Rao, K P C and Kishore Kumar, G and Subbarao, A V M and Dhulipala, R and Kumar, S (2026) Anticipatory Action and Response Plan for Maharashtra Managing Agricultural Risks from El Niño-Induced Below-Normal Conditions Kharif 2026. Working Paper. ICRISAT, Hyderabad.
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The team acknowledges the support from the Ministry of Earth Sciences (MoES), Government of India through Mission Mausam for enabling this work. We thank the India Meteorological Department (IMD) for providing climate forecasts and the Indian Institute of Tropical Meteorology (IITM) for climate modelling inputs. We also acknowledge the support of the CGIAR Climate Action Science Program (AoW2) in strengthening climate-informed agricultural decision-making. We also acknowledge the contributions of ICAR, State Agricultural Universities of Maharashtra, and collaborating institutions for their technical inputs in developing district-level advisories and contingency plans. Field-level insights and support from extension systems, partner organizations, and stakeholders have been critical in shaping the recommendations presented in this report.
Abstract
Managing variable climatic conditions remains a persistent challenge for millions of rainfed farmers in India. The high variability and inherent unpredictability of weather patterns makes it extremely difficult to plan and manage agricultural systems whose performance is closely tied to environmental conditions during the crop season. Extreme events, particularly droughts, have a profound impact on productivity and farm profitability. Historically, responses to such events have been largely reactive, focusing on relief and recovery rather than anticipatory planning and action. This limitation stems largely from the difficulty of reliably forecasting seasonal conditions in advance. In recent years, meteorological science has made significant advances in understanding large-scale climate phenomena such as the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), alongside improvements in predicting climatic conditions months ahead. In India, this progress has been further accelerated by the establishment of the Monsoon Mission (now Mission Mausam) under the Ministry of Earth Sciences, Government of India. The mission aims not only to enhance forecast accuracy but also to promote the effective use of climate information across weather-sensitive sectors, particularly agriculture. In this context, ICRISAT, in collaboration with ICAR-CRIDA and ILRI, is implementing the project titled “AI-powered Context-Specific Agromet Advisory Services for Climate-Resilient Agriculture at Scale” with support from Mission Mausam. The project seeks to develop an automated system—the Intelligent Systems Advisory Tool (iSAT) that integrates climate and weather forecasts with context-specific agricultural knowledge drawn from a growing database compiled from multiple national and international research institutions. iSAT translates probabilistic climate forecasts into actionable, context-specific guidance, delivering both pre-season and in-season advisories tailored to user needs. The current phase of the project focuses on Maharashtra and aligns with the CGIAR Climate Action Science Program (AoW2), which aims to strengthen climate-informed decision-making in agriculture. This report represents an early effort to test the tool and its capabilities in generating a state-level preseason advisory based on the seasonal climate forecast issued by the India Meteorological Department (IMD) for the Kharif 2026 season. The forecast indicates below-normal rainfall at the national scale, consistent with the emergence of El Niño–like conditions in the Pacific Ocean. Accordingly, the advisory framework emphasizes anticipatory actions to manage agricultural risks associated with a potentially weak monsoon. As the project is still in its early stages of implementation, this report should be considered a work in progress. We expect its quality, precision, and relevance to improve as the underlying database is strengthened, and the AI/ML models are further refined. It is also important to note that a substantial portion of this report (approximately 80%) has been generated using AI-based methods. This report is intended to support policymakers, extension systems, and field-level institutions in enabling timely, coordinated, and evidence-based responses to climate risks. We welcome feedback and suggestions from readers, which will be invaluable in improving future iterations.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Divisions: | Global Research Program - Enabling Systems Transformation |
| CRP: | UNSPECIFIED |
| Series Name: | Working Paper |
| Uncontrolled Keywords: | Agricultural Risks, El Niño, action plan, Climate-Resilient Agriculture, India |
| Subjects: | Others > Climate Smart Agriculture (CSA) Others > Information Technology Others > Data & Analytics Others > Digital Agriculture |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 25 May 2026 07:58 |
| Last Modified: | 25 May 2026 08:01 |
| URI: | http://oar.icrisat.org/id/eprint/13644 |
| Links: |
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