Document(s) 9 of 15

Reflecting on practice

In previous chapters our colleagues have described their experiences in exploring new conceptual and methodological grounds in participatory research (PR) in natural resource management (NRM), often as a complement to existing (‘traditional’) research from both the natural and social sciences. These explorations are producing new and exciting insights into promising alternatives for the management of natural resource systems, including crops, soils, water, trees and animals. These experiences are also resulting in the innovative adaptation of participatory research approaches. Venturing into this still relatively new research terrain of working for rural transformations, however, raises difficult questions about the research process. Researchers are faced with the challenge of critically assessing the kind(s) of participation and processes appropriate to the different stages of the research cycle. This expansion of the research domain and the new knowledge generated require that researchers must be able to identify what is ‘good practice’ in PR in NRM.

While this challenge is starting to be met in some individual research projects, the emergent learning has been somewhat insular. Perhaps because the experience of doing participatory research in one context is not easily compared to another, shared learning between research institutions about ‘what is good practice in PR in NRM’ has been slow. We consider that comparisons and the integration of ideas are necessary elements of identifying good practice. The PRGA (Participatory Research and Gender Analysis) Program and the Natural Resources Institute (NRI) NRM workshop in Chatham, England, created an ideal opportunity to begin such a process of shared learning.

In this chapter we draw on the case studies from the Chatham workshop and other literature to generate a number of principles of good practice in PR in NRM and, potentially, beyond this field.¹ The intention is that these principles may be useful in the planning and assessment of the rigour of participatory research methodologies. As such, we aim to contribute to the growing interest in the development of appropriate methodologies for monitoring and evaluating participatory research (eg, Armonia and Campilan, 1997; Guijt et al, 1998; McAllister, 1999; Estrella et al, 2000; Sun Qiu et al, 2000). We argue that this on-going assessment of rigour, and the subsequent refinement of methodologies are integral parts of participatory research.

Following this introduction, the next section highlights the need to conceptually ‘situate’ all participatory research activities in their own local and research contexts. We suggest a number of key factors that define what is appropriate and feasible in a participatory process. In the third section we present five principles of good practice in NRM research followed by a brief review of other contributions to the definition of good practice. The next section illustrates the principles and associated indicators with (five) Chatham workshop case studies. The final section concludes the chapter with some final thoughts on the potential of the framework; the whole framework of principles and indicators is provided for easy reference in the chapter’s appendix.

Understanding the research context

Participatory research needs to be designed and assessed in the context within which it occurs. Various parameters define what is appropriate and feasible in every participatory research project. These guide what we can realistically expect from the process and results of the research (including what form of learning might occur), and therefore need to be considered as the backdrop for any ‘guideposts’ to good practice, and in monitoring and evaluation. We suggest, and then discuss, five key factors that affect participatory research (Found, 1995; McAllister and Vernooy, 1999):

1. The nature of the research question(s).
2. Researchers’ views on participation, learning and the role of science.
3. The social aspects of the NRM system.
4. Experience and capacity of the stakeholders.
5. External contextual factors which enable or constrain participation.

The nature of the research question(s)

The objectives, scale and scope of the research questions influence the appropriateness and feasibility of the participatory research approach, and affect local people’s willingness to participate. Some social or biophysical research issues may be adequately addressed by research processes with relatively low participation, such as in cases when the research findings have relatively ‘low added value’ for the direct participants (eg, an inventory of species in cases

where the local people already have sufficient knowledge for their own purposes). When research questions involve the generation of new knowledge at the local level, then it may be more likely to call for a higher degree of participation. This would include, for example, generating new ideas or testing management practices or processes.

Researchers’ views on participation, learning and the role of science

Researchers who apply participatory approaches tend to do so for one, or a combination, of two purposes: functionality and/or empowerment. The former refers to the contribution of participatory approaches to increasing the accuracy and validity of research findings. For example, research on an innovation in a complex natural resource management system may require participatory research in order to effectively apply (and thus experiment with) the innovation. The second refers to the empowerment aspect of participatory research, in which research becomes a means for capacity building and social transformation in the research site.

Related to the latter, the researchers’ explicit or implicit assumptions about the role of learning in the research also underpin their decisions about whether and what form of PR to use. Van der Veen (2000) and Loevinsohn et al (2000) describe three principal learning theory approaches and their relevance for PNRM research and (rural) development. The first approach, reproductive learning, assumes that there is a body of objectively verifiable knowledge and that this can be taught by breaking down content into its essential elements. Frequently, scientist-led research and various types of consultative participatory research involve reproductive forms of learning as the basis for the dissemination phase of the research. An example of this is researchers and extension workers showing users how to apply specific practices or technical options through the dissemination of pamphlets, training, and/or on-station demonstrations. A parallel can usefully be drawn between approaches to learning in research and research approaches (in the sense of ‘philosophies’ or ‘paradigms’) themselves. In this case, reproductive learning most closely mirrors the ‘logical positivist’ (or empiricist) research paradigm, in which research seeks the ‘accumulation of objective knowledge through the production of empirically testable hypotheses’ (Braun, 2001).

The second approach to learning, constructivist learning, assumes that important features of the external world are uncertain and disputed, and that people actively construct their understanding of it. (Re)discovery and innovation, not repetition, are essential parts of this construction process. In practice, researchers/development workers often assume roles as facilitators, rather than instructors. They encourage work in groups and shared planning, action and reflection. This type of learning tends to occur in more collaborative forms of participatory research. In terms of parallels to research paradigms, a constructivist learning approach is congruent with a ‘social constructivist’ paradigm, which views the human mind as the source of knowledge (Braun, 2001). In this paradigm:

the function of science is the creation of concepts or theories that expand flexibility and choice . . . [social constructivism] holds that all social action is open to multiple interpretations, none of which is superior in any objective sense . . . Theory and practice are not viewed as separate moments, but rather as inseparable aspects of a single practice . . . and theory is a powerful change agent’ (Braun, 2001, p9, based on Cooperrider and Srivastva, 2000).

The third learning approach is transformative learning. In this approach, ‘learners’ together build a more integrated or inclusive perspective of the world. Through the learning process, they jointly transform some part of their worldview, for example their understanding of social relations in their own community forest. Such transformation is often stimulated by communicative learning, but goes beyond it, in terms of internalization and transformation of understanding. Manifestations of transformative learning in resource management include, for example, new values or patterns of decision-making that farmers generate and apply outside the immediate arena of the learning intervention. This approach to learning has linkages to the people-centred, emancipatory research approaches, such as participatory action research. This can be understood as an ‘emergent paradigm’, which draws from and combines both positivist and constructivist views (Fisher, pers comm). Ideally this approach to research integrates knowledge sharing, systematic enquiry and human interpretations of the world. Moreover, it intentionally and consciously activates the ‘praxis’ (the theory and practice linkage that constructivism highlights) as a means of (self-) empowerment for marginalized people and improvements in human systems.

