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The Role of Information in Agricultural Technology Adoption: Experimental Evidence from Rice Farmers in Uganda
Last registered on June 29, 2016


Trial Information
General Information
The Role of Information in Agricultural Technology Adoption: Experimental Evidence from Rice Farmers in Uganda
Initial registration date
June 10, 2016
Last updated
June 29, 2016 9:10 AM EDT
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
PI Affiliation
Additional Trial Information
In development
Start date
End date
Secondary IDs
Previous research identified information inefficiencies as a major constraint to sustainable crop intensification among rice farmers in Eastern Uganda. The fact that some farmers report not using certain inputs or techniques because they are not aware of them while others report they are aware of them but are not using them suggests information gaps at two levels. First, farmers may lack knowledge about the existence or use a particular input or technology. Second, a farmer may not be aware of the returns to using the technology. In this study we therefore try out two different information treatments at the individual level. In a first intervention, we show farmers the recommend practices and inputs in rice farming. In a second intervention, we point out the returns to investment in a series of simple simulations that also consider the longer run. The study uses a 2 by 2 factorial design with randomization over matched blocks of four farmers.
Registration Citation
Asten, Piet, Bjorn Van Campenhout and Wilberforce Walukano. 2016. "The Role of Information in Agricultural Technology Adoption: Experimental Evidence from Rice Farmers in Uganda." AEA RCT Registry. June 29.
Former Citation
Asten, Piet et al. 2016. "The Role of Information in Agricultural Technology Adoption: Experimental Evidence from Rice Farmers in Uganda." AEA RCT Registry. June 29.
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Experimental Details
We will be using 2 instruments in the field. First, there are the actual interventions that consist of information treatments in the form of short (about 5 minutes) videos and will be shown to individual farmers in the field. The videos will be embedded in a short questionnaire that asks some questions, mainly for validation or to test hypotheses about the impact pathways. The second instrument will be a standard survey to collect end-line information on a range of outcome variables.

The first instrument will be developed from scratch. We will produce one video for the technical information treatment (TIT) and one video for the returns to investment treatment (RIT). For this latter treatment, we may also decide to go through some of the calculations together with the farmer, either by hand or using a simple calculator on the tablet. To make the videos, we will have extensive interviews with farmers and experts on rice growing in the region. From these interviews we will distill the most important steps and converted them into a script. These criteria for the steps were that they should have a large effect on productivity. The choice of what interventions will feature in the videos will also be informed by the relationships found in the baseline data. There we find that both pesticides and fertilizer, especially Urea, are correlated with higher yields. For recommended practices, we find water management to be important (proper bunds construction, correct water levels at different stages of growing). In addition, recommended transplanting practices, related to spacing and plant density, is strongly related to yields. Finally, nursery bed construction and seeding is also correlated with outcomes.

For the TIT, the video will go over each of the inputs and technologies mentioned above. It will show how fertilizer should be applied, at what quantities and at which points in time. It will then also explain how pesticides should be used. There will also be sections on water management, again paying close attention to timings. In this movie, we will avoid alluding to the results of these efforts. In particular, we will avoid contrasting yields from farmers who use fertilizer to farmers who do not. We will also avoid showing how pesticides increases plant health. In short, we want the video to respond to the “how” question, while avoiding the “why” questions.

For the RIT, the video will start of by contrasting the outcomes of a farmer that uses improved technology to one that does not, for instance by visualizing the number of bags of rice that the farmers get from a 1 acre field. We then go over the same inputs and techniques that were explained in the TIT video, but instead of explaining how to use these techniques or inputs, we will highlight the return to using them. For instance, for fertilizer we will explain the cost of applying fertilizer to one acre and subtract this from the value of the expected harvest. We will also highlight how part of this return can be reinvested. This video is therefore trying to achieve the reverse of the TIT and provide answers to the “why” questions while avoiding the “how” question. The videos will be shot by a professional videographer, Mr Nathan Ochole, with extensive experience in producing infomercial for eg. the World Bank and other CGIAR centres ( For the RIT video, we may also decide to add some extra time where the farmer is trained in the basics of cost benefit analysis if try out of the videos in the field prove this is necessary.

The use of information treatments as the interventions has some obvious advantages. First, the use of a pre-recorded video results in a standardized treatment, and all subjects receive exactly the same treatment. While one may argue that providing the information through trainers may be more effective, as the trainer may adapt the message to eg. the education level of the recipient, this may also lead to subtle differences in the message given. The videos will also be administered at the individual level. Again, one may argue that providing the information at a more aggregate level, such as to cooperatives, may be more effective. However, it will be very difficult to control group dynamics, and thus providing information to groups may again lead to heterogeneous treatments. We also use video to reduce spill-over effects. For instance, an alternative to a video would be to provide posters or brochures that explain how to engage in seed selection and proper seed storage and handling. This may actually be more effective, as farmers can keep these materials and get back to them at different points in time. The video will be shown only once and farmers may forget some the recommendations over time. However, providing printed material can more easily be passed on to neighbors and relatives, potentially contaminating other treatment or control groups. Illiterate farmers also are likely to benefit more from videos than from written material. Finally, the provision of a relatively hands-off information treatment (instead of for instance providing inputs ) was also chosen because we want to evaluate an intervention that is cheap and easy to scale up in a setting that is more realistic than the typical experimental field trials used in the agronomy studies.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
rice yields, agricultural technology adoption (fertilizer use, pesticide use, improved seed use, recommended practices), market participation, farm-gate price, consumption expenditure (welfare)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
2X2 factorial design. About 110 households will receive information on technical aspects related to sustainable intensification of rice farming. About 110 households will receive information on profitability of engaging in sustainable crop intensification. This will be done in such a way that there are 55 households that receive both types of information and 50 households that do not receive any information at all (a control group).
Experimental Design Details
Not available
Randomization Method
We have access to baseline data. Randomization will be done on a computer after bloc matching. An algorithm was developed to perform a hierarchical clustering in groups of equal cluster size using nearest neighbor matching. The algorithm also tries to maximize distance between households within each cluster (based on GPS coordinates) to minimize spillover effects. We have used the following cluster variables: household size, age of household head, log(average productivity of rice), gender of household head, total area of potato cultivation , log(consumption per capita), distance to input distributor, amount of credit received in the last year and whether the household had access to extension for rice growing. The code for this is available here ( and the sampling list with allocation of treatments and blocs is here:
Randomization Unit
household level: rice growing farm households in eastern uganda
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
We expect a 20 % average increase in yields. To identify such an effect, we need about 110 observations in each treatment arm. We therefor propose to run an experiment that involves about 220 observations in a 2x2 factorial design. In such a design, about 110 households will receive information on intensification technologies. About 110 households will receive information returns to investment in various intensification techniques. This will be done in such a way that there are 55 households that receive both types of information treatment and 55 households that do not receive any information at all (a control group).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our interventions are expected to reduce knowledge gaps, which in turn increase the adoption of sustainable crop intensification methods such as fertilizer and pesticides among rice farmers. We therefore look at the different in mean yields between farmers that use a particular method and those that do not to get an idea about what effect size to expect. To do so, we restrict ourselves to farm households that reported growing rice in the second season of 2013. We find that average yields are about 2.11 MT/ha for farmers that report not useing fertilizers. Farmers that use fertilizer report yields of about 3MT/ha, which is an increase of about 42 percent. The pooled standard deviation is about 1.29 MT/ha. We would need about 26 observations in each treatment arm (single sided, 80 percent power and alpha level of 0.05). For pesticide use, we find that farmers that do not report using pesticed get yields of around 2.16 MT/ha, while those that do use get about 2.84, corresponding to an increase of about 31 percent. We would need about 45 observations in each treatment arm to detect such an effect. For recommended practices, we find that farmers that maintain water depth of 10-25 cm during cultivation to effectively control weeds have 41 percent higher yields, and we would need about 40 obseravations to identify such an effect with 80 percent power. Finally, farmers that plant in rows have average yields of about 2.71MT/ha, while those who do not plant in rows attain about 2.17MT/ha. To find this effect, we would need about 68 observations in each treatment arm to detect this effect. The above are the effects of implementing a particular technology. However, our information treatment is unlikely to encourage all farmers to start using improved inputs or technologies. Therefore, we expect effects to be smaller and settle on a 20 % average increase in yields. To identify such an effect, we need about 110 observations in each treatment arm.
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents
pre analysis plan

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Uploaded At: June 29, 2016