Forest Ecology and Management 248 (2007) 107–118 www.elsevier.com/locate/foreco
Addressing collaborative planning methods and tools in forest management Helena Martins *, Jose´ G. Borges Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade Te´cnica de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal Accepted 8 February 2007
Abstract Addressing forest sustainability requires negotiation and integration of individual forest management plans of multiple small non-industrial forest owners (NIPF). Recently, Portuguese forest policy prescribed the creation of Areas for Forest Intervention (AFI/ZIF)—forest areas encompassing at least 1 103 ha and 50 NIPF—to address those requirements. Yet, the development of forest management plans for AFI is targeting multiple objectives in the framework of multiple-ownership. This is not trivial as conflict is prone to arise and negotiation is needed to satisfy individual and collective goals and constraints. This paper is prompted by the need to identify methods and tools that may be used to support forest management planning in the framework of an AFI. Emphasis is on the need of specific tools and methods that can support AFI management planning, in order to mitigate conflicts and achieve a consensual plan. This paper thus presents a review of methods and tools used to support group decision-making in forest management planning. It further discusses the potential of hybrid approaches for collaborative planning that may take advantage of the integrated functionality of both quantitative and qualitative decision support methods and tools. Published by Elsevier B.V. Keywords: Forest management; Multiple decision-makers; NIPF; Collaborative planning; Methods and tools
1. Introduction The multiple-owner integrated planning problem emerges when several holdings, each controlled by different decision makers, are bound together by economic, ecological and social goals and constraints (Davis et al., 2001). This is often the case in regions and countries where private forestry is prevalent. Namely, it is the case of Areas for Forest Intervention (AFI) – land management units that must encompass at least 1 103 ha and 50 NIPF according to recent Portuguese forest policy. Forest management decisions are still typically implemented at the stand or holding levels and yet ecosystem sustainability depends on the spatial and temporal interactions of management scheduling at a larger scale. Current wildfire prevention goals further call for the integration of multiple small nonindustrial forest owners (NIPF) forest management plans and thus prompted the creation of AFI. Nevertheless, moving from a single decision maker to a multiple decision maker framework increases the complexity of
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[email protected] (H. Martins). 0378-1127/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.foreco.2007.02.039
the forest management process (Hwang and Lin, 1987). Rather than just selecting the best management alternative according to a single decision maker’s preference structure, targeting multiple objectives in the framework of multiple-ownership encompasses analysis and negotiation to satisfy both individual and collective goals and constraints. This is not trivial as conflict is prone to arise. The solution of such complex management problems has been the scope of collaborative planning, which is based on the development of approaches to support group decision-making (e.g. Hwang and Lin, 1987; Malczewski, 1999; Laukkanen et al., 2002). Collaborative planning will be interpreted in this paper as a special case of participatory planning when all participants involved share decision-making power and are directly affected by management options. These participants have been referred in the literature as active stakeholders (Grimble and Wellard, 1997). In the particular case of an AFI, these stakeholders are the NIPF and those who are directly affected by their management decisions such as livestock producers and hunters. Nevertheless, the distinction between active and passive stakeholders, i.e., between those who determine and those who are solely affected by decisions, is not absolute (Grimble and Wellard, 1997). For example, the solution of a multiple-owner integrated planning
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problem in an AFI comprehending biodiversity hotspots may have to take into account conservation-related amenity rights of other mostly passive stakeholders. Effective collaborative planning encompasses the involvement of stakeholders in all planning steps. It may thus stimulate thinking and exploration of management scenarios by stakeholders, provide a deeper understanding of the management problem and improve the ability to eliminate biases and oversights (Coughlan and Armour, 1992). Nevertheless, this effectiveness depends upon specific methods and tools to promote collaboration and communication during the planning process, in order to mitigate conflicts and to reach a consensual plan. This paper presents a review of methods and tools that can be used to support each step of the group decision-making process in the framework of multi-criteria forest management planning in areas comprehending multiple NIPF. It further discusses the potential of hybrid approaches for collaborative planning that may take advantage of the integrated functionality of both quantitative and qualitative decision support methods and tools.
Secondly, since decision-making is a shared process, the methods and tools have to deal with conflict between individual and group goals and constraints. If there were no conflicts, there would be no need for collaborative planning and forest management planning could evolve as if there was a single decision maker. Conflict management is required to aggregate multiple preference structures and integrate individual NIPF perspectives. The Arrow’s theorem states that there is no method of aggregating individual preferences over three or more alternatives that would satisfy several conditions for fairness and always produce a logical result (Laukkanen et al., 2002). Aggregation may, therefore, lead to a compromise that is uncomfortable and unstable. However, in a collaborative process, stakeholders’ preferences need to be combined. Both quantitative and qualitative approaches have been developed to facilitate and support conflict management and consensus building. Thirdly, issues of transparency and user friendliness are of particular importance in order to ensure that stakeholders perfectly understand the process that leads to a management plan. This is a critical aspect of their engagement in the planning process (Kangas et al., 1996; Sheppard and Meitner, 2005).