The social aspects of the natural resource management system

Natural resources are governed by complex, overlapping and sometimes conflicting social entitlements and traditional norms such as private versus common property rights, tree versus land tenure, differential security of tenure and use rights. Roles negotiated along lines of gender, kinship, ethnicity, socioeconomic status, age and occupation influence access to decision-making and use of natural resources. Representations of ‘community interests’ and ‘local or indigenous knowledge’ in the research process are often produced in the context of struggles over resources through which different parties defend interests and advance claims. Although participatory research processes may provide an opportunity for less powerful groups to contest existing power relations and resource rights, it may also enable more powerful or politically aware groups to assert preferential rights over resources. Researchers need to take this into account, and make continuous adjustments to avoid possible negative repercussions.

Experience and capacity of the stakeholders

Interaction between researchers and local people, and the skills, attitudes and

personalities of researchers have an influence on what local people say, how they feel about the research, and how willing they are to participate. Previous experience of local people with research and development projects, as well as perceptions of potential benefits, can positively or negatively influence community motivation to participate in new research activities, as well as bias their responses. Hence, it is important that researchers be aware of the social and other constraining or enabling factors which influence the interaction process between researchers and local people, and the process of knowledge construction (Long and Long, 1992).

External contextual factors which enable or constrain participation

The political context and the history of local involvement in decision-making will influence the kinds of local participation which are feasible, as well as local people’s willingness to participate. The type of questions researchers need to be responsive to in research design include:

• Is the government supportive of participatory processes?
• What is the history of local participation in decision-making?
• What is the history of local control over resources?
• Is there a history of hierarchy or oppression?
• Has there been recent conflict in the country, or within the local area?

Towards good practice: shared learning from experience

The above sections highlight key elements that differentiate research contexts; clearly there is a great need to treat each research experience as unique. Yet at the same time, a review of the literature and shared learning experiences in fora such as the Chatham Workshop begin to indicate patterns of good research practices which cross-cut these differences. At the Chatham Workshop participants were asked to identify elements of what they considered ‘best practice² in participatory natural resource management research’ in each of the case studies presented. This process generated a list of 36 aspects, which were then further distilled down to 18 general elements through critical review by a small working group in the workshop (see Appendix 2). Following the workshop, Vernooy et al further analysed these 18 aspects for their commonalities, and grouped them into five key principles of good practice in PR in NRM (Vernooy et al, 2000). These are considered good practice in terms of their contribution to both of the goals of PR in NRM: positive local impacts of research; and the generation of valid, trustworthy, and relevant research findings. The latter implies that these findings may be generalized, in other words that they contribute learning that can be applied in some way to other areas beyond the research site.

The proposed principles of good practice in PR in NRM are:

1. The research reflects a clear and coherent common agenda (or set of priorities) among stakeholders and it contributes to partnership building.
2. The research addresses and integrates the complexities and dynamics of change in human and natural resource systems and processes, including local understanding of these.
3. The research applies the ‘triangulation principle’ (multiple sources of information and methods), and links together various knowledge worlds.
4. The research contributes to concerted planning for the future and social change.
5. The research process is based in iterative learning and feedback loops and there is a two-way sharing of information.

Contributions from the literature: establishing trustworthiness

References to similar collective efforts to identify good practice are scarce. Jules Pretty is one researcher who has discussed in some detail the issue of ‘quality control’, or the need to establish criteria of trustworthiness in participatory research. He developed a ‘framework’ of 12 indicators to identify parts of the research process and to check whether key elements have been omitted (1994: 42–45; 1995: 1256). Looking at the framework of indicators listed below, we can see that it is possible to group most of these indicators under the five generic principles that resulted from the Chatham case studies. We have added the corresponding number of our proposed principles in parentheses to illustrate the linkages. The indicators are:

• Prolonged and/or intense engagement between various actors involved in the research cycle (1).
• Persistent and critical observation (2).
• Triangulation by multiple sources, methods and investigators (3) (further detailed by Patton, 1990: 464–470).
• Analysis and expression of difference (3).
• Negative case analysis, or the search for and explanation of cases, or examples that do not fit the general pattern (3) (further elaborated by Patton, 1990: 463).
• Positive impact on stakeholders’ capacity to know and act (4).
• Parallel investigations and team communications (5).
• Participant checking (5).
• Peer or colleague checking (5).
• Reports with working hypotheses, contextual descriptions and visualizations (5).
• Use of reflexive journals (5).
• Use of an enquiry audit (5).

Other authors have also elaborated upon the meaning of one or more of these indicators. Chambers (1997; 156–161; 1994: 1253–1268), using the term

‘principles for participatory learning and analysis,’ pays particular attention to the importance of the ‘reversal of learning’ (researchers learn from local people), ‘handing over the stick’ (local people take a lead role in facilitation, investigation, analysis and learning), sharing of information and ideas, triangulation, the quality of behaviour and interaction, rigorous observation and reflective judgement. McAllister (1999: 38–39) emphasizes that good participatory research allows for the proper identification of stakeholders, does a sound job in disaggregation, and provides space for the adequate representation of stakeholders. It is encouraging to see that the principles or indicators developed by these authors converge with our five generic principles. What we have not been able to find in the literature, however, are many concrete examples of how these research quality-control criteria are used in assessment, either ex ante or ex post. This seems a difficult task to accomplish, but one that we consider to be of great importance. For this reason, we attempt this task in the following section, drawing out concrete examples of good participatory research practices in action.³

Good practice in action: five case studies

Examples of the Chatham case studies that illustrate one or more of the five principles are listed in Table 6.1. We highlight one case study in depth for each principle. The five chosen cases combine different types of research experiences. Three cases illustrate principles 2, 3 and 4 respectively: the International Center for Tropical Agriculture (CIAT) ‘Hillsides Project’; the International Maize and Wheat Improvement Center (CIMMYT) ‘Risk Management Project’; and the Center for International Forestry Research (CIFOR) ‘Adaptive Collaborative Management Research Project’. In these three cases the research highlighted is strategic research initiated by research centres, which then builds an agenda with other stakeholders. Principles 1 and 5, which are illustrated by the International Centre for Research on Agroforestry (ICRAF) ‘Landcare Project’, and CIAT’s ‘Comité de Investigación Agrícola

Table 6.1 Chatham case studies that illustrate the five principles

Local (CIAL) Project’ respectively, are both community-based research processes, which experiment in agriculture at the local level.

