Bundling Weather Forecasts and Agronomic Advisory for Farmers in Pakistan

Last registered on September 06, 2023


Trial Information

General Information

Bundling Weather Forecasts and Agronomic Advisory for Farmers in Pakistan
Initial registration date
August 18, 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
August 18, 2022, 3:24 PM EDT

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

Last updated
September 06, 2023, 3:51 PM EDT

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



Primary Investigator

University of Chicago

Other Primary Investigator(s)

PI Affiliation
Harvard University

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Agricultural production is inherently risky and subject to weather shocks that threaten livelihoods for small-scale farmers worldwide. Short-range weather forecasts can help farmers plan immediate agricultural activities and take preventative measures that lower the likelihood of losses due to unforeseen weather. Advisory services coupled with weather forecasts can speed up learning about how forecasted weather patterns can affect crop production. We propose to test an optimal design strategy to detect treatment and spillover effects in a large population of potential program participants. We do so by developing a research design that accounts for both spillovers and adaptive assignments. We propose to assign clusters to have variation in treatment intensity of the share of users per cluster assigned to any treatment. This will generate exogenous variation in the share of farmers in a community that receive the information. After the first round of assignments, we propose to use information on uptake and engagement to reassign users in an adaptive experiment to maximize benefits to users.
External Link(s)

Registration Citation

Rudder, Jessica and Davide Viviano. 2023. "Bundling Weather Forecasts and Agronomic Advisory for Farmers in Pakistan." AEA RCT Registry. September 06. https://doi.org/10.1257/rct.9945-2.1
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Experimental Details


Precision Development (PxD) is rolling out a new service that provides short-run weather forecasts to cotton farmers in Punjab province Pakistan using a voice message system that automatically calls users and delivers a pre-recorded message. PxD’s core service provides agronomic advisory recommendations via the same voice message system. The intervention will compare outcomes for users randomly assigned to one of two groups: 1. A control group that receives advisory messages only, and 2. A treatment group that receives advisory messages plus weather forecasts. The core user base includes about 500,000 cotton farmers located in 42 tehsils in the cotton-growing belt of Pakistan.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Agricultural input use, timing of input applications, agricultural yields, sharing of information, information spillovers, individual weather forecast/belief, engagement

Outcomes for follow-up survey launched in September 2023: perceptions of climate change, perceptions of risks from climate change, 2022 input expenditure and yields, 2023 short-run adaptation (crop, seed choices, conservation agriculture practices), spillovers on network sample
Primary Outcomes (explanation)
Network sample collected by asking survey respondents to provide phone numbers for up to three friends with whom they discuss agricultural practices

Secondary Outcomes

Secondary Outcomes (end points)
Sources of heterogeneity: variation in climate trends at the tehsil level, variation in 2022 yield expectations compared to realized yields, variation in forecast accuracy, growing zone
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The design consists of at least three consecutive waves of randomization. Each wave assigns treatments with different probabilities across different tehsils (equivalent of a district unit).

We consider three groups of tehsils denoted as high/medium/low saturation, each corresponding to larger treatment probability within each tehsil (approximately 60-40-12% of treated individuals in the first experimentation wave).

Throughout each wave, we increase the treatment probability within each tehsil. By the last wave, the high saturation teshils will present the largest share of treated individuals (approximately 90%).

In the first wave, we induce small local perturbation to treatment probability across pairs of tehsils. In the consecutive waves we assign treatments also using engagement data, and induce local perturbation to treatment probabilities based on individual engagement.

Engagement data is readly available for all experimental participants. For phone surveys, we sample individuals at random in multiple waves (up to 1,800 individuals/month). In each wave, sampling is stratified by treatment status, and tehsil.
Experimental Design Details
Randomization Method
randomization done in office by a computer
Randomization Unit
Clusters and units within clusters
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Approximately 500 000 participants in the experiment with engagement outcomes and up to 10,000 individuals with outcomes from survey data. Follow-up survey number of observations: 2,000
Sample size (or number of clusters) by treatment arms
Intervention starts with 30% of participants assigned to treatment and increases each wave
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
University of Chicago
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials