Site-Specific Agronomic Information and Technology Adoption: A Field Experiment from Ethiopia
Last registered on January 06, 2020

Pre-Trial

Trial Information
General Information
Title
Site-Specific Agronomic Information and Technology Adoption: A Field Experiment from Ethiopia
RCT ID
AEARCTR-0002678
Initial registration date
December 23, 2019
Last updated
January 06, 2020 9:23 AM EST
Location(s)
Region
Primary Investigator
Affiliation
Dublin University, Trinity College
Other Primary Investigator(s)
PI Affiliation
Department of Economics and Trinity Impact Evaluation Unit (TIME), Trinity College Dublin
PI Affiliation
International Maize and Wheat Improvement Center (CIMMYT), Ethiopia
Additional Trial Information
Status
Completed
Start date
2017-11-15
End date
2019-09-30
Secondary IDs
Abstract
Blanket fertilizer recommendations are often used in developing countries to encourage farmers to use particular blends of fertilizer. The response to these recommendations has been poor among smallholder farmers. Using a randomized control trial, we explore whether targeted extension advice leads farmers to align fertilizer usage to the recommended levels and whether this impacts productivity. We also consider whether coupling the targeted information with agricultural insurance to protect fertilizer investments in the event of crop failure enhances adoption rates. Results show that targeted recommendations closed the gap between the amount of fertilizer used and the recommended amounts and that this in turn led to increased productivity. We found no differential effect of the targeted recommendation when coupled with agricultural insurance suggesting that the risk of crop failure is not a binding constraint to fertilizer adoption in this context, or that farmers do not consider agricultural insurance a useful risk-mitigating mechanism.
External Link(s)
Registration Citation
Citation
Ayalew , Hailemariam, Jordan Chamberlin and Carol Newman. 2020. "Site-Specific Agronomic Information and Technology Adoption: A Field Experiment from Ethiopia." AEA RCT Registry. January 06. https://doi.org/10.1257/rct.2678-1.0.
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Experimental Details
Interventions
Intervention(s)
The experiment has two treatment arms:

Households in the first treatment group received site-specific agronomic information, consisting of a recommended amount and blend of fertilizer to use on a particular maize plot, the optimal timing of the application of that fertilizer, and the expected yield outcome for that recommendation. The information was provided both verbally and on paper. Comparing outcomes ex-post with the control group will allow us to test whether fertilizer recommendations are more likely to be adopted when accompanied by agronomic information.

Similarly, households in the second treatment group received the same site-specific agronomic information as in treatment arm one and insurance cover to mitigate the risk associated with potential crop failure. For each farmer in this group, we purchased crop insurance from Oromia Insurance Company (OIC) and informed them that they were insured while we provided the site-specific recommendation. The cover includes any crop failure associated with drought, flood, excess rain, fire, storms, and hail for the 2018 agricultural season. This information was explained face to face to farmers during the planting period.
Intervention Start Date
2018-03-11
Intervention End Date
2018-04-01
Primary Outcomes
Primary Outcomes (end points)
The primary outcome of interest is the adoption of fertilizer recommendations.
Primary Outcomes (explanation)
We measure the adoption rate of fertilizer recommendations by calculating the absolute gap between the actual and recommended values of fertilizer use in kg/ha.
Secondary Outcomes
Secondary Outcomes (end points)
Our secondary outcome of interest are farm productivity and household welfare.
Secondary Outcomes (explanation)
Farm productivity is measured by average maize production per-hectare for all maize plots. Finally, we use average household level per-capita consumption expenditure as a proxy for household welfare. Consumption expenditure is constructed by taking the sum of the values of home production, purchased commodities, and gifts.
Experimental Design
Experimental Design
Our sample was drawn from the main maize growing areas of Ethiopia. We randomly generated four 10 x 10 km sampling grids within each of the four main maize growing zones (Jimma, Bako, West Gojjam, and East Shewa), with each grid subdivided into 100 1km2 grid cells. Within each grid, eight grid cells were chosen randomly. If a randomly selected grid-cell could not be included (either because it was physically inaccessible or if there was no maize production taking place at or near that point), then a replacement location was drawn from the same 10km x 10km grid.

Within each of the randomly selected grid cells, 6 farmers were identified, using the following protocol. First, we identified the farm household closest to the selected point. For example, if the point fell within a field, we identified the farmer who owns this field. If this farmer grew maize in the current year, then this farmer entered the sample, as farmer number 1 for that location. If the farmer did not grow maize in the current year, then we identified the nearest neighbor to that farmer and repeated until farmer number 1 is identified for that location. Second, from farmer number 1 (for that location), 5 neighboring farmers were identified on the basis of spatial proximity and direction. We started with the nearest farmer in the direction due North (0 degrees), and proceeded to the nearest farmer in a clockwise direction at 72 degree intervals. Once it was confirmed that they grew maize in the current season, they were added to the sample. If any of these farmers did not grow maize, their nearest neighbor (not otherwise already included in the sample) was evaluated for suitability, until a total of 6 farmers for the selected point location were identified. If, at the time of the survey, a sample farmer was not available (or unable to be enumerated due to death, leaving the village or no longer planting maize), a replacement was made following the same spatial proximity rules as used in the initial selection. This replacement farmer was given the same household identification number as the drop-out farmer. In our baseline line data, around 27 originally selected households (3.6 percent of the sample) were replaced.

