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iForest - Biogeosciences and Forestry
vol. 5, pp. 60-71
Copyright © 2012 by the Italian Society of Silviculture and Forest Ecology
doi: 10.3832/ifor0608-009

Research Articles

Use of multi-criteria analysis (MCA) for supporting community forest management

C. KhadkaCorresponding author, H. Vacik

Introduction 

Approaches in community forestry management

Community forestry has shifted from traditional participatory, to participatory and collaborative management approaches that integrate local and scientific knowledge (e.g., [72], [39], [63]). The involvement of stakeholders to manage the forests as community forestry has become a widely accepted participatory management philosophy, along with the sustainability concept. In this regard, the concept of sustainability has become a dominant paradigm for the management of the remaining global forests, particularly tropical forests ([32]). The concept of criteria and indicators (C&I) has produced an increasing number of initiatives, which has contributed to the promotion and achievement of Sustainable Forest Management (SFM), such as monitoring, reporting and management instruments at a global, national and community management level. In its early phase, the International Tropical Timber Organization (ITTO) began to develop C&I sets ([23], [24]) and as yet, nine eco-regional forestry processes have been established ([74]), involving 149 countries, whose combined forest area equals 97.5 percent of the world’s total forest area. Among others, the Pan-European and the Montreal processes for temperate and boreal forests, dry zone Africa process for arid zones forests, the African Timber Organization (ATO) Process; the near east process and the regional initiatives for dry forests in Asia process.

The traditional meaning of sustainability in terms of sustained yield was radically expanded ([16]). Sustainable forest management is defined as “stewardship and use of forests and forest land in a way, and at a rate, that maintains their biodiversity, productivity, generation capacity, vitality, and their potential to fulfill now and in the future, relevant ecological, economic, and social functions at local, national, and global levels […]” ([42]). However, there exists a wide variety of planning and management approaches which comprise different schools of thought and allow different impacts. Therefore, in community forest management the practical implementation of the planning approaches is often highly debated and a promising approach is hardly identified. In a traditional participatory planning and management approach, goals are set and one management strategy is selected as the optimal one ([34]). It has been often criticized that in such a setting process information sharing is restricted, stereotypes are reinforced, and that a limited public involvement in the plan development, as well as win-lose solutions, are generally promoted ([14], [66], [7], [39], [79]). Walters ([72]) introduced the concept of an evolutionary (“trial and error”) approach, which is closely linked to the traditional participatory approach, but starts with a haphazard set of choices and progressively winnows these down to a subset to improve results. This approach can produce holistic and equitable solutions with the necessary support to be implemented ([65], [17], [11], [51], [79]).

The collaborative-based planning approaches described in the literature are rich in participatory tools and methods aiming to support natural resource management, including co-management (e.g., [58], [50]) and adaptive management (e.g., [63]). In the literature, co-management is described as a planning approach which supports formal arrangements between governments and local groups involving institution building (e.g., [26]) and emphasizes the sharing of rights, responsibilities, and power between different levels and sectors of government and civil society ([4]). Adaptive management approaches involve the generation of alternative hypotheses, assess the value of additional information, develop models for future learning, formulate policy options and support the identification of criteria to facilitate evaluation and comparison among options ([20]). Adaptive management systematically integrates results of previous interventions to iteratively improve and accommodate change by learning from the outcomes of experimented practices ([40]) and promotes learning-based decision making, monitoring and action ([22], [75]), Thus learning through ad hoc trial and error is replaced with learning by careful design and testing ([73]). Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives, hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration ([1]). Within the adaptive management approach two types (active and passive) can be distinguished which comprise the setting of goals, modeling the system, and selecting and implementing a management strategy ([34]). [33] recently proposed an adaptive monitoring approach that links the development of conceptual models, setting questions, making a experimental design, collecting, analyzing and interpreting data iteratively, and that can be applied to all kinds of monitoring, including question-driven, passive and mandated monitoring programs. Passive adaptive management applies historical data to design and implements management strategies (“single best estimate”) at the same time, with the management decision being made assuming this model is correct ([71], [34]). “Single best estimate” should be actually considered outside the realm of monitoring for adaptive management ([12]). In active adaptive management, multiple experimental alternatives are examined, and feedback loops allow the reiteration of alternatives as well as goals and criteria weightings ([34]), requiring to measure the initial state of the systems and to monitor trends over time to track system responses to management practices ([12]), which are distinguished primarily by the degree to which they emphasize the reduction of uncertainty ([75]). Adaptive co-management combines the dynamic learning characteristic of adaptive management (e.g., [22]) with the features of collaborative management (e.g., [10])

