more rapid but more expensive regeneration method is used.

The PDF includes a summary in English.

• Lappi, E-mail: jl@mm.unknown

The planning of timber production in a forestry unit is divisible into two phases. In the first phase, planning provides the decision-maker with a number of possible timber production policies; these policies define the production possibility boundary. After the decision-maker has chosen one of these policies, planning moves to the second phase, in which a detailed programme is prepared with a view to meeting the requirements of the timber production policy accepted. The paper indicates one possibility of solving these two tasks simultaneously. In the first phase, the solution of the primal linear programming problem is employed and in the second phase the respective dual or shadow price solution.

The PDF includes a summary in English.

• Kilkki, E-mail: pk@mm.unknown
• Pökälä, E-mail: rp@mm.unknown
• Siitonen, E-mail: ms@mm.unknown

This paper describes different methods of long-term forecasts in forest management planning with a special attention on intention forecasts for a total forest property or district. Methods for calculating the sustained yield on the basis of the actual increment or the yearly area cut are discussed. It is concluded that a better estimate of the sustained yield is obtainable by the application of a long-term forecast technique. Forecasts for 100 years should not be viewed as plans, but as a background for making short-term decisions. Some of the long-term-type programmes, such as the programme of maximum profit, sustained yield in volume and in money are discussed briefly.

It is pointed out that there is often present a conflict between the various elements of the policy formulated by a forest owner. This leads to the conclusion that the calculations of the profitability of single projects may be misleading.

The precision of a long-term forecast is discussed, and how under certain assumptions the error of the allowable cut is influenced by errors in area, volume, age etc. It is shown that the precision in area and volume is more important in this connection than, say, the precision in increment. In conclusion, existing knowledge, methods and equipment for calculations constitute a basis for long-term forecasts which make them an important instrument in forest management planning.

The PDF includes a summary in English.

• Seip, E-mail: hs@mm.unknown

Silva Fennica Issue 64 includes presentations held in 1947 in the third professional development courses, arranged for foresters working in the public administration. The presentations focus on practical issues in forest management and administration, especially in regional level. The education was arranged by Forest Service. Two of the presentations were published in other publications than Silva Fennica issue 64.

This presentation discusses different ways of organizing felling cycle, forest management practices used in the forests of Finnish forest research institute, and how good practices developed in the institue could be applied in state forests.

• Heikinheimo, E-mail: oh@mm.unknown

The aim of the study was to develop a method for calculating a cutting budget that is adapted to the present forest management practices. The cutting budget determines the volume of annual cuttings for a forest holding in a certain period of time. Effect of fellings on the cutting budget depends on the cutting methods used. The study aimed at proving that growth of the forest can be estimated based on growing stock and structure of the forests for a certain time period. Accordingly, adequate drain can be defined in advance. The cutting budget is based on age-class distribution of the forest holding, which is most applicable for even-aged forestry. Calculation is based on area of the forest land and estimated volume of the growing stock. Also, the quality of the forest soil can be taken into account when age-class distribution is used. A suitable period for estimating a cutting budget is suggested to be 20 years, which is divided in two 10-year periods. The cutting budget it is included in a forestry plan. An example of a cutting plan based on the method is presented.

The PDF includes a summary in German.

• Lihtonen, E-mail: vl@mm.unknown

The paper examines the needs, premises and criteria for effective public participation in tactical forest planning. A method for participatory forest planning utilizing the techniques of preference analysis, professional expertise and heuristic optimization is introduced. The techniques do not cover the whole process of participatory planning, but are applied as a tool constituting the numerical core for decision support. The complexity of multi-resource management is addressed by hierarchical decision analysis which assesses the public values, preferences and decision criteria toward the planning situation. An optimal management plan is sought using heuristic optimization. The plan can further be improved through mutual negotiations, if necessary. The use of the approach is demonstrated with an illustrative example. Its merits and challenges for participatory forest planning and decision making are discussed and a model for applying it in general forest planning context is depicted. By using the approach, valuable information can be obtained about public preferences and the effects of taking them into consideration on the choice of the combination of standwise treatment proposals for a forest area. Participatory forest planning calculations, carried out by the approach presented in the paper, can be utilized in conflict management and in developing compromises between competing interests.

