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ICT-mediated agricultural knowledge transfer in Uganda: What works?

Last registered on June 15, 2017


Trial Information

General Information

ICT-mediated agricultural knowledge transfer in Uganda: What works?
Initial registration date
June 13, 2017

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
June 15, 2017, 11:05 AM EDT

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



Primary Investigator


Other Primary Investigator(s)

PI Affiliation
PI Affiliation
University of Antwerp

Additional Trial Information

In development
Start date
End date
Secondary IDs
In information dissemination campaigns by agricultural extension services, seemingly small attributes, such as the way it is delivered or who it is delivered to, can result in significant differences in outcomes, such as knowledge transfer, adoption and yield. In the context of ICT-mediated knowledge transfer, this study investigates the role of the gender composition of the person(s) who provide the information and the gender composition of the person(s) who receive the information in making the information transfer more effective. In addition, video as a way to deliver extension information is augmented with a more demand-driven ICT solution, such as Interactive Voice Response (IVR). Effectiveness is assessed in terms of knowledge gain, adoption, yield increase, and poverty reduction. These research questions are answered through field experiments, where farmers are randomly assigned to particular interventions.

Registration Citation

Lecoutere, Els, David J Spielman and Bjorn Van Campenhout. 2017. "ICT-mediated agricultural knowledge transfer in Uganda: What works?." AEA RCT Registry. June 15.
Former Citation
Lecoutere, Els, David J Spielman and Bjorn Van Campenhout. 2017. "ICT-mediated agricultural knowledge transfer in Uganda: What works?." AEA RCT Registry. June 15.
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Experimental Details


We will use video to convey information to farmers with the aim of changing adoption behaviour. We will focus on providing information on seed selection, soil nutrient management (including promoting organic fertilizer application), weeding, timely planting and plant spacing. From our conversations with experts, we learn that many farmers may already be aware of the existence and use of these technologies or practices. Therefore, simply providing information about the existence of modern technologies and recommended practices and on how to uses them may not be sufficient to change behavior. Often, access and afford ability was mentioned as a problem. In our videos, we will thus also try to alter the belief that seeds and fertilizers are “too expensive” by pointing out the costs and benefits of the different technologies and practices we promote. In addition, we will encourage farmers to start small, using fertilizer and seeds on a small area of their field to experiment and see for themselves, and reinvest in subsequent years. Inter-temporal decision making, where costs today have to be compared to uncertain outcomes in a distant future, is often challenging for poorly educated farmers. Furthermore, Duflo et al. (2011) point out farmers may have difficulties committing to fertilizer use in Kenya. In our video, we attempt to make farmers aware of this, and suggest some techniques to overcome this bias. We also pay considerable attention to the way the message is delivered. For instance, the message if brought by “peer farmers”, as it is found that farmers find communicators who face agricultural conditions and constrains most comparable to themselves to be the most persuasive (BenYishay et al., 2014). The information is also presented as a success story, which is assumed to affect a range of non-cognitive farmer characteristics such as aspirations, locus of control and self-esteem (Bernard et al., 2015).

The video starts with a farmer (a male farmer, a female farmer, or a couple, see below) introducing themselves. He talks about how he used to struggle with his maize gardens and how at one point in time, he decided things needed to change. It is shown how the farmer sells a hen, and obtains a small loan from a friend. This money is than used to buy small quantities of improved seed and fertilizer in a local shop. It is then shown that, before planting the improved seed, the farmer prepares the garden. He is shown collecting manure wherever he can find it, and applies it to a small corner of the field of 20m by 20m. Next, it is shown in detail how the maize seeds are spaced 75cm x 30cm with 1 plant per hill and how the DAP should be applied. The viewer is reminded to plant in time. The next scene depicts the field after about 10 to 12 days when the maize has emerged from the ground. At this stage, it is recommended that the farmer engages into gap filling to replace seeds that did not germinate with new seeds to preserve optimal plant density. The next shot shows the field at 18 to 20 days after planing, when first weeding is done. Particular attention is paid to identification of striga in an early stage. It is also advised to weed again two to three weeks later. The next scene zooms in on urea fertilizer application. Here, the field is shown at about 4 weeks after planting when the maize is knee high. It is shown how Urea topsoil dressing should be applied. Finally, it is recommended to do one more round of weeding around the tasseling stage of the maize.

We then spend some time comparing the costs to the benefits the different improved inputs and recommended practices. For fertilizer and improved seed, the costs for one acre is calculated and compared to the value of what is harvested. The profit is then compared to the value of what would have been harvested on that acre without improved seed and fertilizer. This would be less than half of what the profit would be when improved inputs are used. For recommended practices, we report what the expected yield increase would be if, for instance, recommended spacing was used or weeding is done in time.

Next, we try to promote a long run perspective, where the farmer is encouraged to start small (one tenth of an acre) and grow bigger over time. We also pay some attention to the commitment problem. We advise the farmer to, at time of harvest when the farmer sells most of his maize, immediately go to the farm supply store and purchase seeds and fertilizer, and store this in a safe place. If inputs are not available, farmers are encouraged to keep the money needed to buy the inputs in a separate, labeled container. It is sometimes argued that such mental accounting can be an effective commitment mechanism (Dupas et al., 2013). In a last scene, the farmer recapitulates and once more directly addresses the viewer an encourages him or her to try this as well.

A total of three such videos will be produced. They will be identical in terms of the information that will be in the video, but they will differ according to the gender of the messenger, corresponding to one of the factors in the factorial experimental setup. In one version of the video, the actor will be a man. In a second version of the video, the actor will be female. In a third version, the video will feature a couple as the messenger. Here, both male and female will feature in the different scenes, and the person talking to the viewer will be altered between the man and the women. Finally, we will use a placebo video for the control. This will be a neutral video on a non-related topic, such as the tourist potential of the region. Farmers will be shown any of these four videos according to random treatment allocation. Videos will be shown twice to each farmer in the sample, once before planing (July) and once immediately after planting (August).
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
knowledge related to what is in the videos, behavioural change of things that are featured in the video, such as spacing, soil nutrient management, maize yields, consumption expenditure and poverty. All of these will be measured at the individual level (male/female), women's empowerment in agriculture index.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This research aims to answer the questions related to information asymmetries within the household, homophilly effects in learning and the projection of farming as a household business. investigate the relative importance of (i) the gender composition of the messenger, and (ii) the gender composition of the audience for effective agricultural extension information messages to encourage sustainable crop intensification in smallholder household farms and for improving gender equity in household farming. In particular, we will compare outcomes at the household level between:

• households that are shown a video with agricultural extension information and households that are shown a random video (placebo);

• households that are shown a video with agricultural extension information where the animator is an individual “peer” farmer (ie. a man or a women) and households that were shown a video providing the same information by a couple of peer farmers (ie. man and woman who are shown to participate as equals in the family farm and deliver the message as a couple);

• households in which one adult individual (husband or wife) is shown a video with agricultural extension information and households in which the couple (husband and wife) is shown a video with the same information;

• households where the gender composition of (the individual) messenger(s) and (individual) audience is matched and households where the gender composition of the messenger and audience differ.

To assess the effect the gender composition of messenger and receiver on equity within the household, we will compare outcomes within the household between:

• households where the video was shown to a man versus households where the video was shown to the husband

• households that were shown the video that promotes a household approach featuring a couple versus households that were shown a video where a single farmer provides the messages

• households that are both shown the video that also projects a household approach to all the other households

For this research question, we will be particularly interested in how these interventions reverberate through intra-household decision-making and the allocation of time and resources to agriculture between the different individuals within the farm household. The outcomes of interest will therefore be disaggregated by gender and age, and interpreted in light of the interplay between efficiency at the household farm level, equity within the farming household and women’s empowerment.

The tools and technologies through which the information is transferred are also likely to influence effectiveness. There are many different ways of delivering agricultural extension messages through ICT. Broadly, one can differentiate between two different approaches. In one approach, extension resembles the traditional teacher-pupil model, where the farmer is assumed to absorb knowledge from experts. Showing videos to farmers or pushing information over a mobile phone would fall into this category. A second approach relies more on a consultative model, where the farmer is assumed to know his or her information needs are and requests this information from a service provider. Call centers and Interactive Voice Response (IVR) technology are examples of this second approach. In a third research question, we will compare the effectiveness in terms of changing knowledge, practices and outcome such as yields and poverty of video messages to more or less demand based options such as IVR. In particular, we will compare videos to videos augmented with IVR and videos augmented with IVR where the recipient is also provided with reminders of the existence of the IVR service.

The questions will be answered through field experiments, where farmers are randomly assigned to a group that receives particular interventions (de Janvry et al., 2017). An identification strategy that is based on randomization allows us to quantify the causal linkage between an intervention and the outcomes. In particular, we establish the causal link between extension videos and the knowledge gained, between extension videos and yield changes, and between extension videos and poverty (measured by income).

The research design itself will take the form of a mixed level factorial design. To answer the first research question, we define two different factors, each with three levels. The first factor relates to the messenger and has three levels (male, female and male+female). Similarly, the second factor relates to the recipient of the message and also has these three levels (male, female and male+female). The second research question, where video is now augmented with a demand-driven technology such as IVR, corresponds to adding an extra factor with two levels (no IVR, IVR). In practice, we therefore add the IVR treatment to half of the participants who get to see a video. Finally, we will add a pure control group to the design, such that we do not only investigate relative effectiveness of the different attributes, but also relative to someone who did not get to see a video at all.

The interventions will be targeted at the individual (household) level as opposed to group screenings. Videos will be shown in the house or in the field if necessary. This option guarantees consistency in the treatment and requires the least amount of observations. The videos will be developed in house by the research team and produced by a professional media production company such as NOTV. For IVR, we will partner with Human Network International. Human Network International’s 3-2-1develops content for farmers on a variety of crops and agricultural practices, and make the service available to farmers for free or at low cost.
Experimental Design Details
Randomization Method
computer at the village headquarter.
Randomization Unit
farm level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
this is going to be an experiment at the individual farmer level
Sample size: planned number of observations
3600 farmers
Sample size (or number of clusters) by treatment arms
Control=257, MM=385, MF=385, MB=369, FM=385, FF=385, FB=369, BM=342, BF=342, BB=369. In each treatment cell except control, half will receive the the treatment augmented with IVR.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
power calculations are based on simulations using data on maize yields from the UNHS 2005/06. As effect size is based on maize yield (kg/acre) Control = 0, MM= +10 percent, MF= +5%, MB= +15%, FM=+1.5%,FF=+7.5%, FB=+12.5%, BM=+15%, BF=+12.5%, BB=+20%

Institutional Review Boards (IRBs)

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IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents


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