UPSC Daily Current Affairs 2nd March 2026
Relevance: GS Paper III – Economy – Agriculture, Food Security, Water Resources, Agricultural Marketing, Sustainable Agriculture
For Prelims:
- Conventional Transplanting-Cum-Continuous Flooding System, Basmati vs Non-Basmati Export Value, 2-Acetyl-1-Pyrroline (Aroma Compound), GI Region for Basmati 6.2 Million Hectares, Pusa Basmati-1509, Marker-Assisted Selection, Bacterial Leaf Blight, Rice Blast Disease, Predictive Breeding, Genomic Selection.
For Mains:
- Virtual Water Exports, Groundwater Depletion Punjab-Haryana, Water-Intensive Paddy Cultivation, Commodity vs High-Value Agricultural Exports, GI-Protected Aromatic Rice Varieties, Crop Diversification Strategy, Floor Price Mechanism for Basmati, Sustainable Rice Farming, Genetic vs Chemical Crop Protection, Climate-Resilient Varieties, Machine Learning in Crop Breeding.
Why in News?
India has:
- Remained the world’s largest rice exporter since 2011–12.
- Exported 21.69 million tonnes (mt) of rice in 2024–25.
- Surpassed China to become the world’s largest rice producer (150 mt) in 2024–25.
However, experts warn that sustaining this leadership raises serious concerns regarding:
- Groundwater depletion
- Water footprint of exports
- Financial efficiency of rice trade
- Long-term sustainability of Punjab-Haryana rice system
The debate calls for moving from commodity rice exports to high-value, less water-intensive rice exports.
India’s Global Rice Leadership
Export Position (2024–25)
- India: 21.69 mt
- Thailand: 7.86 mt
- Vietnam: 8.06 mt
India’s dominance is clear both in production and exports. But sustainability is the challenge.
Environmental Sustainability: The Water Crisis
Paddy is a Water-Guzzling Crop
- Rice cultivation under conventional transplanting and flooding system is extremely water-intensive.
Irrigation Requirement
- One irrigation covering 1 acre at 2.5 cm depth requires:
101,171.5 litres of water - Paddy fields maintain water level of 5 cm continuously.
- Number of irrigations: 20–30
Total Water Use
- 25 irrigations × 5 cm depth
- Total ≈ 5 million litres per acre
Water Footprint
- Average yield: 2.5 tonnes per acre
- Water per kg paddy ≈ 2,000 litres
- Water per kg rice (after milling) ≈ 3,000 litres
Therefore, for every kg of rice exported, India indirectly exports 3,000 litres of water.
This raises serious groundwater sustainability concerns, particularly in:
- Punjab
- Haryana
Paddy is a Water-Guzzling Crop
- Rice cultivation under conventional transplanting and flooding system is extremely water-intensive.
Irrigation Requirement
- One irrigation covering 1 acre at 2.5 cm depth requires:
101,171.5 litres of water - Paddy fields maintain water level of 5 cm continuously.
- Number of irrigations: 20–30
Total Water Use
- 25 irrigations × 5 cm depth
- Total ≈ 5 million litres per acre
Water Footprint
- Average yield: 2.5 tonnes per acre
- Water per kg paddy ≈ 2,000 litres
- Water per kg rice (after milling) ≈ 3,000 litres
Therefore, for every kg of rice exported, India indirectly exports 3,000 litres of water.
This raises serious groundwater sustainability concerns, particularly in:
- Punjab
- Haryana
Financial Sustainability: Basmati vs Non-Basmati
Key Insight
- Non-basmati exports are double in volume.
- Yet export earnings are similar.
- Basmati earns nearly 2–2.5 times more per kg.
Basmati provides more value per litre of water used.
Why Basmati is More Sustainable
1. Transplanting Time Advantage
- Non-basmati: transplanted in June (peak summer)
- Requires frequent irrigation due to high temperatures.
- Basmati: transplanted in July (monsoon period)
- Less irrigation requirement.
2. Ideal Grain Development
Basmati grain filling occurs in October when temperatures fall to 30–31°C.
This helps formation of: 2-acetyl-1-pyrroline (2-AP)
→ Responsible for basmati’s characteristic aroma.
If planted earlier (June), high September temperatures reduce aroma formation.
Thus, basmati is both climatically suitable and water-efficient.
Strategic Shift Needed
According to experts:
India should:
- Expand basmati cultivation.
- Promote GI-protected aromatic short-grain varieties.
- Move from “commodity rice” to “premium rice exporter.”
GI Aromatic Varieties to Promote
Instead of bulk non-basmati exports, focus on:
- Kalanamak (UP)
- Adamchini (UP)
- Katarni (Bihar)
- Gobindobhog (West Bengal)
- Badshah Bhog (Chhattisgarh)
- Koraput Kalajeera (Odisha)
- Wayanad Jeerakasala (Kerala)
- Seeraga Samba (Tamil Nadu)
Benefits:
- Higher export value
- Regional diversification
- Lower water pressure on Punjab
Basmati Expansion Potential
GI-Registered Region
Total basmati GI region area: 6.2 million hectares
Break-up:
- Punjab: 3.1 mh
- Haryana: 1.3 mh
- Western UP: 1.5 mh
- Uttarakhand: 0.12 mh
- Jammu: 0.1 mh
- Himachal Pradesh: 0.05 mh
Current basmati cultivation: 2.1 mh
Scope exists to expand basmati to entire 6.2 mh GI region.