2. Methods and tools to support collaborative planning 2.1. Problem identification Specific methods and tools are required to support the planning process within a collaborative framework at an AFI (Table 1). In this paper, a simplified three-step planning process is suggested that takes into account former classifications (e.g. Kangas et al., 1996; Davis et al., 2001; Reichert et al., 2007; Sheppard and Meitner, 2005). Problem identification involves the acquisition and analysis of information to understand and to define the AFI management problem. Problem modelling involves model building to represent both the relations between management alternatives and outcomes of interest and the management policy scenarios. Problem solving involves the design of the AFI forest management plan. The planning process involves feedback loops between steps. For example, problem solving may underline the need for more information and/or other policy scenarios. Moreover, the same methods might be used at different stages with different purposes. This is the case of methods that allow preferences structuring (e.g. both at the modelling – objectives weighting – and the solving – alternatives prioritization – steps). Participatory methods used in problem identification can also be useful to guide stakeholders through the modelling and the solving steps. They can help NIPF represent their knowledge and expectations and they can support decision analysis. The methods and tools presented herein have been either developed or adapted to attend to the specificities of the collaborative planning process. Firstly, since collaborative planning implies the direct involvement of stakeholders in decision-making, these methods and tools encompass a collective rather than an individual process of data and information acquisition and analysis.
The understanding and the definition of the AFI forest management planning problem depends on those involved in and affected by management options. Therefore, knowing who should be involved is the first step of problem identification. In order to be eligible for funding, an AFI must encompass at least 1 103 ha and 50 NIPF. This should be taken into account when designing outreach efforts that may bring in NIPF together in order to encourage the development of an AFI joint management plan. The entity conducting the outreach effort (e.g. NIPF Association, Forest Services) must decide who should be involved in order to meet representation requirements. Moreover, the methods used should promote a transparent and comprehensive selection process (Buchy and Hoverman, 2000). The identification of stakeholders and their representation can be undertaken with an informal procedure based on criteria such as property rights, history with planning processes, reputation, influence and importance (e.g. Grimble and Chan, 1995; Harrison and Qureshi, 2000; Sheppard and Meitner, 2005). Grimble and Wellard (1997) and Colfer et al. (1999) mentioned formal methods based on matrices that represent the influence and the importance of different stakeholders as perceived by others. Harrison and Qureshi (2000) further referred to a procedure based on interactive identification, where previously unknown stakeholders reveal others. In the framework of the large-scale multiple-owner integrated planning problem, stakeholder identification should evolve to bring together as many NIPF as possible to ensure their adequate representation along the planning process. This is key to ensure the fairness and credibility of the decision process (Sheppard and Meitner, 2005). Other stakeholders should be taken into account at this stage as they may be directly or indirectly affected by forest management at the AFI.
Table 1 Methods and tools that can be used to support a collaborative planning process Steps of the planning process
Tasks
Methodological approaches
Problem identification (acquisition and analysis of information to understand the AIF management problem encompassing multiple NIPF)
To identify the relevant stakeholders
Informal methods based on subjective selection of stakeholders
Examples of methods
Auxiliary tools
Examples of tools
Sources of information
Representativity
Grimble and Chan (1995), Grimble and Wellard (1997)
Fairness, transparency, simplicity and representativity
Colfer et al. (1999), Harrison and Qureshi (2000), Sheppard and Meitner (2005)
Interviews, questionnaires, Delphi method
Undertaken individually
Brainstorming, workshop, nominal group technique
Undertaken collectively
Hwang and Lin (1987), Coughlan and Armour (1992) McDaniels and Roessler (1998), Laukkanen et al. (2002) CIFOR, Pukkala (2004), Kyem (2002), Thomson (2000), Belton and Stewart (2002), Cai (2005)
Formal methods to represent the importance of stakeholders as perceived by others
To identify goals and objectives, management alternatives, forest policies, resources, conflicts and interactions
Participatory methods
To increase stakeholders’ perception of the collaborative planning process. To document the planning process Problem modeling (building a model that represents the AFI management problem)
To model quantitatively the impact of management alternatives on the objectives
To model qualitatively the relation between management alternatives and their impact on the objectives
Computational tools to support problem definition in a structured way
Operations Research techniques (objective functions)
Linear programming, goal programming, heuristics
Multi-criteria methods
AHP, Multi-attribute utility functions
Soft-OR approaches
Cognitive mapping, qualitative systems dynamics
Automated prescription writers
Computational tools to guide problem modelling
To weight objectives according to individual preferences
The Bridge, Monsu GIS Internet
SODA
Computational resources. Availability of quantitative information to support predictions of forest management outcomes
Romero and Rehman (1987), Pukkala (2002), Borges et al. (2002), de Steiguer et al. (2003) Malczewski et al. (1997), Ananda and Herath (2005)
