Embracing the complexity: Multiple interests and debated resolutions in the pineapple value chain in Uganda
Keywords:actor-oriented, cognitive mapping, food value chains, pineapple, systems learning
Strengthening horticultural value chains can be used for improving food and nutrition security while reducing rural poverty. However, the complexity of local situations challenges the effectiveness of blueprint development strategies and calls for actor-oriented approaches. The fresh pineapple value chain in Uganda is illustrative of such a complex situation. The market supply is not organized by dominant lead firms. In contrast, individually negotiated and context specific actor relationships and their purposeful activities form and sustain this human activity system. As value chain actors take multiple factors for their business activities into account, the aim of our system analysis is to elicit their perspectives on the influence of these factors. This provides a more contextualized understanding to inclusively increase local actors’ benefits.
We used a systems learning approach, in which farmers, traders, brokers and scientists were seeking a better understanding of the local value chain. Cognitive mapping and additional qualitative methods were used to reveal internally held perceptions about the factors and their influences on income generation from engaging in the pineapple value chain. Several meetings with participants from single actor groups informed subsequent multi-actor meetings: four with farmers (4-8 each) and four with traders (2-7 each). Group cognitive maps served as a starting point for ten meetings which included participants from several actor groups (4-13 each). To foster a feeling of connectedness between actors along the chain, these consecutive multi-actor meetings evolved around the factors and situations that participants had identified as influential to all actor groups, such as prices, markets, quality and communication. Semi-structured interviews and participant observation further complemented the analysis.
The approach resulted in a contextualized picture of how multiple natural, technical and social factors influenced actors’ income generation in the pineapple value chain, e.g. farm and market price, market size, quality, seasonality, production methods and skills, buyer-seller relationships and transportation. There was little disagreement about the rationale of the influence of factors during the single actor group meetings. However, the number of factors and the perceived cause-effect relations differed markedly between actor groups. The dialogue during multi-actor meetings revealed different aspects of problem-situations. Participants expressed solutions and also explained barriers to them. For all actors in the chain to profit from their respective business activities, awareness of prices and other market information is particularly important. However, problematic communication patterns between actors pose current challenges and dissatisfactions. The flow of information was disrupted by the intertwined patterns of changes in prices, supply and demand, along with structural constellations, such as many small-scale farmers, relatively few brokers linking production areas to distant market centers and many, dispersed traders in different markets. Moreover, prices were individually negotiated and generally competitively formed. The occurring fragmentation among actors is a result and also a cause for communication problems, fluctuations and actor relations. The controversial debates regarding proposed solutions, showed that the feedback cycles are difficult for actors to break given the contextual constraints and their conflicting interests.
The participatory activities and shared explanations allowed surfacing of problematic patterns and value chain structures that caused friction and hindered broader collaboration. The approach helped to trigger dialogue and understanding between otherwise often competing market actors. While actors are aware of the benefits from improved collaboration, the gained contextualized system understanding revealed why this is difficult to implement. Participatory system learning can reveal actors’ room of maneuver, and contribute to a process that enables actor-driven system change.