Impact Evaluation of the Use of Pod-Borer Resistant Cowpea in Nigeria

Last registered on November 30, 2022


Trial Information

General Information

Impact Evaluation of the Use of Pod-Borer Resistant Cowpea in Nigeria
Initial registration date
November 25, 2022

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
November 30, 2022, 3:28 PM EST

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


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Primary Investigator

International Food Policy Research Institute

Other Primary Investigator(s)

PI Affiliation
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Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
The primary objective of the study is to generate evidence on the impact of the use of the new pod-borer resistant (PBR) cowpea variety in Nigeria and its consequential effects on a variety of economic and food security conditions, including household and farm impacts and associated value chain effects. This study will evaluate key outcomes related to changes in cowpea yields and productivity. To achieve this objective, the study will use a cluster randomized controlled trial (c-RCT) to quantify the overall and relative impacts of the incremental treatment arms of the intervention. The c-RCT will use an encouragement design and will have two treatment arms: PBR cowpea adoption (T1), PBR cowpea adoption plus inputs (T2), and a control arm of conventional cowpea adoption plus inputs (C). The study will collect data from baseline, midline, and end-line surveys of all groups and will use mean differences and regression analysis to determine causal impacts of interventions. In addition to the c-RCT, the evaluation will use qualitative methods to collect and analyze information through key informant interviews and focus group discussions. A related value chain component will identify and describe value chain aspects of PBR cowpea use in Nigeria, including the identification of potential issues that may affect PBR cowpea adoption in Nigeria, such as factors which may enhance or hinder the functioning of the PBR cowpea value chain.
External Link(s)

Registration Citation

Adetunji, Fasoranti et al. 2022. "Impact Evaluation of the Use of Pod-Borer Resistant Cowpea in Nigeria." AEA RCT Registry. November 30.
Experimental Details


The intervention of interest is the introduction of the PBR cowpea variety SAMPEA-20T developed by the USAID-funded “Feed the Future Innovative Maize and Cowpea Technologies to Increase Food and Nutrition Security in Africa, PBR cowpea Sub-activity (2020-25)”. This sub-activity, implemented by the African Agricultural Technology Foundation (AATF) and partners, has entered the commercial release stage in Nigeria, with future releases expected in Ghana and Burkina Faso. The overall, expected outcome for this sub-activity in Nigeria is that PBR cowpea will be adopted by 25% of cowpea producers, generating yield gains of at least 20% for adopting farmers during the lifespan of the project (USAID 2021, 4). The Impact Evaluation (IE) will generate evidence regarding household and farm impacts of PBR cowpea adoption, and value chain effects from the commercial release and adoption of PBR cowpea in Nigeria. Other activities under this with the program improved seed delivery systems, development of profitable seed companies, sustainable BPR cowpea production system, increased confidence in the tools of modern biotechnology, among others – are generally outside the scope of the proposed IE. On a broader level, this IE will provide quantitative and qualitative data and analyses to assess the success and limitations of the “USAID development strategy and partner interventions supporting insect resistant variety release and commercialization, USAID Agricultural Biotechnology Program, and Global Food Security Strategy goals, and inform Feed the Future programming for better results” (USAID 2021, 1).
The primary objective of this evaluation is to generate evidence of the impact of the newly introduced PBR cowpea on cowpea yield and productivity. We expect to evaluate this impact under somewhat optimized production conditions for a PBR cowpea variety. The optimized conditions include establishing refugia, providing product information support, input use, etc.) that are standardized between experimental and control group to incentivize adoption at levels needed to support a c-RCT approach. While our research design will allow us to quantify the overall and relative impacts of the incremental treatment arms of the intervention, it will also facilitate further heterogeneity analyses on potential differential impacts across groups of individuals. Identifying the relative, heterogeneous and dynamic impacts of these interventions is crucial to designing cost-effective and scalable approaches to address gender gaps in PBR cowpea agricultural productivity in Nigeria.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. Cowpea yield
2. Cowpea productivity
3. Pesticides use and costs
Primary Outcomes (explanation)
1. Cowpea yield, which will be measured based on total cowpea production, kg per hectare
2. Cowpea productivity, which will be based on cowpea net income (gross revenue minus costs of production) per hectare.
3. Pesticides use and costs, which will be measured based on pesticide use, kg and costs of pesticide used per hectare.