Principle 1: The research reflects a clear and coherent common agenda

The following ‘guideposts’ or indicators underscore issues of quality partnerships and collaboration between local stakeholders and researchers and of local empowerment. The latter includes empowerment not only via outcomes of research but also through local participation and leadership in the process of the research. As Chambers (1994) expresses it, this requires researchers to have confidence in local abilities and to thus ‘hand over the stick’. The indicators include:

1.1 The research (and extension) agenda has been set collaboratively and transparently.

1.2 The research (and extension) design allows space for the meaningful participation of local stakeholders, including marginalized groups, and takes into account potentially differentiated perspectives and interests (based on gender, class, age, ethnicity or other aspects).

1.3 Partnerships among stakeholders have been created and strengthened through dialogue, joint actions and mutual benefits (friendship and fun included).

1.4 The research initiative respects the commitments made with partners, and the follow-through strategy is defined.

1.5 The research includes a clear strategy for action/change, which has been defined in terms of expected material results and increased social capital (or more broadly, for empowerment).

1.6 There is good documentation of the participatory process, including the use of tools.

1.7 The analysis of results and authorship of published materials have been shared between researchers and other stakeholders.

This principle is explored with reference to ICRAF’s ‘Landcare Initiative’ (see Box 6.1 and Garrity’s case study, this volume). The first four indicators (all relating to ‘partnership’) are discussed together here with reference to Landcare⁴ as a model and as a research activity. In terms of the original design of the ICRAF research agenda, the decision to study contour hedgerow technologies was initially driven by the research institution, based on experience in these areas. The strategy to address hedgerow stakeholder issues was developed through the researchers’ observations and interactions with farmers during the on-going on-farm research. Thus, although not originally conceptually developed in partnership, ICRAF’s decision to ‘experiment’ with Landcare as a dissemination strategy, was sparked by the demands of local farmers. This experimentation, emerging out of a necessity to increase the impact of the ‘main research,’ was initially viewed as an extension approach. As researchers became aware of the multitude of research questions relating to social capital formation,

sustainability, efficiency, and scaling up, however, they engaged increasingly vigorously in Landcare as a form of multistakeholder participatory action research (PAR). The PAR tackles head on the hypothesis that initiatives for sustainable upland development should be able to address both the technical and socioeconomic–institutional constraints faced by poor farmers (Catacatun, pers comm, 2001).

Landcare as an institutional model incorporates all four partnership indicators. According to Catacutan and Mercado (2001), Landcare:

relies on the active participation of participants in building networks and harnessing support for communal activities. Founded by the convergence of common agenda, it enhances the development of social capital among three key actors: the farmers, the Local Government Units, and the Technical facilitators and other service providers. Landcare is not typified as a set of prescribed rules and approaches. However, it aims to maintain and preserve a demand-driven process where the agenda is set by the members themselves and Resource Organizations are there to support their interests . . . It is about people, and the key to success is based on a mature social capital and a close bond between and among farmers – communities – and governments. (Catacutan and Mercado, 2001, p12)

The extent to which each Landcare group is able to fulfil the indicators, and follow the Landcare model in its ideal form, depends on each group’s members, facilitator and processes.

In terms of the fifth indicator, the partnership approach described above is also the basis for a clear strategy for action/change and well-defined empowerment objectives and strategies.⁵ The theory underpinning this strategy is the ‘convergence model of institutional development’. It suggests that social capital formation is triggered when there is a convergence of interests and desired goals among participating communities, institutions and individuals. The Landcare model offers a forum where the key actors can share the costs and benefits of the programme. Typically, farmers share their time, knowledge, skills and leadership, as well as labour and low-cost materials for group activities and projects. Local governments provide leadership, financial, technical and policy support.⁶ Technical facilitators, such as ICRAF staff, and service providers contribute the necessary technical support and facilitation for group formation. The driving force for this social capital formation is the relevance of the issues to the participants. The essence of the concept is that each group ‘responds to issues that affect them and are more likely committed to find and implement solutions on their own ways, than those imposed by external agencies’ (Catacutan and Mercado, 2001). Landcare groups’ interests have embraced such diverse issues as adapting soil and water conservation technologies, stream rehabilitation, education, training and policy advocacy.

In terms of the sixth and seventh indicators (documentation of the participatory process and shared analysis of results and authorship), these rely, again, on the motivations and capacities of the local farmer groups and the researchers with whom they interact. Some of the groups in Mindanao are engaged in participatory self-assessment workshops to monitor and assess the maturity stage of their own groups. In terms of the whole Landcare experience, ICRAF has documented many of its experiences in a range of publications; the president of Landcare in Claveria, who is a farmer himself, has also authored two papers on Landcare (Catacutan, pers comm, 2001).

Principle 2: The research addresses and integrates the complexities and dynamics of change in human and natural resource systems

Sound natural resource management research deals with the intersections of biophysical and social forces. It embraces the complexities of resource dynamics, such as multiple and often conflicting uses and users, individual and common property resources, space and time interdependencies and scales, the self-regulating nature of ecological processes, and off-site effects. This not only demands that researchers integrate social and biophysical science, but because of limited time and resources, that they assess and manage the tradeoffs between the various kinds and depth of information (Chambers, 1994).⁷ Because of the dynamic nature of ecosystems, NRM research also needs to be adaptive in its approach. Researchers need to critically assess the research design, process, and

learning and outcomes on an on-going basis, and adjust it and themselves accordingly. Indicators for this principle include:

2.1 The analysis balances and integrates natural (biophysical) resource dynamics with human (social) changes and innovations in NRM. (The latter includes people’s relationships with the natural resource system and changes, and their perceptions of it.)

2.2 The analysis gives equal attention to both the inherent site characteristics and to (the impacts of) innovative management practices (locally generated or the results of research interventions).

2.3 The research uses an iterative research and learning approach, in other words cycles of diagnosis–intervention–assessment–diagnosis–intervention–assessment, etc.

