Telephonic farmer feedback surveys were conducted in Madhya Pradesh and Bihar through Kisan Call Centres after the forecasts were sent. The survey revealed that 31–52% farmers adjusted their planting decisions, primarily through changes in land preparation and sowing timing, which included crop and input choice
An AI-based pilot was conducted in collaboration with the Development Innovation Lab- India on agriculturally relevant local monsoon onset forecasts across parts of 13 states in India for Kharif 2025.
An open source blended model was used, including Google’s NeuralGCM, the European Centre for Medium-Range Weather Forecasts’ (ECMWF) Artificial Intelligence Forecasting System (AIFS), and historical rainfall data from 125 years from the India Meteorological Department (IMD).
The probabilistic forecasts predicted only the local onset of monsoon, which is essential for taking a decision on the date of sowing of crops. Local monsoon onset forecasts were sent via SMS through the M-Kisan portal to 3,88,45,214 farmers in 13states in five regional languages- Hindi, Odia, Marathi, Bangla, and Punjabi. No financial assistance was provided for this pilot.
Telephonic farmer feedback surveys were conducted in Madhya Pradesh and Bihar through Kisan Call Centres after the forecasts were sent. The survey revealed that 31–52% farmers adjusted their planting decisions, primarily through changes in land preparation and sowing timing, which included crop and input choice.