Kenya LR 2020 Cropping Series
KEN -20 -1502Last modified on December 19th, 2025 at 10:39 am
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Abstract
PxD operates the MoA-INFO platform in collaboration with the Kenya Ministry of Agriculture to provide free agricultural recommendations to farmers via SMS messages. Farmers can access information whenever they like by sending the word “MENU” or “ORODHA” to access a complete list of topics. In addition, farmers can opt-in to weekly cropping series (CS) messages which offer advice on crop management practices—from land preparation to harvesting and storage—throughout the season. While all the content in the CS is available on the menu for farmers to access at any time, most engagement with the platform comes from farmers who opt-in to the weekly CS advisory.
We previously found that the MoA-INFO platform could increase farmers’ knowledge about topics such as Fall Armyworm (FAW; for example, see “Kenya Fall Armyworm Trial 2019”). In this trial we evaluate the effects of the MoA-INFO service on farmers’ behavior, by randomizing farmers to receive invitations to specific CS topics. During the long rainy season 2020 (LR 2020), we selected seven CS topics that pertain to seed and fertilizer choice and post-harvest storage, and we randomized farmers to receive invitations to those seven CS topics. In September 2020, we collected information on the adoption of recommended practices and on crop yields, via a phone survey. Complementary information on platform engagement came from administrative data from the MoA-INFO platform.
We find that higher adoption of recommended practices, as reported by farmers and measured using an aggregate index of practices, is correlated with higher yield. This finding suggests that recommended practices can improve agricultural outcomes. However, receiving SMS advice did not lead to statistically significant changes in the adoption of recommended practices or crop productivity (yield and harvest). These results suggest that our agronomic content and approach (i.e., advising on a set of good agricultural practices) has potential, and underscore the importance of enhancing the effectiveness of our information provision. -
Status
Completed
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Start date
Q1 Jan 2020
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Experiment Location
Kenya
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Partner Organization
Kenya Ministry of Agriculture
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Agricultural season
Long Rains
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Experiment type
Impact Evaluation
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Sample frame / target population
MoA-INFO Platform Users (farmers)
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Sample size
2,939
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Outcome type
Farming practices, Agricultural production / yield, Input adoption, Service engagement
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Mode of data collection
Phone survey, PxD administrative data
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Research question(s)
What are the effects of MoA-INFO CS messages on farmer practices and yields?
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Research theme
Agricultural management advice, Communication technology
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Research Design
The trial sample consisted of all 11,336 MoA-INFO farmers from early planting constituencies who had opted-in to both maize and bean CS, had not opted-out of these CS at the time of randomization, had self-reported their ward location, and were in a ward with at least one other farmer who met these criteria (otherwise the ward would be dropped due to ward-level fixed effects).
The sample was stratified at the ward level and randomized at the individual level because farming recommendations vary by the predominant agro-ecological zone (AEZ) at the ward level. Out of the 11,336 farmers in the sample, 1,417 (12.5%) were randomized to be part of the control group that did not receive the selected seven CS topic invitations: maize seed, maize fertilizer, maize topdressing, maize post-harvest storage, bean seed and fertilizers, bean planting, and bean post-harvest storage. The remaining 9,919 treatment farmers received all seven of the selected CS invitation messages (unless they opted-out).
A phone survey of a subsample of 2,939 farmers gathered information on their farming practices, yield, and demographics.
Given that the farmers had opted-in to receive CS recommendations, we wanted to send CS invitations to as many farmers as possible. Therefore, we chose to have a small control group and measure a limited set of outcomes on behavior change.
For further information on a previous trial, see “Kenya Fall Armyworm Trial 2019”.
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Results
Adopting more recommended practices, measured using an aggregate index of practices, was correlated with higher yields, yet there was no evidence suggesting that the text message advice led to statistically significant changes in the rate of adoption of recommended practices or in farm outputs (yield and harvest).
We analyzed the results by sub-groups for maize monocropping (n = 1,260), bean monocropping (n = 381), and maize and bean intercropping (n=1,635). We do not detect a significant impact on these three sub-groups, yet differences between these groups provide some valuable insights. In the case of beans, treatment impacts on yield and on individual practices are always in the expected (positive) direction. For maize, treatment impacts on practices are sometimes in the expected (positive) direction and sometimes in the opposite (negative) direction, while the impacts on maize yield are consistently negative. This difference suggests that bean results are somehow more promising than maize results.
We also explored heterogeneous effects across different sub-groups (previous CS opt-ins, superusers, gender, smartphone ownership, location) and find little evidence of such effects.