This principle is explored with reference to the CIAT ‘Hillsides Project’ (see Box 6.2 and the Vernooy case study, this volume). The first two indicators (balancing of biophysical and social science aspects and attention to both the inherent site characteristics and management innovations) can be explored in the three main aspects of this project’s action research activities: natural resource mapping, natural resource analysis, and the application of indicators to the resource systems. In each of these, the researchers made efforts to understand and apply the farmers’ perspectives. This perspective inherently integrates social and biophysical aspects of resource management issues.

Each of the studies started with the participatory design of a local resource map (as per the now well-known participatory rural appraisal (PRA) mapping exercises). The maps included the borders of the area according to local definitions, the hills, principal and secondary roads and paths, the rivers, creeks, springs and reservoirs as well as the principal drinking-water pipelines, infrastructure (schools, churches, health-care centres, cemeteries, coffee-washing/drying facilities, haciendas and farms), agroecological zones, production systems, vegetation (forest types) and soil types. With one or two exceptions the maps gave a detailed picture of the micro watershed landscape. All maps were returned to the local cooperators for future use and reference.

The maps also served to define the line for the transect walk, which would criss-cross the major zones and production systems, passing the other important resource features of the area. During the transect walk, the informants (facilitated by the research team) undertook resource analyses, which were recorded in a table. Factors examined included landscape characteristics and use (agroecological zones, and changes over time), the ‘state’ of forests, water resources, crops, wildlife, domesticated animals, pastures and local soil indicators. In addition, participants identified the limitations as well as opportunities for agricultural production and NRM in the area. The CIAT research team later complemented the information with data from other sources (previous studies, aerial photographs, GIS-based data, local poverty profiles study, and soil analyses).

The third and final step in the micro watershed analysis process constituted the development of a set of ‘simple to understand and use’ local indicators and values for each of the factors. The set of indicators, for use by inhabitants of the micro watersheds, was developed through the following consultative exercise: a draft set was formulated by the research team based on the findings of the combined 15 resource analyses, reviewed and then refined with the informants, and subsequently applied by the informants to their own micro watershed during a workshop. Values given to the indicators (options were: bad, regular and good) were tabled and grouped together by component (water, forests, crops, etc; note that soils were added based on the outcomes of the soil analyses conducted during the transect walks). In order to compare and discuss results with the informants, a second workshop was organized. The results were discussed both by micro watershed (site comparisons) and by component (resource comparisons).

The third indicator – the use of an iterative research approach – is illustrated by the last step of this cycle of the research. Following the indicator development and assessment, the research team and other stakeholders used the research

findings for the selection of potential new sites where CIALs could be formed and other forms of experimentation could be started (in other words they drew on learning from this stage to create the next stage). Among these new initiatives by CIAT and its collaborators are a number of NRM experiments to test alternatives including ‘new’ crops (for example, soy beans) or crop combinations, and soil improvements. As well as providing this kind of direction to the research project, the outcomes also had external impacts. The research team presented the results to key local decision-makers such as the mayor of San Dionisio, state agencies and NGOs operating in the watershed, as well as to the newly created Association of Community Organizations. Decision-makers discussed priority zones for action where natural resources are in bad shape or are at high risk. The analysis was also helpful as a pre-hurricane Mitch overview of the state of the natural resource base and allowed for comparison with the post-Mitch situation (a study completed in 1999).

Principle 3: The research applies the ‘triangulation principle’ and links knowledge worlds

Triangulation is a means of increasing confidence in results by assessing and cross-checking findings from multiple ‘points’, including various sources, various methods or in various conditions. Given the complexity of NRM research, triangulation is particularly important. In the context of participatory research, one potent form of building confidence in results is through the explicit sharing of findings and learning with local stakeholders on an on-going basis. This creates the opportunity for distortions or misunderstandings to be corrected relatively rapidly and easily. Relatedly, research findings will be more sound and more widely accepted if they build on and link the ‘best of diverse knowledge worlds’; in other words, they expand beyond scientific views of the world to also incorporate local and traditional knowledge and views. Potential indicators for this principle include:

3.1 The research process links the local, traditional and scientific knowledge worlds.

3.2 The research methodology uses a diversity of methods and tools.

3.3 Information generation is based on multiple sources.

3.4 Information dissemination (sharing of learning and findings) occurs throughout the process through multiple exchanges between researchers and stakeholders, including at the local level.⁹

This principle is explored with reference to CIMMYT’s ‘Risk Management Project’ (RMP) (see Box 6.3 and the Vaughan and Shamudzarira case study, this volume). The first two indicators of this principle relate to the linking of knowledge worlds and to triangulation through diversity of research methods. In the RMP, we can track these indicators by looking at the project’s use of participatory agroecosystem modelling maps as a shared learning and decision-support tool for researchers and farmers to understanding the soil fertility systems. The linking of local and scientific knowledge was not an explicit goal

at the beginning of the project; the experience gained during the methodological process, however, changed this somewhat. The feedback interface stage (in other words, the role playing and discussion of ‘if and when’ scenarios relating to the model) was critical to the integration of various types of knowledge that did take place. In order to build on the synergy that was created during this stage, the team developed a simplified participatory model that allows for full farmer–researcher involvement in the communication and research process.¹⁰ By linking farmers’ and researchers’ knowledge, these tools have the potential to contribute to a more holistic and thus effective evaluation of soil fertility management technologies, under highly variable and risky climate conditions. Vaughan (2001, per comm) noted from their experience that it is challenging to keep the integration of knowledge worlds present and functioning if it is not built into the framework of the project from the beginning; ideally a project would incorporate these objectives at the outset, and assess and adjust as needed through a dynamic review process.

In the RMP, a diversity of tools and techniques were used, refined, adapted or discarded as necessitated by the process and project stakeholders. The research team used focus group discussions, transects, soil sampling, agroecosystem maps and climatic timelines to undertake the participatory development of farmers’ soil and climate taxonomies (including indications of

production and management practices and constraints by climate and soil type). These were then linked across a matrix format that identifies clustered management practices (rules of thumb) for different typologies of farmers under different soil, climatic and socioeconomic conditions. This enables the model to run different crop, management and climatic scenarios based on each farmer group’s classification criteria for soils, climate conditions and management practices. While these methods were needed in order to integrate different kinds of knowledge, they also provided the opportunity to build the trustworthiness of results through triangulation.

