A protocol for the role of incentives for adoption of climate-smart agricultural innovations: An experimental evaluation in Uganda
Last registered on June 01, 2021

Pre-Trial

Trial Information
General Information
Title
A protocol for the role of incentives for adoption of climate-smart agricultural innovations: An experimental evaluation in Uganda
RCT ID
AEARCTR-0007729
Initial registration date
May 27, 2021
Last updated
June 01, 2021 8:40 AM EDT
Location(s)

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Primary Investigator
Affiliation
Wageningen University & Research
Other Primary Investigator(s)
PI Affiliation
Wageningen University & Research
PI Affiliation
Wageningen University & Research
Additional Trial Information
Status
On going
Start date
2020-02-01
End date
2022-06-30
Secondary IDs
Abstract
Agricultural productivity in Uganda is low and declining for many crops, including soybean. This is caused by limited adoption of climate-smart agricultural (CSA) innovations due to risks and uncertainties associated with increased incidences of droughts, floods, changes in prices, crop diseases/pests, and storm. High dependence on rain-fed agriculture means that farmers are vulnerable to climatic shocks and their impacts. To take risks and invest in CSA innovations, farmers will need incentives. Such incentives should address production and market risks as well as informational constraints, contemporaneously. The SNV-led ‘Climate Resilient Agribusiness for Tomorrow (CRAFT)’ program offers incentives including (1) farm-level training in CSA practices and technologies; (2) agricultural extension services; and (3) value-chain linkages (VC) through contracts and index-insurance to encourage smallholder farmers in Uganda, Kenya, and Tanzania to adopt climate-smart agricultural innovations. Improved adoption would enhance productivity and household welfare. Through a field experiment, we assess the effectiveness of these incentives for the adoption of CSA innovations, and ensuing impacts on farming system resilience, productivity and household welfare. The key research questions are: what is the optimal combination of push and pull incentives for adoption of CSA practices and technologies; what are the impacts of CSA practices and technologies on factor productivity productivity, income and household welfare?
External Link(s)
Registration Citation
Citation
Bizimungu, Emmanuel, Ruerd Ruben and Robert Sparrow. 2021. "A protocol for the role of incentives for adoption of climate-smart agricultural innovations: An experimental evaluation in Uganda." AEA RCT Registry. June 01. https://doi.org/10.1257/rct.7729-2.2.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-02-01
Intervention End Date
2022-02-28
Primary Outcomes
Primary Outcomes (end points)
For the first objective, the primary outcomes include the proportion of households participating agricultural-related trainings; proportion of households visiting (visited by) demo sites (extension agents) and number of visits; proportion of households that report having applied acquired knowledge about CSA innovations; proportion of farm-households participating in collective bulking and marketing; proportion of households reporting participation in contract farming; proportion of households using agricultural insurance; and gender empowerment.

In regards to the second objective, the primary outcomes include: land productivity; as well as labour productivity. Land productivity is measured as the volume of output (Kg or value of production (UGX)) produced per hectare, while labour productivity is the volume of output (Kg or value of production (UGX)) produced per man-day in a given cropping year.
Primary Outcomes (explanation)
The main variables of interest with regard to the first objective include trainings, defined as participation and intensity of participation, by farm-household’s main decision-maker and/or their spouse, in organized training workshops concerning climate-smart agricultural (CSA) practices and technologies in a given cropping year. Intensity of participation is defined as the number of times the decision-maker and/or their spouse participated in organized training workshops. Farm-household visits, defined as the number of times the farm-household’s main decision-maker and/or their spouse visited farmer field schools (FFSs) or demo sites in a given cropping year. Extension visits, defined as the number of times any agricultural extension agent or officer visited the household or farm in a given cropping year.

Collective bulking and marketing, defined as farm-household’s participation in collective bulking and marketing of soybean and any other crop and livestock products in a given cropping year. Contract farming, defined as participation by the farm-household in contract farming arrangement for soybean and other crop or livestock products in a given cropping year. Agricultural insurance, defined as the use of agricultural insurance including index-based insurance for soybean and any other crop or livestock in a given cropping year.