• Kangas, E-mail: jk@mm.unknown
• Loikkanen, E-mail: tl@mm.unknown
• Pukkala, E-mail: tp@mm.unknown
• Pykäläinen, E-mail: jp@mm.unknown

Forest disturbances challenge our ability to carefully plan for sustainable use of forest resources. As forest disturbances are stochastic, we cannot plan for the disturbance at any specific time or location. However, we can prepare for the possibility of a disturbance by integrating its potential intensity range and frequency when developing forest management plans. This study uses stochastic programming to integrate wind intensity (wind speed) and wind event frequency (number of occurrences) into the forest planning process on a small coastal Finnish forest landscape. We used a mechanistic model to quantify the critical wind speed for tree felling, with a Monte Carlo approach to include wind damage and salvage logging into forest management alternatives. We apply a stochastic programming model to explore two objectives: maximizing the expected forest net present value or maximizing the even-flow of income. To assess the effects of improper wind risk assumptions in planning, we compare the results when optimizing for correct versus incorrect wind intensity and frequency assumptions. When maximizing for net present value, the impacts of misidentifying wind intensity and frequency are minor, likely due to harvests planned immediately as trees reach maturity. For the case when maximizing even-flow of income, incorrectly identifying wind intensity and frequency severely impacts the ability to meet the required harvest targets and reduces the expected net present value. The specific utility of risk mitigation therefore depends on the planning problem. Overall, we show that incorporating wind disturbances into forest planning can inform forest owners about how they can manage wind risk based on their specific risk preferences.

• Eyvindson, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Norway; Natural Resource Institute Finland (Luke), Bioeconomy and Environment, Laatokartanonkaari 9, 00790 Helsinki, Finland https://orcid.org/0000-0003-0647-1594 E-mail: kyle.eyvindson@nmbu.no
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, Yliopistokatu 6, 80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
• Nahorna, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Norway https://orcid.org/0000-0002-5497-0315 E-mail: olha.nahorna@nmbu.no
• Hunault-Fontbonne, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Norway https://orcid.org/0009-0004-1864-5162 E-mail: juliette.hunault@nmbu.no
• Potterf, Ecosystem Dynamics and Forest Management Group, Technical University of Munich, Hans Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany https://orcid.org/0000-0001-6763-1948 E-mail: maria.potterf@tum.de

Carbon sequestration and income generation are competing objectives in modern forest management. The climate commitments of many countries depend on forests as carbon sinks which must be quantified, monitored, and projected into the future. For projections we need tools to model forest development and perform scenario analyses to assess future carbon sequestration potentials under different management regimes, the expected net present value of such regimes, and possible impacts of climate change. We propose a scenario analysis software tool (GAYA 2.0) that can assist in answering these types of questions using stand level simulations, detailed carbon flow models and an optimizer. This paper has two objectives: (1) to describe GAYA 2.0, and (2) demonstrate its potential in a case study where we analyze the forest carbon balance over a region in Norway based on national forest inventory sample plots. The tool was used to map the optimality front between the carbon benefit and net present value. We observed changes in net present value for different levels of carbon benefit as well as changes in optimal management strategies. We predicted future changes in several forest carbon pools as well as albedo and illustrated the impact of gradual increase in forest productivity (i.e., due to climate warming). Having been updated and modernized from its previous version with increased attention to forest carbon and energy fluxes, GAYA 2.0 is an effective tool that offers multiple opportunities to perform various types of scenario analyses in forest management.