The third indicator refers to triangulation by generating information from multiple stakeholders. The RMP used stakeholder analysis at an early stage as the basis for participation in the research; this process is critical to this indicator. The preliminary stakeholder analysis (to identify different institutions and influential individuals with interest or influence in the project) was followed up with the participatory development of farmer typologies utilizing wealth ranking and other methods. This enabled an identification of the key differentiating variables between the various socioeconomic groups, and was predominantly based on seasonal access to resources. As described above, this enabled the project to develop different scenarios for the different farmer groups, based on their various perspectives and management practices. This illustrates the ‘spin-off effect’ of triangulation of sources; in this case it increased the applicability of the research findings through the diversification of models.

One lesson that emerged from this research experience is that the successful implementation of a multiple knowledge worlds, multi-stakeholder, and multi-research tool approach depends on the team members internalizing a sense of value of this new and time-consuming approach,¹² as opposed to a desire to ‘just getting models running’. This, in fact, also relates to the fourth indicator (dissemination/information exchanges locally throughout the research), which suggests that researchers need to share their findings locally during the research with the explicit purpose of seeking improvement in them.¹³ A challenge that can be highlighted from this is that there is a temptation in science to ‘keep things in the lab’ or ‘in the model’, and not adequately value the knowledge and input of local people. This includes the temptation for scientists/modellers to undertake into local interactions with the (conscious or unconscious) goal of getting their models validated (as opposed to building them based on field-derived input). This is also closely intertwined with issues of farmer ownership over the research. According to Vaughan (2001, pers comm),

the process of resource mapping was critical for sharing ideas and getting farmers to have ownership over a systems view of their resources, for collecting and analyzing information, and for providing the modelers with a framework for developing and interpreting model questions and outputs. Conceptually it was a big leap for the modelers and the farmers to do this. The modelers may have initially under-estimated the farmers’ ability and level of existing understanding regarding outcomes of their resource trade-offs and allocations. (Vaughan, 2001, pers comm)

In addition, the experience pointed to the value of having social scientists integrated in the team, alongside the more technical researchers, as well as having good access to external support from outside specialists.¹⁴

Principle 4: The research contributes to concerted planning for the future and social change

The fourth principle relates to the concept of the research contributing in a concrete and grounded fashion to positive future impacts in the research site (indicators 1 and 2) and beyond it (indicator 3). In referring to options and scenarios development, the first indicator implies that the research process should include some means of explicitly considering various options for activities and future directions, and their ‘pros’ and ‘cons’. The second indicator refers to the research being designed so that any related processes and benefits can continue beyond the research project window, including there being a plan for how the research project staff and resources can withdraw without undermining the momentum that has been developed. This is a critical indicator, because if the research project addresses an on-going issue, but does not successfully ‘work itself out of a job’ at the local level, then it can be argued that it has either: created/contributed to dependency during its work (and thus the process cannot continue without it); or that it has developed a process (as a research ‘product’) that does not accrue sufficient net benefits to the participants for them to be interested in continuing it after the withdrawal of the external agents (thus there is not sufficient local will to continue, without external incentives). The last indicator of this principle refers to the utility of the findings for (geographic) areas or (organizational) arenas other than those directly involved in the research, or in other words, the generalizability of the research results. The indicators are:

4.1 The research process allows for options and scenario development.

4.2 The research methodology has a sustainability focus and an ‘exit strategy’ built in from the outset.

4.3 The research incorporates a ‘scaling up’ or extrapolation strategy, including an analysis of the uptake environment.

This principle is explored with reference to CIFOR’s ‘Adaptive Collaborative Management (ACM) Project’ (see Box 6.4 and the case study by McDougall et al, this volume). The first indicator, which refers to options and scenario development, is illustrated in the ACM research project through its use of local visioning exercises, development of local criteria and indicators of sustainability, and simulation modelling. The ACM project action research process integrated the consideration of various future pathways at two levels: the community ‘direction setting’ level, and the level of ‘micro-actions’. At the ‘direction setting level’, for example, in the research communities in the Philippines, and Nepal,¹⁵ the PAR began with community-based workshops. Participants in the workshops, including women and men of a range of different ethnic (and caste), wealth and interest groups, undertook a joint visioning exercise, in which small groups of participants drew pictures of their ideal future for their forest user

group and community. A facilitator led the process of identifying commonalities that existed across all the visions, and the group also added any other key aspects that were missing from their joint vision. In the Philippines, for example, eight main themes emerged: education, organization, livelihoods, forests and forest management, coastal resource management, infrastructure, health and policy. The participants then used these themes as a framework for developing criteria and indictors for their community forestry self-assessment. In an iterative process, through facilitated small group discussions, participants created possible ‘indicators’, and refined those through discussions based on the question of ‘what if’ that was the case – ‘would that really get us to where we want to be?’ The visioning process was critical to the PAR because it enabled the community members to frame the overall future scenario to which they were jointly aspiring. The development of the criteria and indicators proved to be an excellent process tool to stimulate detailed discussion of the many facets of that vision, including that it made explicit the divergent and convergent local views on the ‘ideal’ community and resource system. It enabled diverse stakeholders to make explicit their experiences and perceptions on a wide range of forest and community issues; the process then encouraged the accommodation of these diverse views through a negotiated development of specific criteria and indicators.¹⁷

The research supported options development by the communities at the ‘micro level’ as well. In the Philippines and Nepal workshops, for example, participants identified the strong and weak aspects of their situation based on their criteria and indicators for self-assessment. They then prioritized several of the weak areas, and set about identifying possible options for actions as part of the action research process. This process of identifying options and assessing them has been followed to date in a preliminary way in these two country sites,¹⁸ and teams are currently considering if and what further forms of ‘soft systems’ scenario development may be useful in weighing those future options. In Zimbabwe, the researchers are exploring participatory modelling as one option for scenario assessment (as described in PRGA, 2000). The process focused on several levels of analysis, from the local to the rural district council. They are using a framework called FLORES (Forest Land Oriented Resource Envisioning System) to guide them in developing a computer simulation model that will allow the exploration of alternative scenarios for NRM in the three villages where they have been working.¹⁹

The second indicator addresses the need for local benefits to continue beyond the life of the research project in the field. In the participatory action research phase of the ACM research project, community members and some other supporting stakeholders (such as District Forest Office representatives, and Federation of Community Forest Users in Nepal), worked together to develop a set of agreed and easily understood criteria and indicators, and then jointly assessed their situation. While it is too early in the project to determine its success, the hope is that the PAR has established a locally appropriate framework for a monitoring and assessment process (embedded in a ‘learning approach’ to management). Local stakeholders should be able to re-apply and adapt these in subsequent years as a means of continuing a feedback and learning process in the community forest management system. It may thus serve to enhance the responsiveness and effectiveness of local management decisions, and ultimately increase the benefits and sustainability of the community’s forest resources.

