AI INTELLIGENCE AND AGRICULTURAL RESILIENCE: A FUTURE DIRECTION

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.

- ANANDAJIT GOSWAMI & SWATI HANS

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 the 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.