Evaluating the Impact of a Digital Agriculture Advisory Program in India

Last registered on April 04, 2022

Pre-Trial

Trial Information

General Information

Title
Evaluating the Impact of a Digital Agriculture Advisory Program in India
RCT ID
AEARCTR-0008560
Initial registration date
November 18, 2021

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
November 21, 2021, 5:07 PM EST

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

Last updated
April 04, 2022, 5:27 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

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Primary Investigator

Affiliation
Precision Development

Other Primary Investigator(s)

PI Affiliation
Harvard Business School
PI Affiliation
University of Maryland
PI Affiliation
Precision Development

Additional Trial Information

Status
On going
Start date
2021-02-01
End date
2023-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
With the rapid increase in access to mobile phones, there is an increasing effort among governments and policymakers to provide timely and customized agricultural information via mobile phones. However, rigorous evidence on how and for whom such interventions work is still limited. This study aims to understand whether and how agricultural extension delivered via mobile phones increases the agricultural productivity and profitability of smallholder farmers. The research is motivated by the large and persistent yield gap that exists among poor farmers in developing countries. We plan to examine this question using a large-scale randomized controlled trial over a two-year period in one state of India. Moreover, this study will test low-cost approaches to measuring yields. Specifically, it will assess the feasibility of using satellite images to estimate farmers’ rice yields in an eastern state of India. This work will help address a common empirical challenge of evaluating digital extension and other agricultural interventions.
External Link(s)

Registration Citation

Citation
Cole, Shawn et al. 2022. "Evaluating the Impact of a Digital Agriculture Advisory Program in India ." AEA RCT Registry. April 04. https://doi.org/10.1257/rct.8560
Experimental Details

Interventions

Intervention(s)
Treatment farmers receive access to a toll-free agricultural helpline through which farmers receive interactive voice response (IVR) calls that provide relevant agricultural information based on the agricultural cycle, crop, and weather content, at an approximately weekly schedule. During the Kharif season, treatment farmers receive IVR calls that share agricultural advisories on paddy; during the Rabi season, treatment farmers receive IVR calls with advisories on two crops that farmers have selected.

Moreover, farmers can also use this service to listen to and respond to agricultural questions posed by other farmers, and record their own questions to local agronomists. The use of this service is completely voluntary, and it does not involve any monetary contribution by the farmers.
Intervention Start Date
2021-06-09
Intervention End Date
2023-04-30

Primary Outcomes

Primary Outcomes (end points)
Vegetation index, self-reported yield, revenue, and profit, knowledge and adoption indices
Primary Outcomes (explanation)
We will use remote sensing satellite data to create multiple vegetation indices, which will then be used to examine the impact of treatment on “greenness”. In addition, we will collect self-reported yield, revenue, and profit, and agricultural knowledge and adoption behavior through in-person and phone surveys. Summary indices for agricultural knowledge and adoption will be constructed using individual questions and weighting matrix.

Secondary Outcomes

Secondary Outcomes (end points)
Agricultural investment, agricultural diversification, non-agricultural self-employment income generating activities in the household.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We use a randomized controlled trial (RCT) to investigate the impact of a digital agricultural advisory service on smallholder farmers’ yield. We recruited eligible households cultivating rice across approximately 903 villages in 19 blocks using a random-walk method and randomly selected half of the households to receive a free digital advisory service.

We selected 19 blocks in five districts in the state for inclusion in the study. All the included blocks had low (<18.5 percent) “penetration” of the information service, as measured by the estimated share of farmers that were enrolled prior to the study, and were covered by the same satellite path. Within these 19 blocks, we selected all villages that met the following criteria during the 2011 Census of India: (i) classified as “rural”; (ii) had more than 50 households; (iii) located in a panchayat with service “penetration” below 10 percent. A total of 1,313 or 39 percent of the villages met these conditions and constituted our study sites. In each village, we selected 5 - 10 percent of households using a random-walk sampling method. Households are eligible for inclusion in the study if they meet a set of criteria and are willing to allow the research team to collect the GPS coordinates of their primary rice plot.

The eligible households (one person per household) are invited to participate in a baseline survey, including questions on demographics, farm characteristics, agricultural knowledge and practice, yield, and GPS coordinates of the primary rice plot.

We then randomize those farmers who have completed the baseline survey and have not registered for this study’s digital extension service before into control and treatment groups. Randomization is conducted at the individual farmer level, and stratified by panchayat group, baseline survey verison, an indicator for participants’ self-reported productivity at baseline being above the median or not, and (for cohort 2 only) an indicator for participants’ gps measured plot size at baseline being above the median or not.

Farmers in the treatment group start to receive free digital advisory service in the coming agriculture season post their baseline survey. Moreover, those farmers are administered a phone-based compensation and service enrollment survey, which collects necessary information to send farmer compensation for their baseline survey participation and explains the digital advisory service to farmers.

The study began implementation of the random walk sampling method in March 2021, but suspended all field operations on 23rd April following a steep rise in COVID-19 cases in the state. 5,204 households were enrolled in the study before the fieldwork suspension (aka “initial” sample, or cohort 1), of which half were randomized to the treatment group and invited to enroll in the information service via phone. 2,571 (99.5%) members of the treatment group (2,602 farmers) opted-in to the service and began receiving advisory messages from 9th June. As the COVID-19 situation alleviated in the study location, we resumed our field activities in October, 2021. We used the same random-walk method in the same locations as previously and sampled a further 8,471 households (aka. cohort 2).

After the 2021-22 and 2022-23 Kharif seasons, we will conduct a midline survey and an endline survey with cohort 1 farmers, allowing us to measure their knowledge, practice, yield and profit.
Experimental Design Details
Not available
Randomization Method
We conduct randomization using the Stata program on a computer in an office.
Randomization Unit
Individual farmer level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
Approximate 13,600 rice farmers.
Sample size (or number of clusters) by treatment arms
Control group: approximate 6,800 farmers.
Treatment group: approximate 6,800 farmers.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
If this study achieves its planned number of observations (13,600), it will be able to detect a 2.8% increase in self-reported rice yields and a 0.78% increase in vegetation index, with 80% power at 5% significance.
IRB

Institutional Review Boards (IRBs)

IRB Name
The Committee on the Use of Human Subjects (CUHS) - Harvard University Area Institutional Review Board
IRB Approval Date
2021-02-01
IRB Approval Number
IRB20-1776
Analysis Plan

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