APPENDIX 7. Exploration of model output.

Radar diagrams can be used to illustrate the variation between NTFP case studies in the availability of capital assets (Fig. A7.1), as inferred by the BBN from the factor scores for individual cases. The results highlight the pronounced differences that exist between NTFP case studies. For example, whereas Hongos (Cuajimoloyas) scored highly in terms of the availability of human and social capital, others (notably Copal, Pucasucho and Jipi Japa, El Carmen Surutú) scored much less well for these variables. In contrast, the case of Hongos (Cuajimoloyas) was characterized by relatively low availability of natural capital, in marked contrast to Cacao (San Silvestre) and Copal (Pucasucho). These diagrams illustrate the potential value of the BBN as a diagnostic tool for identifying the relative strengths and weaknesses of NTFPs proposed for commercialization in specific socio-economic situations, and providing a basis for identifying appropriate interventions to support commercialization efforts.

Fig. A7.1. Radar diagrams illustrating the contrasting availability in different asset types for commercialization of different NTFPs. Six NTFP case studies are presented, illustrating the range in outputs derived for the entire set of 19, namely: A, Hongos (Cuajimoloyas); B, Cacao (San Silvestre); C, Maguey (La Esperanza); D, Palma tepejilote (Yagavila); E, Copal (Pucasucho); F, Jipi Japa (El Carmen Surutú). The values presented are probabilities associated with the asset being of “high” availability, as inferred by the BBN when instantiated with the factor scores for the individual case studies. Abbrevations for capital asset types: N–natural; P–physical; S–social; H–human; F–financial.


The model can also be used to make generalizations regarding the availability of the different capital assets required to support commercialization by combining the factor scores of all 19 case studies. Results indicate that for the NTFP case studies considered here, when considered together, the availability of human and physical assets was relatively high (with probabilities of availability being high of over 0.63 being recorded in both cases). In contrast, BBN inference indicated that natural and social capital were more likely to be classified as ‘low’ (with probabilities of 0.53 and 0.56 respectively) rather than ‘high’, suggesting that in general, these assets are more likely to constrain NTFP commercialization. Again, such analyses could potentially inform the planning of interventions designed to support communities in their development of NTFP resources.

The BBN can also be used to examine the potential impacts of commercialization on livelihoods, by referring to the nodes representing availability of assets post-commercialisation. Two examples are provided here to illustrate the range in responses obtained for the CEPFOR case studies (Figs. A7.2, A7.3), produced by instantiating the factor nodes of the network with the values for the individual case studies. In both cases, the impacts are represented as a probability distribution associated with the degree of change in the availability of different assets resulting from commercialization. In the case of Hongos (Cuajimoloyas), the most likely outcome is a small increase in all capital types. In contrast, in the case of Jipi Japa (El Carmen Surutú), the most likely outcome was no change in any of the capital asset types. The fact that outputs are represented as a probability distribution indicates how BBNs can be used to illustrate the uncertainty surrounding outcomes. For example, in the case of Jipi Japa (El Carmen Surutú), increases in the availability of all five assets could potentially occur as a result of commercialization, as indicated by probabilities greater than zero. However, the probability of assets declining in availability is greater than those of assets increasing in every case, suggesting that negative outcomes are more likely than positive outcomes in this particular case.

Fig. A7.2. The impacts of commercialization on availability of assets, as predicted by the BBN, for the case study of Hongos (Cuajimoloyas). These outputs were generated by instantiating the network with factor values for these individual case studies (see text).

Fig. A7.3. The impacts of commercialization on availability of assets, as predicted by the BBN, for the case study of Jipi Japa (El Carmen Surutú). These outputs were generated by instantiating the network with factor values for these individual case studies (see text).


Use of the BBN to explore policy options

The BBN can also be used to evaluate the potential impact of different policy interventions on livelihoods. As an illustration, three different interventions are considered here: (1) provision of credit for community level NTFP-based enterprises, (2) improvements to rural transport and communication infrastructure, and (3) promotion of better management of the communal natural resource, for example through the provision of incentives. To explore these potential policy impacts, three case studies were chosen to represent the range of values encountered in the CEPFOR case studies, with respect to the availability of capital assets: Hongos (Cuajimoloyas), Maguey (La Esperanza) and Jipi Japa (El Carmen Surutú). Policy specialists within the research team then defined the likely impact of the interventions on the state of 66 factors identified in the research as influencing the commercialization process, based on expert judgement. Each of the interventions was adjudged to affect a suite of different factors. The number of factors affected was different between the three interventions, being 22, 16 and 16 respectively. Details of the potential impact of the interventions on the factors are described on an appended spreadsheet (App. 4).

To determine the potential impact of these interventions on livelihoods, the BBN case file for each of the three products was amended by changing the state of factors adjudged to be affected by the intervention, and by instantiating the nodes appropriately. The predicted effects of the intervention on the availability of assets required to support livelihoods, according to the DFID livelihood framework, was then illustrated using radar diagrams (Fig. A4). The results illustrate how the impacts of particular policy interventions are likely to differ among NTFP case studies, reflecting their different asset availabilities and the state of the factors prior to the intervention. Impacts were generally more pronounced for Jipi Japa (El Carmen Surutú) than for the other case studies considered, reflecting the relatively low availability of capital assets at the outset in this case study (Fig. A4). Interventions also differ in terms of the capital that they most affect. For example while Intervention 1 generally increased financial capital assets, it had little effect on natural capital. In contrast, Intervention 3 had a pronounced impact on availability of natural capital (Fig. A7.4).

These results highlight the value of the BBN as a decision support tool, enabling the potential impact of different policy interventions to be evaluated. The results suggest that it might be difficult to generalize among NTFPs regarding the potential impact of an intervention. Effective interventions depend upon understanding the factors limiting NTFP commercialization in each specific case, as represented here by the factor scores. Where availability of assets is relatively high, such as human and social capital in the case of Hongos (Cuajimoloyas), the impacts of any intervention on these assets are likely to be relatively slight. However, in cases where assets are lacking, policy interventions can be highly effective in increasing the availability of assets on which livelihoods depend, and therefore in increasing the probability of NTFP commercialization being successful. The relationship between policy interventions and the BBN is illustrated by an expanded analytical framework (Fig. A7.5).


Fig. A7.4. Radar diagrams illustrating the predicted impacts of different policy interventions on the availability of different asset types for commercialization of different NTFPs. Three NTFP case studies are presented, illustrating the range in outputs derived for the entire set of 19, namely: A–Hongos (Cuajimoloyas); B–Maguey (La Esperanza); C–Jipi Japa (El Carmen Surutú). The values presented are probabilities associated with the asset being of “high” availability, as inferred by the BBN when instantiated using data for the individual case studies. Abbrevations for capital asset types: N–natural; P–physical; S–social; H–human; F–financial. The first column presents values for the NTFP case studies as determined by the CEPFOR research, representing the current situation. The three subsequent columns illustrate predictions according to three policy interventions, respectively: (1) provide credit for community-level NTFP-based enterprises, (2) improve rural transport and communication infrastructure, and (3) promote better management of the communal natural resource.

Fig. A7.5. Expanded analytical framework for assessing the impact of policy interventions on NTFP commercialization processes and on livelihoods. We propose that policy interventions mediated through appropriate institutions and processes will influence both the factors influencing commercialization and the process of NTFP commercialization itself, as well as alternative livelihood strategies. Policy context will also influence the vulnerability context of actors involved in commercialization, reflecting the impact of external shocks, market trends, seasonal variation, etc. The impact of policy interventions on livelihoods is determined by their impact on availability of the five types of asset before and after commercialization.