Gender, defined as Women’s Empowerment in Agriculture Index (WEAI). This index will be developed using twelve indicators namely, autonomy in income; attitudes about intimate partner violence against women; respect among household members; input in productive decisions, ownership of land and other assets; access to and decisions on credit and financial accounts; control over use of income; work balance; visiting important locations; group membership; and membership in influential groups.

For the second objective, the main variables of interest include quantity harvested (kilograms) per crop, quantity sold (kilograms) per crop, quantity of livestock (cattle, goats, sheep, pigs, etc.) and poultry sold; market price (UGX) for crops and livestock sold; land area (hectares) under each crop; family and hired labour (man-days); value of production (total quantity produced multiplied by the farm gate price); production costs (UGX) for crops and livestock; household size; and types of food consumed. These variables will be used to construct both the primary and outcome indicators.
Secondary Outcomes
Secondary Outcomes (end points)
The secondary outcomes include: (i) proportion of households accessing input (or financial) credit; (ii) proportion of households reporting access to better quality inputs such as improved seed; (iii) proportion of households reporting having sold high quality output; (iv) proportion of households reporting having sold output at a relatively higher than average market price; (v) proportion of households adopting CSA practices and technologies; and (vi) the intensity of adoption of CSA practices and technologies. We define adoption intensity in two ways. First, as the number of CSA practices and/or technologies used by the farm-household in a given cropping year. Second, as the number of hectares or the proportion of total cultivated land that is under the CSA practice such as zero/minimum tillage and soybean-cereal intercrop or CSA technology such as drought-resilient seed and rhizobia inoculants. For CSA innovations, a third definition applies as the amount (or value) of improved seed and rhizobia inoculants applied per hectare.

For the second objective, the secondary outcomes include revenues, total household income, share of soybean income in total household income; and nutrition. Revenue, defined as market price multiplied by quantity sold will be computed for all crop and livestock/poultry products sold by farm-households in a given cropping year. Total revenue will be the summation of revenues from crop and livestock/poultry enterprises. We shall also compute the share of soybean revenue in both the total revenue and revenue from all the crops.
Secondary Outcomes (explanation)
Practices. Defined as climate-smart agricultural (CSA) practices such as minimum/zero tillage or intercropping used by the farm-household in the production of soybean and other crop and livestock enterprises in a given cropping year. Technologies. Defined as climate-smart agricultural (CSA) technologies such as drought-resilient seed or rhizobia inoculants used by the farm-household in the production of soybean and other crop and livestock enterprises in a given cropping year (two cropping seasons).

Household income will entail total income earned by all members of the farm-household from all the crops grown (per hectare); livestock and poultry kept as well as other incomes from other farm and off-farm sources and activities in a given cropping year. We shall deduct all costs involved to secure each type of income. Income per adult equivalent will also be computed for well-being comparisons across households with different number of members. We shall then compute the share of soybean income in total household income.

Nutrition, a proxy indicator namely women’s dietary diversity score (WDDS) will be developed from different food groups consumed by women in a household over the 24-hour recall period. The different food groups are formed from different food categories including cereals, roots/ tubers/plantains, legumes, oil seeds, vegetables, fruits (including juices), meats, dairy products, fats/oils, among others.
Experimental Design
Experimental Design
Three small and medium agribusiness enterprises (SMAEs) namely: ACILA enterprises, ALITO Joint, and OKEBA operating in the eastern, northern, and western and central regions, respectively are implementing an intervention involving three key incentives. First, farmers organized in groups are trained through training workshops to create awareness and their understanding of different CSA practices and technologies. Thereafter, trained farmers begin to access agricultural extension services through demonstration sites or farmer field schools (FFSs) established within their communities. At the demo sites, the extension agents also residing within the same communities practically teach farmers how to apply different CSA practices and technologies. Second, the SMAEs facilitate smallholders’ access to quality inputs either directly through direct sales on cash/credit, or indirectly through brokered linkages with agro-input dealers. Upon harvest, farmers sell their output to the SMAEs at a higher than average market price. This SMAE-farmer relationship is established through either a written or verbal contract. Through this relationship, input and output market risks are addressed. Finally, the SMAEs with support from SNV identify key insurance service providers to sell index-based insurance to farmers. Farmers pay half of the premium while the other half is subsidized by the government of Uganda. Index-based insurance insulates smallholder farmers against risks associated with weather changes including excessive rains or seasonal drought.

