Speaker
Description
Selecting a timber harvesting system is a context-dependent decision that requires substantial expertise to balance economic performance, environmental impacts, operator safety, and technical feasibility. In practice, system choice is influenced by stand and site conditions (e.g., slope, extraction distances, soil conditions), machine characteristics and operational constraints, and trade-offs between sustainability dimensions are inevitable. This work presents the concept and architecture of a decision support system (DSS) that supports harvesting planners in selecting the most sustainable harvesting system – i.e., machine combinations for felling/processing and extraction – for a given stand.
The DSS follows a multi-criteria decision analysis (MCDA) framework. Sustainability is represented by nine core criteria operationalized through 15 indicators covering the three pillars (economy, environment, social/work safety). The workflow comprises seven steps: (1) User Input: scenario definition (stand, site, operation) and criteria weighting, (2) configuration of potential machine chains, (3) feasibility screening, (4) calculation and harmonization of indicator values using an evidence-based data layer, (5) transformation of indicators into normalized utilities, (6) weighted aggregation into overall scores and rankings, and (7) reporting of machine chain ranks and trade-off visualizations.
The MCDA framework is underpinned by a data layer that provides reference values and parameter-dependent ranges for indicator estimation, while feasibility screening uses these operating envelopes to constrain system recommendations under the specified conditions. Using this foundation, a combination of data-driven and process-based models is employed to derive indicator values from user-defined stand and operational inputs. These indicators form the basis for multi-criteria evaluation and optimization, allowing machine chain alternatives to be assessed and ranked according to trade-offs between economic, environmental, and social objectives. The relative importance of these objectives can be adjusted through user-defined criteria weightings, allowing the system to reflect different stakeholder priorities and decision contexts. The overall aim is to generate context-specific machine chain recommendations that balance sustainability dimensions in timber harvesting operations.
Our contribution highlights the modular system design (data layer, rule layer, formal model and user interface) and discusses open research needs, in particular systematic evidence expansion, harmonization standards, and the derivation of a minimal dataset to guide future field data collection and model calibration.
| Keywords | DSS; Sustainability_Indicators; Harvesting_Systems; Criteria_Weighting |
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