Adequate facilitation
Wolstenholme ¨ zesmi (1999), O ¨ zesmi and O (2004), Purnomo et al. (2004) Eden (1988), HjortsØ (2004) Reynolds et al. (2000) Bousquet and Le Page (2004), Purnomo et al. (2005)
Fuzzy approaches
Logic models
Computational resources. Adequate facilitation Computational resources
AI-based approaches
MAS
Computational resources
Multi-criteria methods for estimating weights according to preferences
Swing weights, direct rating and pairwise comparisons (especially AHP)
Expert Choice
Reduced number of objectives. Preferences easily converted into scores
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Malczewski (1999), Mendoza and Prabhu (2000), Ananda and Herath (2003) and Herath (2004)
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Requisites of application
‘‘Who counts’’ matrix
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Table 1 (Continued ) Steps of the planning process
Methodological approaches
Examples of methods
Requisites of application
Sources of information
To weight objectives according to a common preferences structure
Participatory methods together with multi-criteria methods
Interviews, questionnaires, Delphi method
Auxiliary tools
Examples of tools
Undertaken individually
Brainstorming, workshop, nominal group technique
Undertaken collectively. Adequate facilitation
Hwang and Lin (1987), Coughlan and Armour (1992) McDaniels and Roessler (1998), Laukkanen et al. (2002) Malczewski (1999), Mendoza and Prabhu (2000), Ananda and Herath (2003) and Herath (2004) Martin et al. (1996), Kangas et al. (2006)
Geometric and arithmetic mean
Deterministic methods for assessing consistency of preference structure
Social choice theory
Voting models
Minimization of a consistency ratio
The CR of AHP
Kangas (1992)
Compromise programming Fuzzy sets Discussion for consensus building Stochastic methods for assessing consistency of preference structure Problem solving (choice of a AFI forest management plan)
To prioritize management alternatives
Adequate facilitation Further expansions of AHP
Operations Research techniques
Linear and goal programming, heuristics
Multi-criteria methods
AHP, ranking, rating
Outranking methods Multiattribute value elicitation Logic models
DSS with visualization interfaces
Computational resources
Only a reduced number of alternative plans can analysised
KBS MAS
Participatory methods
SADflOR, DTRAN, MONSU, MONTE
Power-to-Change EMDS, CORMAS
Phua and Minowa (2005) Bantayan and Bishop (1998) Belton and Stewart (2002) Alho et al. (2001), MacKay et al. (1996), Phua and Minowa (2005) Rose et al. (1992), Kangas et al. (1996), Davis et al. (2001), Borges et al. (2003), Kurttila and Pukkala (2003), Jumppanen et al. (2003), Romero and Rehman (1987), Schmoldt et al. (2001), Pukkala (2002) Kangas et al. (2001) McDaniels and Roessler (1998) Mendoza and Prabhu (2002), Reynolds (2001), Bousquet et al. (1998), Ligtenberg et al. (2004)
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This is the case of hunters and livestock producers from local communities. Once having the stakeholders identified, the aim of this planning step is to obtain information regarding the identification and justification of the various management goals, objectives and constraints that guide NIPF in their individual management decisions and that characterize their assumptions about the way the AFI forest area should be managed. Grimble and Chan (1995) list a few ‘‘key questions for local stakeholders in background research on use and management of natural resources’’ that can help identify the information that should be gathered at this planning step: – What direct goods and services do they extract from the resource? – What indirect (including environmental) goods and services do they provide? – What restrictions do they face over the use of the resource? – What de jure and de facto rights or claims to they have over using and managing the resource? – What are the forms and degree of management of the resource in question? – What are the stakeholder’s views on other stakeholders’ use of the resources, and how he or she interacts with other stakeholders over the use and management of that resource? – What trade offs are stakeholders making and what decisionmaking criteria are they using when they choose a particular management or resource-use strategy? – What are the actual and perceived costs and benefits to stakeholders of following their chosen behaviour or actions? – Do they perceive any external costs and benefits of their actions and decisions and, if so, are these considered in their decision-making? – What stakeholders see as their decision-making environment? What factors they perceive as lying within their control and what lie outside it? An essential aspect of the analysis of this information is to recognize patterns and contexts of interactions among forest owners and other relevant stakeholders. Based on this information it is possible to discover sources of conflict and examine ways to address them so that the multiple-owner integrated planning problem is amenable to a consensual solution. Examples of common conflicts that arise at an AFI are the use of forest parcels by cattle that might destroy natural regeneration and the access to private areas by hunters. Obtaining this information from stakeholders will be instrumental for the next step, the construction of a management model. The latter will be substantially easier if the information obtained is somehow structured. This requires specific participatory tools and methods. In order to be effective, these tools and methods should support and motivate constructive and creative ways for stakeholders to provide information. They should guide stakeholders for enhanced perception of the AFI integrated planning problem. Moreover, they should help to document the planning process making it easier to track later the rationale for management decisions.