Famers do not pay for the supply-driven training and extension services. However, farmers may incur other costs associated with travels to and from the training venues and demo sites. We combine training and extension and label these push incentives since both incentives address informational constraints and any uncertainties associated with the technologies. On the other hand, demand-driven technologies/inputs such as improved seed, and insurance services are accessed through production/marketing contract, and index-based insurance incentive instruments, respectively. In other words, farmers have to decide whether or not to participate (buy) in contract farming arrangements (index-based insurance). Therefore, production/marketing contract is labelled pull-1 while index-based insurance is labelled pull-2 incentive.

We form different combinations (bundles) of push and pull incentives and assess their effectiveness on adoption intensity of CSA practices and technology before assessing impacts of CSA innovations on factor productivity, incomes and household welfare. The bundles include: (i) push; (ii) Push + Pull-2; (iii) Push + Pull-1; and (iii) Push + Pull-1 + Pull-2. Incentive bundles/combinations of push and pull incentives can potentially effectively address the multiplicity of risks and other constraints faced by smallholders thereby improving adoption of seemingly profitable innovations such as improved seed and rhizobia inoculants. In other words, raising adoption might require incentive bundles that address various risks and constraints, contemporaneously. This is particularly important because standalone incentives are normally offered to address specific risks or constraints but not a continuum of risks/constraints.

The SMAEs roll-out the intervention to the participants in phases by targeting new farmer groups every season for two years (Table 2). Also important to note is that, index-based insurance becomes available to farmers in the second year due to the delays in the identification and selection of insurance service providers. Therefore, we implement a concurrent stepped-wedge Cluster Randomized Controlled Trial (CRCT) with SMAEs to assess among their farmer groups the effectiveness of the four incentive bundles. Randomization is done in three steps. First, we randomly assign farmer groups across seasons due to phased roll-out of the intervention where new farmer groups are enrolled at the beginning of a new season. Using block-cluster randomization approach, we randomized farmer groups (clusters) into three instead of four cropping seasons because the SMAEs (blocks) had already selected beneficiaries for the first season of 2020 by the time of randomization. Random assignment of farmer groups to the seasons helps us to randomly determine the sequencing of enrolment of farmer groups into the CRAFT program. Second, farmer groups were randomly assigned to push or push + pull-1 incentive bundles. Finally, we randomize access to index-based insurance among farmer groups that begin to receive push or push + pull-1 in either year 2020 or year 2021.

Each farmer group starts with (1) the business as usual condition or the baseline phase in which usual farming without incentives is assessed, followed by (2) a second (mid-line) phase in the year 2020 in which 73 farmer groups receiving push (T1-1 and T2-1) and/or 76 farmer groups receiving push+pull-1 (T3-1 and T4-1) incentive bundles are compared with 92 and 88 control farmer groups that begin to access push (T1-2 and T2-2) and push+pull-1 (T3-2 and T4-2) incentives in the year 2021, respectively (Table 1). In the final (end line) phase, five more comparisons can be made. That is, 89 farmer groups receiving Push+Pull-1+Pull-2 (T4-1 and T4-2) are compared with: (i) 87 farmer group receiving push (T1-1 and T1-2) incentives; (ii) 77 farmer groups receiving push+pull-1 (T3-1 and T3-2); and (iii) 78 farmer groups receiving push+pull-2 (T2-1 and T2-2) incentives. Similarly, recipients of Push+pull-2 incentives are also compared with recipients of (i) push, and (ii) push+pull-1 incentives. Noteworthy, 180 farmer groups that wait to receive interventions in the year 2021 serve as the control groups for assessment at the mid-line period. Farmer groups that began to receive push and push+pull-1 incentives in the first year (2020) continue to receive the same incentives in the second year (2021) except that part of these are randomly assigned to receive index-based insurance as an additional incentive (the last two bars in seasons 1 and 2 of year 2021 in Figure 1). We collect data from 2,533 households on several variables of interest at the baseline period in the year 2019 before implementing the CRAFT program to facilitate measurement and estimation of outcomes of interest such as adoption intensity.

