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Introducing ICT-enabled climate services to build smallholder farmers resilience: Evidence from an experimental study in Benin

Last registered on July 08, 2022

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0009294

Initial registration date

July 05, 2022

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

July 08, 2022, 9:30 AM EDT

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

Locations

Country

Benin

Region

Primary Investigator

Name

Rosaine N. Yegbemey

Affiliation

Faculty of Agronomy, University of Parakou (Benin)

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Habiba Djebbari

PI Affiliation

Aix-Marseille University, France

Contact Investigator

PI Name

Esther Ogouniyi Adimi

PI Affiliation

University of Parakou, Benin

Contact Investigator

PI Name

Michael Tanner

PI Affiliation

University of Hamburg, Germany

Contact Investigator

PI Name

Marta Talevi

PI Affiliation

Vrije Universiteit Amsterdam, Netherlands

Contact Investigator

Additional Trial Information

Status

In development

Start date

2022-04-01

End date

2024-12-31

Keywords

Agriculture, Gender, Labor

Additional Keywords

Climate variability, climate services

JEL code(s)

Secondary IDs

Prior work

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

Abstract

In smallholder farming systems, climate variability is one of the major challenges that lead to inefficiency in resource allocation, lower productivity and income. Climate services have increasingly been acknowledged to help build resilience through efficiency gains in the form of production cost decreases and productivity increases. Most of the existing climate service initiatives are implemented as pilots and their impact evaluations so far indicate positive effects on productivity and income, for instance. Despite these promising results, there is no evidence on how the successful pilot initiatives could be scaled-up. We design an experimental study to test at a large scale the effectiveness of selected scaling approaches to promote climate services for smallholder farmers. The initiative that we will test is a service package that involves a one-time group training, the provision of village specific precipitation forecasts through mobile phone SMS and a technical follow-up either in person by extension officers or via mobile phone. Our identification strategy will rely on a Clustered Randomized Controlled Trial (CRCT) design, involving two treatment groups that differ on the type of technical support (in-person vs phone-based). We assess the impact of the intervention on labor allocation (including female farmers labor), crop yield and income. We also consider secondary outcome variables such as knowledge, attitude and beliefs with respect to climate services service in general and weather forecasts in particular. Our study has the potential to generate rigorous evidence on the effectiveness of different approaches to promote climate services. This can inform policy decision makers but also contribute to the current literature on the impact of climate services and the role of ICT-enabled extension in building farmer’s resiliency.

External Link(s)

Registration Citation

Citation

Djebbari, Habiba et al. 2022. "Introducing ICT-enabled climate services to build smallholder farmers resilience: Evidence from an experimental study in Benin." AEA RCT Registry. July 08. https://doi.org/10.1257/rct.9294-1.0

Sponsors & Partners

Interventions

Intervention(s)

The intervention that we intend to test is a climate service designed as a package that involves a one-time group training, the provision of village specific precipitation forecasts through mobile phone SMS and a technical follow-up either in person by extension officers or via mobile phone.

Intervention (Hidden)

The CS initiative that we will test is a package that is designed to inform, training, advise and support farmers as far as weather information is concerned. The intervention includes three components:

>> A training component: This is a one-time group (at village level) training meant to provide farmers with an overview of the services and technical information that can help them to interpret and make good use of the weather forecasts in their farming activities. The group training is organized before the onset of the rainy season. Participation in the training is a must to have access to the basic weather information and the technical support services.

>> A basic weather information service: Two types of weather information will be provided: seasonal forecasts that will be shared during the one-time group training and daily precipitation forecasts that will be disseminated trough SMS campaigns every three days. All the weather information will be village specific. We will focus on rainfall forecast only and agreement is made with the Benin meteorological office, Meteo-Benin, to get their support in accessing accurate weather forecast information. For the purpose of this study, the service will be free of charge for farmers.

>> A technical agro-meteorological support: This component is meant to provide continuous support to farmers who will be exposed to the weather information. The support will cover among others the facilitation of the interpretation of the weather forecast and information on production inputs availability and price. The technical support will be provided either through in-person interactions by extension officers or through phones. Farmers who will receive the phone-based support will also have access to an Infoline that they could call for information, if needed. The costs related to the calls will be supported by farmers.

Intervention Start Date

2022-05-22

Intervention End Date

2022-12-31

Primary Outcomes

Primary Outcomes (end points)

Labor allocation to farming activities
Yied
Crop income

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Farmers’ awareness and behaviors (KAP) towards climate services

Secondary Outcomes (explanation)

Experimental Design

We will randomly assign villages in of the following groups: Pure control group, CS + In-person support group and CS + Phone support group.

Experimental Design Details

Our experiment involves three groups:

> Pure control group: This is the status quo group. Farmers in this group will not receive any intervention

> CS + In-person support: This treatment will involve the group training on climate services based on the seasonal forecasts at the brink of the new crop season, daily SMS weather forecasts and technical support provided by extension officers via in-person monthly or bi-weekly visits to farmers (traditional extension approach).

> CS + Phone support: is treatment will involve the group training, daily SMS weather forecasts and phone-based technical support. The phone-based technical support will have two aspects: i) monthly or bi-weekly calls to farmers to follow-up on the use of daily weather forecasts and ii) Infoline for farmers who can initiate at any time and at their own cost calls to ask questions (e.g., availability and price of inputs such as seeds, fertilizers, pesticides, etc.) related to agriculture.

Randomization Method

Public lottery

Randomization Unit

Villages

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

105 Villages

Sample size: planned number of observations

30 farmers per village, implying 3,150 farmers in total

Sample size (or number of clusters) by treatment arms

35 villages in pure control group
35 villages in CS + in-person follow-up group
35 villages in CS + phone-based follow-up group

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

To compare two groups, with a total number of 70 clusters with 5 randomization sites and 25 farmers per clusters we can detect a small to medium effect sizes between 0.20 SD and 0.33 SD with 80% power. Considering the baseline values as suggested by the pilot study on CS in Benin (Yegbemey et al., 2021), the anticipated effects sizes this implies MDEs between 4,366 and 7,719 XOF/ha for labor, 305 and 500 kg/ha for yield. Considering that the average daily pay in agriculture is about XOF 2,000, the saving in term of labor can be between 5 to 10 days. These MDEs are realistic given the pilot experiment. Considering that attrition rate will not exceed 10% the final sample we hope to have up to 30 farmers (about 30% more) involved per village, implying up to 3,150 farmers.

Supporting Documents and Materials

IRB