AI Advisory, Risk Communication, and Investment under Uncertainty: Evidence from Nigerian Smallholders

Last registered on June 22, 2026
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Pre-Trial

Trial Information

General Information

Title
AI Advisory, Risk Communication, and Investment under Uncertainty: Evidence from Nigerian Smallholders
RCT ID
AEARCTR-0018919
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

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Primary Investigator

Name
Oyakhilomen Oyinbo
Affiliation
CIMMYT
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Other Primary Investigator(s)

PI Name
Jordan Chamberlin
PI Affiliation
CIMMYT
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PI Name
Bisrat Gebrekidan
PI Affiliation
CIMMYT
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Additional Trial Information

Status
On going
Start date
2026-05-25
End date
2027-12-31
Keywords
Agriculture, Behavior
Additional Keywords
AI
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Smallholder farmers in rainfed systems in developing regions face high levels of risk and uncertainty in their production and investment outcomes. Yet, such stochasticity of farm-level investment returns is rarely well-defined, and even more rarely communicated to farmers in existing extension systems in SSA, especially amid increasing production and market risks. Technical advances in AI-enabled analytics, large amounts of remotely sensed data, and other large, geographically distributed datasets on market and production characteristics observed over time and space are now being explored to tailor return distributions to a farmer's specific plot, crop, and input choices on demand. However, little is known about the effectiveness of AI-enabled risk parametrization and communication in farm-level investment and modalities for priming farmers to inquire about returns distributions. Using a four-arm cluster randomized controlled trial across 240 villages and 1680 maize-producing households in northern Nigeria, we evaluate whether AI-enabled advisory systems that communicate the stochasticity of fertilizer investment returns can improve smallholder farmer decision-making compared with advisories that provide agronomic and price information without stochastic guidance.
External Link(s)

Registration Citation

Citation
Chamberlin, Jordan, Bisrat Gebrekidan and Oyakhilomen Oyinbo. 2026. "AI Advisory, Risk Communication, and Investment under Uncertainty: Evidence from Nigerian Smallholders." AEA RCT Registry. June 22. https://doi.org/10.1257/rct.18919-1.0
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Experimental Details

Interventions

Intervention(s)
Our interventions include an AI-enabled advsisory chatbot that can be self-administered by farmers via Telegram on Android phones, a short passive video explainer of risk concepts and chatbot capabilities, and an incentivized fertilizer investment game. All study farmers can access the AI-enabled advisory tool in the local language and have the option of English language
Intervention Start Date
2026-06-01
Intervention End Date
2026-06-15

Primary Outcomes

Primary Outcomes (end points)
Fertilizer investment levels and management decisions, advisory experience (trust in advisories, satisfaction with advisory contents, agency), and farm investment returns (yield, gross margin)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Chatbot engagement and risk comprehension
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design is a cluster randomized controlled trial with four treatment arms (T0, T1, T2, and T3) across 240 maize-producing villages in northern Nigeria. Farmers randomly assigned to the four treatment arms are exposed to the following:
T0 (Control): AI chatbot that provides recommended fertilizer package (rate, type, timing, and basic “how-to”), rainfall forecasts, agronomic and investment returns guidance; advice is deterministic and point-estimate based; no stochastic production or price content.
T1 (Risk-Aware Advisory): T0 + explicit stochasticity in production and market outcomes, scenario-based returns, and risk-adjusted profitability guidance.
T2 (Risk-Aware + Passive Explainer): T1 + short passive video explaining risk concepts and how the chatbot can answer probabilistic questions. Holds total dosage and exposure to risk content roughly constant relative to T3.
T3 (Risk-Aware + Risk-Inquiry Priming): T1 + interactive incentivized investment game in which farmers choose fertilizer investments under uncertainty, observe realized payoffs, and are nudged toward asking the chatbot about return likelihoods and distributions
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Village
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
240 villages
Sample size: planned number of observations
1680 maize-producing households
Sample size (or number of clusters) by treatment arms
60 villages in T0 (420 households), 60 villages in T1 (420 households), 60 villages in T2 (420 households), 60 villages in T3 (420 households)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
CIMMYT Internal Research Ethics Committee
IRB Approval Date
2026-05-21
IRB Approval Number
IREC 2026.014
Analysis Plan