Formal seed systems aim to provide farmers with high-quality planting material that meets evolving demands and cultivation challenges. East African banana (Musa sp.) systems rely strongly on informal seed exchange. For seed system interventions to have a larger and more sustainable impact in such a context, it is necessary to better understand the informal seed system. We studied the management and replacement dynamics around banana suckers and mats by smallholder farmers in Central Uganda. Data were collected through Focus Group Discussions (n = 4) and semi-structured interviews (n = 23). This study showed that, on average, banana farmers grew 10 different banana cultivars to ensure year-round harvesting and to accommodate multiple consumption and cultural needs. They included cultivars from the formal seed system within their portfolios of banana cultivars while also conserving cultivar diversity. Farmers used a broad array of evaluation criteria to select suckers and preferred to use known sources to assure plant quality. Household characteristics, such as age or wealth, are determinants of mat management and replacement. We concluded that a flexible blend of formal-informal approaches to developing the banana seed system is needed to meet the multiple needs of farm households and to support them in improving productivity and dealing with emerging challenges.
Means‐end chain analysis has been applied in a wide range of disciplines to understand consumer behavior. Despite its widespread acceptance there is no standardized method to analyze data. The effects of different analyses on the results are largely unknown. This paper makes a contribution to the methodological debate by comparing different ways to analyze means‐end chain data. We find that (1) a construct that is not mentioned can still be important to a respondent; (2) coding constructs at the same basic level or condensing constructs at a superordinate level lead to different results and both an increase and decrease of information; (3) aggregating data can be based on different algorithms which influences the results. Among available software packages there is no consistency in the used algorithm; (4) before applying means‐end chain analysis in a new research area the validity of assumptions underlying the research model should be evaluated. We conclude there is no universal “best way” to means‐end chain analysis, the most suitable approach depends on the research question. Research concerning how products are evaluated can best apply number‐of‐respondents‐based aggregation and low levels of condensation. Research concerning why products are valued can best apply frequency‐of‐responses‐based aggregation and high levels of condensation.
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