The impact evaluation will test the effectiveness of the interventions in the Kirinyaga county of Kenya in increasing the rate of adoption of new technologies and its impact on agricultural productivity and income of smallholder banana farmers.
There are approximately 270,000 smallholder banana farmers in Kenya, and about half of them are women. To enhance the capacity of the farmers and Farmer based organisations (FBOs), they will be trained by the implementing agency, EA-MDA in land preparation and planting, seed variety selection, weed control, harvesting, grading and postharvest handling and record keeping
Drawing on lessons from behavioural literature, the study will also test the extent to which ‘goal-setting’ may affect technology adoption. It will then inform policy makers on how farmers and FBOs can adopt modern technologies and effectively manage their operations.
• Do information and training of smallholder farmers lead to technology adoption? Is goal setting important?
• What are the impacts of training and goal setting on agricultural productivity, income and improving living standard?
• What roles do social connections and social learning play in technology adoption and diffusion?
• What is the spillover effect of EAMDA interventions on nonparticipants?
The interventions involve the provision of information, training and marketing links. The training will provide farmers with necessary agronomic practices and expertise on the handling of planting materials. EA-MDA will also provide training on postharvest technologies so that farmers acquire the technical expertise and there are no losses when banana is harvested. In addition, EAMDA will work with intermediaries who procure for large supermarket chains so that farmers can sell their produce to them and receive a high price. Finally, it will also work with FOs to build their technical and institutional capacity for sustainability and scaling up.
Theory of change
By providing training and disseminating information, inputs and marketing services, farmers may be more willing to adopt profitable technologies to increase yield. This can in turn fetch a higher price for harvested output, leading to higher income and welfare.
The evaluation will follow a randomised controlled trial (RCT). The study uses a multi arm study involving two crosscutting sets of treatments: (1) fraction of eligible farmers provided training, and (2) the same training followed by a goalsetting exercise. Randomisation will take place at both the group and individual levels. The 90 villages (clusters) in the Kirinyaga County in Central Kenya will be randomly assigned into five fractions of treated farmers including the control group: (a) 20% of eligible farmers will be included, (b) 40% of eligible farmers will be included, (c) 60% of eligible farmers will be included, (d) 100% of eligible farmers will be included, and (e) Control group: zero per cent of eligible farmers will be included. In the first stage of randomisation, each village will be randomly assigned to one of the five fractions listed above. There will be 15 villages in each group in the first four groups, and 30 villages in the last group. In the second stage, randomisation will be at individual farmer level. Combining RCT with mixed methods will help explain the interplay of constraints. The study will also assess the heterogeneity in impact based on the education level of the household head, their gender and prior experience with growing bananas.