|Home | Archives | About | Login | Submissions | Notify | Contact | Search|
Copyright © 2006 by the author(s). Published here under license by The Resilience Alliance.
Go to the pdf version of this article The following is the established format for referencing this article:
Newton, A. C., E. Marshall, K. Schreckenberg, D. Golicher, D. W. te Velde, F. Edouard, and E. Arancibia. 2006. Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods. Ecology and Society 11(2): 24. [online] URL: http://www.ecologyandsociety.org/vol11/iss2/art24/
Research, part of Special Feature on Do we need new management paradigms to achieve sustainability in tropical forests? Use of a Bayesian Belief Network to Predict the Impacts of Commercializing Non-timber Forest Products on Livelihoods
1Bournemouth University, 2UNEP-World Conservation Monitoring Centre, 3Overseas Development Institute, 4El Colegio de la Frontera Sur, 5Methodus Consultora
Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of NTFPs is represented in the model as the conversion of one form of capital asset into another, which is influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are determined by the availability of the five types of assets following commercialization. The model, implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes of success and failure in NTFP commercialization, and can be used to explore the potential impacts of policy options and other interventions on livelihoods. The potential value of this approach for the development of NTFP theory is discussed.
Key words: conservation; NTFP; sustainable development; tropical forest
In recent years, commercialization of non-timber forest products (NTFPs) has been widely promoted as an appropriate means of developing tropical forest resources (Lawrence 2003, Arnold and Ruiz Pérez 1998, Ruiz Pérez and Arnold 1996). This reflects a growing recognition of the contribution made by many NTFPs to rural livelihoods, both in terms of supporting subsistence and as a means of generating financial income (Arnold and Ruiz Pérez 1998, Belcher 2003). At the same time, because harvesting of NTFPs is generally considered to be less damaging to forest resources than timber extraction, NTFP exploitation is widely believed to be relatively compatible with forest conservation (Arnold and Ruiz Pérez 1998, Belcher 2003, Peters 1996). Thus, commercialization of NTFPs potentially offers a means of achieving both conservation and development goals concurrently (Plotkin and Famolare 1992, Counsell and Rice 1992), by increasing the value of forest resources to local communities (the “conservation-through-use” or “use it or lose it” principle; Dickinson et al. 1996).
Recent reviews suggest that approaches to NTFP commercialization have not, however, been universally successful, and that the scope for improving rural livelihoods through NTFPs is in doubt (Ros-Tonen and Wiersum 2005). For example, Sheil and Wunder (2002) suggest that donor investments for the development of NTFP resources have often been misdirected, and expectations of local income generation potential have frequently been unrealistic. Many NTFPs have been harvested destructively or unsustainably, resulting in resource degradation (Peters 1996). In a comprehensive review of NTFP commercialization, Neumann and Hirsch (2000) indicate that sale of NTFPs often tends to provide a low level of income for the poorest section of communities, rather than providing a method of socioeconomic advancement. The NTFP trade may actually perpetuate poverty rather than alleviate it (Neumann and Hirsch 2000). Belcher (2003) notes that the term “NTFP” encompasses a very wide range of forest products and marketing systems, and although some NTFPs appear to be successful in alleviating poverty and contributing to forest conservation, others are harvested very intensively in a manner that results in some stakeholders being excluded from the process.
Given that NTFPs are highly diverse in terms of their ecological and socioeconomic characteristics, there is a need to define which NTFPs have particular potential for development, and under what conditions their use is likely to make a positive contribution to both human livelihoods and forest conservation (Belcher 2003, Lawrence 2003). Such information would help reduce the misdirection of donor investments identified by Sheil and Wunder (2002). However, the high diversity of NTFPs challenges the development of a firm theoretical framework that would enable the potential impacts or outcomes of NTFP commercialization to be predicted. The scientific literature relating to NTFPs, although copious, tends to be characterized by detailed investigations of individual case studies, with relatively little attention given to synthesis, generality, or theory (Neumann and Hirsch 2000). Such developments have been hampered by the lack of an analytical framework that would enable the results from different case studies to be integrated and compared (Arnold and Ruiz Pérez 1996).
