Precision weather forecasting at the sub-district level can be a force multiplier for climate-resilient agriculture. Discuss with reference to IMD's recent initiatives.

Q. Precision weather forecasting at the sub-district level can be a force multiplier for climate-resilient agriculture. Discuss with reference to IMD's recent initiatives. (15 marks, 250-350 words)

The block is India's lowest administrative unit, and shifting monsoon advisories from roughly 700 districts to over 7,000 blocks turns a broad seasonal signal into a farm-level decision tool. With IMD projecting 2026 southwest monsoon rainfall at 90% of the Long Period Average under developing El Niño conditions [1], such granularity is now a resilience necessity, not a refinement.

Why sub-district precision multiplies resilience - Sowing accuracy: onset varies sharply within a district; blocks can stay rainless after "arrival" is declared, causing costly re-sowing. In the Kharif 2025 AI pilot, 31–52% of surveyed farmers in Madhya Pradesh and Bihar altered planting decisions, mainly land preparation [2]. - Scale of reach: probabilistic onset forecasts were disseminated by SMS through the mKisan portal to over 3.88 crore farmers in five languages across 13 States [2]. - Input and water efficiency: knowing that onset is delayed locally discourages premature irrigation and fertiliser application, easing groundwater stress. - Risk management: localised onset data can sharpen crop-insurance loss assessment and block-level contingency planning through KVKs.

IMD's recent initiatives - A blended modelling approach combining NeuralGCM, ECMWF's AI Forecasting System and 125 years of IMD rainfall data, generating weekly probabilistic onset forecasts [2]. - Seasonal skill rests on the Multi-Model Ensemble of coupled global climate models including IMD's MMCFS, operational for long-range forecasts since 2003 [3], benchmarked against an LPA of 87 cm (1971–2020) [4]. - Scaling into an in-house national system through IITM–IMD–ISRO collaboration, with outputs routed to farmers via ministry APIs and Agri Stack [2].

Limitations - Coverage remains partial and pilot-stage; a residual error of a few days still matters for short-duration crops. - Onset timing is not seasonal quantum — a timely onset can precede a deficient season [1]. - Last-mile interpretation and sustained farmer trust depend on agromet extension.

Precision forecasting therefore multiplies resilience only when paired with delivery and advisory capacity. Institutionalising block-level onset products across all States, with transparent uncertainty communication and KVK-led extension, would align climate services with SDG-2 and India's sustainable agriculture mission.

(~330 words)

Sources: 1. Updated Long Range Forecast for the Southwest Monsoon Seasonal Rainfall during June–September 2026, PIB/MoES — 90% of LPA, below-normal season, El Niño development 2. Government Conducts AI-Based Pilot for Local Monsoon Forecasting to Support Kharif Sowing Decisions, PIB — pilot across 13 States, NeuralGCM + ECMWF AIFS + 125-year IMD data, mKisan reach of 3.88 crore farmers, 31–52% behavioural change, IITM–IMD–ISRO scaling 3. Long Range Forecast For the 2026 Southwest Monsoon Season Rainfall, PIB/MoES — Multi-Model Ensemble of CGCMs and MMCFS; LRF issued since 2003 4. Ministry of Earth Sciences, Long Range Forecast for the 2026 Southwest Monsoon (PDF) — LPA of 87 cm for 1971–2020