www.elsevier.com/locate/foreco

Forest Ecology and Management 248 (2007) 107–118

Addressing collaborative planning methods and tools in

forest management

Helena Martins *, Jose G. Borges

Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade Tecnica 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 manage-

ment scheduling at a larger scale. Current wildfire prevention

goals further call for the integration of multiple small non-

industrial 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

* Corresponding author. Tel.: +351 21 365 3343; fax: +351 21 364 5000.

E-mail address: hmartins@isa.utl.pt (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

H. Martins, J.G. Borges / Forest Ecology and Management 248 (2007) 107–118108

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 involve-

ment of stakeholders in all planning steps. It may thus stimulate

thinking and exploration of management scenarios by

stakeholders, provide a deeper understanding of the manage-

ment 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.

2. Methods and tools to support collaborative planning

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).

� P

roblem identification involves the acquisition and analysis

of information to understand and to define the AFI

management problem.

� P

roblem modelling involves model building to represent both

the relations between management alternatives and outcomes

of interest and the management policy scenarios.

� P

roblem 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.

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 quantita-

tive 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).

2.1. Problem identification

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 require-

ments. 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 Examples of methods Auxiliary tools Examples of tools R quisites of application Sources of information

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

‘‘Who counts’’ matrix R presentativity Grimble and Chan

(1995), Grimble

and Wellard (1997)

Formal methods to represent

the importance of stakeholders

as perceived by others

F irness, transparency,

s plicity and

r resentativity

Colfer et al.

(1999), Harrison

and Qureshi

(2000), Sheppard

and Meitner (2005)

To identify goals and

objectives, management

alternatives, forest policies,

resources, conflicts and

interactions

Participatory methods Interviews, questionnaires,

Delphi method

U dertaken individually Hwang and Lin

(1987), Coughlan

and Armour (1992)

Brainstorming, workshop,

nominal group technique

U dertaken collectively McDaniels and

Roessler (1998),

Laukkanen et al.

(2002)

To increase stakeholders’

perception of the

collaborative planning

process. To document

the planning process

Computational tools to

support problem

definition in a

structured way

The Bridge, Monsu

GIS Internet

CIFOR, Pukkala

(2004), Kyem

(2002), Thomson

(2000), Belton

and Stewart (2002),

Cai (2005)

Problem modeling

(building a model that

represents the AFI

management problem)

To model quantitatively

the impact of management

alternatives on the

objectives

Operations Research

techniques (objective

functions)

Linear programming, goal

programming, heuristics

Automated prescription

writers

C mputational resources.

A ailability of quantitative

i ormation to support

p edictions of forest

m nagement outcomes

Romero and

Rehman (1987),

Pukkala (2002),

Borges et al.

(2002), de

Steiguer et al. (2003)

Multi-criteria methods AHP, Multi-attribute

utility functions

Malczewski

et al. (1997),

Ananda and

Herath (2005)

To model qualitatively

the relation between

management alternatives

and their impact on

the objectives

Soft-OR approaches Cognitive mapping,

qualitative systems dynamics

A equate facilitation Wolstenholme

(1999), Ozesmi

and Ozesmi

(2004), Purnomo

et al. (2004)

Computational tools to

guide problem modelling

SODA C mputational resources.

A equate facilitation

Eden (1988),

HjortsØ (2004)

Fuzzy approaches Logic models C mputational resources Reynolds et al.

(2000)

AI-based approaches MAS C mputational resources Bousquet and

Le Page (2004),

Purnomo et al. (2005)

To weight objectives

according to individual

preferences

Multi-criteria methods for

estimating weights

according to preferences

Swing weights, direct rating

and pairwise comparisons

(especially AHP)

Expert Choice R duced number of

o jectives. Preferences

e sily converted into scores

Malczewski (1999),

Mendoza and Prabhu

(2000), Ananda and

Herath (2003)

and Herath (2004)

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Table 1 (Continued )

Steps of the planning process Tasks Methodological approaches Examples of methods Auxiliary tools Examples of tools 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

Undertaken individually Hwang and Lin

(1987), Coughlan

and Armour (1992)

Brainstorming, workshop,

nominal group technique

Undertaken collectively.

Adequate facilitation

McDaniels and

Roessler (1998),

Laukkanen et al.