Policy Recommendations
1. Floor Price for Basmati
- Declare minimum mandi auction price.
- Prevent distress sale.
- Basmati’s premium status allows enforceability.
2. Shift Non-Basmati Procurement to Eastern India
Increase procurement in:
- Eastern UP
- Bihar
- West Bengal
- Assam
Reason:
- Lower groundwater stress
- Farmers not receiving MSP benefits
Breeding Innovations: Making Rice Sustainable
Blockbuster Varieties
Developed by IARI scientists:
- Pusa Basmati-1509
- Pusa Basmati-1121
- Pusa Basmati-1401
Example: Pusa Basmati-1509:
- 2.5 tonnes per acre yield
- 115–120 days maturity
Compared to traditional:
- 1 tonne per acre
- 155–160 days maturity
Higher productivity + shorter duration = lower water use.
Disease-Resistant Breeding
Using marker-assisted selection, genes introduced for resistance to:
- Bacterial leaf blight
- Rice blast fungus
New varieties:
- Pusa Basmati-1847
- Pusa Basmati-1885
- Pusa Basmati-1886
Benefits:
- Reduced need for antibiotics and fungicides
- Maintains export quality
- Preserves global premium image
Genetic vs Chemical Approach
Instead of heavy pesticide use:
Focus on:
- Genetic resistance
- Indigenous landrace screening
- Drought tolerance
- Heat tolerance
- Salinity tolerance
Future of Rice Breeding: Predictive Breeding
Next breakthrough: Predictive Breeding
Combines:
- Genomic selection
- Machine learning models
- Phenotypic data
- DNA-based prediction
Advantages:
- Faster variety development
- Reduced time and cost
- Higher efficiency
Also supported by:
- Speed breeding techniques
Broader Concerns
- Groundwater depletion in Punjab-Haryana
- Chemical overuse affecting export quality
- Climate change threats (heat, salinity, pests)
- Low value realisation from bulk exports
Way Forward
India must:
- Reduce water-intensive non-basmati in stressed regions.
- Promote high-value basmati and GI aromatic varieties.
- Expand cultivation in ecologically suitable regions.
- Encourage predictive and disease-resistant breeding.
- Move from volume-based strategy to value-based strategy.
Conclusion
India’s rice success story is remarkable. But exporting large quantities of low-value, water-intensive rice is environmentally and financially unsustainable.
The future lies in:
- Premium branding
- Water-efficient cultivation
- Scientific breeding
- Regional crop diversification
India must shift from being the largest rice exporter by volume to becoming the most sustainable and highest-value rice exporter in the world.
UPSC PYQ
Q. In the context of India, which of the following is/are considered to be practice(s) of eco-friendly agriculture? (2020)
- Crop diversification
- Legume intensification
- Tensiometer use
- Vertical farming
Select the correct answer using the code given below:
(a) 1, 2 and 3 only
(b) 3 only
(c) 4 only
(d) 1, 2, 3 and 4
Ans: (a)
CARE MCQ
Q. Consider the following statements regarding India’s rice production and export strategy:
- India exported about 21.69 million tonnes of rice in 2024–25 and is the world’s largest rice exporter.
- Basmati rice exports generate nearly similar export earnings as non-basmati rice despite being exported in smaller quantities.
- Conventional paddy cultivation under continuous flooding requires approximately 3,000 litres of water per kg of rice produced.
Select the correct answer using the code given below:
- I and II only
- II and III only
- I, II and III
- I and III only
Answer: C
Explanation:
Statement I – Correct
India exported 21.69 million tonnes in 2024–25 and remains the world’s largest rice exporter.
Statement II – Correct
Basmati exports (5–6 mt) earned nearly $5.8–5.9 billion, comparable to non-basmati exports (11–14 mt), showing higher unit value realisation.
Statement III – Correct
Water consumption under conventional transplanting-cum-continuous flooding equals roughly 3,000 litres per kg of rice.
Relevance: GS Paper III – Science & Technology, Agriculture, Inclusive Growth, Digital Infrastructure GS Paper II – Governance, Social Justice, Rural Development, Welfare Schemes
For Prelims:
- IndiaAI Mission, National Strategy for Artificial Intelligence (2018), AI Governance Guidelines (2025), #AIforAll, Digital Public Infrastructure (DPI), SabhaSaar, eGramSwaraj, Gram Manchitra, AIKosh, BhuPRAHARI, Digital ShramSetu Mission, Kisan e-Mitra, National Pest Surveillance System, DIKSHA Platform, YUVAI Programme, Suman Sakhi Chatbot, BHASHINI, BharatGen AI, Adi Vaani, India–AI Impact Summit 2026
For Mains:
- Inclusive AI Governance, Responsible AI Framework, Fairness and Accountability in AI, AI for Rural Development, AI in Panchayati Raj Institutions, Geospatial AI for Asset Monitoring, AI in Agriculture Decision Support, AI in Informal Sector, Multilingual AI Platforms, Voice-Enabled Governance, Digital Inclusion in Rural India, AI-Augmented Service Delivery, Linguistic Inclusion, AI for Social Equity, Viksit Bharat@2047
Why in News?
