Developing Effective Technology Dissemination Strategies through Farmer-to-Farmer Networks in Ghana

Last registered on February 19, 2026

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Pre-Trial

Trial Information

General Information

Title

Developing Effective Technology Dissemination Strategies through Farmer-to-Farmer Networks in Ghana

RCT ID

AEARCTR-0017887

Initial registration date

February 18, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

February 19, 2026, 7:44 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name

Guenwoo Lee

Affiliation

Japan Research Center for Agricultural Sciences

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2024-01-01

End date

2027-03-31

Keywords

Agriculture

Additional Keywords

Agricultural technology adoption; Social networks; Information diffusion; Extension services; Integrated Soil Fertility Management (ISFM); Ghana; Network targeting; Complex contagion

JEL code(s)

O13; Q12; Q16; Z13; C93

Secondary IDs

Prior work

This trial is based on or builds upon one or more prior RCTs.

Abstract

This study aims to improve agricultural productivity in Northern Ghana by identifying the most effective method for selecting "seed farmers" to disseminate information about Integrated Soil Fertility Management (ISFM) technologies (e.g., improved maize varieties, fertilizer application, and intercropping). While selecting central individuals in a social network is known to speed up technology diffusion, traditional methods of mapping these networks are often too expensive and time-consuming for practical use in developing countries. This Randomized Controlled Trial (RCT) tests a novel, low-cost targeting method designed to identify influential farmers without a full census. The study compares four specific strategies for selecting seed farmers:
a) Network Theory-Based Targeting: Using full network data to find optimal spreaders.
b) Gossip Method: Asking villagers to nominate people who are good at spreading news.
c) Chairman’s Informants (New Method): Using a confidential nomination process involving the village Chairman's close associates to identify influential farmers.
d)Benchmark: Selection by government extension officers based on their standard criteria.
Conducted across 40 villages in five districts of Northern Ghana, the study tracks the spread of knowledge and adoption of ISFM practices among farmers from 2023 to 2027 to determine if the new "Chairman's Informants" method offers a cost-effective alternative to expensive network surveys.

External Link(s)

Registration Citation

Citation

Lee, Guenwoo. 2026. "Developing Effective Technology Dissemination Strategies through Farmer-to-Farmer Networks in Ghana." AEA RCT Registry. February 19. https://doi.org/10.1257/rct.17887-1.0

Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)

The study evaluates the effectiveness of four different targeting methods to select "seed farmers" for the dissemination of Integrated Soil Fertility Management (ISFM) technologies in Northern Ghana. In each of the 40 participating villages, two seed farmers are selected based on one of the following four randomly assigned methods:
a) Network Theory-Based Targeting: Seeds are selected based on eigenvector centrality calculated from full network data.
b) Gossip Method: Seeds are selected based on nominations by villagers identifying those capable of spreading information effectively.
c) Chairman’s Informants (New Method): Seeds are selected through a confidential nomination process involving the village Chairman's associates.
d) Benchmark: Seeds are selected by government extension officers based on their standard criteria.

Selected seed farmers receive a one-day intensive training on ISFM provided by MOFA extension agents. The training covers improved maize varieties, fertilizer application, compost production, intercropping, and avoiding slash-and-burn agriculture. Seed farmers are provided with improved seeds and fertilizers to demonstrate the technology.

Intervention Start Date

2024-04-22

Intervention End Date

2026-12-31

Primary Outcomes

Primary Outcomes (end points)

a) Technology Adoption: Adoption of ISFM practices by seed farmers and their neighboring farmers, specifically "improved seeds," "fertilizer application," and "crop rotation."

b) Knowledge Diffusion: Whether farmers have heard of ISFM and whether they know how to implement it.

Primary Outcomes (explanation)

a) Adoption Rate: Constructed as an indicator variable for whether a respondent (seed farmers and their neighbors) has adopted specific ISFM practices (improved seeds, fertilizer, crop rotation) in the given year.
b) Knowledge: Constructed as indicator variables for "Heard of ISFM" and "Knows how to implement ISFM.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

a) Agricultural Yields: Log of maize yields.
b) Information Exchange: Frequency of conversations about ISFM with seed or shadow partners.

Experimental Design Details

Not available

Randomization Method

Randomization done in office by a computer (implied by "randomly assigning" 40 villages and use of Python/simulations).

Randomization Unit

Village (Community).

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

40 villages

Sample size: planned number of observations

1,290 Farms (Sample Survey). (Note: A full census of ~5,600 farms is also conducted for network mapping, but the main analysis sample is 1,290).

Sample size (or number of clusters) by treatment arms

10 villages Network Theory-Based Targeting, 10 villages Gossiper Method, 10 villages Chairman’s Informants, and 10 villages the extension officer's selection (benchmark).

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

To determine the minimum detectable effect size (MDES) accounting for the cluster randomized design, we performed power calculations based on results from our preliminary network simulations. The unit of randomization is the village, and the primary outcome is the number of adopting households per village. Based on the simulation, the baseline adoption level under the extension officer's selection (benchmark) is estimated at 2.2 households per village. The expected adoption levels for the treatment arms are 10.4 households (Network theory-based), 2.9 households (Chairman’s informant), and 2.9 households (Gossipers) per village. Assuming a standard deviation of 5.5 households derived from the Network group simulation and accounting for the cluster design (e.g., intra-cluster correlation), a sample size of 10 villages per arm (40 villages in total) was determined to be sufficient. This design provides >80% power to detect the expected difference of 8.2 households per village (between the Benchmark and the Network theory-based method) at a 5% significance level. The smaller differences observed in other treatments (e.g., Chairman’s informant) will be examined exploratorily.

Supporting Documents and Materials