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Experimental study of Monsoon Onset Forecast Dissemination (Round 1)

Last registered on October 22, 2025
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Pre-Trial

Trial Information

General Information

Title
Experimental study of Monsoon Onset Forecast Dissemination (Round 1)
RCT ID
AEARCTR-0017028
Initial registration date
October 14, 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 22, 2025, 1:01 PM EDT

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

Locations

Country
India
Region

Primary Investigator

Name
Jess Rudder
Affiliation
Oregon State University
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Other Primary Investigator(s)

PI Name
Sabareesh Ramachandran
PI Affiliation
Indian Business School
Contact Investigator
PI Name
Tomoko Harigaya
PI Affiliation
Precision Development
Contact Investigator
PI Name
Michael Kremer
PI Affiliation
University of Chicago
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PI Name
Amir Jina
PI Affiliation
University of Chicago
Contact Investigator

Additional Trial Information

Status
On going
Start date
2025-05-22
End date
2025-12-31
Keywords
Agriculture
Additional Keywords
JEL code(s)
O
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study aims to understand how smallholder farmers in Odisha respond to monsoon onset forecasts delivered through voice calls (IVRS) in addition to text messages with forecasts sent to all farmers. It will examine how farmers interpret these forecasts and whether the information influences key agricultural decisions, such as field preparation, sowing timing, and crop choice, during the 2025 Kharif season. The study will evaluate the impact of the monsoon onset forecast dissemination program implemented by the Government of Odisha (GoO) through its Krishi Samruddhi (KS) IVRS platform. The GoO has randomly selected blocks to receive IVRS messages with onset forecasts starting May 22, 2025, through June 16, 2025.
External Link(s)

Registration Citation

Citation
Harigaya, Tomoko et al. 2025. "Experimental study of Monsoon Onset Forecast Dissemination (Round 1)." AEA RCT Registry. October 22. https://doi.org/10.1257/rct.17028-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The intervention, being implemented by GoO independently of the proposed research, comprises pre-recorded voice calls sharing forecasts of the monsoon onset date specific to each region. These messages are delivered via the KS system and are generated using AI-based weather models.
Intervention Start Date
2025-05-22
Intervention End Date
2025-06-16

Primary Outcomes

Primary Outcomes (end points)
Forecast receipt and recall, crop choice, crop damage, planting dates. Engagement on forecast messages: Pickup rate, average listening rate, listening to X% of the message. Engagement on other advisories: Pickup rate, average listening rate for other advisories in the same season.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Expected and realized monsoon onset, reports of dry spells, probability comprehension, sharing messages with others, and input use.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study aims to understand how farmers interpret and respond to monsoon onset forecasts delivered through voice calls (IVRS) during the 2025 Kharif season in Odisha, India. The study will evaluate whether access to these forecast messages influences farmers' early-season agricultural decisions. Forecast messages will be disseminated via the GoO’s KS platform.

GoO is implementing IVRS monsoon onset forecast message dissemination via the KS system across a subset of 118 blocks in Odisha. Given the KS system call handling capacity constraints, GoO randomly selected 57 of these 117 blocks to begin receiving monsoon onset forecast messages starting May 22, 2025. The remaining 60 blocks did not receive these messages. Farmers in all blocks received text messages with the forecasts sent by the Ministry of Agriculture and Farmers’ Welfare, Government of India. The list of farmers in the state government’s list may be slightly different from the list of farmers receiving the text messages.

The study team intends to leverage this randomized rollout - where 57 of the 117 eligible blocks (treatment group) are receiving the IVRS messages and 60 are not (comparison group) - to evaluate the impact of forecast dissemination on farmer message recall, message comprehension, decision-making, and other agricultural outcomes. A first round of phone-based surveys was conducted in August 2025, focusing on early-season agricultural decisions, including total area of cultivation, crop and variety choices, and planting date. Each survey round is expected to last approximately five weeks.

The anticipated sample includes up to 2000 farmers per survey round selected from administrative databases. The study sample will be drawn from all 117 blocks that were suitable for the monsoon forecast dissemination - 57 treatment and 60 comparison blocks. Within each block-engagement stratum, simple random sampling was used to select respondents.
Experimental Design Details
Not available
Randomization Method
Randomization by computer
Randomization Unit
Administrative blocks
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
117 total blocks
Sample size: planned number of observations
Engagement data will utilize the part of the population of the administrative records. We reach 9.6 lakh farmers through IVRS. Survey sample size will be 2,000, including both treatment and control farmers.
Sample size (or number of clusters) by treatment arms
57 treatment blocks and 60 control blocks
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
DAI Research & Advisory Services Pvt Ltd. Human Subjects Committee
IRB Approval Date
2025-07-02
IRB Approval Number
OR-MOFD-01/2025-2026
Analysis Plan