(2002)

Geometric and arithmetic mean Malczewski (1999),

Mendoza and

Prabhu (2000),

Ananda and

Herath (2003)

and Herath (2004)

Social choice theory Voting models Martin et al. (1996),

Kangas et al. (2006)

Deterministic methods

for assessing consistency

of preference structure

Minimization of a

consistency ratio

The CR of AHP Kangas (1992)

Compromise programming Phua and Minowa

(2005)

Fuzzy sets Bantayan and

Bishop (1998)

Discussion for consensus

building

Adequate facilitation Belton and Stewart

(2002)

Stochastic methods for

assessing consistency of

preference structure

Further expansions of AHP Alho et al. (2001),

MacKay et al.

(1996), Phua and

Minowa (2005)

Problem solving (choice

of a AFI forest

management plan)

To prioritize management

alternatives

Operations Research

techniques

Linear and goal programming,

heuristics

DSS with visualization

interfaces

SADflOR, DTRAN,

MONSU, MONTE

Computational resources 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)

Multi-criteria methods AHP, ranking, rating Only a reduced number

of alternative plans

can analysised

Outranking methods Multi-

attribute value elicitation

KBS Power-to-Change

EMDS, CORMAS

Kangas et al. (2001)

Logic models MAS McDaniels and Roessler (1998)

Participatory methods Mendoza and Prabhu (2002),

Reynolds (2001), Bousquet

et al. (1998), Ligtenberg

et al. (2004)

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H. Martins, J.G. Borges / Forest Ecology and Management 248 (2007) 107–118 111

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 identifica-

tion 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:

– W

hat direct goods and services do they extract from the

resource?

– W

hat indirect (including environmental) goods and services

do they provide?

– W

hat restrictions do they face over the use of the resource?

– W

hat de jure and de facto rights or claims to they have over

using and managing the resource?

– W

hat are the forms and degree of management of the

resource in question?

– W

hat 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?

– W

hat trade offs are stakeholders making and what decision-

making criteria are they using when they choose a particular

management or resource-use strategy?

– W

hat are the actual and perceived costs and benefits to

stakeholders of following their chosen behaviour or actions?

– D

o they perceive any external costs and benefits of their

actions and decisions and, if so, are these considered in their

decision-making?

– W

1 http://www.cifor.cgiar.org/docs/_ref/research_tools/index.htm.

hat stakeholders see as their decision-making environ-

ment? 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 manage-

ment 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.

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 result-

ing 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 (Laukka-

nen 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 computer-

based 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

H. Martins, J.G. Borges / Forest Ecology and Management 248 (2007) 107–118112

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). Quanti-

tative approaches may help address the large-scale character-

istics 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; Falcao 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 compli-

cated 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 alter-

natives 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 Soft-

OR methods may also be used to model the AFI integrated

planning problem.

H. Martins, J.G. Borges / Forest Ecology and Management 248 (2007) 107–118 113

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 manage-

ment 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

reader is referred to Ozesmi and Ozesmi (2004) for an extensive

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 (Wol-

stenholme, 1999). Purnomo et al. (2004) have demonstrated its

application in collaborative planning of community-managed

resources.

Qualitative approaches for multiple NIPF forest manage-

ment 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 pre-

ference structures, the emphasis is solely on facilitation of

discussions for consensus building (Belton and Stewart, 2002).

No formal ways for aggregating individual preferences are

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 algo-

rithms. 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’’ – optimiza-

tion 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.

H. Martins, J.G. Borges / Forest Ecology and Management 248 (2007) 107–118114

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 goal-

seeking 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; Falcao and Borges, 2005). For example, the Minnesota’s

Generic Environmental Impact Statement (GEIS) on Timber

Harvesting and Forest Management (Jaakko Poyry Consulting,

1994) demonstrated the potential of quantitative approaches

and Decision Support Systems to promote collective multi-

disciplinary 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 out-

ranking 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 Sys-

tems) 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 identifica-

tion 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 manage-

ment 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; Falcao 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. Falcao 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;

Hetemaki 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.

H. Martins, J.G. Borges / Forest Ecology and Management 248 (2007) 107–118 115

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 pre-

ferences 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; Falcao 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

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 process-

oriented 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 sophis-

ticated 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 measur-

ing 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

H. Martins, J.G. Borges / Forest Ecology and Management 248 (2007) 107–118116

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 methodo-

logical 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 commu-

nication 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 monitor-

ing 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.

Acknowledgements

The Portuguese Science Foundation and the European

Social Fund within the framework of the IIIQCA provided

support to this research work.

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