The sample size was chosen on the basis of power calculations carried out for our main outcome variables. These are the absolute difference between recommended and actual fertilizer use in kilogram per hectare (kg/ha) for the maize area planted, total maize production in kg per hectare, and average per-capita household income. Randomization took place at the grid-cell level with each grid-cell randomly assigned to one of three groups. We stratified by four blocks, defined by administrative zones. Since the total number of grid-cells cannot be evenly assigned to the three groups, we first randomly assigned an extra grid-cell to one of the three groups. The first treatment arm was randomly selected to get one extra grid-cell. This means that treatment arm one has 44 grid-cells, whereas treatment arm two and the control group have 43 grid-cells each.

In addition, while each zone should have an equal number of grid-cells across the three groups, the uneven number of grid-cells prevents this. Hence, we randomly allocate the extra grid-cell in each zone to a particular group. Two zones take an extra grid-cell in treatment group one given that there is an extra grid cell assigned to this group, with the Bako and East Shewa zones randomly selected to have an extra grid-cell. Jimma and West Gojjam are randomly selected to have an extra grid-cell in treatment group two and the control group, respectively.

Households in the first treatment group received site-specific agronomic information (SSI), consisting of a recommended amount and blend of fertilizer to use on a particular maize plot, the optimal timing of the application of that fertilizer, and the expected yield outcome for that recommendation. The information was provided both verbally and on paper. Comparing outcomes ex-post with the control group will allow us to test whether fertilizer recommendations are more likely to be adopted when accompanied by agronomic information.

Similarly, households in the second treatment group received the same SSI as in treatment arm one and insurance cover to mitigate the risk associated with potential crop failure. For each farmer in this group, we purchased crop insurance from Oromia Insurance Company (OIC) and informed them that they were insured while we provided the site-specific recommendation. The cover includes any crop failure associated with drought, flood, excess rain, fire, storms, and hail for the 2018 agricultural season. This information was explained face to face to farmers during the planting period. As discussed above, the aim of this treatment is to allow us to test whether the downside risk associate with fertilizer investment plays a role in the take-up of the site-specific fertilizer use recommendation. During our sample period, around 25.6 percent of farmers were experienced biophysical shocks (drought, flooding, pests and diseases). Moreover, in the 2017 agricultural season, farmers lost around 14.7 percent of their crop income due to weather related shocks. As such, weather related risks are salient for these farmers. Despite this, the rate of crop insurance in rural farm households in Ethiopia is very low. From the baseline Agronomic Panel Survey (APS) data, less than one percent of households bought crop insurance in the main maize producing areas of Ethiopia.

Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
A 1 km by 1 km grid cell in four main maize growing zones of Ethiopia.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
130
Sample size: planned number of observations
738
Sample size (or number of clusters) by treatment arms
43 grid cells Control, 43 grid cells Treatment arm one and 44 grid cells Treatment arm two.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We conducted power calculations for our main outcome variable - the absolute deviation of farmers’ nitrogen applications from recommended rates in kilogram per hectare (kg/ha) – as well as other productivity and welfare outcomes of interest, i.e. farm-level maize productivity (kg/ha), net value of maize production per capita in Ethiopian birr (ETB), and average per capita consumption expenditure (ETB). B1. Absolute deviation of farmers’ actual and recommended amount of macronutrient use (kg/ha) To calculate the minimum detectable effect of the absolute deviation of farmers’ actual fertilizer application and the recommended amount, we estimate the intra-cluster correlation coefficient using the baseline data. The intra-cluster correlation between the control and treatment farmers is 0.065, whereas 0.075 for treatment arm one and two. Figure B1 presents the relationship between the number of clusters and the minimum detectable effect associated with the absolute deviation between actual farmers practice and the recommended value. The solid line shows the relationship between minimum detectable effect and cluster size between treatment and control farmers, whereas the dashed line presents the relationship between farmers in treatment arm one and two. For a power of 0.8, cluster size of 6, test size of 0.05 and intra cluster correlation of 0.08, we found a minimum detectable effect of 0.237 for 130 clusters. That is, we will be able to detect a treatment induced reduction of nutrient supply gaps of at least 23.7 percent with only a 20% chance of a type II error, assuming a test size of 0.05. This effect is more or less the same between treatment arm one and two. B2. Productivity and welfare indicator variables Figure B2 shows the relationship between the number of clusters and the minimum detectable effect for productivity and welfare measures. The solid line shows the relationship between minimum detectable effect and cluster size between treatment and control farmers, whereas the dashed line presents the relationship between farmers in treatment arm one and two. We estimate the minimum detectable effect for a power of 0.8, cluster size of 6 and cluster number of 130. For intra cluster correlation of 0.36 and 0.34, we found a minimum detectable effect of 0.338 and 0.335 between farmers in treatment and control groups, and farmers in treatment arm one and two respectively. That is, we will able to detect treatment induced improvement of farm productivity of at least 33.5 percent for plots managed by treatment farmers with only a 20% chance of a type II error, assuming a test size of 0.05. Similarly, we found a minimum detectable effect of 0.30 for the average per capita net cost maize value production and consumption expenditure.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Faculty of Arts, Humanities and Social Sciences Research Ethics Committee Decision
IRB Approval Date
2018-01-23
IRB Approval Number
N/A
Analysis Plan
Analysis Plan Documents
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
April 30, 2019, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
April 30, 2019, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
130 clusters
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
738 households
Final Sample Size (or Number of Clusters) by Treatment Arms
43 grid cells Control, 43 grid cells Treatment arm one and 44 grid cells Treatment arm two.
Data Publication
Data Publication
Is public data available?
No

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Program Files
Program Files
No
Reports and Papers
Preliminary Reports
Relevant Papers