Supporting community forestry in Nepal

In making plans and managing natural resources, Multi-Criteria Analysis (MCA) helps to consolidate the multiple views and knowledge of stakeholders to support decision-making in complex environments ([28], [43], [38]). MCA techniques have proved to be useful in structuring forest management problems ([2]), supporting participatory decision-making ([48]), negotiation, and mediation processes ([8], [3]). Additionally, Criteria and Indicators (C&I) provide a common framework to describe, conceptualize, organize, and interpret information related to sustainable forest management ([80], [53], [74]). They have proved to be a useful communication tool among stakeholders and local communities ([59]). Prabhu et al. ([52], [54], [55]) proposed the combined use of C&I sets and MCA and their applicability for the development of adaptive management programs. The hierarchical structure of defining indicators allows a complex problem to be broken down into manageable elements that can lend themselves to formal analysis ([45]). In this context the Analytic Hierarchy Process (AHP) is a robust, ratio-scaled MCA method for analyzing complex decisions with multiple attributes ([60]). The AHP has been applied to elicit public preferences in a vast range of natural resource policy areas, including forest management ([62], [37]), and was applied in multi-objective forest management for structuring and solving complex decision problems ([36], [27], [69]).

Community forestry in Nepal can be described as a laboratory for participatory resource management, where collaboration and coordination among all stakeholders is practiced daily ([31]). However, the existing collaborative planning approaches in community forest management in Nepal are currently not utilizing MCA for evaluating forest management. Although Hjortsø et al. ([21]) applied multiple-objective programming and goal programming for a land-use planning case in the protected area-buffer zone management of the Chitwan National Park, they failed to analyze the decision problem in a collaborative manner. As the concept of C&I has emerged in an increasing number of initiatives at global, national and forest management unit level, it has been applied in community forest management in Nepal as well ([32]). However, a number of applications evaluating forest planning and management approaches with MCA techniques describe limits in the practical implementation ([44], [45], [46], [19], [78]). From this study it became evident that appropriate management requires: (i) harmonizing and integrating different datasets; (ii) selecting the right indicators; (iii) fitting the right concept to the right scale; and (iv) integrating data, indicators and concepts into systems that allow both a high level of participation and flexibility in application to different questions ([15]).

This study intends to support the practical implementation of community forest management in the Shree Gyneshwar Community Forest in the central development region, Nepal, by utilizing MCA techniques for evaluating different management strategies for community forest management (CFM). The objectives of this work were:

  1. to assess the relative importance of a proposed set of C&I with respect to sustainable forest management, by the elicitation of stakeholders preferences;
  2. to evaluate perceptions of the overall performance of community forest management strategies by the use of Analytic Hierarchy Process (AHP);
  3. to perform a sensitivity analysis to identify an overall compromise option in the case study area;
  4. to draw policy implications for supporting community forest management in Nepal.

