Monsoon Onset Forecast Dissemination to Farmers in India–Kharif 2025
INDIND -25 -2709Last modified on December 19th, 2025 at 10:42 am
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Abstract
Climate change is increasing variability in the timing of agricultural growing seasons, thus creating significant challenges for farmers planning their agricultural decisions. Building on a successful pilot “Weather Forecast Dissemination 2024” in 2024 when PxD sent monsoon onset and total rainfall forecasts to 9.45 million farmers across 5 states, in 2025 PxD partnered with the Ministry of Agriculture and Farmers Welfare (MoAFW), the Department of Agriculture and Farmers’ Empowerment of Odisha (DAFE), Human-Centered Weather Forecasts Initiative (HCWF), and the Development Innovation Lab India (DIL–India) at the University of Chicago to scale up this initiative and deliver agriculturally relevant monsoon-onset forecasts to the mobile phones of farmers across India. Approximately 38 million farmers across 13 states received AI-driven monsoon onset forecasts, produced by the HCWF team and disseminated by MoAFW via SMS, two to four weeks in advance of the monsoon onset in May and June 2025, to support agricultural decision-making for the Kharif season.
In Odisha, we randomized the roll-out of forecast delivery such that farmers in some blocks received forecasts via both Interactive Voice Response (IVR), disseminated by DAFE through the Krushi Samruddhi program, and SMS, while farmers in other blocks received forecasts only via SMS. This allowed for a comparison of outcomes between IVR+SMS and SMS-only blocks. We examined whether farmers recalled receiving the forecasts, listened to IVR messages, comprehended the forecast content, utilized the forecast information in agricultural decision-making, and adjusted planting choices (including crop/variety selection, area, timing, or replanting), and assessed the impact on crop outcomes. -
Status
Completed
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Start date
Q2 May 2025
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Experiment Location
India / Odisha, India
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Partner Organization
Development Innovation Lab - University of Chicago, India Ministry of Agriculture, Government of Odisha, Human Centered Weather Forecasts Initiative
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Agricultural season
Kharif
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Experiment type
Other
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Sample frame / target population
Pradhan Mantri-Kisan Scheme and Krushi Samrudhi registered farmers in 13 states
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Sample size
38,000,000
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Outcome type
Agricultural production / yield, Beliefs or perceptions, Crop choice or land use, Information sharing, Service engagement, User experience
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Mode of data collection
Partner administrative data, Phone survey
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Research question(s)
In a roll-out of weather-forecast delivery:
1. Do farmers recall receiving the forecast messages?
2. Do farmers listen to the IVR forecast messages?
3. Do farmers comprehend the content of the forecast messages?
4. Do farmers use the forecasts for agricultural decision-making?
5. How do farmers utilize the forecasts in their agricultural decision-making?
6. Do the forecasts influence farmers’ planting decisions, including about crop or variety selection, the area cultivated, and the timing of planting and replanting?
7. Do the forecasts contribute to reducing farmers’ production costs?
8. Do the forecasts affect crop outcomes, including crop losses, the severity of loss, and harvest yields? -
Research theme
Message framing, Service design, Weather information
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Research Design
Intervention details and treatment arms:
We sent monsoon onset forecasts via SMS to approximately 38 million farmers across 13 states (Uttar Pradesh, Madhya Pradesh, Haryana, Punjab, Himachal Pradesh, Jharkhand, Bihar, West Bengal, Odisha, Rajasthan, Maharashtra, Chhattisgarh, and Uttarakhand). In Odisha, we randomized the rollout of forecast delivery such that farmers in 57 blocks received forecasts via both IVR calls and SMS messages, while farmers in 61 blocks received forecasts only via SMS messages, thus allowing for a comparison between IVR+SMS and SMS-only blocks.Treatment group (n = 1,007): Received forecasts via IVR calls and SMS messages.
Comparison group (n = 1,051): Received forecasts via SMS messages only.Data collection and measurement methods:
Using administrative and platform data for all 38 million recipients, we gathered information on the delivery of and engagement with forecast messages. We conducted monitoring surveys via telephone, and collected farmer feedback on their message recall, message comprehension, and use of forecasts for agricultural decision-making. We conducted experimental surveys via telephone calls with treatment and comparison groups, to estimate the causal impact of monsoon onset forecasts on farmer behavior and crop outcomes, thereby leveraging the randomized rollout across selected blocks.Sample frame and selection criteria:
Monitoring surveys: In Odisha, farmers were sampled from 57 blocks in the state where IVR-based forecasts had been disseminated to them. A total of 15,000 farmers were randomly selected for surveys from those registered under the Krishi Samruddhi program, which is a free IVR service providing agricultural advisory to farmers. In Bihar and Madhya Pradesh, farmers were sampled from 203 sub-districts in Bihar and 391 sub-districts in Madhya Pradesh, where SMS-based forecasts had been disseminated to them. A total of 4,050 farmers were randomly selected in Bihar, and 7,275 farmers in Madhya Pradesh; these farmers were registered with the Pradhan Mantri–Kisan scheme, which provides financial support to landholding farmers.Experimental survey: In Odisha, a total of 2,058 farmers were randomly sampled from 118 blocks. Of these, 57 blocks had received forecast calls while 61 blocks had not.
Randomization protocol with clustering or stratification:
Monitoring survey: In Odisha, the sample was stratified by block, forecast grid box, and historical engagement with voice-call forecast dissemination in 2024. For the first survey round, we classified farmers as high-engagement if their pick-up and listening rates (conditional on pickup) exceeded the average for the dissemination sample during May and June 2024. We oversampled high-engagement farmers using a 70:30 allocation between the high- and low-engagement categories. For the second survey round, we applied the same classification only to farmers who had picked up the forecast calls in round 3 of the dissemination cycle. In Madhya Pradesh and Bihar, farmers were equally allocated within each stratum, where strata were defined by the combination of state, forecast grid box, and district. We determined the number of farmers selected per stratum within each state based on the operational capacity of the respective Kisan Call Centers that conducted the phone surveys.Experimental survey: In Odisha, blocks were randomly assigned to the treatment or control groups in each forecast grid box stratum. Within each block, we used simple random sampling to select respondents for the survey.
For more information on the previous trial, see “Weather Forecast Dissemination 2024”.