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Hwang and Lin (1987) and Coughlan and Armour (1992) discussed a broad range of participatory methods that can potentially be useful to structure the information as provided by NIPF at this stage of the planning process. Some methods target individuals (e.g. interviews, questionnaires, Delphi method) while others require interaction among stakeholders (e.g. brainstorming, workshops). The latter have advantages resulting from an open discussion, namely they contribute to consistency (e.g. McDaniels and Roessler, 1998; Laukkanen et al., 2002). Yet, when costs and time constrain participation, the former may be a good option since it is easier to undertake. In spite of guidance provided by participatory methods, obtaining adequate information for problem identification is a process that requires cognitive effort from stakeholders. If the AFI integrated planning problem involves a large number of participants, methods with low information requirements may be more adequate. However, the acknowledgment of the value of information has motivated further development of approaches to alleviate the cognitive burden and to render information acquisition a more spontaneous and flexible process (e.g. Ananda and Herath, 2003; Laukkanen et al., 2004; Kangas et al., 2006). They complement rather than replace face-to-face meetings and human interaction (Laukkanen et al., 2004). The goal is to enhance capabilities to explore different aspects of the AFI forest management problem and generate further information. This is the case, for example, with methods and models automated in The Bridge, a computerbased tool developed by the Centre for International Forestry Research (CIFOR1) and in Monsu (Pukkala, 2004). The former is a knowledge-based visioning tool that helps stakeholders to express their goals and objectives. The output is a structured vision of the problem that aims to facilitate the devising of new management strategies. The latter is a decision support system (DSS) that facilitates interactive identification of goals. User-friendly computational interfaces and visualization tools may thus help facilitate meetings and collaborative work sessions in order to promote communication, exchange of information, awareness, understanding and trustworthiness. Both DSS and knowledge-based systems (KBS) may thus provide capabilities for enhanced problem definition. Modules of DSS such as Geographic Information Systems (GIS) have also been applied. For example, Kyem (2002) used GIS to settle a dispute over allocation of forest resources. GIS provided a consistent approach to data processing and user-friendly display. Internet based development tools may also be instrumental for successful production of information as they enable dispersed and asynchronous working (e.g. Thomson, 2000; Belton and Stewart, 2002; Cai, 2005). 2.2. Problem modelling Quantitative approaches for problem modelling have been used in forestry when a considerable amount of quantitative information is available. Conversely, qualitative approaches 1
http://www.cifor.cgiar.org/docs/_ref/research_tools/index.htm.
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have been used when most information is subjective, unstructured and scarce. In both cases, the information acquired in the previous step is instrumental to guide the NIPF through the modelling process. Models should be perfectly understood and validated by NIPF. Methods and tools used to acquire information in the framework of problem identification can also be useful for problem modelling. In the case of the latter they are used in order to further clarify both the relations between management alternatives and outcomes of interest and the management policy scenarios. Problem modelling helps NIPF review and further structure information and assumptions produced in problem identification. Moreover, it may also identify additional information needs. Collaborative planning is thus an adaptive process that calls for feedback loops between problem identification and modelling. Quantitative approaches for modelling the AFI management problem may include linear and goal programming, heuristics with penalty functions and multi-attribute utility functions, which have been proved suitable for integrating multiple and conflicting objectives (e.g. Pukkala, 2002; de Steiguer et al., 2003). The models developed with these techniques express the impact of management alternatives on management objectives. The magnitude of the impact is expressed by coefficients that ponder the decision variables. In order to be able to calculate these coefficients, it is necessary to have data that characterize the current situation (e.g. forest inventory data) and reliable estimates of outcomes and conditions that may result from a management option (e.g. growth and yield models). Quantitative approaches may help address the large-scale characteristics of many NIPF integrated planning problems. Yet, in this case, automation of processes such as prescription writing is needed to generate large resource capability and policy models (e.g. Rose et al., 1992; Falca˜o and Borges, 2005). Multi-criteria methods have been proved useful in providing a framework for structuring different aspects of the forest management problem (e.g. Ananda and Herath, 2005; Malczewski et al., 1997). Readers are referred to Pukkala (2002) and de Steiguer et al. (2003) for an overview of multiple criteria methods and a detailed description of their characteristics. The representation of the NIPF preference structure complicates problem modelling. It is not trivial to assign weights to management objectives. Participatory methods may be used to facilitate the elicitation of NIPF individual preferences. These preferences are converted into scaling constants using simple multi-criteria methods techniques such as swing weights, direct rating, and pairwise comparisons (namely using the Analytic Hierarchy Process – AHP). This weight elicitation may be further facilitated by graphical software such as Expert Choice2. The individual weights should then be aggregated into a single weight. In spite of Arrow’s theorem, a suitable method of aggregating multiple preferences can probably be found for most problems (Kangas et al., 2006). Namely, the group weights may be estimated by either
2
http://www.lionhrtpub.com/orms/orms-8-96/software.html.