Multi-Criteria Analysis (MCA) for supporting community forest management 

Case study area: Shree Gyneshwar community forest

The Shree Gyneshwar Community Forest (SGCF) is located in Mangalpur Village Development Committee (VDC), Chitwan district of central development region, Nepal. It covers an area of 208 hectares and 2300 Households (Hhs - year 2009). Gyneshwar CF lies in sub-tropical lowlands of the inner terai region, located at the alluvial plain of Narayani river, and is dominated by young stands (25 years) and riverine forests. Main species are: Sissoo (Delbergia sissoo Roxb.), Gutel (Trewia nudiflora L.), Khayar (Acacia catechu L.f. Willd.), Karma (Adina cardifolia Hook.), Ipil Ipil (Leucaena leucocephala [Lam.] de Wit) and Bakaino (Melia Azedirach L.). Climatic conditions of this subtropical region are relatively warm: highest temperatures reach 38 °C during the winter season and drop to a minimum of 6°C in the post-monsoon period (October to January), when dry northerly winds from the Himalaya and Tibetan Plateau are prevalent ([9]). Mean annual rainfall is 2400 mm with about 90% falling in the monsoon from June to September. The soils of this area are highly variable but mostly sandy and alluvial soil.

Owing to human settlements, increasing fuel (wood) demands, population pressures, and the conversion of forest land to agriculture land and illegal practices, the area was completely deforested in the 1980s. In 1981 the Timber Corporation of Nepal (TCN) initiated a plantation programme in this area with Sissoo (Dalbergia Sissoo), and handed over the plantation areas to the District Forest Office (DFO), Chitwan. Gyneshwar CF runs its own community nursery consisting of a variety of fast growing and multi-purpose tree species, e.g., Sissoo (Dalbergia sissoo), Bakaino (Melia azedarach), Ipil-ipil (Leucaena leucocephala), and others. Local associates and farmers are trained for plantation and other forest management activities. CF has tremendously worked on biodiversity conservation, wildlife habitat management and plantation work and invested lots of efforts to restore the forest condition. They are highly motivated to conserve biodiversity, protect the river-bank and improve forest condition through multi-purpose forest management system and promote eco-tourism and forest recreation activities in their forest.

The governmental local authorities adapted the traditional participatory approaches with the consultation of local users, and formed the constitution and operational plan with an ad hoc committee, without specifying objectives and activities, and handed over the forests to local communities in 2001. After that, several participatory tools (e.g., social mapping, stakeholder analysis, participatory resource mapping, historical trend and seasonal calendar) have been applied during the amendment of constitution and operational plan at period 2006 and 2009, which increased users’ awareness of forest protection, management and utilization of forest resources. As the demand of local users for natural resources is quite high, there is a need to increase the forest’s productivity by applying a regular management. In order to support the Community Forest User Group (CFUG) in moving towards a more efficient and effective management a Multi-Criteria Analysis (MCA) approach was initiated.

Methods

Multi Criteria Analysis (MCA) refers to a suite of techniques in which multiple values reflecting different objectives are quantified and used to provide a decision outcome ([18]). In this study, a methodological approach was adopted that seek to take explicit account of multiple criteria in helping individuals or groups explore decisions that matter ([5]). Mendoza & Prabhu ([44], [46]) describe a number of features that make MCA valuable for community based forest management, in that it accommodates individual concerns and opinions of a number of stakeholders by a set of criteria being measured and evaluated simultaneously. For applying the MCA process to the case study, five phases were distinguished: (i) awareness building, (ii) criteria and indicators development, (iii) elicitation of preferences, (iv) formulating of forest management options, and (v) evaluation of management options (Fig. 1).

Fig. 1 - Application of Multi-Criteria Analysis (MCA) in evaluating Community Forest Management (CFM).

In the awareness building phase the community forest user group (CFUG) started to develop a vision and goals based on the results of studies on the socio-economic, institutional, historical, policy and bio-physical conditions of the case study site. The CFUGs and researcher jointly identified and classified stakeholders’ interests, and their roles and involvement in the decision-making process. In the C&I development phase the participants discussed the main principles of Sustainable Forest Management (SFM) and negotiated objectives and outcomes for different levels. The research team and local facilitators organized 20 hamlet-level meetings to derive the opinions and experiences from all stakeholder groups. The number of participants in each tole/hamlet varied between 20 and 25 persons. In the C&I development workshop, some 71 users arranged in 12 stakeholder groups developed and assessed a set of 6 criteria and 44 indicators. A series of workshops were held in the Gyneshwar CFUG, from April 2007 to March 2009. For the case study, participants were selected by the following criteria: executive committee of forest user groups (who have executive power for decision-making and implementation work); general users (e.g., interested in resource management or affected by decisions in the areas); advisory committee members (e.g., having expertise on the historical background of the forest or influence the planning process) and local facilitators (engaging stakeholder in the process), with a total of 72 participants (see also [32]). The representatives of the stakeholder groups had sound knowledge of the region and the community forest user group was selected by the executive committee of the Shree Gyneshwar CFUG.

