Spatiotemporal analysis of farmer queries from Kisan call centre data

Agricultural data analytics has emerged as a transformative tool for improving farmers’ decision-making, forecasting agricultural trends, and addressing localized challenges, thereby fostering sustainable and resilient farming practices. This study investigates the evolving information requirements of farmers in Gujarat through a detailed analysis of 1,937,276 call records from the Kisan Call Centre (KCC) database, spanning the period from 2009 to 2024. These calls represent a subset of the national KCC repository, which contains a total of 40,774,833 records, making this one of the largest state-level explorations of its kind. The research analyzes month-wise, district-wise, and query-type distributions, providing valuable insights into the dynamic nature of agricultural inquiries across Gujarat. Visualizations of the data reveal significant temporal fluctuations in call volumes, with peaks typically aligning with major cropping seasons. Spatial analysis identifies districts with consistently high engagement, enabling more targeted extension strategies. Queries were classified across multiple dimensions, including crop type, season (Kharif, Rabi, Jayad), and sector (Agriculture, Horticulture, Animal Husbandry, Fisheries). Weather-related concerns showed a sharp rise during monsoon months, while pest and disease-related inquiries were distributed throughout the cropping cycles. The study also highlights variations in the number of unique crop queries per district, ranging from below 100 to over 340 distinct crops. These insights provide a granular understanding of farmer needs and enable the development of region-specific, season-sensitive advisory frameworks. The findings empower policymakers, researchers, and agricultural extension agencies to design proactive, data-driven support systems that are responsive to real-world conditions. Beyond Gujarat, this analytical framework offers a scalable model for enhancing farmer engagement and agricultural efficiency through intelligent use of call centre data.