Researchers at the Center for International Forestry Research (CIFOR), in Indonesia, have recently developed an analytical approach for classifying NTFP case studies and assessing their development potential, using multivariate statistical approaches. The method aims to simplify the diversity of NTFP case studies by identifying “typologies,” or groups of case studies sharing common characteristics. The initial analysis using this method described by Ruiz Pérez and Byron (1999) was based on only nine case studies, limiting the generality of the results (Marshall et al. 2003). However, more recently, the approach has been applied to a much larger number (61) of case studies (Ruiz Pérez et al. 2004). Results were used to group cases into classes according to different household economic strategies, each of which varied in their degree of dependence on NTFPs as a source of income. Such analyses may be of value in highlighting broad groupings of NTFP case studies. However, the existence of discrete strategies remains open to doubt; rather, the data presented by CIFOR illustrate continuous variation among the case studies with respect to the variables considered. In addition, these analyses provide little insight into the relationship between the characteristics of an NTFP and the impact of its commercialization on livelihoods or natural resources. The main limitation of the CIFOR approach is its dependence on correlation; even when relationships are defined, no causation can be inferred.
In this paper, we describe an alternative approach to analyzing the characteristics of different NTFP case studies. Specifically, our objective was to develop a method that would enable the impact of NTFP commercialization on livelihoods to be predicted on the basis of scoring a common set of attributes. In order to make such predictions, some form of modeling approach is required. Progress with developing predictive models of NTFP commercialization has been very limited to date (Arnold and Ruiz Pérez 1998). Most of the models that are available are entirely conceptual in nature (Wilkie and Godoy 1996, Homma 1996). As noted by Arnold and Ruiz Pérez (1998), deterministic models for the management and use of NTFPs are likely to have limited applicability because of the complexity and variability of the situations under which NTFP commercialization takes place. Furthermore, as interdisciplinarity is an essential feature of NTFP research (Neumann and Hirsch 2000), an analytical framework is required that enables different forms of information to be integrated and analyzed, including qualitative and quantitative data drawn from ecological, economic, and social research.
We propose that Bayesian Belief Networks (BBNs) provide an appropriate method for developing predictive models of NTFP commercialization. This method represents information in the form of probabilities, enabling many different sources of data to be integrated and analyzed according to a common framework. The use of a probabilistic, rather than a deterministic, approach to modeling also avoids many of the problems described by Arnold and Ruiz Pérez (1998). Presentation of model output in the form of probabilities has the added advantage of being relevant to the needs of decision makers, who in the context of NTFPs require an assessment of risk associated with a particular investment option.
Here, we provide a brief description of BBNs and their application to management of natural resources (Append. 1). We then describe the construction of a BBN to predict the livelihood impacts of NTFP commercialization, according to an analytical framework focusing on the different capital assets required to support livelihoods. The BBN described here was designed to incorporate information generated by an interdisciplinary research project (CEPFOR) that examined the factors influencing NTFP commercialization in 19 case studies from Mexico and Bolivia (Marshall et al. 2003, 2006). We validate the BBN using field data gathered by this project, and illustrate how the BBN can be used as a model to predict the impacts of NTFP commercialization on livelihoods and to explore the potential impacts of policy interventions. The contribution of this model to development of a general theory of NTFP management and use is then discussed.
Development of an Analytical Framework
We employed the Department for International Development (DFID) “livelihoods framework” described by Ashley and Carney (1999) and DFID (1999) as a basis for constructing the BBN (Append. 2). This assumes that people require a range of assets (including both material and social resources) in order to achieve positive livelihood outcomes. Five different types of capital asset are considered: natural, physical, human, financial, and social (for definitions, see Append. 2). Following this approach, we consider that communities and individuals involved in NTFP commercialization will require access to each of these five types of assets in order for commercialization to be successful. Furthermore, we propose that the process of NTFP commercialization can be considered as the conversion of one form of capital asset into another. Principally, during NTFP commercialization, natural capital will be converted into financial capital, but during this process the availability of other forms of capital (human, social, and physical) is also likely to change (Fig. 1). The dynamics of the availability of different assets define the potential impact of NTFP commercialization on livelihoods.