At the preference elicitation stage, the comments and inputs of stakeholders were accommodated to refine the set of criteria and indicators. A final set was used throughout all group meetings for weighting consistency purposes. Ranking, rating and pairwise comparison techniques, which are commonly used in C&I assessment studies, have been applied for preference elicitation (see [53], [62], [57], [70]). In this study the participants were instructed to express their weights for each criterion by applying rating and ranking techniques. For the rating a score between 0 and 9 was assigned to each element and the ranks were assigned following a 9-point scale, depending on the number of indicators related to each criterion. Moreover, pairwise comparisons were done by the local facilitators based on the ordinal input (ranking, rating) provided by the stakeholder groups according to each single indicator, and the priorities were calculated using the Eigenvalue method ([60]). Each participant had the chance to argue different opinions in their own group and in the plenary as well, and a consensus had to be found based on the different preferences of the members within one group. As a consequence, the individual judgments of each member within a group were used to formulate one single representative judgment for the entire group in a negotiation process. However, in order to allow a synthesis of the individual group priorities within the AHP, the judgments had to be combined in a manner so that the reciprocal of the synthesized judgments is equal to the synthesis of the reciprocals of these judgments ([61]). If groups or individuals had different priorities of importance, it was suggested that their synthesized judgments (final outcomes) should be raised to the power of their priorities, leading to calculate the geometric mean of the group priorities for that purpose ([61]).

Based on the discussions in the preference elicitation workshops the CFUG developed management strategies to improve the livelihood of the poor and promote social inclusion. Each stakeholder group was asked to express their favorite measures for improving the overall situation and all actions were complied in four management strategies (Tab. 4). The groups had different perceptions on the magnitude of improvement of the forest conditions and livelihood of rural users. The stakeholders were asked for a qualitative assessment of the strategies using a five-point scale similar to other studies ([56], [77]): very high improvement to the current situation (+++); high improvement (++); fairly good improvement (+); no significant improvement at all (0); and in conflict with the current SFM objectives (-). The qualitative evaluation of stakeholder groups was related to the available information of context studies (e.g., socio-economic assessment, bio-physical assessment, institutional assessment, and constitution and operational plan) but in some cases stakeholders were uncertain about a likely improvement (stating “?”). A final decision on the performance of the management strategies was made by members of the executive committee and approved by the general assembly of forest user groups.

Tab. 4 - Characteristics of the management strategies (MS I-IV).

The presentation of the findings of the case study focuses on the preference elicitation (stage three), formulating forest management options (stage four) and evaluation of management strategies (stage five) in order to demonstrate the practical implementation of the MCA approach (Fig. 1).

Adopting the multi-criteria analysis approach 

Preference elicitation

The stakeholder groups comprising a total of 71 users were classified into twelve subgroups (15 Advisory members in 3 groups, 13 CFUGC members in 2 groups, 23 general members in 3 groups, 20 local facilitators in 4 groups) and asked to assess each criterion according to its perceived importance, with respect to sustainable community forest management and improvement of their livelihood. According to the judgements (n=12) of the four different stakeholder groups for each criterion, the criteria for environmental and forest health (C3) and community relations (C5) were found to be the most relevant, followed by silvicultural prescriptions (C2) and monitoring (C6). The management plan (C4) and policy framework (C1) had less importance. Tab. 2 indicates that the stakeholder groups expressed their priorities for the criteria differently as each group identified its own favorite. (C1) was preferred by the Advisory Members, (C2) by the local facilitators, (C3) by the CFUGC members, and (C5) by the general users, respectively. (C4) was not prioritized by any of the stakeholder groups.

