Social learning to scale-up the agroecological transition

Last registered on March 30, 2023


Trial Information

General Information

Social learning to scale-up the agroecological transition
Initial registration date
March 21, 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
March 30, 2023, 2:55 PM EDT

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


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Primary Investigator

French National Research Institute for Agriculture, Food and Environment (INRAE)

Other Primary Investigator(s)

PI Affiliation
French National Research Institute for Agriculture, Food and Environment (INRAE)
PI Affiliation
Université Grenoble Alpes
PI Affiliation
French National Research Institute for Agriculture, Food and Environment (INRAE)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Peer effects are a subject of increasing attention in many areas of economic research. Peer influence can create social multiplier effects, whereby an initial investment targeting one small group can lead to larger changes, as individuals close to the target group are directly influenced by its actions. When it comes to the green transition, a central question is that of learning about green agricultural technologies. The adoption of a new technique sometimes requires specific technical assistance, which cannot easily be provided to all eligible farmers because of the high costs it would entail. In this context, observational learning can play a crucial role in the diffusion of new practices. However, there are many reasons why social learning might not ultimately happen; and it is often difficult to identify and measure it accurately. The literature provides several examples of the diffusion of agricultural innovations through social networks and peer effects in developing countries. There is, however, no prior evidence of the existence of the diffusion of agroecological practices through social learning in developed countries. We aim at filling this gap by studying the knowledge spillovers of an intervention that trains a number of volunteer farmers in new agricultural practices, with the aim of indirectly transforming those of the members of their network – we call a network of farmers a cluster. The experiment requires the implementation of a pilot program, in which the clusters are randomly assigned to training. The proportion of farmers within each cluster who are offered training varies between treatment arms. We call them the injection points of new knowledge. This design allows first to infer the causal effect of training on treated groups, then to check the presence of the diffusion of new knowledge inside the clusters, and finally to highlight a threshold in the proportion of injection points necessary for the diffusion of new knowledge.
External Link(s)

Registration Citation

Deperrois, Rose et al. 2023. "Social learning to scale-up the agroecological transition." AEA RCT Registry. March 30.
Experimental Details


The objective of this experiment is to measure the dissemination, within farmer clusters, of new technical knowledge enabling the transition to pesticide-free agriculture. The experience is based on the implementation of a training program, which a randomly chosen proportion of farmers within each cluster are invited to follow. The treatment is the proportion of trained farmers within a cluster.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The main outcomes are scores of technical knowledge related to the agroecological transition.
Primary Outcomes (explanation)
The training provided in groups A and B aims to improve the knowledge necessary for a transition to pesticide-free agriculture. The acquisition of this knowledge by the farmers (whether they are injection points or ordinary members of the cluster) can be assessed using a knowledge test. If the injection points pass the knowledge test, the proportion of successful tests within the treatment groups will be that of the injection points (1/3 and 1/2 respectively). If the ordinary members also pass the test (through knowledge sharing), the proportion of successful tests within the treatment groups will be higher than 1/3 and 1/2. If there is a threshold effect, i.e. it takes at least a certain proportion of injection points for diffusion to take place, then the proportion of successful tests in group B will be greater than 1.5 times that in group A.

Secondary Outcomes

Secondary Outcomes (end points)
Other outcomes include indicators of the environmental performance of farmers, the use of chemical pesticides and the adoption of alternative practices notably.
Secondary Outcomes (explanation)
Insofar as the training provided has adapted to the problem faced by the participants, it is interesting to look beyond the simple acquisition of new knowledge and to evaluate the change in practices likely to be triggered by the acquisition of this new knowledge.

Experimental Design

Experimental Design
We will run a randomized control trial (RCT) to compare the efficiency of treatments A and B in improving the technical knowledge needed to switch to pesticide-free agriculture. Each volunteer participant will be asked to sign an FPIC form specifying that they understand that their participation in the pilot program does not guarantee that they will benefit from free training at the end, as the pilot includes two phases of randomization.
Upstream of the randomization, we will form clusters of farmers on the basis of criteria defined by the project's partner cooperatives. The main criterion will be the geographical location of the members of the cluster, the latter having to be geographically close. In any case, each of the farmers volunteering to participate in the experiment must be a member of one and only one cluster.
In the first phase of randomization (lottery #1), the clusters are randomly assigned to the arms of the trial, which include one control group and two treatment groups. People in the treatment groups then participate in a second lottery (lottery #2), which will designate individuals inside each cluster who will be trained. People in the control group are not offered such an opportunity during the experimental period.
Experimental Design Details
Not available
Randomization Method
Random assignment to the arms of the trial (lottery #1), as well as random selection of injection points (lottery #2) will be performed using computer software.
Randomization Unit
We will randomize clusters.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
480 clusters of 6 farmers each
Sample size: planned number of observations
Total number of farmers is 2880.
Sample size (or number of clusters) by treatment arms
Sample size by treatment arm is 960 farmers (160 clusters)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With alpha = 0.05 and power = 0.8 the minimum detectable effect size, i.e. the minimum difference in knowledge test pass rates is 0.0832.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number