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
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.
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).