Tab. 2 - Priorities for the criteria based on geometric mean of the synthesized stakeholder group judgments.

Tab. 3 presents the derived relative weights and standard deviation of the preference values for the indicators using rating (columns 2 and 3), ranking (columns 4 and 5) and geometric mean of the priorities of the pairwise comparisons technique (column 6). The results obtained from the 12 stakeholder groups by rating, ranking and pairwise comparisons do not indicate large differences, depending on the preference elicitation technique. In addition, the small variability in the individual preferences reflects a high level of similarity among the twelve stakeholder groups. The indicators I1.4, I2.5, I3.6, I4.1, I5.1 and I6.1 were rated with first priority by all methods. However, most of the indicators ranked second or third based on priorities received from different techniques. However, all activities related to protection measures (illegal and unauthorized activities, plantations, conservation measures) and participatory management have been listed with high priority.

Tab. 3 - Preferences of criteria based on rating, ranking and pairwise comparisons (PWC) methods (n=12). (a): indicates the means for the most preferred indicator under each criterion.

Formulating forest management options

The Shree Gyneshwar CFUGs mostly adopted conservative and protection oriented management strategies where the collection of dead, dying and diseased trees for fuel was undertaken, where non-wood products, such as grasses, leaf litter and bedding materials, were collected, and where the use of timber for furniture production was applied to a minor extent. Although the plantations required regular silvicultural treatment protection, measures are mostly preferred by government authorities and local elites. Tab. 4 highlights the characteristics of each strategy, whereas all management approaches from traditional participatory, to evolutionary (trial and error), passive and active adaptive management were identified by the stakeholder groups.

  • Management Strategy I (MS I): focuses mainly on protection measures (e.g., no grazing, making fire lines, patrolling by forest guards, no harvesting) and was proposed by traditional users, the old committee and elite members.
  • Management Strategy II (MS II): focuses mainly on plantations to support the forest protection management regime and was proposed by executive committee members.
  • Management Strategy III (MS III): allows a multiple use of natural resources introducing production-oriented measures and supporting the active inclusion of all members; it was proposed by executive members and hamlet representatives.
  • Management Strategy IV (MS IV): focuses on a sustained sawn timber production and other ecosystem services by advocating new institutional arrangements; it was proposed by local facilitators dominated by young members.

Evaluation of management strategies

The total 71 users in 12 stakeholder subgroups carried out the qualitative assessment regarding the performance of all management strategies against each indicator. The stakeholders assessed the future effects of a management strategy if they would cause no change in, make a positive improvement in, or conflict with, the current situation (Tab. 5).

Tab. 5 - Qualitative assessment of management strategies against indicators.

For the assessment of the management strategies the evaluation hierarchy of the AHP was used based on the 6 criteria and 44 indicators. The overall goal was to “select the best strategy with regard to livelihood enhancement and sustainable forest management” on top, the criteria and indicators on the two next lower levels, and the four management strategies at the bottom (Fig. 2). Pairwise comparisons have been used to evaluate the performance of all management strategies according to each single indicator using the qualitative assessment by the stakeholders as input (see Tab. 5). The preferences for the criteria and indicators were derived based on the pairwise comparisons (Tab. 3).

Fig. 2 - Analytic Hierarchical Process (AHP) model for the evaluation of management strategies according to C&I set.

The preferences derived for the criteria and indicator levels, done by pairwise comparisons of the 12 individual stakeholder groups, were aggregated using the geometric mean to obtain group preferences for the local priorities. The priorities and ranks for the four management strategies, based on the geometric mean of the synthesized judgments with respect to the stakeholder groups, are shown in Tab. 6. For the advisory and committee members groups, MS III and MS IV are ranked first and second. For the general members and local facilitator groups, MS IV and MS III are ranked first and second, respectively. In considering the priority of all stakeholder groups it was found that MS III - a passive adaptive management strategy focusing on a multiple use of natural resources and introducing production-oriented measures - was identified as the most preferable option, but in general MS III and MS IV are very close.

