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.
In Sub-Sahara Africa, adoption rates of improved crop varieties remain relatively low, which is partly due to farmers’ limited access to information. In smallholder settings, information often spreads through informal networks. Better understanding of such networks could potentially help to spur innovation and farmers’ exposure to new technologies. This study uses survey data from Tanzania to analyze social networks and their role for the spread of information about improved varieties of maize and sorghum.