**6. Conclusion**

Criteria weights Utility value and Simulator position

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

The last row contains the sum of the position points.

**Table 3.** Results of the sensitivity analysis.

planning and management processes.

**5. Future steps**

[37].

i) C1–C8 =0.125 0.269 1 0.262 2 0.224 4 0.246 3 ii) C1 Model approach =0.5 0.207 4 0.289 1 0.267 2 0.237 3 C2 Range of application =0.5 0.261 1 0.257 2 0.235 4 0.248 3 C3 Calibration specification =0.5 0.318 1 0.226 3 0.204 4 0.252 2 C4 Simulator input =0.5 0.272 1 0.268 2 0.246 3 0.213 4 C5 Sub‐models for growth =0.5 0.255 2 0.252 3 0.202 4 0.291 1 C6 Simulator output =0.5 0.264 2 0.273 1 0.237 3 0.226 4 C7 Environmental influence =0.5 0.190 3 0.311 2 0.181 4 0.318 1 C8 Software =0.5 0.384 1 0.218 2 0.217 3 0.181 4 Sum of position points (lowest = best) 16 18 31 25 Note: Calculated utility values if the normalised criteria weights are changed, (i) same weighting of each criteria (0.125), (ii) with one criterion in each case being considered as very important (0.500) and the rest as equally 'unimportant' (0.071).

The next phase involves parametrising the chosen simulator using data from Switzerland and then checking its validity, that is, how accurately it reflects reality [36]. During the parametr‐ isation process, the simulator's key sub‐models, such as diameter and height increment, mortality and ingrowth, will be described climate‐sensitively and optimally adapted to the available data using appropriate functions. Consequently, it only makes sense to validate the simulator results once the reparametrisation has taken place. Furthermore, the individual‐tree simulators in question consist of multiple sub‐models. Pre‐validating the sub‐models would

At a later stage, the chosen simulator will be integrated into an overarching DSS covering forest

DSSs are crucial because the management of a forest is a challenging task as all the major ecosystem services, such as timber production, recreation, biodiversity conservation and carbon storage, occur side by side on a small scale and are highly demanded by society. Wrong management decisions can have huge consequences, ranging from a loss of ecological and social values up to a loss of economic income. This situation underlines the urgent need for appropriate concepts and tools for decision support that consider all ecosystemservices and can help forest managers to find an optimal management strategy to fulfil the diverse societal and political demanded services [3]. Climate change additionally exacerbates the problems

be highly resource intensive and not justified for the purposes at hand.

BWINPro SILVA MOSES PrognAus

AHP analysis provides a structured evaluation process to support simulator comparison. Using the pairwise comparison matrix, AHP‐enabled quantitative ratios were obtained based on the individual qualitative assessments. This made comparison of the simulators much easier by providing a transparent and traceable basis for:


The comparison criteria were established based on the recommendations concerning the uniform and standardised description of forest growth simulators [35]. On the one hand, these enabled the requirements for the chosen simulator to be defined. On the other hand, they provided a framework for the decisions based on which the individual simulators could be thoroughly scrutinised, particularly in terms of their model and software structure and functionalities and which enabled a critical argumentation and assessment. Particularly when comparing such complex instruments, AHP provides a sound basis for ensuring that all key factors are taken simultaneously into account in the decision. Thanks to quantitative repre‐ sentations, the results are rendered transparent and comparable and 'gut decisions' are significantly reduced.

The main weakness of the AHP method is that it becomes very complicated when a large number of criteria are involved. In such cases, evaluation of all the pairwise comparisons is much more time consuming than when the number of pairwise comparisons is kept to a strict minimum. Furthermore, a comprehensive assessment often results in 'unnecessary evalua‐ tions' and this overdetermination can lead to inconsistencies [9].

In general, AHP has proved well suited in evaluating a forest growth simulator. However, it should be noted that AHP itself only provides a subjective basis for decision‐making, although the margin of subjectivity can be controlled by means of a sensitivity analysis. This shows whether the result would be significantly different if the criteria weightings were slightly changed. In this case, the ranking of the alternatives proved relatively robust, even with the sensitivity analysis. However, the sensitivity analysis also revealed that, with different criteria weights, SILVA and PrognAus receive higher utility values than BWINPro in some cases. In addition to the sensitivity analysis, therefore, several different decision makers could poten‐ tially be involved in performing the AHP analysis. This could generate alternative results, enabling a 'final' end comparison.

The AHP model presented here found BWINPro to be a suitable simulator for the defined objectives. The main reasons for this choice were BWINPro's free availability as open‐source software and its modular structure in the Java programming language. On the one hand, this enables the parametrisation of sub‐models for Swiss conditions or the replacement of indi‐ vidual components with components developed in‐house. In addition, the transparent model structure provides the basis for further development into a climate‐sensitive version. The model also offers a broad range of treatment variants commonly found in forestry practice, including those aimed at nature conservation and promoting biodiversity.

Using AHP, a simulator was evaluated which will go on to be parametrised for Swiss condi‐ tions with data from long‐term forest experimental plots and the National Forest Inventory. Furthermore, the detailed analysis of the different functionalities of the four simulators found that the simulator in question lends itself particularly well to implementation in a DSS. This DSS could support forest planning and management processes in the future.