Construction of the BBN
The BBN was constructed, using Hugin Developer 6.3 (http://www.hugin.com/), by creating nodes for each of the five types of capital asset, each of which was given two possible states, “high” and “low,” representing the amount of capital available before commercialization. A second set of five nodes was created representing the change in capital assets available resulting from commercialization; these were given five possible states, namely “Large decrease,” “Small decrease,” “No change,” “Small increase,” and “Large increase.” Each of the nodes representing the availability of capital assets before commercialization was linked to each of the nodes representing change in capital assets resulting from commercialization (Fig. 2).
Results from CEPFOR were used to identify a list of factors that were found to most influence the process of NTFP commercialization. The CEPFOR project investigated 19 NTFP commercialization case studies in Bolivia and Mexico (Marshall et al. 2003, 2006, te Velde et al. 2006) (Table 1), with the explicit aim of identifying the factors influencing success of commercialization. Factors were included on the list if they were identified by the participatory research methods or the statistical analysis of household data collected (see Append. 3, and Marshall et al. 2006). A total of 66 factors were identified, which included the biological characteristics of the products being traded, the socioeconomic characteristics of the producer communities, and the characteristics of the market chains (Append. 3). Each of the factors was then scored for all of the 19 case studies. Scoring was performed by members of the research team familiar with all of the case studies, and was based on all the evidence collected during the CEPFOR research. A complete set of the factors and scores is appended (Append. 7); those factors that most commonly limited commercialization are listed in Table 2. In the BBN, each factor was represented as an individual node, linked to one of the five nodes representing capital asset types available before commercialization. Factors were grouped according to capital type, such that each factor was linked to a node representing only a single type of capital asset. The assignment of factors to capital types was performed by the project team, on the basis of expert judgement. Definition of the states of each node, and the Conditional Probability Tables (CPTs) associated with each node, are described in Append. 4.
For the purposes of validating the BBN, two entirely independent data sets were used: one was used as input to the model, and the other was used to test the predictions made. For the former, the scores for each factor generated by the project team on the basis of the research results were used to instantiate the factor nodes for each NTFP case study individually (see Append. 1). Each of the 19 NTFP case studies was saved as a separate BBN case file that could subsequently be reloaded into the network.
A second data set was developed to test the predictions made using the model. This focused on an assessment of the impacts of NTFP commercialization on the communities and households that are actively involved in the commercialization process. A scoring exercise was performed by the staff of partner NGOs associated with the CEPFOR project, who were each familiar with the individual NTFP case studies, having worked directly with the communities involved. This group of experts was invited to assess the impacts of NTFP commercialization on livelihoods using a set of indicators developed by CIFOR (Kusters et al. 2005). These “CIFOR indicators” were developed according to the DFID Livelihood Framework, and organized according to the five types of capital asset described previously. For each of the indicators, the experts were invited to score the impact of NTFP commercialization on the actors in the case study area over the past 10 years using a five-point scale: 2 (“strongly positive”), 1 (“positive”), 0 (“neutral”), -1 (“negative”), and -2 (“strongly negative”). Separate sets of indicators were employed for assessing impacts at both household and community scales. Full details of the indicators used are appended (Append. 8). Technical assistance was provided to each partner by one member of the core research team, to assist in the interpretation of the indicators, and to ensure that the scoring was undertaken in a consistent manner across all case studies.
The impact indicator scores were summed individually for each NTFP case study, pooling data for both household- and community-level indicators. These values were then compared with model predictions for each individual NTFP case study. For this purpose, we used the summed probability values for the “Small increase” and “Large increase” states inferred for the nodes representing the change in assets resulting from commercialization. Data for the different asset types were pooled together for this analysis, but data for individual case studies were kept separate. When analyzed by regression, these two independent sets of values were found to be significantly related (r2 = 0.66, P < 0.001; Fig. 3). This indicates that the model is able to predict livelihood impact accurately, in terms of the degree of change in assets available, purely from the factor scores used to instantiate the factor nodes, which refer to the biological characteristics of the products being traded, the socioeconomic characteristics of the producer communities, and the characteristics of the market chains. Predicted values were also analyzed as ranks, and again a significant positive relationship was found (Spearman rank correlation, r = 0.80, P < 0.001), indicating that the model is also able to predict the relative impacts of different NTFP cases on livelihoods.
