An impact assessment of EAMDA's banana initiative to increase technology adoption by smallholder farmers in Kenya

Publication Details

3ie Funded Evaluation, TW4.1027. A link to the completed study will appear here when available.


Author
Shyamal Chowdhury, Jane Kabubo­Mariara, Munshi Sulaiman, Uttam Sharma
Country
Kenya
Region
Sub-Saharan Africa (includes East and West Africa)
Sector
Agriculture and Rural Development
Subsector
Agricultural Reform, Agricultural Extension, Agro-Industry & Marketing, Agricultural Research
Gender analysis
Yes
Equity Focus
Gender
Evaluation design
Randomised Control Trials (RCT), Mixed Methods
Status
Ongoing 3ie Funded Studies
3ie Funding Window
Agricultural Innovation Thematic Window

Synopsis

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. 

Context

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.

Research questions

•    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? 

Methodology

Intervention design

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. 

Evaluation design

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.

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