geometric or arithmetic mean (e.g. Malczewski, 1999; Mendoza and Prabhu, 2000; Ananda and Herath, 2003; Herath, 2004). Voting models of social choice theory may also be used to aggregate individual preferences (e.g. Martin et al., 1996; Kangas et al., 2006). Conflict resolution is further problematical as most methods rely on two assumptions for aggregating preferences, namely that individual preferences are independent and that there is consistency among them. For example, actions that enhance the welfare of one group may be detrimental to the welfare of some other group. This would violate the assumption of additive separability that is a requisite to most aggregation schemes (Martin et al., 1996). Testing the independence assumption requires complex and thorough checks that are difficult to conduct in real-world management problems (e.g. Martin et al., 1996; Ananda and Herath, 2003). To our knowledge, in most studies independence is just assumed. Consistency is complicated when stakeholders are classified into groups for representation purposes. It implies that intra-group preferences are homothetic, or quasi homothetic (Martin et al., 1996). Nevertheless, Wang and Archer (1994) pointed out that there is uncertainty about the overall preferences of the group because individuals are likely to have different preference structures. Presenting this uncertainty in a useful form to decision makers is thus a key issue to improve the effectiveness of conflict resolution in the framework of the AFI integrated planning problem. The literature reports methods to address consistency considerations. For example, a critical step of the use of AHP for modelling preference structure is the computation of a consistency ratio (CR) (e.g. Kangas, 1992). A CR 0.10 indicates a reasonable level of consistency among pairwise comparisons. Otherwise, it is indicative of inconsistent judgements and one should reconsider and revise the original values in the pairwise comparison matrix. MacKay et al. (1996) suggested a probabilistic extension of AHP that assumes ratio judgments are probabilistic rather than deterministic to take into account the variance that is likely to exist in group judgements (Easley et al., 2000). Alho et al. (2001) further developed AHP to analyse intra-group preference uncertainty. Phua and Minowa (2005) used compromise programming to measure consistency within the framework of an application of AHP to establish forest conservation priorities. Bantayan and Bishop (1998) further addressed uncertainty using a fuzzy set approach to land use allocation in a forest reserve. The application of quantitative modelling approaches is limited by the amount of quantitative information available. In the case of small private forest holdings in Portugal, information for assessing the impact of management alternatives on objectives is often scarce and subjective (e.g. lack of inventory data and of growth and yield models). Moreover, there are social and cultural aspects that influence management and NIPF expectations that are hard to capture. In these cases qualitative modelling approaches within the framework of SoftOR methods may also be used to model the AFI integrated planning problem.
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Involvement facilitation for preference modelling and problem structuring are at the core of Soft-OR approaches to seek a consensual solution. At the stage of problem model building, Soft-OR methods support the development of structured models that provide a focus and language for discussion (Belton and Stewart, 2002; Rosenhead and Mingers, 2002). For example, the Strategic Option Development and Analysis (SODA) tool emphasizes the understanding and the agreement within the group by a series of interviews, workshops and analysis. It provides a way of identifying and structuring subjective concerns and multiple conflicting objectives. HjortsØ (2004) demonstrated the use of SODA for guiding stakeholders along the planning process thus enhancing public participation within a tactical forest management planning process. SODA includes a module (Decision Explorer) to support cognitive mapping (Eden, 1988), a widely used qualitative modelling technique. Concepts, ideas and their relationships are represented as nodes and arcs in a network. The result is a qualitative and comprehensive problem representation. In the context of collaborative planning, cognitive mapping can also be seen as a structured way for group members to convey concepts and their understanding of the planning problem. Again, it is more effective and efficient to base this task upon the information gathered at the problem identification step. The ¨ zesmi and O ¨ zesmi (2004) for an extensive reader is referred to O review of cognitive mapping and its extensions applied to ecological modelling and environmental management. An alternative approach to cognitive mapping is qualitative systems dynamics. This method provides more explicit relationships between elements (e.g. concept and ideas and the corresponding nodes and arcs) of the decision network. Emphasis is put on explaining causality relationships (Wolstenholme, 1999). Purnomo et al. (2004) have demonstrated its application in collaborative planning of community-managed resources. Qualitative approaches for multiple NIPF forest management problem modelling further encompass innovative and computationally AI-based advanced approaches such as logic models and Multi-Agent Systems (MAS). The former involves fuzzy logic, first suggested by Zadeh (1965) to extend substantially the ability to address imprecise information. Reynolds et al. (2000) report the development and application of logic models to assess watershed conditions. MAS is an approach to reproduce the knowledge and the reasoning of several heterogeneous agents that need to be accommodated in addressing multiple decision makers planning problems. Bousquet and Le Page (2004) presented a review of its applications to ecosystem management. It has also been used by Purnomo et al. (2005) to develop a multi-agent simulation model of a community-managed forest. Strict qualitative approaches to problem modelling address conflict resolution using a relatively informal procedure. Rather than focussing on modelling multiple and conflicting preference structures, the emphasis is solely on facilitation of discussions for consensus building (Belton and Stewart, 2002). No formal ways for aggregating individual preferences are