Tab. 6 - Priorities of management strategies based on geometric mean of the synthesized judgment with respect to the stakeholder groups.

Fig. 3 shows the priorities of the management strategies assigned by different stakeholder groups at criteria level (C1-C6). In case of the advisory members group, they considered a similar perception as the CFUGC members, while MS I is the best option under the policy framework (C1), followed by MS IV under silvicultural prescriptions (C2), and MS III under Criteria 3-6. The priorities of the general members and local facilitators differed slightly. The local facilitators preferred MS IV as a best alternative under criteria 1, 2 and 4. In the case of general members groups, MS IV was the best option under criteria 2, 4 and 5, and MS III at criteria 3 and 6. In most of the cases, it becomes evident that MS III is either the best or second best option for all stakeholder groups.

Fig. 3 - Priorities of the management strategies derived from the AHP from the perspective of different stakeholder groups. (A): Priorities of management strategies based on geometric mean of the synthesized judgment of one Advisory members group; (B): Priorities of management strategies based on geometric mean of the synthesized judgment of one Executive members group; (C): Priorities of management strategies based on geometric mean of the synthesized judgment of one General members group; (D): Priorities of management strategies based on geometric mean of the synthesized judgment of one Local facilitators group.

Tab. 7 shows the performance of the management strategies with regard to the criteria level. Applying the geometric mean of the synthesized stakeholders’ preferences reveals some interesting insights. MS IV was highly ranked in relation to the silvicultural operations (C2), management plan (C4) and monitoring and assessment (C6), and ranked with a low priority at forest health (C3). MS I was found as the best alternative with regard to policy framework (C1), whereas MS II was the best choice under management plan (C4). However, although management strategy III was ranked in second place for almost all criteria it seems to be the overall best compromise strategy (compare Tab. 6).

Tab. 7 - Priorities of management alternatives based on the geometric mean of the synthesized preferences of stakeholders groups with respect to the criteria. (C1): policy framework; (C2): silvicultural prescriptions; (C3): forest health; (C4): management plan; (C5): community relations; and (C6): monitoring and assessment.

From Tab. 7 it can be found that MS I is the best alternative option according to policy framework (C1), followed by MS III, MS IV and MS II as the least priority options. According to the C2 (Silvicultural practices and other management system), C4 (Appropriate management plan) and C6 (Regular monitoring and assessment), MS IV is the best management option, followed by MS III, MS II and MS I. The best option is MS II, taking environmental and forest health (C3) as the primary objective. MS III is the best management option considering only the long-term social and economic benefits to the local users (C4) as primary objective. In overall, MS III is the best management option for all stakeholders, whereas MS I has the lowest performance for all stakeholder groups.

