Demand and supply factors constraining the emergence and sustainability of an efficient seed system - three experiments in Uganda
Last registered on June 01, 2021

Pre-Trial

Trial Information
General Information
Title
Demand and supply factors constraining the emergence and sustainability of an efficient seed system - three experiments in Uganda
RCT ID
AEARCTR-0006361
Initial registration date
August 25, 2020
Last updated
June 01, 2021 4:04 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Ifpri
Other Primary Investigator(s)
PI Affiliation
KU Leuven
PI Affiliation
Wageningen University
PI Affiliation
NARO
PI Affiliation
Makerere University
PI Affiliation
IFPRI
PI Affiliation
ISSD
Additional Trial Information
Status
In development
Start date
2020-09-15
End date
2021-12-31
Secondary IDs
Abstract
Agricultural technology remains under-adopted among smallholder farmers in Sub-Saharan Africa. We investigate how the quality of an agricultural technology – improved maize seed – affects its adoption. The research entails three hypotheses that will be tested in a series of randomized controlled trials among agro-input dealers and smallholder farmers in Uganda. In a first hypothesis, quality concerns that constrain uptake are caused by information inefficiencies at the level of the agro-input dealer, who is assumed to lack knowledge about proper storage and handling. An intensive training program is expected to increase improved maize seed quality and subsequent adoption by farmers. A second hypothesis conjectures that information asymmetry between seller and buyer with respect to the quality of seed – a classic lemons technology – leads to under-adoption. We implement a crowd-sourced information clearinghouse similar to yelp.com to test this hypothesis. This hypothesis targets the interaction between farmers and input dealers. A third hypothesis targets farmers directly, as sub-optimal adoption is assumed to be caused by learning failures: Farmers might attribute disappointing outcomes to poor input quality, while in reality many input dimensions like the time of planting, weeding and fertilizer application co-determine outcomes. An ICT-mediated information campaign that stresses the importance of paying attention to all input dimensions is implemented to test this hypothesis.
External Link(s)
Registration Citation
Citation
Bagamba, Fredrick et al. 2021. "Demand and supply factors constraining the emergence and sustainability of an efficient seed system - three experiments in Uganda." AEA RCT Registry. June 01. https://doi.org/10.1257/rct.6361-2.0.
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Experimental Details
Interventions
Intervention(s)
3 interventions will be implemented in a 2x2x2 factorial trial. A first intervention consists of an intensive agro-input dealer training to improve seed handling and storage at this level. A second intervention consists of a crowd-sources information clearing house that will be set up at the catchment area level.
Intervention Start Date
2020-12-01
Intervention End Date
2021-08-15
Primary Outcomes
Primary Outcomes (end points)
quantity of quality seed sold by input dealers, adoption rates of quality seed by farmers
Primary Outcomes (explanation)
quantity (kg) of improved seed varieties of maize (hybrids and OPV) sold by the input dealer over the last agricultural season; indicator value =1 of farmer used improved seed (hybrid of OPV) obtained from an agro-input dealer on any maize plot
Secondary Outcomes
Secondary Outcomes (end points)
input dealer skill/knowledge, farmers' seed quality perception, farmers' input dealer quality perception, farmer skill/knowledge, yield/profit/income/consumption
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
To test the three hypotheses, we will implement three interventions that are combined in a field experiment where various treatment and control groups are randomly assigned to either a treatment or control condition. The randomized control trial (RCT) will take the form of a 2x2x2 factorial design, with each intervention corresponding to one hypothesis. To test the first hypothesis, a random sub-sample of input dealers will receive training on proper seed handling and storage. To test the second hypothesis, a rating system will be set up among a random sub-sample catchment areas of input dealers catchment areas, and farmer and input dealers will receive feedback on the ratings before the start of the planting season. To test the third hypothesis, a video that points out the importance of combining improved seed with other inputs and careful crop management will be shown to a random subset of farmers.
Experimental Design Details
Not available
Randomization Method
The randomization of the interventions are the catchment area level were done by a computer based on a census of input dealers. The randomization of the farmers will be done through se
Randomization Unit
the first and second treatment are implemented at the catchment area level. For the third treatment, treatment status will also be determined by the computer, but a geographic sampling approach will be used where enumerators are supplied with gps coordinate from where they have to find the closest household to include in the study.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
112 catchment areas
Sample size: planned number of observations
3200
Sample size (or number of clusters) by treatment arms
in 56 catchment areas, treatment 1 will be implemented and in 56 not; in 56 catchment areas, treatment 2 will be implemented and in 56 not; 1600 farmers will receive treatment 3 and 1600 farmers will serve as control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
159 kilograms sold (standard deviation of 454 kilograms); MDE for adoption is < 6.5% (from a base of63 percent)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IFPRI IRB #00007490 FWA #00005121
IRB Approval Date
2019-06-05
IRB Approval Number
DSG-19-0627
IRB Name
Makerere University School of Social Sciences Research Ethics Committee
IRB Approval Date
2020-05-19
IRB Approval Number
MAKSS REC 05.19.291
Analysis Plan
Analysis Plan Documents
pre-analysis plan

MD5: c1f7b44ddb3ba6878e2da798c522d175

SHA1: 2d10fc2f961a6ace96df8e4fa7fd98d48dfec129

Uploaded At: May 04, 2021

registered report (only balance tables for now)

MD5: b922cd7fa67e097ea12d1153e7acc062

SHA1: 040d6e1cb875e4919e7f3766b1a2b8732a2fadec

Uploaded At: June 01, 2021