As a further test, the frequencies of the different scores provided for the CIFOR livelihood indicators were calculated by pooling household- and community-level responses together, but treating each capital asset type separately. These were then regressed against the proportions of each of the five states inferred for the nodes representing the change in assets resulting from commercialization, including each capital type as a separate set of data points (Fig. 4). For this analysis, predictions were made using the BBN instantiated with probabilities of factor states calculated by pooling together values for all NTFP case studies. Again, the regression was highly significant (r2 = 0.86, P < 0.001). This indicates that the model is able to capture variation in the availability of assets among different asset types in a way that is consistent with independent measures of livelihood impact.
Explorations of model output, including predicting the potential impact of different policy interventions on livelihoods are described in Append. 5. A decision-support tool based on the BBN has also been made available as a free download (Append. 6).
The BBN described here represents the first model of NTFP commercialization to be developed that permits quantitative predictions to be made regarding the potential impacts of NTFP commercialization on livelihoods. The model could potentially be of direct value to decision makers involved in supporting NTFP commercialization initiatives, enabling financial support and other interventions to be focused on those products and socioeconomic circumstances with highest potential for success. In addition, the model could be used to diagnose problems or constraints affecting NTFPs currently undergoing commercialization, and to explore the potential impacts of different policy interventions. The decision-support tool described here (Append. 6) based on the model was developed with such practical applications in mind.
However, the value of the model as a decision-support tool is clearly limited by the fact that information was based on a sample of only 19 case studies, albeit drawn from more than one country. A key question is the extent to which results obtained here are applicable to other NTFPs and other areas. An important issue for any project taking a case study approach is how widely its findings can be applied; as noted by Belcher (2003), caution is advised in extrapolating results from any single group of NTFPs. However, the aim of the CEPFOR research was not simply to analyze a group of individual case studies in detail, but rather to identify patterns and processes that are generally applicable, and to evaluate under what conditions these apply (Marshall et al. 2006). The research findings are likely to be of relevance to many other communities in regions of Latin America that share similar key socioeconomic and geographical characteristics, including poverty level, dependence on forest resources, access to markets, etc. (Marshall et al. 2006). One of the advantages of the BBN approach is that additional information provided by further case studies could readily be incorporated into the model. Conditional probabilities can be updated as additional evidence becomes available through a process of sequential updating, also known as adaptation or sequential learning. The software employed here (Hugin Developer) uses an adaptation algorithm developed by Spiegelhalter and Lauritzen (1990) for this purpose.
The extent to which the list of factors considered here is generally applicable to NTFPs remains to be tested. It is likely that the precise set of factors that influences the process of commercialization will differ between individual NTFP case studies, therefore it might be necessary to include additional factors in the model when extending it to other NTFPs. The analytical framework adopted here enables additional factors to be readily incorporated, simply by identifying the type of capital asset that the factor is deemed to influence and amending the CPTs accordingly. Although the number of factors considered here is large (66), it is substantially fewer than the 114 variables considered by the CIFOR research, which were used to characterize a wide range of attributes of the NTFP case studies considered (Belcher and Ruiz Pérez 2001, Ruiz Pérez et al. 2004).
Differences between the list of factors considered here and the characteristics employed in the CIFOR research may be attributed to the contrasting research approaches adopted. Whereas characteristics were selected by expert judgement in the CIFOR study (Belcher and Ruiz Pérez 2001, Ruiz Pérez et al. 2004), in the current investigation factors were identified through a process of participatory research involving actors participating in NTFP commercialization. Also, in this study, only those factors that had been shown to influence the commercialization process were included. This approach contrasts with the CIFOR research, where an attempt was made to provide a comprehensive characterization of each case study, including a large number of descriptive variables of uncertain value (Belcher and Ruiz Pérez 2001). It is possible that accurate model predictions could be obtained with fewer than the 66 factors considered here, although such a reduction of the factor list would require further evidence to be acquired regarding their relative influence on the commercialization process.
