Budget 2025 of India specifically highlights the importance of climate resilient districts to tackle climate vulnerability and enhance agricultural export earnings from these districts in order to attain the vision of doubling the farmer income of India. Currently, India’s agriculture is extremely vulnerable to climate change, and each district has different degrees of climate vulnerability because of varying regional climatic and meteorological conditions. According to recent studies, agricultural output in Indian districts gets impacted by unpredictable rainfall patterns, warming temperatures, extreme weather events, endangering food security, rural livelihoods and impacting the goal of doubling the farmer income. Therefore, understanding district-specific agricultural vulnerabilities is essential for creating focused interventions that can improve resilience in India’s agricultural sector through means of state and district specific climate action plans for high risk climate vulnerable zones.
A Machine Learning based LSTM Model has been developed to identify such high-risk districts of India requiring immediate action and thus creating, enabling a customized policy mapping for those districts. A district-specific policy offers a more detailed understanding of climate-related hazards assisting farmers and policy makers towards implementing climate adaptation measures. The model uses climate variability factors across districts of India. Relationship between climate variables and agricultural production is mapped by the model with the help of following process –
Taking account of past data, LSTM Model predicts agricultural production with reference to climatic variation across the Rabi and Kharif Seasons. Amongst districts of India, the following results emerge out of the LSTM model –
In Kharif Season: Thane shows drastic future production decline whereas Ratnagiri, Gwalior shows notable decline requiring attention. East Godavari and Panna show increase in production. Hoshangabad, Jabalpur, Vellore also show gradual decline but less than Ratnagiri and Thane.
In Rabi Season: East Godavari (Andhra Pradesh) and Thane (Maharashtra) should be monitored closely, although their rate of decline is slowing. Continuous intervention might be necessary to further slow down or halt the negative trend of production. Ratnagiri shows positive trend in production but still requires support to maintain such growth momentum.
Risk Zones and Priority Districts
Based on the model predictions for the next 70 years, districts are divided into different risk zones after mapping the future variation in their agricultural vulnerability and exposure to climate variability:
High-Risk Zones: These areas show sharp drops in agricultural productivity and are extremely vulnerable to climate change. Immediate measures are crucial, including crop diversification, drought-resistant seed varieties, and upgradation of irrigation infrastructure.
Moderate-Risk Zones: These regions are subject to sporadic disturbances, but they can reduce risks by implementing climate-smart farming methods, better soil conservation, and more effective irrigation systems.
Low-Risk Zones: Although these regions are not affected much in future, proactive steps like early warning systems and sustainable farming methods are needed to maintain their resilience against future climate unpredictability.
Based on the above specific zoning, the model suggests the below mentioned policy matrix for future consideration –
Policies for Different Risk Zones
As per our LSTM modelling, for the two seasons of Kharif and Rabi, the following findings have emerged:
For the Kharif Season, East Godavari District of Andhra Pradesh is in a low risk zone owing to a decreasing pattern of future variation by 2070
For the Kharif Season, Thane and Ratnagiri are in a high risk zone owing to a large pattern of climatic agricultural variation in the future by 2070
The above findings are a snapshot of a large set of findings from our model for each district of India. These findings enhance the importance of strengthening the importance of implementation of state specific climate action plans. State level implementation of central schemes dealing with crop insurance, climate resilient agricultural practices have to increase manifold to address the above zone-specific climate and agricultural vulnerability. A state-specific strategy that incorporates district-level data, its assessment, measurement and monitoring for future policy making and execution must be immediately integrated into central and state level policy frameworks in a cogent way. A systematic approach to climate adaptation that considers the socioeconomic and ecological circumstances of each district must be implemented.
Some of the state specific policies may include –
Creating State-Specific Climate Policies as an additional lever to the State Level Climate Action Plans: To guarantee that policies adequately address issues unique to a given region and they should be customized based on regional vulnerability assessments based on the modelling outputs as suggested above.
Investing in Climate-Resilient Infrastructure: Climate impacts can be considerably reduced by integrating renewable energy sources, enhancing soil health management, and upgrading water conservation systems by promotion of new investments in the green infrastructure for climate resilient agriculture.
Encouragement of Climate-Smart Agricultural Practices: Through incentives and awareness campaigns, robust crop types, precision farming, and sustainable soil practices should be promoted.
Data for Climate Resilient Agricultural Policy: To enhance data-driven decision-making, governments and farmers can benefit from the use of AI-driven forecasting, predictive analytics, and real-time climate monitoring.
Strategic climate adaptation efforts must incorporate localized vulnerabilities if India moves towards a prosperous climate resilient agricultural future for doubling the farmer income. With the advent of changing, upcoming climate challenges, policy makers can protect food security, sustain rural livelihoods, and guarantee sustainable agricultural expansion by giving district-level climate resilience top priority by means of localized data based decision making tools using AI tools. Within the current and future context, this is the only option that the country has for now!
Anandajit Goswami is a Professor, Director, Research Director at Manav Rachna International Institute of Research and Studies (MRIIRS), Swati Hans is an Assistant Professor at Manav Rachna International Institute of Research and Studies (MRIIRS). Views are Personal. The authors acknowledge the contributions by Ms. Tanu Dua and Prof. Indu Kashyap for the article.