The Effects of Decentralized and Video-based Extension on the Adoption of Integrated Soil Fertility Management – Experimental Evidence from Ethiopia

Last registered on July 04, 2019

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0004406

Initial registration date

July 02, 2019

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 03, 2019, 3:17 PM EDT

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

Last updated

July 04, 2019, 3:51 AM EDT

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

Locations

Country

Ethiopia

Region

Africa

Primary Investigator

Name

Denise Hörner

Affiliation

University of Göttingen

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Meike Wollni

PI Affiliation

University of Göttingen

Contact Investigator

PI Name

Markus Frölich

PI Affiliation

University of Mannheim and Center for Evaluation and Development (C4ED) and J-PAL Affiliate

Contact Investigator

PI Name

Adrien Bouguen

PI Affiliation

University of California, Berkeley

Contact Investigator

Additional Trial Information

Status

On going

Start date

2015-12-01

End date

2020-05-01

Keywords

Agriculture

Additional Keywords

JEL code(s)

Q01 Q15 Q16

Secondary IDs

Abstract

The slow adoption of new agricultural technologies is an important factor in explaining persistent productivity deficits among smallholders in Sub-Saharan Africa (SSA). Farmers delay in particular the joint uptake of technology packages consisting of several practices. Since knowledge constraints are an important barrier to adoption, effective extension approaches are key to overcoming technology deficits. In recent decades, extension systems in many SSA countries have moved away from “top-down” towards decentralized “bottom-up” models that involve farmers as active stakeholders.
In this study we assess the effects of a decentralized extension program and an additional video intervention on the adoption of integrated soil fertility management (ISFM) among 2,382 small-scale farmers in Ethiopia using a randomized controlled trial, in cooperation with the German Agency for International Cooperation and Development (GIZ). ISFM should enhance soil fertility and productivity and ultimately combat land degradation by using organic and inorganic soil amendments simultaneously. We find that both extension-only and extension combined with video induce ISFM adoption as well as gains in knowledge. We further find evidence for increased adoption of ISFM practices at the household level among farmers in extension communities that do not actively participate in the extension activities. The additional video intervention shows a significant complementary effect for these non-actively involved farmers, in particular when it comes to the integrated use of the practices on the same plot, and thus, appears especially beneficial for this group. A causal mediation analysis further reveals that increases in knowledge explain part of the treatment effects on adoption.

External Link(s)

Registration Citation

Citation

Hörner, Denise et al. 2019. "The Effects of Decentralized and Video-based Extension on the Adoption of Integrated Soil Fertility Management – Experimental Evidence from Ethiopia ." AEA RCT Registry. July 04. https://doi.org/10.1257/rct.4406-2.0

Former Citation

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Experimental Details

Interventions

Intervention(s)

The extension intervention has been implemented by the German Agency for International Cooperation and Development (GIZ) with its national partners from the agricultural ministry and offices at different administrative levels.
The core of the extension treatment consists of the (non-random) selection of model farmers, who are trained by public extension agents and provided with inputs to maintain ISFM demonstration plots on their farms. Model farmers lead and train so-called "Farmer Research and Extension Groups" (FREG) consisting of 50 farmers in each mws. In addition, two farmer field days per cropping cycle are conducted. During these field days, model farmers share and discuss their experience with FREG members regarding the implementation of ISFM and its results. Field day activities are mainly targeted to FREG members, although in some communities other farmers do also participate. The extension treatment mainly aims at creating awareness and know-how about ISFM through a knowledge sharing process from development agents to model farmers, and from model farmers to FREG members. Through that entry point, information should diffuse to the broader population of farmers in the communities.

The additional video intervention should serve to provide an additional stimulus to adoption by exposing farmers to information about the ISFM concept with all its individual components, in order to overcome potential incomplete information diffusion between farmers and resulting knowledge gaps. The movie is composed of two parts: A narrative and documentary part which presents the example of a farmer couple who has already successfully implemented the ISFM technologies and visibly increased yields, serving as (potential) role models for treated farmers. The second component of the film consists of animations that visualize processes taking place in the soil – such as hydrological cycles, the “work” of roots, soil organic matter, microorganisms and nutrients. Complex soil processes and the relationship between the individual ISFM components, soil fertility and improved yields are presented in a simplified way.

Intervention Start Date

2016-02-01

Intervention End Date

2018-09-01

Primary Outcomes

Primary Outcomes (end points)

- The number of adopted ISFM practices
- The likelihood of integrated ISFM adoption (all practices jointly on one plot)
- ISFM knowledge

Primary Outcomes (explanation)

- Adoption variables rely on self-reported data
- Knowledge variables are constructred based on an ISFM knowledge exam made with farmers; we calculate knowledge scores ranging from 0 to 1 based on the number of correct answers a respondent gave relative to the total number of questions

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The study builds on an RCT design with two treatment arms and a control group. We used microwatersheds (mws) as units of randomization, which are common implementation units for natural-resource-related interventions in Ethiopia. One group of treatment mws (T1) receives the decentralized extension treatment only, while the second group of treatment mws (T2) receives the extension treatment plus the additional video intervention. The extension intervention is implemented at the level of mws as an an-going process since 2016, all activities are regionally aligned with the course of the main harvest cycle. The video screenings were conducted in T2 communities in early 2017, around six weeks prior to the start of the main growing season. Typically, the video was shown in public spaces such as farmer training centers, health posts or schools, and followed by group discussions that were facilitated by extension agents. In each microwatershed, the 15 households from our sample were invited by village heads a few days prior to the screenings orally and with written invitation cards. In the case of double-headed households we invited both spouses, otherwise only household heads.

Experimental Design Details

Randomization Method

Lottery

Randomization Unit

Cluster randomization at the level of micrwatersheds; within microwatersheds, random selection of households

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

161 microwatersheds

Sample size: planned number of observations

2,416 households

Sample size (or number of clusters) by treatment arms

36 microwatersheds extension (only)
36 microwatersheds extension + video
89 microwatersheds control

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

Minimum detectable effect size: 20% of a standard deviation, with 161 clusters and 15 households per cluster, assuming 10% intra-cluster correlation; for the number of adopted practices (0-5) with a mean of 2.5 and SD of 1.4, this would be 0.28

Supporting Documents and Materials

IRB