Regardless of the precise selection of factors, we believe that the analytical approach adopted here provides a valuable framework for integrating information from different NTFP case studies, and could potentially be applied to any group of NTFPs. The associations between factors and capital assets can readily be visualized, enabling the relevance of different product characteristics to be rapidly identified and explored. By focusing directly on the different assets required to support livelihoods, the impact of NTFP commercialization on livelihoods can readily be assessed. Adoption of a livelihood framework also facilitates communication of research results to policy makers. Livelihood frameworks and similar asset-based approaches are increasingly being used by a range of aid agencies and development organizations to target development aid and identify appropriate policy interventions (Ashley and Carney 1999, Carney 1998, 2002, DFID 1999). Although the DFID approach was employed here, similar approaches are being used by CARE, the United Nations Development Programme (UNDP), Oxfam, and the FAO (Warner 2000). For example, Siegel (2005) describes use of an asset-based approach to examine policy issues and investment priorities for the World Bank in Latin America and the Caribbean, and Ambrose-Oji (2004) provides details of how sustainable livelihood frameworks have been applied in eight different countries in a range of different production systems. Warner (2000) considers application of the livelihood approach specifically to the forest sector, and highlights its value for defining how forests can contribute to achieving sustainable livelihoods and alleviating poverty. However, livelihood frameworks have never previously been used as anything other than a conceptual tool. For the first time, using a BBN, we have demonstrated here that such frameworks can be operationalized as quantitative analytical models, enabling predictions to be made regarding the potential impacts of different policy interventions on livelihoods.
Speculation: Implications for Theory
One of the principal features of previous NTFP research is its theoretical weakness. As noted by Neumann and Hirsch (2000), much NTFP research “appears to be conducted in a theoretical vacuum.” Few attempts have been made to explicitly develop or test theories in NTFP research, and those theories that have been developed are generally conceptual or highly qualitative in nature (Neumann and Hirsch 2000, Wilkie and Godoy 1996, Homma 1992, 1996). As a result, many of the research designs employed in NTFP research are based on flawed assumptions, and there is a widespread inability to predict or explain outcomes (Neumann and Hirsch 2000). We support Peters’ (1991) assertion that the defining characteristic of theory is its ability to make testable predictions. Given its ability to make such predictions, could the BBN presented here, therefore, be viewed as contribution toward development of general NTFP theory?
Two key questions have been at the center of much NTFP research: (i) does commercialization of NTFPs alleviate poverty? (ii) does NTFP commercialization contribute to forest conservation? The first question arises out of concern that NTFP commercialization strategies may either result in many people only earning a small supplementary income, or a few people earning a significant contribution and disadvantaging others (Neumann and Hirsch 2000). The second question arises out of the belief that commercialization could be combined with forest conservation, and even act as an incentive for it—the so-called “use it or lose it” principle (Dickinson et al. 1996, Freese 1997, Godoy et al. 2000). This hypothesis is based on three key assumptions: (i) forests have a greater long-term economic value if they are left standing, rather than being converted to some other land use; (ii) local communities will be more likely to manage forest resources sustainably if they gain direct economic benefits from harvesting forest products; (iii) poverty in rural tropical areas is both a cause and a result of forest loss and degradation (Neumann and Hirsch 2000). Resource depletion may prompt different responses, including moving to different harvesting areas or initiating new management regimes. Homma’s (1992, 1996) well-known conceptual model proposes that increasing commercialization will inevitably result in over-exploitation of wild resources, leading to two possible scenarios: domestication or synthesis/substitution of the product.
Could the model described here be of value in addressing such questions? The BBN is designed to predict the impact of different interventions on the assets required to support livelihoods. The approach is also designed to enable generalizations to be made on the basis of information from individual case studies. The impact of policy interventions on components of poverty alleviation such as income generation could be examined, for example by assessing changes in the availability of financial capital to actors involved in commercialization. Similarly, the impact of commercialization on forest resources could be evaluated by assessing changes in the availability of natural capital. However, in order to address these questions fully, information is needed on how people make decisions regarding the trade-offs between different capital assets, such as natural and financial capital, in relation to their immediate and long-term needs. Furthermore, it should be remembered that NTFPs offer just one among several alternative livelihood strategies that may be available. A comprehensive theory would, therefore, incorporate the decision-making processes of the actors involved in NTFP commercialization regarding which livelihood strategies to adopt under different circumstances. Although trade-offs between different assets could be explored using the model as described here, incorporation of decision-making processes would require extending the BBN to include factors that influence selection of alternative livelihood strategies. Alternatively, other approaches such as agent-based models might be of value in this context (Lambin et al. 2003). Our belief is that understanding how the availability of different livelihood options affects people’s decisions about NTFP commercialization currently lies at the frontier of NTFP research.
Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link
ACKNOWLEDGMENTSThe authors thank the communities and traders who participated in this work. Finbarr O’Sullivan (URL: http://mapinformatics.com/) designed the Java interface that is incorporated in the CEPFOR Decision Support Tool. This publication is an output from a research project funded by the U.K. Department for International Development (DFID) for the benefit of developing countries. The views expressed are not necessarily those of DFID. Project R7925 Forestry Research Programme.
Ambrose-Oji, B. 2004. Livelihoods synthesis study: key determinants of poor people’s livelihood strategies and natural resources-related management opportunities. Department for International Development (DFID) Natural Resources Systems Programme, Hemel Hempstead, UK.
Arnold, J. E. M., and M. Ruiz Pérez. 1996. Framing the issues relating to non-timber forest products research. Pages 1–18 in M. Ruiz Pérez and J. E. M. Arnold, editors. Current issues in non-timber forest products research. Center for International Forestry Research (CIFOR), Bogor, Indonesia.
Arnold, J. E. M., and M. Ruiz Pérez. 1998. The role of non-timber forest products in conservation and development, Pages 17–42, in E. Wollenberg and A. Ingles, editors. Incomes from the forest: methods for the development and conservation of forest products for local communities. Center for International Forestry Research (CIFOR), Bogor, Indonesia.
Ashley, C., and D. Carney. 1999. Sustainable livelihoods: lessons from early experience. Department for International Development (DFID) Issues series, London, UK.
Belcher, B. M. 2003. What isn’t an NTFP? International Forestry Review 5(2):161–168.
Belcher, B., and M. Ruiz Pérez. 2001. An international comparison of cases of forest product development: overview, description and data requirements. CIFOR Working Paper Number 23. Center for International Forestry Research (CIFOR), Bogor, Indonesia. [online] URL: http://www.cifor.cgiar.org/publications/ntfpsite/docs/details/detail-wp23.htm.
Bromley, J., N. A. Jackson, O. J. Clymer, A. M. Giacomello, and F. V. Jensen. 2005. The use of Hugin to develop Bayesian networks as an aid to integrated water resource planning. Environmental Modelling and Software 20(2):231–242.
Burgman, M. 2005. Risks and decisions for conservation and environmental management. Cambridge University Press, Cambridge, UK.
Cain, J. D., C. H. Batchelor, and D. K. N. Waughray. 1999. Belief networks: a framework for the participatory development of natural resource management strategies. Environment, Development and Sustainability 1:123–133.
Carney, D., editor. 1998. Sustainable rural livelihoods: what contribution can we make? Department for International Development (DFID), London, UK. [online] URL: http://www.livelihoods.org.
Carney, D. 2002. Sustainable livelihoods approaches: progress and possibilities for change. Department for International Development (DFID), London, UK. [online] URL: http://www.livelihoods.org.
Counsell, S., and T. Rice, editors. 1992. The rainforest harvest: sustainable strategies for saving the tropical forests? Friends of the Earth Trust Ltd., London, UK.
Crome, F. H. J., M. R. Thomas, and L. A. Moore. 1996. A novel Bayesian approach to assessing impacts of rain forest logging. Ecological Applications 6:1104–1123.
Department for International Development (DFID). 1999. Sustainable livelihoods guidance sheets. DFID, London, UK. [online] URL: http://www.livelihoods.org.
Dickinson M. B., J. C. Dickinson, and F. E. Putz. 1996. Natural forest management as a conservation tool in the tropics: divergent views on possibilities and alternatives. Commonwealth Forestry Review 75(4):309–315.