Clearly, however, a ‘technology transfer’ of such a self-monitoring process would not be useful if it: (a) was not needed by the community; and (b) did not include a build-up of the necessary local skills and capacities to continue using the self-monitoring system after the end of the research project. The latter was assessed before the workshops, primarily through discussions between community members and the researchers (who had been working in the communities for six months or more and so were familiar with the local situation). In all the communities the local forest management committee and researchers tried to design the workshops in such a way that they maintained a high sense of community ownership. Following the development of the self-assessment systems, the participants expressed that it was a tool that they wanted to continue using on their own. Researchers have initiated the local development of skills and capacities to maintain the initiatives through starting to engage local stakeholders, including community members, as facilitators in this process. This will be further supported through local training in facilitation, as needed. To enable benefits from the research project to continue well beyond the life of the research project, the researchers and community members are integrating

the self-monitoring processes and other ACM elements into the existing decision-making and planning systems in the communities. In this sense, the research outcomes (self-assessment processes) become practical and flexible means of strengthening these local institutions over the long term.

The third indicator, ‘producing generalizable results’, is also a CIFOR (and thus an ACM project) mandate. Research based on a small number of isolated PAR initiatives would have presented the project with some significant challenges in regard to this goal. In order to address this, the project embedded the PAR in a larger multi-site framework of scientific analysis. Specifically, in all the research sites in every country, researchers have laid the foundations for comparative analysis by conducting a series of parallel background studies.²⁰ The background studies used a consultative form of participation,²¹ but simultaneously allowed researchers the time to build relations and the groundwork with local stakeholders for the main phase of the research. Additionally, to contribute to the larger CIFOR research goal, as well as local recording, processes and learning in all sites are regularly recorded by researchers in a framework and format that is comparable across countries and sites. Furthermore, these site findings are integrated with additional studies being undertaken in each country; less intensive, but more extensive, these shorter studies (which are more consultative in nature, and less action-oriented than the sites), add breadth to the analysis. Other methods are also being used to triangulate the results across sites, countries and regions, key among these being the use of multivariate analysis across all sites, and the analysis of the outcomes of the participatory modelling in Zimbabwe, Indonesia and Cameroon. Whereas the multivariate analysis is expected to provide a quantified picture of key drivers for the success or failure of adaptive collaborative management processes, the analysis of the simulation models (including the discussion with local partners of the scenarios they deliver) is expected to provide insights into the reasons for failure or success arising out of the structure and behaviour of these processes. Ultimately, these elements of the larger framework for analysis will enable greater depth of understanding within each site and highlight findings that emerge across varying community forestry conditions.

Principle 5: The research process is based in iterative learning and feedback loops and there is a two-way sharing of information

Continuous learning (leading to people’s increased ability to solve problems) is one of the key features of participatory research. It is therefore crucial that the research design allows for systematic, regular and critical exchange and reflection upon both the research process and the results (learning and outcomes).²² A central aspect should be the meaningful participation by the different stakeholders in these activities. Indicators for this may include:

5.1 The research includes regular exchange and reflection events involving key stakeholders.

5.2 The research has regular monitoring events, involving key stakeholders.

5.3 The outcomes of monitoring activities are translated into revised actions.

5.4 There is an appropriately high quantity and quality of participation in exchange and monitoring events.

5.5 The flow of information sharing in the research is multi-directional.

This principle is explored with reference to CIAT’s ‘Local Agricultural Research Committee (CIAL) Project’ (see Braun case study, this volume). The first three indicators, all discussed together here, refer to participants in the research meeting regularly to discuss processes and results, systematically assessing the processes and results, and then translating that reflection into adjustments of research process and management activities. The CIALs case offers a clear illustration of these three indicators, because the essence of the CIAL process is that of joint experimentation and learning.²³ CIALs follow a cyclical process (reminiscent of a PAR cycle), often depicted as a staircase (Figure 6.1). It has the following stages:

• Diagnosis: The CIAL’s research topic is chosen through a group diagnostic process that takes place at an open community meeting. Communities base
  
  their decisions on criteria such as chances of success, the number and groups of beneficiaries and the likely costs of the research. They commonly choose subjects such as the evaluation of new crops or crop varieties, measures for controlling crop pests and diseases, and fertilizer use.
• Planning: With the support of the facilitator, and other farmers and resource persons, CIAL members decide on the objectives of the experiment, the treatments and control, the materials and methods to be used, the inputs needed, the data to be collected, and the criteria for evaluating results.
• Experimentation: The CIAL members implement the experiment, and collect data, using the CIAL fund to pay for inputs. Other members of the community may assist them.
• Evaluation: The CIAL meets with the facilitator to evaluate the data collected. Conclusions are drawn and preparations made to present the results to the community.
• Analysis: The CIAL asks itself, ‘What have we learned’? This stage in the process is especially important when new crops fail or the experiment produces unexpected results.
• Feedback: The CIAL presents its activities, results and expenditures at regular, open meetings of the community. Committee members may support their presentation with simple posters showing research results. If the CIAL feels confident about these results, it may make recommendations based on them. The community then decides whether the CIAL should continue with the experiment, switch to a new topic, or cease its activities altogether.

Source: Ashby et al, 2000

Figure 6.1 Stages of a CIAL process

Monitoring and evaluation, like facilitation, take place throughout the CIAL process. The purpose is to ensure that the process operates as it should and that those responsible for it are held accountable. The community monitors the performance of the CIAL and is free to add, remove, or replace committee members at any time. The CIAL is expected to keep records of its experiments and to make these available to community members, upon request. It must also account to the community for its use of the CIAL fund. The CIAL, in turn, monitors the performance of its facilitator, who is held responsible for the quality of support provided. The results of this evaluation are made publicly available. The research process may be adapted based on monitoring findings within this framework. Revision of ‘actions’ (indicator 3) beyond the immediate CIAL process (for example, ‘adoption’ of research outcomes) is illustrated most clearly by changes in local farming practices. While not all CIALs would have as high a record, Pescador, Cauca, Columbia, offers a strong example of impact: as a result of CIAL monitoring of its trials, over 80 per cent of the farmers adopted a bean variety recommended by the local committee (Ashby et al, 2000:6).

