UPSC CARE Mains Practice - 2nd March 2026
Mains Practice Questions for the Day
- India’s rice export leadership raises concerns regarding environmental and financial sustainability. Examine and suggest a way forward. (GS Paper III – Economy – Agriculture, Food Security)
- Artificial Intelligence (AI) is emerging as a foundational driver of inclusive rural development in India. Examine how India’s AI governance framework and sectoral integration are transforming rural ecosystems. (GS Paper III – Science & Technology, Agriculture, Inclusive Growth, Digital Infrastructure)
Q. India’s rice export leadership raises concerns regarding environmental and financial sustainability. Examine and suggest a way forward. (GS Paper III – Economy – Agriculture, Food Security)
Introduction:
India is the world’s largest rice exporter and producer. In 2024–25, exports reached 21.69 million tonnes, and production touched 150 million tonnes. However, sustaining this leadership poses serious environmental and economic challenges.
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Environmental Concerns
- Paddy is a highly water-intensive crop.
- Around 5 million litres of water are used per acre under continuous flooding.
- Approximately 3,000 litres of water are embedded in every kg of exported rice.
- Excessive cultivation in Punjab and Haryana has led to groundwater depletion.
This results in large “virtual water exports”.
Financial Sustainability Issues
- Non-basmati exports dominate in volume but yield lower returns (₹34–39/kg).
- Basmati exports, though smaller in quantity, fetch higher value (₹82–92/kg).
- Current strategy prioritises quantity over value.
This reduces export efficiency per unit of water used.
Technological and Policy Dimensions
- High-yield basmati varieties such as Pusa Basmati-1509 reduce crop duration.
- Marker-assisted selection enables disease-resistant varieties.
- Predictive breeding using genomic selection and machine learning can enhance sustainability.
Policy reforms needed:
- Expand basmati cultivation within GI region.
- Promote GI-tagged aromatic varieties.
- Introduce floor price for basmati.
- Shift non-basmati procurement to eastern states with lower groundwater stress.
Conclusion:
India’s rice dominance must be aligned with ecological sustainability and export value optimisation. A strategic shift toward high-value, less water-intensive rice exports is essential for securing both economic and environmental resilience.
Q. Artificial Intelligence (AI) is emerging as a foundational driver of inclusive rural development in India. Examine how India’s AI governance framework and sectoral integration are transforming rural ecosystems. (GS Paper III – Science & Technology, Agriculture, Inclusive Growth, Digital Infrastructure)
Introduction:
Artificial Intelligence (AI) refers to the ability of machines to perform cognitive tasks such as learning, reasoning, and decision-making. In India, AI is being developed within a social-purpose framework aligned with inclusive welfare and Viksit Bharat@2047. Its integration into rural governance, agriculture, healthcare, and skilling is positioning AI as a public good for equitable development.
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AI Governance Framework for Inclusive Development
India’s AI approach rests on a dual framework:
- National Strategy for AI (NITI Aayog, 2018 – #AIforAll)
Focuses on access, affordability, and quality of services in agriculture, healthcare, and education. Emphasises augmentation of human labour rather than displacement. - India AI Governance Guidelines (MeitY, 2025)
Establish principles of fairness, accountability, transparency, and India-specific risk mitigation.
Promote system-level governance through Digital Public Infrastructure and whole-of-government coordination.
This ensures responsible and context-sensitive AI deployment in welfare delivery systems.
AI in Rural Governance and Service Delivery
1. Panchayati Raj Integration
- SabhaSaar generates structured Gram Sabha minutes.
- eGramSwaraj digitises planning, budgeting, and asset management (2.53 lakh Gram Panchayats onboarded).
- Gram Manchitra enables GIS-based planning and evidence-driven GPDPs.
2. Agriculture and Livelihoods
- AI-enabled weather forecasting, pest surveillance, and Kisan e-Mitra advisories reduce production risks and enhance income security.
3. Rural Infrastructure Monitoring
- BhuPRAHARI integrates AI and geospatial tools for MGNREGA and rural asset tracking.
4. Multilingual Inclusion
- BHASHINI, BharatGen, and Adi Vaani reduce linguistic barriers, enabling voice-first and tribal-language governance.
Challenges
- Data privacy concerns
- Algorithmic bias
- Capacity gaps in rural institutions
- Infrastructure disparities
Conclusion:
AI in India is evolving from isolated innovation to institutionalised public infrastructure. When embedded within ethical safeguards and multilingual inclusivity, AI strengthens last-mile service delivery, participatory governance, and rural resilience. Its responsible deployment will be central to achieving inclusive and sustainable development under Viksit Bharat@2047.
