Fall Armyworm
ZAM -19 -1371Last modified on September 16th, 2021 at 11:20 am
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Experiment rationale and/or abstract
Fall armyworm (FAW) is a pest that spread from the Americas to Sub-Saharan Africa in 2016. It is a fast-reproducing species and causes substantial crop damage. Precision Agriculture for Development (PAD) and our partner, the Centre for Agriculture and Bioscience International (CABI) implemented an SMS campaign in four provinces (Southern, Luapula, Central, and Eastern) of Zambia to provide smallholding farmers with timely advice on how to prevent and manage FAW infestations throughout the 2019-2020 season using a digital platform owned by the Zambian Ministry of Agriculture.
We investigated the impact of providing detailed and actionable advice to farmers on their knowledge acquisition and behavior change of recommended practices. -
Status
Completed
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Start date
Q4 Oct 2019
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Experiment Location
Zambia
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Partner Organization
CABI , Zambia Ministry of Agriculture , Smart Zambia Institute
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Agricultural season
Rainy Season
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Experiment type
Impact Evaluation
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Sample frame / target population
FISP-registered Farmers
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Sample size
3,028
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Outcome type
Crop/Livestock loss, Yields, Knowledge
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Method of Measurement
Phone survey
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Research question(s)/hypotheses
1. Does SMS advice on FAW improve farmers’ knowledge and nudge them to adopt the recommended practices?
2. Are there differential effects by gender of the message recipient? -
Research theme
SMS, Impact Evaluation, Pest Management
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Results
We found that the treated farmers knew more about FAW and adopted more recommended practices than farmers who did not receive the FAW advisory. The treatment group had a knowledge index score that was 2.64% higher than the control group, and an action index score that was 4.67% higher than the control. These effects do not vary by gender of the participant or use of smartphones.
We find no statistically significant effect of treatment on self-reported yield or crop recovery, but we note that the sample of this study was not large enough to detect expected effects on these outcomes.