Data-driven insights from farmer call center logs in India: Developing a scalable framework for digital agroclimate advisory

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cg.authorship.typesCGIAR single centre
cg.contributor.affiliationInternational Center for Tropical Agriculture
cg.contributor.initiativeClimate Resilience
cg.contributor.initiativeDigital Innovation
cg.contributor.programAcceleratorClimate Action
cg.coverage.countryIndia
cg.coverage.iso3166-alpha2IN
cg.coverage.regionAsia
cg.coverage.regionSouthern Asia
cg.creator.identifierDegefie Tibebe Degefie: 0000-0002-5340-6077
cg.creator.identifierAniruddha Ghosh: 0000-0003-3667-8019
cg.creator.identifierJemal Seid Ahmed: 0000-0001-6136-9184
cg.creator.identifierMastawesha Misganaw Engdaw: 0000-0001-8224-2369
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.actionAreaSystems Transformation
cg.subject.alliancebiovciatAGRICULTURE
cg.subject.alliancebiovciatCLIMATE CHANGE ADAPTATION
cg.subject.alliancebiovciatEXTENSION
cg.subject.impactAreaClimate adaptation and mitigation
cg.subject.sdgSDG 13 - Climate action
dc.contributor.authorTibebe, Degefie
dc.contributor.authorGhosh, Aniruddha
dc.contributor.authorAhmed, Jemal Seid
dc.contributor.authorEngdaw, Mastawesha
dc.date.accessioned2026-01-19T13:20:36Z
dc.date.available2026-01-19T13:20:36Z
dc.identifier.urihttps://hdl.handle.net/10568/180119
dc.titleData-driven insights from farmer call center logs in India: Developing a scalable framework for digital agroclimate advisoryen
dcterms.abstractFarmer call centers capture high-frequency, location-specific information needs and constraints, offering a farmer driven dataset for designing scalable agroclimatic advisory services. This progress report documents the development of an AI-enabled pipeline that converts India’s call center logs into operational agroclimatic intelligence for targeting, prioritization, and message refinement. Using on-disk processing (DuckDB) for multi-million-record repositories, the workflow standardizes dates and administrative locations, de-identifies sensitive fields, and applies multilingual natural language processing (NLP) to (i) classify calls into WEATHER_DIRECT, WEATHER_TRIGGERED_AG, and OTHER intents and (ii) map climate-related calls into interpretable topic groups (e.g., rainfall/monsoon, sowing/onset timing, irrigation and water management). Spatiotemporal aggregation produces routine monitoring indicators (annual and monthly demand trends, state year heatmaps, and district-level intensity distributions) and supports hotspot ranking and anomaly flagging for expert review. Service-quality metrics are prototyped (answer rate, response length, and a preliminary actionability proxy) to identify topics that benefit from standardized scripts, decision trees, and escalation protocols. Initial descriptive outputs demonstrate strong monsoon-driven seasonality, concentration of demand in a subset of states and districts, and topic dominance by rainfall/monsoon queries. These results are preliminary and will be refined through full-scale reprocessing, improved text normalization, and validation with domain experts. The report concludes with an operational scale-up pathway human-in-the-loop playbooks and message templates integrated with Kisan Call Centre (KCC), IMD–GKMS advisories, and mKisan and an evaluation plan to estimate productivity and resilience impacts as the system moves from prototype to deployment.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationTibebe, D.; Ghosh, A.; Seid, J.; Engdaw, M. (2025) Data-driven insights from farmer call center logs in India: Developing a scalable framework for digital agroclimate advisory. 25 p.en
dcterms.extent25 p.
dcterms.issued2025-12-29
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.subjectclimate actionen
dcterms.subjectadvisory servicesen
dcterms.subjectdigital recordsen
dcterms.typeReport

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