Leveraging the Transformational Potential of AI to Empower Small-Scale Farmers in Ethiopia

Last registered on June 22, 2026
View Trial History

Pre-Trial

Trial Information

General Information

Title
Leveraging the Transformational Potential of AI to Empower Small-Scale Farmers in Ethiopia
RCT ID
AEARCTR-0018829
Initial registration date
June 15, 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
June 22, 2026, 6:35 AM EDT

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

Locations

Country
Ethiopia
Region
Oromia

Primary Investigator

Name
Jérémy Do Nascimento Miguel
Affiliation
Université de Bordeaux
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Gashaw Abate
PI Affiliation
PI Name
Tanguy Bernard
PI Affiliation
Université de Bordeaux
PI Name
Joshua Deutschmann
PI Affiliation
University of Chicago

Additional Trial Information

Status
On going
Start date
2026-05-01
End date
2028-01-01
Keywords
Agriculture
Additional Keywords
Artificial Intelligence, Agriculture, Technology Adoption
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project evaluates whether artificial intelligence (AI) can help improve agricultural advice and decision-making among smallholder farmers in Ethiopia. The study focuses on FarmerChat, an AI-powered mobile application that allows farmers and agricultural extension agents to ask questions using text, voice, or pictures and receive tailored recommendations on crop production, livestock management, weather conditions, and agricultural practices in local languages.

The project will be implemented in 317 villages across 15 districts of the Oromia region and will involve more than 2,600 farming households and over 300 agricultural extension agents. Villages will be randomly assigned to one of three groups: a control group, a group where extension agents receive training on FarmerChat, and a group where extension agents are trained and encouraged to actively introduce the application to farmers. This randomized design will allow us to rigorously measure the impact of AI-based agricultural advice.

Using household surveys and administrative data from the application, we will examine whether access to FarmerChat increases engagement with digital advisory services, improves agricultural knowledge and confidence, encourages the adoption of improved farming practices, and ultimately increases agricultural productivity and incomes. The study will also assess whether AI-powered advisory services can help reach farmers who are traditionally underserved by extension systems, including women and farmers living in remote areas.
External Link(s)

Registration Citation

Citation
Abate, Gashaw et al. 2026. " Leveraging the Transformational Potential of AI to Empower Small-Scale Farmers in Ethiopia." AEA RCT Registry. June 22. https://doi.org/10.1257/rct.18829-1.0
Sponsors & Partners

Partner

Name
Digital Green
Type
ngo
URL
https://digitalgreen.org
Experimental Details

Interventions

Intervention(s)
The project will be implemented in 317 villages across 15 districts of the Oromia region and will involve more than 2,600 farming households and over 300 agricultural extension agents. Villages will be randomly assigned to one of three groups: a control group, a group where extension agents receive training on FarmerChat, and a group where extension agents are trained and encouraged to actively introduce the application to farmers. This randomized design will allow us to rigorously measure the impact of AI-based agricultural advice.
Intervention Start Date
2026-06-14
Intervention End Date
2027-12-31

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes of interest are engagement with FarmerChat, agricultural awareness and knowledge, confidence in agricultural decision-making, adoption of improved agricultural practices, and agricultural productivity.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes include perceptions of FarmerChat, food security, food consumption, technology-related anxiety, aspirations, and other behavioral measures.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study uses a cluster-randomized controlled trial conducted in 317 kebeles (villages) across 15 woredas in the Oromia region of Ethiopia. Kebeles are randomly assigned to one of three groups: (i) a control group receiving no FarmerChat onboarding, (ii) a treatment group in which Development Agents (DAs) receive training and onboarding to the FarmerChat platform, and (iii) a treatment group in which DAs receive the same onboarding and are additionally encouraged to actively onboard farmers with mobile phones in selected villages. Randomization is stratified by woreda and implemented at the kebele level to minimize spillovers.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Kebele: smallest administrative units in Ethiopia
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
317 kebeles
Sample size: planned number of observations
2,600 farmers and 317 development agents
Sample size (or number of clusters) by treatment arms
105 kebele control, 106 kebele DAs only treatment, 106 kebele DAs + farmers onboarding treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power calculations are based on 317 kebeles and approximately 2,627 farming households, with treatment assigned at the kebele level. Assuming a two-sided test, 80 percent statistical power, and a 5 percent significance level, the study is powered to detect standardized effect sizes of approximately 0.17 standard deviations for chemical fertilizer adoption and row planting, 0.19 standard deviations for improved seed adoption, and 0.18 standard deviations for agricultural revenue. These correspond to minimum detectable effects of about 3.5 percentage points for chemical fertilizer adoption (baseline mean: 45 percent), 1.3 percentage points for improved seed adoption (baseline mean: 6 percent), 3.4 percentage points for row planting (baseline mean: 21 percent), and ETB 21,900 in annual agricultural revenue (baseline mean: ETB 103,346), equivalent to approximately 21 percent of the baseline mean.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
International Food Policy Research Institute
IRB Approval Date
2025-12-02
IRB Approval Number
00007490
Analysis Plan