Agricultural adaptation: Scaling-up the adoption of rainwater harvesting techniques

Last registered on June 12, 2024


Trial Information

General Information

Agricultural adaptation: Scaling-up the adoption of rainwater harvesting techniques
Initial registration date
December 19, 2023

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
December 21, 2023, 8:02 AM EST

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

Last updated
June 12, 2024, 11:00 AM EDT

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


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Primary Investigator

UC Santa Barbara

Other Primary Investigator(s)

PI Affiliation
Tufts University
PI Affiliation
University of Diffa

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In situ rainwater-harvesting (RWH) techniques have the potential to increase agricultural yields in the face of low and erratic rainfall, reversing land degradation and combatting desertification. We propose to study the scale-up of an intervention that has been shown to increase the adoption of RWH techniques in Niger, with relevance for most degraded land in the Sahel. A randomized control trial (RCT) conducted in Niger between 2018 and 2021 found that training alone was highly effective at increasing adoption. This, in turn, led to increases in agricultural revenue of around 0.14 s.d. up to three years after the initial training. This project will build upon the completed RCT to scale the adoption of RWH techniques in Niger, with three distinct contributions relative to the completed work. First, it will focus on expanding the adoption of two techniques (demi-lunes and zai) that are appropriate for severely degraded soils in Niger. Second, it will assess the relative costs and benefits of using in-person versus remote sensing to monitor initial and sustained adoption. Third, it will investigate whether larger trainings, which are cheaper to implement, deliver similar adoption impacts.
External Link(s)

Registration Citation

Aker, Jenny, Kelsey Jack and Malam Assane Maigari. 2024. "Agricultural adaptation: Scaling-up the adoption of rainwater harvesting techniques." AEA RCT Registry. June 12.
Experimental Details


The basic intervention follows the training program described in Aker and Jack (2023), which involved training farmers on a specific rainwater harvesting technique (demi-lunes) and collected data on adoption outcomes using in person measurement. This trial introduces village-level variation in training and monitoring protocols as described below. The overarching study objective is to refine the training design to facilitate further scale up by the Ministry of Environment.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Rainwater harvesting adoption, measured in two ways as described below
Primary Outcomes (explanation)
In-person measurement: In villages assigned to the in-person monitoring treatment, we will measure number of demi-lunes and zai constructed, and will measure the area under adoption. We will also ask questions about the labor and tool costs involved during construction, and households’ beliefs about the benefits of these technologies prior to the agricultural season. We will analyze these outcomes for the subset of villages that receive in person monitoring.

Remote measurement: We will also use the in person monitoring data to train a predictive model using remote sensing imagery. (At the moment, we have access to PlanetScope and Sentinel-1 and -2 datasets; if higher resolution data become available, we will use it too.) One predicted outcome will be adoption, which will be consistently available for all villages, regardless of the monitoring treatment. The model will also be used to predict adoption outside of the study sample of 16 households per village.

Secondary Outcomes

Secondary Outcomes (end points)
Agricultural productivity
Land degradation
Climate resilience
Secondary Outcomes (explanation)
ii) Agricultural productivity
- Survey data: At endline, we will ask households to report total agricultural production, by crop, and total revenue from agriculture, also by crop.
- Remote measurement: We will measure NDVI and other agricultural production proxies.

iii) Land degradation
- Survey data: At endline, we will ask households to report land retirement, new land brought into production and self-reported soil quality.
- Remote measurement: We will predict land degradation from remote sensing imagery using labels collected at baseline.

iv) Climate resilience
- Survey data: At endline, we will follow Chomitz et al. (or any new instruments developed between now and endline) and measure adaptation and resilience using household self reports. We will also collect survey-based self reports on weather realizations relative to average.
- Remote measurement: We will merge village locations with high resolution temperature and precipitation data to measure deviations from long run weather patterns.

Experimental Design

Experimental Design
Villages were assigned to a control group or one of the treatment arms. Three main cross cutting treatments are assigned at the village level.

1) Rainwater harvesting technique:

Farmers are trained on where and how to construct rainwater harvesting techniques. Specifically, villages are trained on (a) demi-lunes, (b) zai or (c) both. The training order was randomized among villages trained in both techniques.

2) Monitoring approach

Villages were assigned to receive either in person or remote monitoring. Households in the in person monitoring arm will be visited by an enumerator and Ministry staff to record adoption in May-June 2024, just before planting. The monitors will visit fields where adoption occurred and count the number and spatial extent (i.e., perimeter) of adoption. Villages assigned to in-person monitoring are informed of the monitoring protocol at the time of the training. This announcement was made during the end-of-training survey, rather than as a public and village-level announcement.

All treatment villages are monitored remotely, by satellite. Additional detail on satellite-based monitoring is discussed below. Villages assigned to remote monitoring were not informed about this type of monitoring (as satellite data collection is not at the discretion of the research team), nor were they informed that they would receiving a monitoring visit later in the year.

Funding permitting, selected villages in the remote treatment arm will be monitored in May/June 2024, both to further improve the prediction model, as well as to better understand the role of monitoring in adoption.

3) Training size

In small-training villages, only the sampled 16 households are invited to attend the training. In large-training villages, the sample 16 households are invited to attend the training, as well as all households interested. In both cases, invitations are done by the village chief.

4) Goal setting

In addition to the village-level treatment variation, one additional treatment is introduced as part of the training. Randomly-selected households are asked to set a goal for adoption as part of a brief survey administered during the training.

A) Goal – self-select: Households are asked if they would like to set a goal of how much land to cover in demi-lunes or zai or both. The decision to set a goal and the goal are recorded by the enumerator.

B) Goal – requested: Households were asked how many demi-lunes or zai they would like to adopt. This is recorded by the enumerator.

C) Goal – control: No goal set and recorded by enumerator.

In villages in the in-person monitoring treatment, the timing of the goal setting treatments are varied to either before or after the in person monitoring is announced.
Experimental Design Details
Not available
Randomization Method
Treatments were assigned within geographic strata (department) using the randtreat command in stata and assigning misfits independently across strata using treatment weights (wstrata). All village level treatments were block randomized, for a fully saturated design.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
The sample consists of 406 villages in the Maradi and Zinder regions of Niger. The village sample was constructed during a listing visit starting in December 2022. To be eligible for the study, villages needed to simply have severely degraded land.
Sample size: planned number of observations
Within each eligible village, we conducted a census of all households with severely degraded land (which was self-reported). Amongst eligible households, we then randomly selected 16 households. The baseline survey was conducted immediately after the random sampling, whereby households were asked a number of questions. In addition, the team visited the household’s most severely degraded plot of land and took the GPS coordinates of the perimeter. Some villages did not have 16 households with severely degraded land; as a result, some villages had fewer respondents. Thus, the intended sample was 6,406 respondents, with a final sample of 6,403 respondents. A spillover sample of 6 additional households was also selected from the randomly ordered list of eligible households. The spillover households were not part of the baseline survey.
Sample size (or number of clusters) by treatment arms
Number of clusters by treatment:
Control, remote sensing 21
Control, in-person 21
DL, Remote, 16 30
DL, Remote, All 30
DL, In-person, 16 29
DL, In-person, All 32
Zai, Remote, 16 30
Zai, Remote, All 31
Zai, In-person, 16 29
Zai, In-person, All 31
Both, Remote, 16 30
Both, Remote, All 32
Both, In-person, 16 29
Both, In-person, All 31
Total 406
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Tufts University
IRB Approval Date
IRB Approval Number
Analysis Plan

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