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considered. Thus, as opposed to quantitative approaches to modelling, applications of qualitative approaches do not objectively analyse consistency among individual preferences. 2.3. Problem solving The focus of this step of the planning process is decision analysis aiming the solution of the management planning problem, i.e., the design of the AFI forest management plan. It encompasses the selection of a combination of management alternatives according to its impact on AFI-wide objectives. Efficient and effective problem model solving is essential to support ‘‘what-if’’ and goal-seeking analysis during the development of an AFI management plan. Scenarios can be many and reliable methods and tools are required to analyse results and outcomes of interest and select a solution and thus help NIPF owners develop the AFI plan. Linear and goal programming are mathematical techniques that increase substantially the ability to analyze alternative multi-objective and multiple-owner scenarios at this step (e.g. Kurttila and Pukkala, 2003). Yet, they are unable to convey the geographical location of forest activities. In this context, the information produced by its solution may be of little value to understand the management problem and to support effectively decision-making (Borges et al., 2002). Spatial recognition is crucial to analyze the multiple-forest owner integrated planning problem as conflict management and consensus building must address management options in each individual forest holding. The computational complexity of these problems suggests the use of heuristic approaches. These techniques are generally more flexible and capable of addressing more complicated objective functions and constraints than exact solving algorithms. Although a sub-optimality cost is incurred, heuristics like exact methods can be very useful as learning devices. They may be used to provide more insight into planning problems and suggest topics for further analysis. The distinction made by Geoffrion (1976) between the mathematical programming ‘‘ostensible purpose’’ – optimization of a particular problem, and its ‘‘true purpose’’ – generation of information to support decision-making is illuminating. Development of heuristics for forest management scheduling thus emerged as workable and appropriate option, particularly when multiple objectives were considered (Borges et al., 2002). The forestry literature reports heuristic’s applications to support collaborative planning and scenario analysis (e.g. Rose et al., 1992; Kangas et al., 1996; Jumppanen et al., 2003; Kurttila and Pukkala, 2003). Nevertheless, specific formulation methods (e.g. Murray and Church, 1996; Snyder and ReVelle, 1997; McDill and Braze, 2000) may also enable integer programming to address the multiple-owner integrated planning problem. Huge gains have been made in the last years in optimization software packages. These gains are a credit to more than just faster computers. Heuristic techniques are utilized directly in optimization packages to help in key aspects of the solution process (Borges et al., 2002). Differences between heuristic and exact approaches are becoming blurred as both have the potential to be used in combination (e.g.
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McDill et al., 2002; Rebain and McDill, 2003). For example, Davis et al. (2001) reported a mixed integer programming application to solve a multiple-owner integrated forest planning problem. Automation is important to successful use of mathematical programming and heuristic techniques for ‘‘what-if’’ and goalseeking analysis of large-scale problems such as AFI management planning. Decision Support Systems (DSS) have been proved as suitable platforms for this automation and are most useful for complex and strategic large-scale planning problems (e.g. Rose et al., 1992; Borges et al., 2003; Pukkala, 2004; Falca˜o and Borges, 2005). For example, the Minnesota’s Generic Environmental Impact Statement (GEIS) on Timber Harvesting and Forest Management (Jaakko Po¨yry Consulting, 1994) demonstrated the potential of quantitative approaches and Decision Support Systems to promote collective multidisciplinary management scenarios development. Quantitative information to support problem model solving is often incomplete. In this situation, simulation, mathematical programming, heuristic techniques and decision support systems may still be used as learning devices. They may help experts to present a set of alternative feasible AFI plans and further support the selection and design of the plan that best meets NIPF individual and joint objectives. Methods for prioritising management alternatives used for problem modelling (e.g. for eliciting, scaling and aggregating preferences regarding objectives’ weights) may also be used to help select the AFI management plan. For that purpose, they may be used to convert preferences into grades so that management alternatives can be prioritised (e.g. Romero and Rehman, 1987; Schmoldt et al., 2001; Pukkala, 2002), These methods assume that NIPF have a well-defined preference structure, yet when they are faced with less familiar goals (e.g. levels of wildfire protection or social sustainability criteria) some guidance may be needed for searching consistent preferences. In these cases, other approaches such as outranking methods (e.g. Kangas et al., 2001) and multi-attribute value elicitation (e.g. McDaniels and Roessler, 1998) may be useful. The former does not require the assignment of scores to management alternatives but only their ranking according to pairwise comparisons in terms of preference. The latter consists of a constructive and collective elicitation approach that can help people with defining and expressing preferences. These approaches to circumventing the lack of data and information limits problem model solving to the comparison of a few alternative plans, as it requires cognitive effort. Qualitative approaches to forest management problem solving have been developed that use projections made by stakeholders based on their experience and expectations. This has been the assumption underlying the development of The Power to Change software, a team game specifically designed to support collaborative planning that can be used to explore various future scenarios within Co-View – Collaborative Vision Exploration Workbench (Mendoza and Prabhu, 2002). The automation of logic models within a knowledge-based system (KBS) (e.g. Reynolds, 2001) may be used also to project and assess AFI alternative management plans efficiently and