Secondary Outcomes

Secondary Outcomes (end points)
1. Household cowpea commercialization
2. Household cowpea consumption
3. Self-reports of symptoms associated with pesticide poisoning
Secondary Outcomes (explanation)
1. Household cowpea commercialization, which will be measured based on the proportion of sold cowpea.
2. Household cowpea consumption, which will be measured based on the cowpea consumption per person and per adult equivalent.
3. Self-reports of symptoms consistent with pesticide exposure, which is measured based on number of symptoms (number) and medical expenses incurred to address symptoms (naira per capita).

Experimental Design

Experimental Design
Our experimental design will combine c-RCT and qualitative approaches in selected communities in Adamawa and Kwara states.
Random Assignment:
A c-RCT is a field experiment in which clusters of farmers (communities) rather than farmers are randomly allocated to intervention groups. A key property of c-RCT is that inferences are intended to apply at the farmer (individual) level, while randomization is at the cluster or group level. Thus, the unit of randomization (community) is different from the unit of analysis (farmer). The intervention will follow a clustered randomized approach. The randomization takes place at the community level while selection of eligible households in each community is performed by community-based organizations operating in each community. LGAs and Communities will be randomly assigned into three groups/arms, based on the type of treatment and benefit they receive. The c-RCT will have two treatment arms, T1and T2 and a control group C:
• T1: will receive PBR cowpea (Sampea 20-T),
• T2: will receive PBR cowpea (Sampea 20-T) plus inputs.
• C: will receive conventional cowpea seed

We will use a multistage sampling procedure to select the states, LGAs, communities and households included in the survey. In the first stage, we purposively select Adamawa and Kwara state in North-East of Nigeria. Both states are among the top cowpea-producing states in Nigeria and are states that also have a relatively high share of female-headed households engaged in cowpea production. No bias will be introduced by targeting these states with high proportion of females, because we expect randomization to even out any differences between treated and control groups. In other words, similar proportions of female-headed households in both treated and control group. We also consider the overall security situation in the states compared with some of the other major cowpea producing states. Adamawa and Kwara provide an interesting case study to analyse the impact of PBR cowpea on cowpea yield, productivity, and costs of cowpea production. Nevertheless, it is important to note that although the proposed study sites in Adamawa and Kwara are currently safe for field work, the security situation has deteriorated in many parts of Nigeria recently. If this trend continues our ability to conduct field work could be subject to some level of uncertainty.
The second stage involves the purposive selection of four LGAs from 25 LGAs of Adamawa state and four LGAs from 16 LGAs of Kwara state. Those eight LGAs from both states are chosen because there is no expected PBR cowpea penetration in these areas by the time of the intervention. The four selected LGAs in each state will be similar in terms of several important contextual factors (size, socioeconomic and agroclimatic conditions, and road and market access). From the eight LGAs, we will select 240 communities that are also similar in terms of the contextual factors, with 80 communities for the control group and the remaining 160 communities for the treatment groups. Within each community, we then list all households, from which we will randomly select 5 cowpea farmers to participate in the study, including from the control communities, for the baseline and end line surveys.
The second stage randomization will include selecting farmers randomly for PBR cowpea interventions within each of the 160 treatment communities from our study sites in Adamawa and Kwara states, where cowpea is an important crop. There are two cowpea cropping seasons: wet season and dry season. For this analysis, we will use the wet crop season. It is the main cowpea-growing season and runs from approximately July to October in the Northern Guinea Savanna agro-ecological zone, where Adamawa and Kwara are predominantly located. The intervention will take place before the beginning of the wet season of 2023.

