·The Hindu·15 marks·250–350 wordsPolityS&T

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.

In this answer
  1. Why sub-district precision multiplies resilience
  2. IMD's recent initiatives
  3. Limitations

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.

Sources

  1. 1Updated Long Range Forecast for the Southwest Monsoon Seasonal Rainfall during June–September 2026, PIB/MoES90% of LPA, below-normal season, El Niño development
  2. 2Government Conducts AI-Based Pilot for Local Monsoon Forecasting to Support Kharif Sowing Decisions, PIBpilot 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. 3Long Range Forecast For the 2026 Southwest Monsoon Season Rainfall, PIB/MoESMulti-Model Ensemble of CGCMs and MMCFS; LRF issued since 2003
  4. 4Ministry of Earth Sciences, Long Range Forecast for the 2026 Southwest Monsoon (PDF)LPA of 87 cm for 1971–2020
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