effectively. KBS have been developed so far by integrating expert knowledge but they have potential for incorporating also NIPF knowledge. They may be further used to assess outcomes generated by DSS for an AFI plan. Another approach, CORMAS (Common-pool Resources and Multi-Agent Systems) adopts a Multi-Agent Systems (MAS) application specifically designed for renewable resource management (Bousquet et al., 1998). Projections of outcomes of individual management plans are defined with role-playing games and can be visualized with GIS. Ligtenberg et al. (2004) have also explored MAS to simulate spatial scenarios based on modelling multi-actor decision-making within a spatial planning process. Problem solving is an iterative process that may require going back to problem modelling or even problem identification as it may suggest the re-assessment of objectives and preferences by NIPF and it may further provide a deeper understanding of the way the problem should be modelled. User-friendly computer interfaces and visualization tools may facilitate it, what explains that GIS have been widely used to visualize solutions of forest management scenarios (e.g. Malczewski et al., 1997; Jankowski and Nyerges, 2001; Phua and Minowa, 2005). Recent advances in computing resources and graphics hardware that provide the functionality to integrate visual landscape elements within the forest management planning process have further enhanced problem model solving (e.g. Pukkala, 2002). This is the case with more sophisticated 3D visualization techniques (e.g. Pukkala, 2004; Sheppard and Meitner, 2005; Falca˜o et al., 2006). Yet, the representation of forested landscapes within a graphics framework and real-time navigation over these representations are complicated due to the high geometric complexity of such systems. Falca˜o et al. (2006) developed a real-time landscape 3D-visualization tool for very large areas that is able to produce dynamic simulations of prospective management scenarios. Progress made on the development of these visualization tools anticipate an increasing realistic representation and projection of alternative AFI forest management plans for NIPF to assess. Nevertheless, these tools should be used with caution in order to avoid misinterpretation (e.g. Wilson and McGaughey, 2000; Hetema¨ki et al., 2005). 3. Discussion This review has underlined the iterative nature of the three steps of the collaborative planning process. Thus, in spite of the usefulness of the proposed classification of the collaborative planning process to organize methods and tools and to understand their application, it is important to bear in mind that the planning steps are interdependent. Moreover, some methods are useful at more than one step of the planning process for different purposes. These two considerations might be of great usefulness when assessing requirements of a specific AFI forest management planning process. This review has also demonstrated that there is a broad range of methods and tools available to support specific aspects of a forest collaborative planning problem. It suggests the need for an integrated approach to the AFI forest management problem.
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Rosenhead and Mingers (2002) propose a multi-methodology approach to such complex problems. This has been attempted, for example, by Malczewski et al. (1997) who combined the use of Analytic Hierarchy Process (AHP) and integer linear programming. The former was used as a tool for structuring the decision problem, namely for incorporating conflicting preferences of different stakeholders in relation to the importance of the decision criteria. The latter was used to solve the problem. Other authors have also advocated the combination of qualitative and quantitative modelling approaches to natural resources management planning (e.g. Pukkala, 2002; Mendoza and Prabhu, 2002; Kangas et al., 2006). Yet there is still little experience in combining methods and tools to better address AFI management planning. This review further suggests the synergistic use of both quantitative and qualitative modelling approaches to address the complexity of the multi-objective and multiple decision-makers planning problem. For example, qualitative approaches may be used to better understand the management problem and frame quantitative modelling. The former may also enhance and facilitate the integration of stakeholders in the planning process. The latter are important to address the multiple-owner integrated forest planning problem at different temporal and spatial scales. The number of management options may be huge and assessing its impacts on outcomes and conditions of interest requires numerical tools to build adequate resource capability models. Further, quantitative techniques may enable a more rigorous, structured and systematised representation and analysis of forest management policy scenarios as they provide ways to address preference aggregation and the integration in the decision-making process. The development of computational platforms for the integration of methods and tools to support collaborative planning is thus a promising research area. Information and communication technologies (e.g. DSS and KBS) have already proved to be suitable platforms for integrating a wide range of data, models, methods and tools to support forest management planning and scenario analysis (e.g. Rose et al., 1992; Borges, 1996; Rauscher, 1999; Reynolds, 2001; Vacik and Lexer, 2001; Borges et al., 2003; Pukkala, 2004; Falca˜o and Borges, 2005; Reynolds et al., 2005). Web-based interfaces may further support participation and the rapid registration of a larger number of individual opinions (Thomson, 2000; Jankowski and Nyerges, 2001, Kangas and Store, 2003; Reynolds et al., 2005). The computer-based platform Co-View, developed by CIFOR, illustrates the potential for integrating several software tools— (1) a Visioning Guide and the software The Bridge to facilitate problem definition, (2) a modelling module and the software The Power to Change for problem analysis (e.g. Mendoza and Prabhu, 2002). Yet, further development of Group Decision Support Systems (GDSS) is needed for enhanced support of collaborative planning. The architecture of GDSS encompasses three main levels (Palma dos Reis, 1999). The first level includes communication systems (e.g. e-mail and conference systems including capabilities for brainstorming and for classification and prioritisation of ideas). The second level brings in the standard
