This chapter examines processes to inform decision making and manage innovation at four generally defined levels of the innovation system for agriculture; policy, investment, organization, and intervention and also identifies methods relevant at each level for assessing, prioritizing, monitoring, and evaluating innovation processes so that practitioners have the information needed for decision making and for managing limited resources effectively.
Successful cases of innovation invariably demonstrate a range of partnerships, alliances and network-like arrangements that connect together knowledge users, knowledge producers and others involved in enabling innovation in the market, policy and civil society arenas. With this comes the realisation that public agricultural research needs to strengthen links to a wider set of players from the private and civil society sectors and, of course, farmers themselves. Public agricultural extension services have traditionally played the role of linking farmers to technology.
Agricultural innovation is a process that takes a multitude of different forms, and, within this process, agricultural research and expertise are mobilised at different points in time for different purposes. This paper uses two key analytical principles to establish how research is actually put into use. The first, which concerns the configurations of organisations and their relationships associated with innovation, reveals the additional set of resources and expertise that research needs to be married to, and sheds light on the types of arrangements that allow this marriage to take place.
Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low.
Most micro-level studies on the impact of agricultural technologies build on cross-section data, which can lead to unreliable impact estimates. Here, we use panel data covering two time periods to estimate the impact of tissue culture (TC) banana technology in the Kenyan small farm sector. TC banana is an interesting case, because previous impact studies showed mixed results. We combine propensity score matching with a difference-in-difference estimator to control for selection bias and account for temporal impact variability.