It is possible to have cases of non-compliance or partial compliance within treatments. Due to different number of incentives in each incentive bundle, each treatment can have different degrees or levels of compliance. We can define two types of non-compliers: never-takers that will always reject a new intervention if they are offered it, and always-takers that will always receive a new intervention even if they are not offered it (Ye et al., 2014). For instance, a member of a farmer group in the push treatment is regarded as a complier if he or she takes both the training and extension services, but would not have done so if it was not offered. A member is regarded as a partial complier if he or she takes only extension services or only training but not both. There is a possibility for farmers to access inputs and technologies and other services under a (verbal or written) contract and, for some reasons, still sell soybean output outside the contract. In this study, a farmer takes up pull-1 incentive if he or she sells the largest share or all of marketed soybean output to the SMAE, regardless of receipt of inputs and other services. A farmer takes up pull-2 incentive if he or she uses it, irrespective of whether or not it was bought. By design, receipt of pull-1 and pull-2 incentives depends on receipt of push incentives. Therefore, it is not possible that a farmer can access only pull-1 or pull-2 incentives. However, a farmer can choose not to take push incentives although such cases are expected to be very rare. Instead, there might be cases where some farmers take only extension services or training. Similarly, a farmer can take both or part of push incentives but chooses not to take pull-1 or pull-2 depending on assigned treatment.

The effects of the four incentive bundles will be assessed in two ways. First, we estimate the average intention-to-treat (ITT) effect among those assigned to treatment (incentive bundles). The ITT effect reflects how farmers respond to te randomized offer regardless of their actual take-up of the treatment. Second, we can account for non-random compliance by using the random offer as instrumental variable for non-random take up of the treatment, yielding a local average treatment effect (LATE).
Experimental Design Details
Not available
Randomization Method
Randomization was done using a computer
Randomization Unit
A farmer group (cluster) is the unit of randomization.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
329 farmer groups
Sample size: planned number of observations
2533 households
Sample size (or number of clusters) by treatment arms
87 farmer groups Push + Pull-1 + Pull-2, 77 Push + Pull-1, 78 Push + Pull-2, 87 Push
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The Minimum Detectable Effect (MDE) for adoption intensity between recipients of Push or Push + Pull-1 incentives and those in the control condition is 11.9 percent and 11 percent, respectively. The detailed description of power calculations is indicated in the pre-analysis plan. At a power of 0.8, we would detect an increase in adoption intensity by about 15.4 percent due to exposure to push+push-1+pull-2 treatment relative to a case where push recipients remain in a counterfactual scenario. Relative to counterfactual scenarios for push+pull-2 and push+pull-1, a power of 80 percent would, respectively, detect an increase in adoption intensity by 23.2 percent and 23.5 percent due to exposure to push+pull-1+pull-2 incentive bundle. Similarly, at a power of 80% we would detect an increase in the number of CSA practices and technologies by 23.5 percent and 24.4 percent due to exposure to push+pull-2, compared to push and push+pull-1 in the counterfactual situation. At a power of 0.8, we can detect an increase in investment in soybean seed by about UGX.37,500 due to exposure to push+push-1+pull-2 treatment relative to push recipients in the counterfactual situation. A power of 80 percent can detect an increase in investment in soybean seed by UGX.40,000 and UGX.60,000 due to smallholders’ access to push+pull-1+pull-2 incentives relative to exposure to push+pull-2 and push+pull-1 at the counterfactual scenarios. Similarly, at a power of 80% we would detect an increase in investment in soybean seed by UGX.30,000 and UGX.55,000 due to exposure to push+pull-2, compared to push and push+pull-1 in the counterfactual situation.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Uganda National Council for Science and Technology
IRB Approval Date
2021-03-24
IRB Approval Number
SS502ES
IRB Name
Wageningen University Social Sciences Ethics Committee
IRB Approval Date
2020-07-09
IRB Approval Number
09215846
Analysis Plan
Analysis Plan Documents
RCT Pre-analysis Plan

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Uploaded At: May 26, 2021