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capabilities of DSS. The third level includes a capability to conduct the decision process according to the nature of the management problem. At the first level, adequate user interfaces, both processoriented and data-oriented, should provide easy and friendly access both to alphanumeric and geographic data (e.g. 2D and 3D maps and images) and to modelling and analysis techniques. Such interfaces should further facilitate information exchange, electronic submission of solution options and voting. Those capabilities would expand the usefulness of DSS to support facilitated meetings and allow for the information exchange to proceed among group members, and between group members and the facilitator. At the second and third levels a modular design should help users to select tools and procedures. The model and method bases should offer a number of decision space exploration tools and evaluation techniques. The knowledge base should contribute to enhance knowledge representation and facilitate explanation and analysis of proposed solutions. Transparency and interactivity are key aspects to address in the development of computing technology to be used in the collaborative development of forest management plans for AFI. The former is essential for effectively capturing and representing local knowledge for problem definition and modelling, and for the acceptance of the problem solution. Formal and rigorous methods sometimes imply more sophisticated approaches to planning and there is the danger that technology may be misused. Planners should not oversell the reliability and accuracy of information provided by problem analysis (Davis et al., 2001). Transparency is crucial for the social acceptance of these tools (Reynolds et al., 2005). Interactivity, on the other hand, should be ensured through adequate interfaces and visualization tools (Jankowski and Nyerges, 2001; Pukkala, 2004; Sheppard and Meitner, 2005). Problem solving is followed by plan implementation. Two issues should be addressed at this stage. Firstly, the proposed solution induces resource changes and these must be managed and monitored. There is hardly ever-perfect information at the beginning of the planning process. Unpredictability and uncertainty often frame the multiple-owner forest planning problem. Monitoring may provide information to better characterize the forest management problem. The success of monitoring encompasses identifying and periodically measuring of a set of indicators related to the outcomes and conditions of interest. Secondly, the proposed solution further impacts NIPF expectations and these must be addressed by conflict resolution techniques when plan implementation unfolds. It is likely that conditions that led to a consensual plan will change, due to both the availability of additional information about the management problem and changes of initial assumptions to problem definition (e.g. new subsidies to production or set aside, fire occurrence). Therefore, it is important to develop a protocol for monitoring these assumptions and for triggering new discussion rounds to redefine the problem and gather new consensus. This is a continuous process that requires a monitoring procedure adapted to collaborative planning. Further information analysis capabilities are called for and
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the need for collaborative computing technologies such as modular GDSS is reinforced. 4. Conclusions Designing an AFI forest management plan is interpreted in this paper as a special case of participatory planning when stakeholders (NIPF) share effective decision-making power and are actively involved in decision analysis and planning rather than just provide information and validate solutions. Therefore, it is a collaborative planning that requires specific methodological protocols, methods and tools that might both enhance the potential for effective group decision-making and help manage a complex planning process. Identifying stakeholders and their representation is a necessary step as they actively participate in the planning process. The definition of the multiple-owner forest planning problem involves approaches that may support NIPF communication of goals and the management context. Effective problem modelling and analysis further requires the iterative assessment by NIPF. This paper discussed the potential of qualitative and quantitative techniques to provide needed support. It further considered the role of information and communication technology in the framework of collaborative planning. The need for transparency and interactivity to promote communication and exchange of information between stakeholders and modellers was emphasized. It was further pointed out that plan implementation presents a challenge to effective collaborative planning. Combining resource monitoring and conflict management approaches may be a flexible option. This review of collaborative planning methods and tools that can be used to support the elaboration of management plans in areas integrating multiple NIPF reveals further research needs. Defining and prioritizing forest management goals requires evolving approaches to address preferences aggregation, consistency and vagueness. Developing hybrid methods may take advantage of the combined potential of qualitative and quantitative approaches. Developing technological platforms may promote the effective integration of methods and tools. Enhancing the ability of stakeholders to analyse more information and more facets of the forest management problem and the support of group decision-making thus requires an interdisciplinary approach to forest management planning. Communication between forestry and social sciences may play an important role in addressing effectively collaborative planning in forest management. Computer science may further provide needed input to develop Group Decision Support Systems guided by the principles of modularity/flexibility, interactivity, user-friendliness and transparency to facilitate the acceptance by NIPF of collaborative planning methods and tools. Research of adequate collaborative planning methods and tools may enhance outreach work to demonstrate the usefulness of collaboration and group decision-making to address NIPF goals (e.g. fire protection) and thus support the effectiveness of policy-making.
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