Ellison, A. M. 1996. An introduction to Bayesian inference for ecological research and environmental decision-making. Ecological Applications 6:1036–1046.
Freese, C. H. 1997. The “use it or lose it’ debate: issues of a conservation paradox. Pages 1–48 in C. H. Freese, editor. Harvesting wild species. Implications for biodiversity. John Hopkins University Press, Baltimore, Maryland, USA.
Gertner, G. Z., and H. Zhu. 1996. Bayesian estimation in forest surveys when samples or prior information are fuzzy. Fuzzy Sets and Systems 77:277–290.
Ghazoul, J., and M. McAllister. 2003. Communicating complexity and uncertainty in decision making contexts: Bayesian approaches to forest research. International Forestry Review 5(1):9–19.
Godoy, R., D. Wilkie, H. Overman, A. Cubas, G. Cubas, J. Demmer, K. McSweeney, and N. Brokaw. 2000. Valuation of consumption and sale of forest goods from a Central American rain forest. Nature 406:62–63.
Haas, T. C. 1991. A Bayesian belief network advisory system for aspen regeneration. Forest Science 37:627–654.
Heckerman, D. 1996. A tutorial on learning with Bayesian networks technical report MSR-TR-95-06. Microsoft Research Advanced Technology division. [online] URL: http://research.microsoft.com/research/pubs/view.aspx?msr_tr_id=MSP-TR-95-06.
Homma, A. K. O. 1992. The dynamics of extraction in Amazonia: a historical perspective. Pages 23–32 in D. C. Nepstad and S. Schwartzman, editors. Non-timber forest products from tropical forests: evaluation of a conservation and development strategy. New York Botanical Garden, New York, New York, USA.
Homma, A. K. O. 1996. Modernisation and technological dualism in the extractive economy in Amazonia. Pages 59–82 in M. Ruiz Pérez and J. E. M. Arnold, editors. Current issues in non-timber forest products research. Center for International Forestry Research (CIFOR), Bogor, Indonesia.
Jensen, F. V. 2001. Bayesian networks and decision graphs. Springer, New York, New York, USA.
Kusters, K., B. Belcher, M. Ruiz Pérez, and R. Achdiawan. 2005. A method to assess the outcomes of forest product trade on livelihoods and the environment. CIFOR working paper No. 33. Center for International Forestry Research (CIFOR), Bogor, Indonesia.
Lagos, N. A., and J. C. Castilla. 1997. Bayesian statistical inference in ecology: an example of its use in a Chilean marine preserve. Revista Chilena de Historia Natural 70:565–575.
Lambin, E. F., Gesit, H. J., and Lepers, E. 2003. Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources 28:205–241.
Lauritzen S. L., and D. J. Spiegelhalter. 1988. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B 50:157–224.
Lawrence, A. 2003. No forest without timber? International Forestry Review 5(2):87–96.
Marcot, B. G., R. S. Holthausen, M. G. Raphael, M. Rowland, and M. Wisdom. 2001. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management 153(1-3):29–42.
Marshall, E., A. C. Newton, and K. Schreckenberg. 2003. Commercialising non-timber forest products: first steps in analysing the factors influencing success. International Forestry Review 5(2):128–137.
Marshall E., K. Schreckenberg, and A. C. Newton, editors. 2006. Commercialization of non-timber forest products. Factors influencing success. Lessons learned from Mexico and Bolivia and policy implications for decision-makers. UNEP World Conservation Monitoring Centre, Cambridge, UK.
Neil, M., N. Fenton, and L. Nielson. 2000. Building large-scale Bayesian networks. The Knowledge Engineering Review 15(3):257–284.
Neumann, R. P., and E. Hirsch. 2000. Commercialisation of non-timber forest products: review and analysis of research. Center for International Forestry Research(CIFOR), Bogor, Indonesia. 176 p.
Pearl, J. 1986. Fusion, propagation and structuring in belief networks. Artificial Intelligence 29:241–288.
Pearl, J. 1988. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers, Inc., San Mateo, California, USA.
Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4):669–688.
Peters, R. H. 1991. A critique for ecology. Cambridge University Press, Cambridge, UK.
