**1. Introduction**

With rising demand for timber and the ecological and social services provided by forests, as well as sustainability requirements, there is an increasing need for reliable forecasts concern‐ ing the likely impacts of alternative silvicultural treatment strategies on forest growth [1].

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To produce such long‐term forecasts at forest enterprise level, a computer simulation model for forest growth is essential. In this context, a growth simulator means a computer program used to predict and simulate silvicultural scenarios, consisting of one or more growth models that biometrically and mathematically reproduce the biological growth process [2].

Simulators increasingly play a key role in decision support for forest management, particularly in the context of climate change and the associated adaptation strategies. They provide information on the likely outcomes of different forest‐management strategies, allowing users to select the most suitable strategy for achieving their management goals [3].

To meet the broad range of demands associated with forest management, simulators ideally need to be able to support both economic and ecological decisions by simulating management strategies and disturbances and their ecological and economic impacts on different tree species and stand types at different sites. No such 'comprehensive' simulator is currently available in Switzerland.

**Figure 1** shows the basic elements of forest growth modelling. A tree's increment depends on its initial state, site factors and stand structure. The stand structure has a crucial impact on competition for water, nutrients and light. It is shaped by anthropogenic interventions, natural mortality processes and tree growth. The increment of an individual tree is described by diameter and height growth functions, which depend on competition, site factors and the tree's initial dimensions.

**Figure 1.** Modelling forest stand dynamics [4].

Stand structure is influenced by anthropogenic thinning and final harvest as well as natural mortality processes. These are modelled by appropriate algorithms. Ingrowth or regeneration models are also required to generate new trees in the system. If one also wishes to produce reliable forecasts of tree‐volume (stemwood) and financial‐yield development, accurate stem‐ form and assortment models are essential, as well as specific models for calculating timber‐ harvesting expenses. The respective state variables of trees can be used to derive other secondary variables such as the biomass of branches, bark, needles and leaves as well as their nutrient content, in order to estimate the nutrient removal that would result from whole tree harvesting. Other types of ecosystem services such as carbon sequestration, biodiversity and the windthrow risk of trees can also be modelled from the tree state variables.

To produce such long‐term forecasts at forest enterprise level, a computer simulation model for forest growth is essential. In this context, a growth simulator means a computer program used to predict and simulate silvicultural scenarios, consisting of one or more growth models

Simulators increasingly play a key role in decision support for forest management, particularly in the context of climate change and the associated adaptation strategies. They provide information on the likely outcomes of different forest‐management strategies, allowing users

To meet the broad range of demands associated with forest management, simulators ideally need to be able to support both economic and ecological decisions by simulating management strategies and disturbances and their ecological and economic impacts on different tree species and stand types at different sites. No such 'comprehensive' simulator is currently available in

**Figure 1** shows the basic elements of forest growth modelling. A tree's increment depends on its initial state, site factors and stand structure. The stand structure has a crucial impact on competition for water, nutrients and light. It is shaped by anthropogenic interventions, natural mortality processes and tree growth. The increment of an individual tree is described by diameter and height growth functions, which depend on competition, site factors and the tree's

Stand structure is influenced by anthropogenic thinning and final harvest as well as natural mortality processes. These are modelled by appropriate algorithms. Ingrowth or regeneration models are also required to generate new trees in the system. If one also wishes to produce reliable forecasts of tree‐volume (stemwood) and financial‐yield development, accurate stem‐ form and assortment models are essential, as well as specific models for calculating timber‐

that biometrically and mathematically reproduce the biological growth process [2].

to select the most suitable strategy for achieving their management goals [3].

220 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

Switzerland.

initial dimensions.

**Figure 1.** Modelling forest stand dynamics [4].

There exists a whole series of simulators with different model functions, spatial resolutions and regional calibration specifications, developed for a wide variety of applications. This makes it difficult to evaluate and select a suitable simulator for one's own purposes.

Different methods exist, which can support the evaluation of software packages. One of the most widely used methods is the analytic hierarchy process (AHP) (e.g. [5–8]). Until now AHP was used only once in forestry for the evaluation of an IT system [9]. Thereby, an IT system which supports the business processes in forest enterprises was evaluated. AHP was not used so far for the assessment and selection of a growth simulator for forest management.

The goal of the project presented here is to evaluate a forest growth simulator that could be adapted to conditions in Switzerland. The simulator needs to not only support stand‐level decision processes within a forest enterprise but also be suitable for use in scientific contexts. It should be able to map climate changes or allow climate sensitivity to be integrated at a later stage [10].

**Figure 2.** Procedural method of the project (phase 3 is not described by this article).

The process for the evaluation of a suitable growth simulator is divided into three phases (**Figure 2**). The first phase involves a literature review in which 14 potentially suitable simulators are identified. In a preselection phase, the 14 forest growth simulators are narrowed down to a few. In the second phase, an AHP model is created for the systematic comparison and assessment of the remaining simulators [11, 12]. Using AHP, the four simulators are evaluated in greater depth in terms of their functionality and software structure. Based on the AHP results, a suitable forest growth simulator is selected. In the third phase, the chosen simulator is reparametrised using real data from Switzerland and the simulation results are checked for validity. The simulator is also intended to be incorporated into a decision support system (DSS), used to assist forest planning and management processes. However, phase three is not described in this article.