The fourth indicator refers to the quantity and quality of participation in exchange and reflection events, both of which are critical components of success in participatory research. Both of these are underpinned by Principle 1, indicator 2, which refers to the question of ‘who participates’:

The research (and extension) design allows space for the meaningful participation of local stakeholders, including marginalized groups, and takes into account potentially differentiated perspectives and interests (based on gender, class, age, ethnicity or other aspects).

Relatedly, the continuity of the group members can offer some insights into this indicator. As Ashby et al (2000) describe, it is not uncommon for a frequent turnover of members early on in the CIAL process:

Being a CIAL member is not for everyone. Many groups experience a turnover of at least one of their members in the opening year. CIALs often go through a difficult period during their early development. This typically occurs a few weeks after foundation, when the initial rush of enthusiasm experienced at the motivational and diagnostic meetings has worn off. Some members feel that the research topic chosen by the community does not match their own priorities and lose interest. Others drop out because the CIAL takes up too much time that they would rather spend doing their own farming. Women in particular find it hard to fit in CIAL activities because of their many other commitments. (Ashby et al 2000)

Ashby and colleagues (2000) also note that participation can be affected by ‘participation fatigue’, for example where past projects in the area have required input by the community, without generating sufficient benefits. This is, unfortunately, an increasingly widespread phenomenon, which reduces the opportunity for future participation in potentially beneficial initiatives.

The fifth indicator, regarding the multi-directional flows of information, can be understood in this case as the regular exchange of information between core and non-core participants of a CIAL. Because the total number of actual CIAL participants is usually small, this type of information exchange is critical because it underlies the goal of providing a service to the community. Adoption of the recommendations of the CIAL also illustrates its effective information flow. Using the same example as above, in Cauca department, Colombia, not only did 80 per cent of the community adopt a variety recommended by the CIAL, but 50 per cent of farmers in three nearby communities with CIALs, and 20 per cent of farmers in four communities without CIALs also adopted the variety (Ashby et al, 2000:6). The other side of this indicator is information seeking by the CIAL. According to Ashby et al (2000:137), ‘as CIALs mature they become more proactive in seeking information: in about 57 per cent of the CIALs, individual members have taken the initiative to contact institutions for advice or seek assistance without waiting for their facilitator to help’. Thus, new (information) linkages are built.

Conclusions: a framework for reflection and change

In this chapter, based on the experiences of the research case studies explored during the Chatham meeting, and other literature, we have proposed a set of ‘principles and indicators of good practice in participatory research on natural resource management’. We have presented this alternative framework –grounded in concrete field experiences – as a tool, or guidepost, to assess methodological options for participatory research. We view the framework as one that encourages reflection and change, and thus is appropriate to the theoretical and philosophical underpinnings of participatory (action) research. In designing the framework, the aim has been to encourage NRM researchers to explore how, and to what degree, a participatory methodology (and particular tools) influences both the effectiveness of NRM research and empowerment of local resource managers. To do so, we have argued that it is necessary to have a clear understanding of the types of learning that guide the research (from reproductive to transformative) and of the contextual variables that influence participatory research in practice (such as the nature of research questions, and local social dynamics and experiences) so that one can work in an informed way with the ‘good practices’ suggested here. The framework represents a potential tool for transformative learning for researchers – learning that enables the application of increasingly inclusive or integrative perspectives to participatory research practice. It also serves as a hypothesis-generating tool to guide future research design and planning. As such, it is an integral part of the iterative learning process in and to which participatory research practitioners are engaged and committed. We hope that this framework can be used to engender reflection and improvement in individual research initiatives. Finally, we hope that it enables comparisons across sites, and thereby contributes to the on-going learning process of the larger community of NRM researchers about what research approaches work best, for whom, when and why.

Appendix 1: Principles and indicators of good practice in PR on NRM

Principles	Indicators
1 The research reflects a clear and coherent common agenda (or set of priorities) among stakeholders and it contributes to partnership building.	1.1 The research (and extension) agenda has been set collaboratively and transparently 1.2 The research (and extension) design allows space for the meaningful participation of local stakeholders, including marginalized groups, and takes into account potentially differentiated perspectives and interests (based on gender, class, age, ethnicity or other aspects) 1.3 Partnerships among stakeholders have been created and strengthened through dialogue, joint actions and mutual benefits (friendship and fun included) 1.4 The research initiative respects the commitments made with partners, and the follow-through strategy is defined 1.5 The research includes a clear strategy for action/change, which has been defined in terms of expected materials results and increased social capital (or more broadly, for empowerment) 1.6 The research involves good documentation of the participatory process, including of the use of tools 1.7 The analysis of results and authorship of published materials have been shared between researchers and other stakeholders.
2 The research addresses and integrates the complexities and dynamics of change in human and natural resource systems and processes, including local understanding of these.	2.1 The analysis balances and integrates natural (biophysical) resource dynamics with human (social) changes and innovations in NRM. (The latter includes people’s relationships with the natural resource system and changes, and their perceptions of it) 2.2 The analysis gives equal attention to both the inherent site characteristics and to (the impacts of) innovative management practices (locally generated or the results of research interventions) 2.3 The research uses an iterative research and learning approach (ie, cycles of diagnosis–intervention–assessment– diagnosis–intervention–assessment, etc).
3 The research applies the ‘triangulation principle’ (ie, multiple sources of information and methods), and links together various knowledge worlds.	3.1 The research process links the local, traditional and scientific knowledge worlds 3.2 The research methodology uses a diversity of methods and tools 3.3 Information generation is based on multiple sources 3.4 Information dissemination (ie, sharing of learning and findings) occurs throughout the process through multiple exchanges between researchers and stakeholders, including at the local level.
4 The research contributes to concerted planning for the future and social change.	4.1 The research process allows for options and scenario development 4.2 The research methodology has a sustainability focus and an ‘exit strategy’ built in from the outset 4.3 The research incorporates a ‘scaling up’ or extrapolation strategy, including an analysis of the uptake environment.

5 The research process is based in iterative learning and feedback loops and there is a two-way sharing of information.

5.1 The research includes regular exchange and reflection events involving key stakeholders 5.2 The research has regular monitoring events, involving key stakeholders 5.3 The outcomes of monitoring activities are translated into revised actions 5.4 There is an appropriately high quantity and quality of participation in exchange and monitoring events 5.5 The flow of information sharing in the research is multi-directional.