Discussion  

This study was meant to support the practical implementation of community forest management in the inner, central terai region of Nepal by utilizing MCA techniques for raising stakeholders’ awareness and their commitment to the process. The approach brought the key stakeholders together and encouraged them to identify their problems and reflect critically on their attitude towards livelihood improvement. It was designed to assist decision-makers in structuring a decision problem, in generating and evaluating decision alternatives and analyze the trade-offs of possible management strategies. In general, MCA techniques are considered to assist the decision-maker in solving complex decision problems ([30]). They are generally considered as promising in allowing a strong representation of stakeholder groups and incorporating their perceptions into the process ([46]). In this study the MCA helped to improve the decision-making process by increasing everyone understanding in the role of preferences for identifying promising management strategies and the importance of tradeoffs between various alternatives. However, application of monitoring, disregarding any methodological approach to explore the consequences of alternatives, may raise relevant problems: heavy reliance on managers’ experience and wisdom inherently and unreliably assumes that management conditions are stable over time ([12]). The applied MCA process demonstrated the potential in selecting the most preferred forest management strategy which can best satisfy the objectives of community forest management. The four management strategies and the evaluation framework have been derived from the identified needs and expectations of the different socio-economic and institutional actors. When stakeholders are not fully involved in framing, analyzing, generating, and implementing management strategies to complex public problems, they might seek other ways of articulating and meeting their interests, hampering the decision process ([6]). In this context, it became evident that compromise strategies have higher possibilities for realization when taking into account the different views of stakeholder groups. MS II was assessed as the best performing strategy under the criteria (C3) “Encourage multiple forest products and services” (Tab. 7), which was identified as the most relevant criteria for all stakeholder groups (Tab. 2). On the contrary, MS III was assessed as the best performing strategy under the criteria (C5) “Long-term social and economic well-being of local communities under community relations”, whereas this criterion was seen to some extent as less important. However, despite the particularly good performance of some strategies, MS III was selected as an overall compromise strategy as it could reflect the major ideas of all stakeholders. MS III has a balanced mix of measures which allows to promote plantations, identify areas of protected areas, put emphasis on the documentation of ecological sensitive areas, as well as identify the potential of each vegetation type to meet the production, biodiversity conservation and other social issues for long-term SFM. The strategy includes effective measures to conserve rare, threatened and endangered species, to map wildlife corridors as well as to stop illegal hunting and trapping practices. The strategy allows promoting the design of plantations and technical measures to implement the operational plan. It also allows a passive adaptive management focusing on a multiple use of natural resources and introducing production-oriented measures, which will enhance livelihood of the local people and set new arrangements for power-sharing.

In the process of designing management strategies the participants were motivated to generate ideas for future improvements, whereas their vision of ideal future conditions was sometimes impractical and unachievable. Youth members generally oriented in the pathway of progressive change and simply ignored the existing power dynamics and institutional capacity of Community Forest User Group. Facilitators helped youth members and general users to develop new concepts as proposed by MS IV. In this context, the role of facilitators has been often described as important to incorporate the issues raised in the whole process, to support the generation of new ideas, alternative strategies and solutions to problems ([41]). Although the MS IV alternative was built taking into account the objectives of CFM, it was far beyond the preferences of all stakeholder groups. In some cases, traditional users, local elites and CFUGC members hesitated to develop new management strategies because they wanted to maintain the status quo. Their proposed management strategies I and II did not take into account the objectives of the CFUG-like wider participation, livelihood enhancement or active forest management. Actually, the promoters of those strategies believed that it is enough to raise the problems and to solve them by applying more of the same. Therefore, MS III seemed to be promising as the best alternative management strategy. The feedback of the participants has shown that decisionmakers can be assisted by the MCA approach in evaluating management options effectively and generating ideas for the long-term strategic planning process of Community Forest Management, even under complex socio-economic and ecological conditions. It could be demonstrated that there is need to have clear, measurable, and agreed-upon management objectives, by which to guide the decision making process and evaluate its performance, which in turn requires an acceptable range of management options and a flexible management environment that allows adaptations as learning occurs ([76]).

Decision-making in community-based institution is typically a complex task, characterized by trade-offs among socio-cultural, political, economic and environmental issues ([46], [64]). MCA techniques can be used to establish consensus and help to find mutually agreed compromises and management options, based on the contrary views of stakeholders. In practice, it has been difficult for the stakeholders to express their preferences for all criteria and indicators. Therefore three different techniques (rating, ranking, pair-wise comparison) have been applied to express the preferences for multiple objectives. Informal feedback from the stakeholders indicated that the rating and ranking was sometimes difficult, when the number of indicators under the criteria was high. In this context, the pair-wise comparisons helped to break down the choice of preferences to a single pair. Caution has to be used in applying any of the weighting procedures, as the process for elicitation of preferences can hamper the overall results ([64]). However, the overall differences among the three techniques were quite low, which allowed to conclude that it was possible for the stakeholder groups to express their priorities in general. The AHP technique provided the possibility to analyze the effect of different stakeholder preferences. The outcomes were used not only to assess the robustness of the evaluation, but also to appraise the influence of each criterion in the selection of the best options. The use of preferences expressed for a given set of C&I provide the opportunity for scenario analysis from different stakeholder perspectives ([19]). In particular, such kind of scenario analysis applied is fundamental for attaining consensus and achieving technically defensible policy options ([8], [13]).

In combination with participatory tools, it is possible to provide data, information and structured knowledge as an essential requirement for decision-making ([48]), as this promotes participation, negotiation, collaboration and social learning, and increases the adaptive collaborative management environment. In particular, Tuxill & Nabhan ([68]) and Myllyviita et al. ([47]) highlight the importance of employing participatory activities that shift the involvement of local communities from passive to more active collaboration. The qualitative nature of the applied techniques in this study can be criticized for their lack of statistical meaning, but they give the participants and the facilitators time to deliberate; i.e., to consider and identify relevant knowledge ([49]). This can improve rationality and transparency of the decisionmaking processes in general, which are recognized as highly important features of policy processes for some stakeholders ([29]).

Conclusions 

The research team and local facilitators were responsible for balancing the objectives of different stakeholder groups, supporting the user in structuring the decision-making process and evaluating alternative management options based on their own opinions. In hamlet meetings, executive committee meetings and C&I development workshops, the participants had the opportunity to express their opinion about objectives, to identify decision problems and possible options to overcome them. Hence, the study promoted a shared understanding and thus increased the ability to change management practices in order to sustain desirable future expectations. However, the operational implementation of the MCA process requires the strong commitment of local elites and executive committee members ([67]). Wealthy and higher caste people (i.e., elites) have most of the decision-making and implementation power, resulting in inequitable decision-making processes and distribution of outcomes ([35], [25]). In particular, it was observed that forestry technicians and key decision-holders of CFUFC wanted to use their power to create obstacles and maintain the status quo, which provided more benefit for them. Nonetheless, the elites and committee members refused to forward the agreed management plan for the final approval to the local district office. Also the staff of District Forest Office (DFO) has considerable power to manipulate or delay the implementation based on their own interests, resulting in perhaps less active CFUG. Government officials may also need to change their attitude from a more traditional command and control approach to participatory forest management to assist and support community forest users in managing forest for their own multiple benefits. However, the general willingness for change is rather low; it seemed that once the executive committee members understood the principles of the MCA process they would facilitate the activities in an effective ways to combat such situation locally.

This study can serve as a milestone to utilize MCA techniques for collaborative-based decision-making processes in CFM in Nepal. An extensive review of the process and its methods to develop future strategies is required, not only to facilitate the process but also to promote an adaptive collaborative management on the long run. In order to integrate and implement adaptive management and multiple criteria decision-making process in community forest management of Nepal, it is essential to have a strong commitment from the responsible agencies for the revision of existing decision making procedures. Therefore, the role and responsibilities of implementing agencies, decision makers as well as policy makers need to be revised by improving their level of understanding, communication and commitment to a learning process including adaptive responses and reduce uncertainty over time. A continuing process of learning and refining management strategies can help to improve the forest conditions and livelihoods of local people by fostering the collaboration among stakeholders within innovative responses. Therefore, we propose that a combined application of MCA and participatory techniques will provide a powerful framework for adaptive management for a wide range of community forest management problems. However, to make use of such MCA techniques in community forest management in the future, there is a need for political will and commitment to engage in multi-stakeholder processes and provide support through organizational and personal means.

Acknowledgements 

We would like to express our thanks to the Shree Gyneshwar community forest user groups and local experts, all of whom expressed their interest in our research activities. This study was supported by the Commission for Development Studies at the Austrian Academy of Science (KEF) and the Austrian Exchange Service (OEAD), by awarding Mr. Khadka a fellowship for his Ph.D studies at the University of Natural Resources and Life Sciences (BOKU), Vienna.

Tab. 1 - Selected characteristics of different management approaches in community forest management.

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Khadka C, Vacik H (2012).
Use of multi-criteria analysis (MCA) for supporting community forest management
iForest - Biogeosciences and Forestry 5: 60-71. - doi: 10.3832/ifor0608-009
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Title Use of multi-criteria analysis (MCA) for supporting community forest management
Authors Khadka C, Vacik H
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