Treatment Effects
We will evaluate the effectiveness of PBR cowpea adoption by comparing average household-level yield between T1 and C. We will also evaluate the incremental effect of interventions by comparing the average household-level yield between T1 and T2. This will give us the average treatment effect for incremental interventions. Randomization solves the problem of selection bias because households in treatment groups and control group are drawn randomly from the same underlying population; therefore, the average characteristics of these groups do not systematically vary, and any differences observed in the outcomes of interest can therefore be attributed to the interventions. To further minimize the possibility that our study design would be compromised (e.g., units selected for treatment may not, in fact, receive the treatment, or may not receive it in the fashion that is intended by the intervention), we will assign a field team (consisting of research assistants and extension agents) that will regularly visit study sites, engage with the local implementing staff, monitor progress, and report back to the evaluation team.

Data Collection
To exploit power gains from repeated observations, we will collect three rounds of data at baseline, midline and endline (e.g., McKenzie, 2012). We will collect baseline survey prior to the experiment’s rollout to investigate balance. Given that agricultural production depends on many exogenous and largely unobservable factors, such as weather, disease, pathogen and pest pressure and others, the baseline survey enables us to average out the noise in measuring yields, productivity, and costs of agricultural production, generating estimates less vulnerable to bias that might arise due to unusual conditions for all subjects during the experimental period (McKenzie, 2012; Rosenzweig and Udry, 2020). The baseline data will improve the power to estimate treatment effects, such as for examining treatment heterogeneity (McKenzie, 2012).

The choice of variables for the baseline survey is based on those used by similar studies in their orthogonality tests. In particular, we will look at variables used in studies that investigate the adoption of yield-improving technologies and practices using RCTs (Duflo, et al., 2011; Karlan et al., 2014; Ashraf, et al., 2009; Bulte et al., 2014). We will collect household information which consist of 12 modules. We will collect household characteristics such as household size, age, education level of household head, and agricultural assets, as well as more specific information related to cowpea farming, such as yield and productivity change in the last wet season, and distance to the nearest agro-input shop and road, access to extension agent and service about cowpea production and varieties, and households input use. The baseline data will improve the power to estimate treatment effects, such as for examining treatment heterogeneity (McKenzie, 2012). We will conduct the midline survey in 2024 one year after experiment’s rollout and the above interventions. An endline survey will re-survey the same households one year later after the midline to measure impacts of the long-term impact of the of PBR cowpea adoption and the change in the adoption behaviour of farmers. Given that our sample households are PBR beneficiaries or potential beneficiaries, we anticipate that attrition rate will be low or at least not systematic. In the endline survey, we will also have a module intended to collect data on unintended (positive and negative) consequences of the adoption of PBR cowpea (e.g., a decrease in number of insects that affect cowpea in the community).

Spillover effects
Finally, we will use statistical analysis to account for other important but less critical limitations of the experimental approach. One such example is related to spillover effects whereby untreated areas may have profited also from the intervention. The existence of spillovers can lead to underestimation of the treatment effect. Randomizing at the community level, rather than at the farmer level, allows the evaluation to account for spillover effects. To further account the spillover effects on our analysis, we will also expand the sample (200) to cover non-treatment households in treatment communities. This will provide useful information on channels and speed of spillover and diffusion of PBR variety. We will compare PBR cowpea adoption among control households (control households within a treatment one communities) versus control households (control households in a control communities).
Experimental Design Details
Not available
Randomization Method
We will randomize at the level of communities using a complete listing from the census ordered cowpea producers for at least two seasons randomly using a random number generator in a computer program such as Stata or Excel.
Randomization Unit
Farmers clustered at community level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
240 communities
Sample size: planned number of observations
1400 farmers
Sample size (or number of clusters) by treatment arms
Planned number of observations:
1400 cowpea producers
Number of clusters by control arms:
C: 80 communities

Number of clusters by treatment arms:
T1 : 80 communities
T2 : 80 communities
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
159.262 Kg is the minimum detectable effect size for main outcome variable with a standard deviation of 770.65, which is a 20 percent increase in yield by 2025.

Institutional Review Boards (IRBs)

IRB Name
International Food Policy Research Institute - Institutional Review Board (IFPRI-IRB)
IRB Approval Date
IRB Approval Number
IRB #00007490
Analysis Plan

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