• Strîmbu, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, NO-1432 Ås, Norway https://orcid.org/0000-0002-0588-2036 E-mail: victor.strimbu@nmbu.no
• Eid, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, NO-1432 Ås, Norway E-mail: tron.eid@nmbu.no
• Gobakken, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, NO-1432 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no

• Bettinger, School of Forestry and Natural Resources, 180 E. Green Street, University of Georgia, Athens, Georgia, USA 30602 E-mail: pbettinger@warnell.uga.edu
• Demirci, General Directorate of Forestry, Ministry of Forest and Water Affairs, Republic of Turkey E-mail: mehmetdemirci@yahoo.com
• Boston, Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, USA E-mail: Kevin.Boston@oregonstate.edu

• Zamora-Cristales, Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, 280 Peavy Hall, Corvallis, OR 97331, USA E-mail: rene.zamora@oregonstate.edu
• Boston, Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, 280 Peavy Hall, Corvallis, OR 97331, USA E-mail: kevin.boston@oregonstate.edu
• Sessions, Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, 280 Peavy Hall, Corvallis, OR 97331, USA E-mail: john.sessions@oregonstate.edu
• Murphy, Waiariki Institute of Technology, Rotorua, New Zealand E-mail: glen.murphy@waiariki.ac.nz

• Nordström, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: eva-maria.nordstrom@slu.se
• Holmström, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: hampus.holmstrom@slu.se
• Öhman, Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: karin.ohman@slu.se

• Nilsson, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-901 83 Umeå, Sweden E-mail: malin.nilsson@slu.se
• Staal Westerlund, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-901 83 Umeå, Sweden E-mail: dianne.wasterlund@slu.se
• Wahlberg, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-901 83 Umeå, Sweden E-mail: ow@nn.se
• Eriksson, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-901 83 Umeå, Sweden E-mail: ljusk.ola.eriksson@slu.se

• Hiltunen, Metsähallitus, Viestitie 2, FI-87700 Kajaani, Finland E-mail: veikko.hiltunen@metsa.fi
• Kurttila, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: mikko.kurttila@metla.fi
• Pykäläinen, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: jouni.pykalainen@metla.fi

• Kangas, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
• Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
• Mäkinen, Simosol Oy, Riihimäki, Finland E-mail: am@nn.fi
• Vanhatalo, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: kv@nn.fi

• Hokajärvi, Oulu University of Applied Sciences, School of Renewable Natural Resources, Metsäkouluntie 4–6, FI-90650 Oulu, Finland E-mail: raili.hokajarvi@oamk.fi
• Hujala, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: th@nn.fi
• Leskinen, University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lal@nn.fi
• Tikkanen, Oulu University of Applied Sciences, School of Renewable Natural Resources, Metsäkouluntie 4–6, FI-90650 Oulu, Finland E-mail: jt@nn.fi

• Kurttila, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mikko.kurttila@joensuu.fi
• Leskinen, Finnish Environment Institute, Research Programme for Production and Consumption, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pl@nn.fi
• Pykäläinen, Metsämonex Ltd., Joensuu, Finland E-mail: jp@nn.fi
• Ruuskanen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tr@nn.fi

• Zhu, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA E-mail: jz@nn.us
• Bettinger, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA E-mail: pbettinger@warnell.uga.edu
• Li, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA E-mail: rl@nn.us

• Eid, Agricultural University of Norway, Dept of Ecology and Natural Resource Management, P.O. Box 5003 NO-1432 Ås, Norway E-mail: tron.eid@ina.nlh.no

• Bettinger, Department of Forest Resources, Oregon State University, Corvallis, OR 97331 E-mail: pete.bettinger@orst.edu
• Graetz, Department of Forest Resources, Oregon State University, Corvallis, OR 97331 E-mail: dgw@nn.us
• Boston, Carter Holt Harvey Forest Fibre Solutions, Tokoroa, New Zealand E-mail: kb@nn.nz
• Sessions, Department of Forest Engineering, Oregon State University, Corvallis, OR 97331 E-mail: js@nn.us
• Chung, Department of Forest Engineering, Oregon State University, Corvallis, OR 97331 E-mail: wc@nn.us

• Boston, Forest Fibre Solutions, Carter Holt Harvey, Tokoroa, New Zealand E-mail: kevin.boston@chh.co.nz
• Bettinger, Department of Forest Resources, Oregon State University, Corvallis, OR 97331 E-mail: pb@nn.us

• Leskinen, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: pekka.leskinen@metla.fi
• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: annika.kangas@metla.fi
• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

• Leskinen, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: pekka.leskinen@metla.fi
• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

• Kangas, Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jyrki.kangas@metla.fi
• Store, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: rs@nn.fi