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Impact Evaluation of Ama Krushi
INDIND -20 -1252Last modified on October 1st, 2025 at 9:04 am
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Abstract
PxD operates Ama Krushi, a free agriculture information service delivered over mobile phones, in collaboration with the State Government of Odisha Department of Agriculture using a two-way IVR platform with "outbound" push calls and an "inbound" hotline service.
We evaluate at scale the impact of a digital agricultural advisory service reaching millions of smallholder farmers, in an eastern state of India. We randomized the rollout of the service among 13,675 rice farmers within five districts, and measured the impact on agricultural outcomes using both survey and remote sensing data. Using survey data, we find that access to the digital service leads to significant improvements in farmers’ knowledge and adoption of recommended practices, a modest increase in rice yield and harvest, and a large reduction in the likelihood of rice crop loss on average. Further analyses suggest that the treatment impact is concentrated in areas hit by certain types of weather shocks, increasing harvest by up to 9% and reducing severe crop loss by up to 21% in affected areas. We use vegetation indices (VIs) to construct an objective yield measure for all farmers in the study sample and confirm that our key survey results are robust against differential attrition, reporting biases, and survey sample selection. While the VI-predicted yield provides valuable validation of survey results, our analysis highlights the need for methodological improvements in the effective application of remote sensing data to measure program impacts on agricultural outcomes. -
Status
Completed
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Start date
Q4 Oct 2020
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Experiment Location
India / Odisha, India
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Partner Organization
J-PAL , Government of Odisha
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Agricultural season
Kharif
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Experiment type
Impact Evaluation
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Sample frame / target population
Paddy farmers
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Sample size
13,675
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Outcome type
Knowledge, Agricultural production / yield, Farming practices, Agricultural profits / revenues, Post-harvest decisions
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Mode of data collection
Remote sensing, Phone survey, In-person survey
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Research question(s)/hypotheses
1. What is the impact of a large-scale digital agricultural extension service on rice farmers’ agricultural outcomes?
2. Examine the potential of using remote-sensing data to measure yields and estimate treatment impacts. -
Research theme
Agricultural management advice, Communication technology, Measurement methods, Weather information
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Research design notes
In 2021, we used a random walk approach to recruit 13,675 rice farmers, including 5,204 Cohort 1 farmers and 8,471 Cohort 2 farmers, and conducted an in-person baseline survey with them. Recruited farmers were randomly assigned to the treatment or control group with equal probability. Farmers in the treatment group were provided access to the Ama Krushi service: Cohort 1 and 2 farmers since the Kharif season of 2021 and 2022, respectively. Cohort 1 farmers were followed up twice at the end of the Kharif seasons of 2021 and 2022, collecting farmers’ self-reported data on agricultural knowledge, practice adoption, rice yield, harvest sales, cultivation costs, and crop losses.
Read full working paper: Cole S., Goldberg J., Harigaya T., Zhu J. (2025)
See also: PxD 2024 Annual Report feature -
Results
Access to the digital service leads to significant improvements in farmers’ knowledge and adoption of recommended practices, a modest increase in rice yield and harvest, and a large reduction in the likelihood of rice crop loss on average. Further analyses suggest that the treatment impact is concentrated in areas hit by certain types of weather shocks, increasing harvest by up to 9% and reducing severe crop loss by up to 21% in affected areas. One key advantage of digital services like Ama Krushi is the extremely low delivery cost. We estimate that, for every dollar invested in the service, Ama Krushi generates benefits to farmers in the range of $12-19.
We use vegetation indices (VIs) to construct an objective yield measure for all farmers in the study sample and confirm that our key survey results are robust against differential attrition, reporting biases, and survey sample selection. While the VI-predicted yield provides valuable validation of survey results, our analysis highlights the need for methodological improvements in the effective application of remote sensing data to measure program impacts on agricultural outcomes.