Disentangling the experiential learning process of coffee farmers in Uganda’s innovation platforms

R. Ochago*, D. Dentoni, T. Lans, J.H. Trienekens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Purpose: While new rich learning opportunities emerged through
the introduction of Innovation Platforms (IPs) in agricultural value
chains, the extent to which IPs enhance farmer experiential
learning is still unclear.
Design/methodology/approach: This paper brings clarity to the
above question by interviewing 91 coffee IP farmers. Data were
analyzed through content analysis to generate overarching
themes for farmers’ experiences, learning activities, and outcomes.
Findings: Results reveal that participation in IP learning activities
generates farmers’ knowledge to cope with coffee value chain
challenges. Specifically, farmers’ making-meaning of challenges
and generating new solutions represents an iteration between
individual critical reflection and experimentation of value chain
activities. The IPs facilitated multi-directional knowledge flows
among farmers by mobilizing necessary resources.
Practical implications: As many Sub-Saharan Africa (SSA)
governments cannot provide sufficient extension support, farmers
increasingly rely on IPs whose processes stimulate members’
learning commitment and endeavors. Nevertheless, Governments
can use these findings to tailor the design and implementation of
IPs to farmers’ experiential learning processes.
Theoretical implications: The study contributes to experiential
theory in the context of agriculture by advancing a model on
how IPs can accelerate farmers’ experiential learning processes
based on the challenges experienced.
Originality/value: This article extends knowledge of experiential
learning in IPs context.
Original languageEnglish
JournalThe Journal of Agricultural Education and Extension
Publication statusE-pub ahead of print - 23 Sep 2021


  • agriculture
  • coffee value chains
  • social networks
  • multi-stakeholder platforms
  • extension approaches
  • problem-based learning


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