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Whereas numerous studies are available that describe the structure and components of food systems, far less attention is usually given to the analysis of drivers for food system change. Since such transformations take place at the interface of multiple stakeholders' interests, involve the use of multiple instruments and may lead to multiple outcomes, it is of foremost importance to understand the underlying dynamics of food system transformation processes. This article identifies different leverage points for improving dietary outcomes and outlines analytical perspectives on three possible interventions along the food value chain that go further than just 'solving the problem'. Real systems solutions intend to address fundamental interactions within the food system and thus provide unusual solutions to change its dynamics. This opens the way to new insights on appropriate policies and innovative incentives for steering food system transformation processes. [ABSTRACT FROM AUTHOR]

Purpose: This study uses data from a sample of 150 oilseed farming households from Arsi Robe, Ethiopia, to assess the impact of different knowledge bases (education, training and experience) and their interactions on linseed productivity. Methodology: A multiple regression analysis was employed to assess the combined effect of the knowledge bases, factors such as age of the household head, land size, marketing channels and geophysical factors (such as land slope) on linseed productivity. Findings: The findings reveal no differences in productivity between trained and untrained farmers. They further show that training and the interaction between training and experience positively influence productivity. Our findings, however, reveal farmer education to have an inverse yet insignificant effect on productivity. Furthermore, we found that factors such as the slope of the land and the choice of marketing channel also play an important role in influencing productivity. Practical implications: Moving away from the traditional top-down approach could be the answer to avoid a mismatch between the information given by trainers and what the farmers actually need. Theoretical implications: The study used interaction variables of knowledge bases, and attempted to demonstrate the importance of a tacit knowledge base, which is often not well documented and therefore neglected in research. Originality/value: The research demonstrates the combined effects that interactions between several knowledge bases have on the productivity of linseed farmers.