Incentivizing Sustainable Agriculture Using Satellite Technology

Last registered on August 18, 2025

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Pre-Trial

Trial Information

General Information

Title

Incentivizing Sustainable Agriculture Using Satellite Technology

RCT ID

AEARCTR-0016577

Initial registration date

August 15, 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

August 18, 2025, 6:59 AM EDT

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

Locations

Country

India

Region

Telangana, Karnataka

Primary Investigator

Name

Steven Brownstone

Affiliation

UC San Diego

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Nicholas Ryan

PI Affiliation

Yale University

Contact Investigator

PI Name

Naveen Kumar

PI Affiliation

BGS College of Engineering & Technology

Contact Investigator

Additional Trial Information

Status

On going

Start date

2025-06-15

End date

2025-12-31

Keywords

Agriculture, Environment & Energy

Additional Keywords

JEL code(s)

O13, Q5

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This study is testing whether satellite technology can be used to support a new system that pays farmers for adopting sustainable rice-growing methods. We are working with 800+ farmers in nine villages in Telangana and Karnataka. Farmers are randomly divided into two groups. One group receives financial payments (about ₹4,500 per acre) for using water-saving methods called Alternate Wetting and Drying (AWD) and Direct Seeded Rice (DSR). The other group does not receive payments. All farmers are offered training in AWD. Satellites are used to check whether farmers are using the practices, and this is confirmed with farmer surveys and in-person visits. We will compare results on water use, crop yields, income, and farmer satisfaction, as well as environmental impacts like methane emissions.

External Link(s)

Registration Citation

Citation

Brownstone, Steven, Naveen Kumar and Nicholas Ryan. 2025. "Incentivizing Sustainable Agriculture Using Satellite Technology." AEA RCT Registry. August 18. https://doi.org/10.1257/rct.16577-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

This study provides training on sustainable rice-growing practices, specifically Alternate Wetting and Drying (AWD) and Direct Seeded Rice (DSR), to all participating farmers. Farmers in the treatment group also receive financial incentives of about ₹4,500 per acre (up to 2 acres per farmer) for successfully implementing AWD. Adoption is verified primarily through satellite imagery, supported by farmer reports and occasional field checks. Farmers in the control group receive training but no payments.

Intervention (Hidden)

Intervention Start Date

2025-08-01

Intervention End Date

2025-11-30

Primary Outcomes

Primary Outcomes (end points)

Environmental:
- Number of AWD drying events (satellite-derived)
- Duration of AWD drying events (satellite-derived)
- Estimated methane emissions flux based on drying event variables using v2.1 of the Source-selective and Emission-adjusted GHG Calculator (SECTOR) tool from the International Rice Research Institute (IRRI).
Agricultural and economic:
- Paddy yields (quintals/acre) - from endline survey
- Net profits (revenue minus input and labor costs) - calculated from output revenue data and cost modules during high-frequency visits

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Adoption and Practice:
- Farmer-reported continuation intention - will continue AWD/DSR next year (endline)
- Method consistency - final method used matches intended method (endline)
- Training attendance rate - from training session records
Environmental:
- Stubble burning incidence - residue burning done (yes/no) from endline + satellite detection
- Soil moisture indicators - from high-frequency monitoring (dry/moderate/wet classifications) [for a subsample of farmers with moisture measurements]
- Estimated water use - We will attempt to extrapolate the relationship between drying events and water use based on pumping hour metres on a sub-sample of plots.
Economic:
- Input cost variations - collected through periodic cost modules during high-frequency visits
- Labor allocation changes - from cost and labor tracking modules
Farmer Experience:
Perceived crop health - visual assessment (good/moderate/poor) from endline

Secondary Outcomes (explanation)

Experimental Design

We are working with 800+ smallholder rice farmers across nine villages in Telangana and Karnataka. Farmers are randomly assigned to two groups: a treatment group that receives financial incentives (about ₹4,500 per acre, up to 2 acres per farmer) for adopting water-saving methods, and a control group that does not. Both groups receive training on Alternate Wetting and Drying (AWD) and Direct Seeded Rice (DSR).

Satellite imagery is the primary way to verify whether farmers are implementing AWD practices, with additional confirmation from farmer reports and occasional field checks. To strengthen results, we will also use a comparison group of non-enrolled plots, monitored by satellite, to help build a “dynamic baseline” that reflects changing weather and environmental conditions.

Experimental Design Details

Randomization Method

The design is a matched pair design within strata defined by village and land size rankings. Farmers were sorted by village, then by total land size, and assigned to pairs. Within each pair, one farmer was randomly assigned to treatment and the other to control. For strata with an odd number of farmers, simple randomization with 0.5 probability was used. All randomization was done by computer.

Randomization Unit

The unit of randomization is the individual farmer. Randomization was stratified by village and landholding size, but assignment was made at the individual level. There is no higher-level (e.g., village-level) randomization.

Was the treatment clustered?