Testing the impacts of cash and knowledge transfer on the adoption of climate-smart agriculture by members of village savings groups

Last registered on November 15, 2024

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0014643

Initial registration date

October 28, 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

November 15, 2024, 12:56 PM EST

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

Locations

Country

Madagascar

Region

Primary Investigator

Name

O. Sarobidy Rakotonarivo

Affiliation

University of Antananarivo

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2024-02-01

End date

2027-06-30

Keywords

Agriculture, Behavior, Environment & Energy, Welfare

Additional Keywords

village savings and loans association, climate-smart agriculture

JEL code(s)

Secondary IDs

Prior work

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

Abstract

Most farmers living near protected areas are inherently isolated and unable to access affordable and safe credit. This constrains their ability to invest in their production and makes them more likely to continue with low input farming practices such as slash and burn agriculture (involving burning and rotational farming). These unsustainable practices drive forest clearing and biodiversity loss and increase farmer vulnerability to the negative effects of climate change. Climate-smart agriculture (CSA) can help develop more sustainable agricultural systems and enhance farmer resilience, thus reducing food insecurity - while generating biodiversity and climate change mitigation co-benefits from reduced deforestation. Despite their demonstrated benefits, adoption of CSA remains very low and robust empirical evidence on the key policy instruments and behavioral factors that support their widespread adoption is very thin. One of the key constraints to adoption is limited access to knowledge. Another major constraint is high upfront investment costs associated with input purchases and labor costs. This study aims to address these constraints in Madagascar by evaluating the impacts of knowledge and credit transfers on CSA adoption and other welfare indicators using a mixed-method approach (a randomized controlled trial, experimental games, and qualitative interviews).

This project aims to address the following research questions:
1. Do credit and knowledge transfer (training in CSA) increase investments in CSA and improve local welfare (farm and non-farm income,
food security and resilience)?
2. Does the effectiveness of the CSA training depend on the availability of the credit?
3. What are the underlying mechanisms driving observed changes in behavior?
4. How do behavioral factors such as risk aversion and willingness to engage in collective action affect farmer’s intention to invest in CSA?
5. Can village savings and loans associations (VSLAs) be a suitable vehicle for encouraging investment in more sustainable farming techniques and income generation?

External Link(s)

Registration Citation

Citation

Rakotonarivo, O. Sarobidy. 2024. "Testing the impacts of cash and knowledge transfer on the adoption of climate-smart agriculture by members of village savings groups ." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.14643-1.0

Sponsors & Partners

Interventions

Intervention(s)

We will implement a randomized controlled trial (RCT) with members of village savings and loans associations (VSLAs) to robustly evaluate the impacts of two interventions: i) training in climate-smart agricultural (CSA) practices, 2) Injection of credit into existing VSLAs on local welfare and farming practices.

The training in CSA will be a two-hours hands-on training that will be provided monthly by field technicians to VSLA members for a total of 12 months. This training will cover a few CSA practices on two key crops (coffee and cassava) such as the use of organic fertilizer (basket compost) and manure fertilizer, cover crops, mulching and contour farming.

The injected credit will match the average total VSLA savings at the end of the previous cycle, and will be implemented for a total of 12 months. The credit is labelled "CSA credit", which aims to spur investments in more productive and resilient farming practices (such as CSA practices).

Intervention Start Date

2024-11-01

Intervention End Date

2025-10-31

Primary Outcomes

Primary Outcomes (end points)

Farm areas under climate-smart agricultural techniques (e.g. use of organic or manure fertilizer, cover crops, mulching and contour farming)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

1) Number of off-farm jobs (livelihood diversification) and off-farm income,
2) Labor allocation (wage labor)
3) Multidimensional poverty index and food security,
4) Total VSLA net savings,
5) Access to new landholdings,

Secondary Outcomes (explanation)

Experimental Design

The RCT has three arms: Control, Training, Training and Credit, and uses a cluster-randomized design in which villages (clusters) are first randomized into the treatment and control groups, and all VSLAs within treatment villages are provided either the training only or the training and the credit.

Experimental Design Details

Not available

Randomization Method

Randomization done in office (Stata) uisng the J-Pal material (https://www.povertyactionlab.org/resource/randomization)

Randomization Unit

The unit of randomization is the village, all VSLAs within a village will be provided the same treatments. The number of VSLAs in a village varies from 1 to 8.

We used a block randomization, with three blocking variables: municipality, multidimensional poverty index (MPI), and village size (number of VSLAs per village). We first ensured treatments are balanced across the eight municipalities of the study sites. We then defined two groups of MPI (> and < median MPI), and two groups of village size (> and < 3 VSLAs per village). We have attached the Stata file we used to conduct the block randomization.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

The RCT has three arms:
1) Control: 31 villages (clusters) with 40 VSLAs (751 household members)
2) Training: 31 villages with 51 VSLAs (966 household members)
3) Training and Credit: 31 villages with 43 VSLAs (789 household members)

Sample size: planned number of observations

2450 households across a total of 134 VSLAs in 93 villages.

Sample size (or number of clusters) by treatment arms

31 villages controls,
31 villages training,
31 villages training and credit

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

The Minimum Detectable Effect Size for our main outcome variable (farm area under organic fertilizer or basket compost) is 0.6 ares (60 m2), with 2.98 of standard deviation and 20% of standard deviation.

Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name

Charles River Campus Institutional Review Board of Boston University

IRB Approval Date

2024-01-30

IRB Approval Number

7205E

Analysis Plan