Peters, C. M. 1996. Observations on the sustainable exploitation of non-timber tropical forest products. Pages 19–39 in D. C. Nepstad and S. Schwartzman, editors. Non-timber forest products from tropical forests: evaluation of a conservation and development strategy. New York Botanical Garden, New York, New York, USA.
Plotkin, M., and L. Famolare, editors. 1992. Sustainable harvest and marketing of rain forest products. Island Press, Washington, D.C., USA.
Reckhow, K. H. 2003. Bayesian approaches in ecological analysis and modeling. Pages 168–183 in C. D. Canham, J. J. Cole, and W. K. Lauenroth, editors. Models in ecosystem science. Princeton University Press, Princeton, New Jersey, USA.
Rieman, B., J. T. Peterson, J. Clayton, P. Howell, R. Thurow, W. Thompson, and D. Lee. 2001. Evaluation of potential effects of federal land management alternatives on trends of salmonids and their habitats in the interior Columbia River Basin. Forest Ecology and Management 153:43–62.
Ros-Tonen, M. A. F., and K. F. Wiersum. 2005. The scope for improving rural livelihoods through non-timber forest products: an evolving research agenda. Forests, Trees and Livelihoods 15:129–148.
Ruiz Pérez, M., and J. E. M. Arnold, editors. 1996. Current issues in non-timber forest product research. ODA/CIFOR, Center for International Forestry Research, Bogor, Indonesia.
Ruiz Pérez, M., and N. Byron. 1999. A methodology to analyze divergent case studies of non-timber forest products and their development potential. Forest Science 45(1):1–14.
Ruiz Pérez, M., B. Belcher, R. Achdiawan, M. Alexiades, C. Aubertin, J. Caballero, B. Campbell, C. Clement, T. Cunningham, A. Fantini, H. de Foresta, C. García Fernández, K. H. Gautam, P. Hersch Martínez, W. de Jong, K. Kusters, M. G. Kutty, C. López, M. Fu, M. A. Martínez Alfaro, T. R. Nair, O. Ndoye, R. Ocampo, N. Rai, M. Ricker, K. Schreckenberg, S. Shackleton, P. Shanley, T. Sunderland, and Y. Youn. 2004. Markets drive the specialization strategies of forest peoples. Ecology and Society 9(2):4. [online] URL: http://www.ecologyandsociety.org/vol9/iss2/art4.
Sheil, D., and S. Wunder. 2002. The value of tropical forest to local communities: complications, caveats, and cautions. Conservation Ecology 6(2): 9. [online] URL: http://www.consecol.org/vol6/iss2/art9.
Spiegelhalter, D. J., and S. L. Lauritzen. 1990. Sequential updating of conditional probabilities on directed graphical structures. Networks 20(56):579–605.
Spiegelhalter, D. J., A. P. Dawid, S. L. Lauritzen, and R. G. Cowell.1993. Bayesian analysis in expert systems. Statistical Science 8(3):219–283.
Siegel, P. B. 2005. Using an asset-based approach to identify drivers of sustainable rural growth and poverty reduction in Central America: a conceptual framework. Policy, Research working paper series; no. WPS 3475. World Bank Group, Washington, D.C., USA. [Online] URL: http://wdsbeta.worldbank.org/.
te Velde D. W., J. Rushton, K. Schreckenberg, E. Arancibia, F. Edouard, E. Marshall, and A. C. Newton. 2006. Entrepreneurship in value chains of non-timber forest products. Forest Policy and Economics 8(7):725–741.
Varis, O. 1997. Bayesian decision analysis for environmental and resource management. Environmental Modelling and Software 12:177–185.
Warner, K. 2000. Forestry and sustainable livelihoods. Unasylva 51(202):3–12.
Wilkie, D. S., and R. A. Godoy. 1996. Trade, indigenous rain forest economies and biological diversity. Model predictions and directions for research. Pages 83–102 in M. Ruiz Pérez and J. E. M. Arnold, editors. Current issues in non-timber forest products research. Center for International Forestry Research (CIFOR), Bogor, Indonesia.
|Home | Archives | About | Login | Submissions | Notify | Contact | Search|