Improving smallholder agriculture with generative AI: The impact of Farmer.Chat in Kenya

Last registered on October 27, 2025
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Pre-Trial

Trial Information

General Information

Title
Improving smallholder agriculture with generative AI: The impact of Farmer.Chat in Kenya
RCT ID
AEARCTR-0017035
Initial registration date
October 26, 2025

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
October 27, 2025, 9:15 AM EDT

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
Gashaw T Abate
Affiliation
IFPRI
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Other Primary Investigator(s)

PI Name
Sedi Boukaka
PI Affiliation
IFPRI
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PI Name
Joshua Deutschmann
PI Affiliation
University of Chicago
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PI Name
Tanguy Bernard
PI Affiliation
University of Bordeaux
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PI Name
David Spielman
PI Affiliation
IFPRI
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Additional Trial Information

Status
On going
Start date
2025-09-01
End date
2027-03-31
Keywords
Agriculture, Behavior, Gender, Welfare
Additional Keywords
Artificial Intelligence (AI)
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study evaluates the impact of an AI-powered agricultural advisory chatbot, Farmer.Chat, developed by Digital Green to enhance knowledge, technology adoption, productivity, and income among smallholder farmers in Kenya. Traditional agricultural extension systems in many low- and middle-income countries face persistent capacity and reach constraints, leaving farmers with limited access to timely, tailored information. Digital advisory tools have begun to bridge this gap, yet rigorous evidence on the effectiveness of AI-enabled solutions remains limited. We will implement a cluster-randomized controlled trial (RCT) in 600 villages in Nakuru County, Kenya, to assess the causal effects of access to Farmer.Chat on farmers’ knowledge, uptake of recommended agronomic practices, yields, and household income. The experiment will also include a cross-randomized video intervention designed to stimulate learning and behavioral change. The trial will be conducted across two agricultural seasons. Findings will provide some of the first causal evidence on the role of generative AI in agricultural extension, informing future efforts to integrate AI tools into scalable, farmer-centered advisory services.
External Link(s)

Registration Citation

Citation
Abate, Gashaw T et al. 2025. "Improving smallholder agriculture with generative AI: The impact of Farmer.Chat in Kenya." AEA RCT Registry. October 27. https://doi.org/10.1257/rct.17035-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
There are two cross-randomized interventions:

(1) Farmers in the Farmer.Chat treatment will have the application installed on their phone.
(2) Farmers in the video treatment will be shown videos to increase aspirations and spark thinking of new ways of doing things, possibly supplemented with text messages and/or phone calls.
Intervention Start Date
2025-11-15
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
Access to and use of agricultural extension
Knowledge and adoption of good agricultural practices
Agency / decision-making capabilities
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Productivity and production decisions (e.g. agricultural portfolio choice)
Income and consumption
Food and nutritional security
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We use a cluster randomized controlled trial (RCT) with a 2x2 factorial design, assigning treatments at the village level to minimize potential spillover effects. The first intervention, Farmer.Chat, involves guiding farm households in treatment villages to download the app and giving some advice to craft queries. The second intervention is a video nudge: a subset of both treatment and control villages will be randomly assigned to receive a short video featuring a local farmer, aimed at lowering cognitive barriers, increasing aspirations, and promoting innovative thinking. Pending additional budget, we may reinforce the video nudge treatment with text messages and/or phone calls.
Experimental Design Details
Not available
Randomization Method
Randomization was done in office by a computer.
Randomization Unit
Village
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
600 villages
Sample size: planned number of observations
3000
Sample size (or number of clusters) by treatment arms
150 villages control, 150 villages Farmer.Chat, 150 villages video nudge, and 150 villages both treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IFPRI-IRB
IRB Approval Date
2025-01-08
IRB Approval Number
MTI-25-0102
IRB Name
USIU Africa
IRB Approval Date
2025-07-21
IRB Approval Number
USIU-A/ISERC/US988-2025
Analysis Plan