Impact of digital advisory services on behavior change, sustainable land management (SLM) practices adoption, and household welfare: Experimental evidence from Uganda and India

Last registered on April 03, 2024
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Pre-Trial

Trial Information

General Information

Title
Impact of digital advisory services on behavior change, sustainable land management (SLM) practices adoption, and household welfare: Experimental evidence from Uganda and India
RCT ID
AEARCTR-0011589
Initial registration date
January 11, 2024

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
January 12, 2024, 3:34 PM EST

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

Last updated
April 03, 2024, 11:52 AM EDT

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

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Primary Investigator

Name
Charles Maina
Affiliation
International Centre of Insect Physiology and Ecology
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Other Primary Investigator(s)

PI Name
Kelvin Mulungu
PI Affiliation
International Centre of Insect Physiology and Ecology
Contact Investigator
PI Name
Roopa Suresh
PI Affiliation
Grameen Foundation, India
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PI Name
Menale Kassie
PI Affiliation
International Centre of Insect Physiology and Ecology
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PI Name
Maurice Tschopp
PI Affiliation
Centre for Development and Environment
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Additional Trial Information

Status
In development
Start date
2023-09-01
End date
2025-03-10
Keywords
Agriculture, Behavior, Welfare
Additional Keywords
Digital advisory services, Sustainable land management practices, Adoption, Impact.
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In many Global South countries, the efficacy of in-person extension systems in providing tailored and timely agricultural information to smallholders and fostering their adoption is constrained by various factors. These include the high cost of reaching farmers in remote villages, limited mobility and interaction of agents with farmers, and the poor delivery of context-specific information. Digital advisory services (DAS) have advanced to overcome these challenges and improve upon the existing face-to-face extension model by reducing the cost of linking farmers with extension officers, delivering tailored and timely advice, and reducing inequalities in access to information, knowledge, and technologies.

This study is a four-arm cluster (village) randomized controlled trial aimed at evaluating the impact of three DAS delivery models on three outcomes: awareness and knowledge of SLMPs, adoption of SLM practices, and their impact on crop productivity. This study uses an agricultural information app, farmbetter, as a proxy for DAS. The study will be conducted in Uganda and India. The interventions are as follows: (1) Agent-only treatment, where the extension agent will use the app and deliver the information to farmers; (2) self-service treatment, where the farmers will use the app independently; and (3) hybrid treatment, where both the farmers and the extension agents will use the app. The unit of randomization is a village in both countries. There are 40 clusters (villages) per arm, and the outcomes will be assessed in 12 households per village in Uganda and about 11 households per village in India. The control villages will be at least 50 km away from the treatment to reduce information spillover. Baseline data will be collected at the beginning of the study and end-line data will be collected after three growing seasons to measure the impact of DAS on the three outcomes.

Findings will explore the most effective DAS delivery approach among the three. Effectiveness will be measured based on the effect on the adoption of SLMPs and the impact on agricultural productivity. This will contribute to the design of DAS in developing countries.
External Link(s)

Registration Citation

Citation
Kassie, Menale et al. 2024. "Impact of digital advisory services on behavior change, sustainable land management (SLM) practices adoption, and household welfare: Experimental evidence from Uganda and India." AEA RCT Registry. April 03. https://doi.org/10.1257/rct.11589-1.1
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Experimental Details

Interventions

Intervention(s)
The intervention for this study involves access to digital advisory services through a mobile-based application called "farmbetter app". This application is customized into three different models that will provide access to digital advisory services (DAS) to farmers. The three models include, Agent-based model, Self-service model, and Hybrid model which form three treatments for this study. Under the Agent-based model, only the extension/village based agents will be able to download the "farmbetter app" to access DAS and train farmers. Under the Self-service model, only farmers will be able to download the app and use it independently to access DAS, while Hybrid model, both the extension agents and the farmers will be able to download the app and share the information together.
Intervention Start Date
2023-11-06
Intervention End Date
2024-12-02

Primary Outcomes

Primary Outcomes (end points)
1. Awareness and knowledge of sustainable land management practices.
2. Adoption of sustainable land management practices.
3. Crop yields (i.e., maize for Uganda; wheat and rice for India).
Primary Outcomes (explanation)
A list of knowledge questions on sustainable land management practices will be prepared and farmers will be expected respond with correct and wrong answers from which a knowledge score will be computed.

Awareness of sustainable land management practices will be measured as a dummy variable taking a value of one if the farmer is aware of sustainable land management practices and zero otherwise.

Adoption will be measured as a dummy variable taking a value of one if a farmer adopt any of the sustainable land management practices and zero otherwise.

Crop yields will be measured as the total output aggregated from all the plots owned by the farmer in kilograms per acre per season. The crop yields data will be obtained from the baseline, phone, and endline surveys. The main crops of interest will be maize in Uganda while in India, the crops will be wheat and rice.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study is a four-arm cluster randomized controlled trial with a total of 160 clusters (or villages) in each country (i.e., Uganda and India). These clusters are formed from the three DAS delivery models customized in the "farmbetter app" (i.e., Agent-based model, Self-service model, and Hybrid model) and a control group. Forty clusters (or villages) are randomly allocated to each treatment and the control with an allocation ratio of 1:1:1:1.

Before the actual randomization of farmers to various treatment groups and control group, a listing exercise of farmers within the selected villages was conducted in Uganda and India. For a farmer to qualify, he/she must be involved in farming (agriculture and allied activities) and own a smartphone (either individually or as a family). The following preliminary information was collected during farmers’ listing exercise; name of the farmer, age, gender, contact number, type of mobile ownership, digital literacy and common crops grown. Once the list was obtained, the households were then randomized into three treatments and a control group.

The trial is expected to run for three growing seasons. Two long seasons and one short season in Uganda, and two wheat seasons and one rice season in India. The impact of DAS delivery models will be evaluated through two main surveys at the start (baseline) and end-line with continuous phone surveys at the end of each season.

Baseline data was collected between August-September 2023 for Uganda and India. The end-line survey will be conducted between December 2024 and February 2025. Each interview will take approximately 1 hour.
Experimental Design Details
Not available
Randomization Method
The randomization was done using a computer following farmers' listing exercise in Uganda and India.
Randomization Unit
Village
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
160 villages
Sample size: planned number of observations
In Uganda: 1,920 households while in India: 1,760 households
Sample size (or number of clusters) by treatment arms
In both Uganda and India; 40 villages for agent based model, 40 village for self-service model, 40 village for hybrid model and 40 village for control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The main outcome variable in Uganda and India is crop yield, therefore a minimum detectable effect is put at 0.3 with a maize yield intra-cluster correlation of 0.18 for Uganda, and rice productivity intra-cluster correlation of 0.15 for India.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Mildmay Uganda Research and Ethics Committee (MUREC)
IRB Approval Date
2023-08-14
IRB Approval Number
MUREC-2023-273
Analysis Plan

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