Technology adoption: agricultural drones and autonomous driving technology for farm machinery

Last registered on April 26, 2023
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Pre-Trial

Trial Information

General Information

Title
Technology adoption: agricultural drones and autonomous driving technology for farm machinery
RCT ID
AEARCTR-0011308
Initial registration date
April 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
April 26, 2023, 5:21 PM EDT

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

Locations

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Primary Investigator

Name
Zhixian Lu
Affiliation
Peking University
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Other Primary Investigator(s)

PI Name
Huang Chen
PI Affiliation
Xiamen University
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Additional Trial Information

Status
In development
Start date
2023-04-29
End date
2024-08-31
Keywords
Agriculture
Additional Keywords
JEL code(s)
C93, D83, O13, O33, Q12, Q16
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Rapid advancement in agricultural machinery and automation technology is transforming traditional agriculture. In developing countries with a large proportion of smallholders like China, intelligent equipment such as drones and self-driving machines have been increasingly applied in agriculture, facilitating precision farming and enhancing agricultural productivity. However, the adoption of these new technologies has been relatively slow, possibly due to information-related factors. Firstly, farmers may have limited access to information on intelligent farming equipment. Secondly, they may lack supply information on agricultural machinery. Given the high costs of machinery investments, smallholders are more likely to use machinery services than to purchase equipment themselves. Therefore, the availability and information on local machinery services are essential for farmers’ technology adoption as well.
We propose a field experiment to investigate whether providing farmers with information on two representative types of intelligent farming equipment and their corresponding local farming services can lead to changes in farmers' perceptions and adoption rates of these technologies. The follow-up survey consists of two steps: firstly, we will assess the impact of the intervention on farmers' perceptions and willingness to pay for the technologies; secondly, we will evaluate the effect of the intervention on farmers’ actual adoption rate of the technologies and their long-term impact on agricultural production.
External Link(s)

Registration Citation

Citation
Chen, Huang and Zhixian Lu. 2023. "Technology adoption: agricultural drones and autonomous driving technology for farm machinery." AEA RCT Registry. April 26. https://doi.org/10.1257/rct.11308-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Education on technology and market information on agricultural drones and autonomous driving technology for farm machinery.
Intervention Start Date
2023-04-29
Intervention End Date
2023-05-07

Primary Outcomes

Primary Outcomes (end points)
The primary outcome variables are farmers’ cognitive scores about agricultural drones and self-driving machines, their willingness to pay for the equipment as well as the service, the adoption rate of the equipment.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The secondary outcome variables are the impacts of these machinery technologies on the agricultural inputs (labor, pesticide, fertilizer, etc.), crop yield, and productivity.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment is a two-stage randomized controlled trial that involves randomization at the village and household levels. First, villages within a township are randomly assigned to one of three groups: treated villages (all sample households are treated), controlled villages (no sample households are treated), and intermediate villages (half of the sample households are treated). Second, households are randomly sampled within each village. In intermediate villages, half of the sample households are randomly assigned to the treatment group. Following the baseline survey of all sample households, an information intervention will be provided to the treatment group using a brochure, while the control group will receive no intervention.
A follow-up survey will be conducted by phone on the day next to the intervention date to measure farmers’ cognition and willingness to pay for the new technologies. After at least one growing season, the other follow-up survey will be conducted to assess farmers’ adoption of the technologies. This survey will provide valuable information on the long-term effectiveness of the intervention and help determine if any improvements are necessary to promote the diffusion of the intelligent agricultural machinery.
Experimental Design Details
Not available
Randomization Method
Randomization done in Excel by a computer.
Randomization Unit
First, villages within a township are randomly assigned to one of three groups: treated villages, controlled villages, and intermediate villages. Second, in intermediate villages, half of the sample households are randomly assigned to the treatment group.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
1,152 farmers in 144 villages, 48 townships, and 24 counties across 8 provinces in China.
Sample size: planned number of observations
1152 rural households
Sample size (or number of clusters) by treatment arms
48 treated villages (all sample households are treated), 48 controlled villages (all sample households are treated), and 48 intermediate villages (half of the households are treated)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board of Finance and Economics Experimental Laboratory, The Wang Yanan Institute for Studies in Economics, Xiamen University
IRB Approval Date
2023-04-21
IRB Approval Number
FEEL230401
Analysis Plan