Appendix 2: List of indicators of good practice generated at the Chatham workshop ‘Continuing to Learn Together’, held at the Natural Resources Institute, Chatham, UK

1. Stimulation of participation by ‘forgotten’ groups (eg, women, landless, lower caste).
2. Shared agenda setting.
3. Realistic objectives and a related, coherent participatory methodology.
4. A clear strategy for action and change.
5. Realistic expectations for empowerment through research.
6. Partnerships created or strengthened through dialogue and joint actions.
7. An eye for incentives and benefits for all parties involved.
8. Use of multiple tools.
9. An eye for the dynamics of change in natural and human-made systems.
10. The best of the various knowledge worlds linked together.
11. Use of multiple sources and triangulation.
12. A built-in exit or sustainability strategy.
13. Possibilities for extrapolation or scaling up of the methodology and results.
14. Good documentation of the use of tools, the participatory process and the results.
15. Horizontal communication.
16. New professional roles accepted and put into practice.
17. Respect for the commitment made with research partners.
18. Shared authorship of results and publications.

These indicators formed the basis of Indicators Framework:

the new proposed Principles and

Principle 1 is based on Indicators 1–7.
Principle 2 is based on Indicator 9.
Principle 3 is based on Indicators 8–11.
Principle 4 is from indicators 12 and 13.
Principle 5 is from indicators 14–18.

Notes

1   In particular, we build on the IDRC discussion paper by Vernooy, Buckles and Schreier, 2000.

2   The term ‘best practice’ – while it held catch-phrase value – gave a misleading impression that there was a single set of ‘correct’ practices. In order to reinforce the notion of ‘good principles’ that can be adapted and applied in a variety of contexts, we opt for the current terminology of ‘good practices’.

3   For reasons of limited space in this chapter, we use only one principle per case in our illustrative ‘assessment’ of good practice. In practice, one would aim to apply all the principles for a well-rounded assessment.

4   The Landcare Case Study is available in the appendix of this book (Garrity, this volume). Key additional information was provided by the discussion paper by D Catacatun and AR Mercado, Jr (2001), and personal communication by D Catacatun (2001).

5   ‘Well-defined’ indicates that they need to be clearly thought out and expressed, and consistent with the research mandate. The actual nature and quality of the empowerment objectives and strategies will, of course, be dependent on the type of PR, and learning and the research goals.

6   In Claveria, for example, the local government has created a legal requirement that it support an annual budget for Landcare-related activities and projects.

7   Chambers (1994) rightly points out the tradeoffs between quantity, relevance, accuracy and timeliness. He notes that this principle includes the notion of ‘optimal ignorance – knowing what is not worth knowing, and then not trying to find it out’ (Chambers, 1994).

8   This case study information was provided by the chapter author, R Vernooy. The full case is available in the appendix (Vernooy, in this volume).

9   This is in addition to, not instead of, a broader more formal dissemination strategy beyond the site; this external dissemination strategy is covered in Principle 4, indicator 4.3.

10  In addition, through a participatory process, a simple agroecosystem map was developed, with either a group or an individual farmer. This process is iterative and dynamic; in other words, the researchers and farmers together and continually revisit and update the map as necessary. After developing the agroecosystem map the researchers asked farmers to demonstrate their soil fertility resource allocations for the past and present seasons by crop and field types. Further discussion and use of focus discussions elaborated on the factors influencing farmers’ targeting of resources. This supported an enabling dialogue between various parties on farmers’ decision-making processes.

11  This case study is based on the case submitted by K Vaughan and Z Shamudzarira, CIMMYT at the Chatham meeting, and personal communication. See also Vaughan and Shamudzarira (this volume).

12  The case study elaborates the fact that the use of participatory tools is ‘time consuming and can be laborious’, but worth it in the end ‘once trust is built and the agenda straight . . .. by backing up (well-documented) participatory field methods with trials based on the PRA work, the research provided some tangible outputs that farmers can use and benefit from’ (Vaughan, pers comm, 2001).

13  The RMP did also have a formal external dissemination strategy, although like many projects, this emerged relatively late in the research process. While this may be a limiting factor (shared by many research initiatives), the project has engaged in

multiple levels of dissemination. These include integration of methods into the university curriculum, as well as the development of a shared research site with ICRISAT, farmers, community institutions, extension and CARE to provide a broad social infrastructure for information dissemination at a local level.

14  This kind of diversity on the research team can also enable the productive use of creative alternative research methods and tools, such as role playing and knowledge analysis mapping.

15  For more information, see the full ACM case study (McDougall with R Prabhu and Y Kusumanto in this volume). Input to the case study analysis in this chapter was also provided by H Hartanto and C Colfer of CIFOR. This project was generously supported by the Asian Development Bank (ADB), EU, DFID, IDRC and WRI.

16  The communities involved were: the San Rafael, Tanabag, and Concepcion Multipurpose Cooperative, Palawan, the Philippines; Deurali-Baghedanda and Bamdibhirkhoria forest user groups in Kaski District, Nepal; and Manakamana and Andheri Bhajana forest user groups in Sankhuwasahba District, Nepal.

17  This process can be viewed as the interlinked transformative learning phenomena of ‘divergent thinking’ (or ‘assimilation’), and ‘convergent thinking’ (or ‘accommodation’), which Van der Veen (2000) describes as central to the concept of perspective transformation.

18  Small groups discussed aspects such as: the history of the issue and lessons learned from past efforts; desired outcomes; possible options for action; and strengths and weaknesses of each option. For the ‘best bet option,’ some small groups tried to anticipate potential obstacles and ways to address them, as well as plans for monitoring the implementation of the actions.

19  The FLORES Adaptation and Calibration (FLAC) package was used in four workshops in Zimbabwe as a means of facilitating the model’s use by non-modelling specialists. Each iteration of the participatory process in FLAC is meant to integrate and acknowledge existing knowledge as the basis for producing a simulation model. A prototype model has been developed and is being subjected to rigorous testing with local partners and against empirical data (CIFOR 2000, Vanclay, 2000).

20  These background studies elaborate stakeholder relations, historical trends, biophysical and socioeconomic contexts and initial levels of adaptiveness and collaboration.

21  The project chose a ‘consultative’ form of research for the background studies phase of the research, because the research questions in this phase (which were the same across all countries) were primarily of interest to researchers rather than to local people. For example, local people were already well aware of the history of their forests; it was the researchers who needed to learn about this. In this case, using a more consultative and less participatory approach was estimated and intended to minimize the time and energy costs of the research to local people.

22  Note the linkage to indicator 3.4, which refers to on-going sharing and assessment of findings.

23  The CIAL model illustrates these in a ‘theoretical’ way; the degree to which CIALs implement these indicators in practice, of course, ultimately depends on each CIAL itself.

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