Leveraging Kisan Call Centre Analytics for Data-Driven Agricultural Support in Gujarat: Trends, Insights, and Policy Implications (2009–2024)

Agricultural data analytics empowers farmers to enhance decision-making, forecast market shifts, and address specific challenges, fostering sustainable farming practices. This research examines farmers' information needs in Gujarat by scrutinizing 1.937276 million calls to the Kisan Call Centre from 2009 to 2024. The analysis tracks call volume changes over time and their geographic origins, facilitating district-tailored support planning. Calls are classified by crop type and query nature, illuminating key farmer concerns. Seasonal query patterns pinpoint critical periods of heightened farmer support requirements. The database further classifies information based on seasonal cycles (Kharif, Jayad, and Rabi) and sectors (Agriculture, Horticulture, Animal Husbandry, and Fisheries). These insights enable policymakers to devise strategies aligned with call types, locations, and timing. The study underscores how data-driven approaches can elevate agricultural support systems, boosting farming efficiency and effectiveness in Gujarat and outside. The KCC database encompasses a total of 40,774,833 records, with 1,937,276 pertaining specifically to the state of Gujarat. Key Words: Agricultural Data Analytics, Decision-Making, Farmers’ Queries, Seasonal Patterns, Crop Types, Predictive Analytics