The India–AI Impact Summit 2026 has highlighted Artificial Intelligence as a key driver of inclusive rural development across agriculture, healthcare, education, and governance.
With the IndiaAI Mission and Digital India accelerating system-wide implementation, and the rollout of the AI Governance Guidelines (2025), India is scaling responsible, multilingual, and citizen-centric AI solutions aligned with the vision of Viksit Bharat@2047.
National AI Policy and Governance Framework for Inclusive Development
India’s AI deployment strategy is anchored in a dual framework:
- A forward-looking national development strategy
- A robust governance architecture
This combined structure ensures that AI is deployed in a manner that is responsible, transparent, equitable, and context-sensitive, particularly in rural and socially vulnerable regions.
The objective is not merely technological advancement, but inclusive growth supported by institutional safeguards.
National Strategy for Artificial Intelligence: #AIforAll
Launched by NITI Aayog in June 2018, the National Strategy for Artificial Intelligence identifies AI as a transformative instrument to address India’s developmental challenges.
Core Objectives:
- Improving access to essential services
- Enhancing affordability
- Improving service quality
The strategy prioritises inclusive and socially oriented growth, particularly in underserved regions where service and infrastructure gaps persist.
Sectoral Focus:
In agriculture, healthcare, and education, AI-enabled decision-support systems and data-driven platforms are designed to:
- Strengthen frontline workers
- Support local institutions
- Extend services to remote populations
- Reduce dependence on heavy physical infrastructure expansion
The strategy explicitly emphasises augmentation rather than displacement of human labour. AI is framed as a support system for:
- Farmers
- Health workers
- Teachers
- Administrators
It also promotes inclusive economic participation through decentralised skilling, digital work opportunities, and technology-aligned training.
Under the #AIforAll framework, AI is positioned as a catalyst for rural growth, strengthened governance, and enhanced human capacity.
India AI Governance Guidelines (2025): Responsible AI Deployment
In November 2025, the Ministry of Electronics and Information Technology (MeitY) launched the India AI Governance Guidelines to shift AI policy from application-focused deployment to governance-centered implementation.
The framework establishes people-centric principles including:
- Fairness
- Accountability
- Transparency
Recognising that global AI risk models may not adequately capture India’s socio-economic diversity, the guidelines advocate for India-specific risk assessments, particularly in welfare systems where automated tools influence beneficiary targeting and service provision.
The Governance Framework Includes:
- Seven guiding principles (Sutras) for ethical AI
- Six pillars of AI governance
- An action plan with short-, medium-, and long-term timelines
- Practical deployment guidelines for industry, developers, and regulators
The framework promotes system-level governance through Digital Public Infrastructure (DPI), embedding privacy, interoperability, and accountability by design.
A whole-of-government approach ensures coordination across ministries and states, strengthening transparency, grievance redressal, and institutional capacity.
AI in Rural e-Governance and Decentralised Administration
AI is being integrated into Panchayati Raj Institutions to enhance grassroots governance.
SabhaSaar
SabhaSaar is an AI-enabled tool that generates structured minutes of Gram Sabha and Panchayat meetings from audio and video inputs.
- Reduces manual documentation burden
- Improves consistency
- Ensures unbiased and timely records
- Integrated with BHASHINI for 14 Indian languages
This allows local officials to focus more effectively on governance outcomes.
Digital Platforms Strengthening Rural Governance
eGramSwaraj
Launched in April 2020 under the e-Panchayat Mission Mode Project, eGramSwaraj consolidates Panchayat functions including:
- Planning
- Budgeting
- Accounting
- Monitoring
- Asset management
- Payments
Gram Manchitra
Gram Manchitra provides GIS-based planning tools that:
- Map rural assets
- Monitor infrastructure projects
- Integrate spatial data into Gram Panchayat Development Plans (GPDPs)
AIKosh: National AI Repository
AIKosh functions as a national repository for AI datasets and models, supporting public-sector innovation.
- 7,500+ datasets
- 273 AI models
- 20 industries covered
- 69.80 lakh visits
- 17,500 registered users
- 5,004 model downloads
It lowers entry barriers and accelerates rural e-governance innovation.
AI Infrastructure and Sectoral Integration
AI infrastructure development integrates data resources, computational capacity, and domain expertise through collaboration between government agencies, academic institutions, and national platforms.
BhuPRAHARI (Launched May 2025)
- Collaboration: Ministry of Rural Development & IIT Delhi
- Monitors MGNREGA and VB-G RAM G assets
- Initially used for Amrit Sarovar water monitoring
- Uses satellite and ground-based data
- Enables real-time asset tracking
This enhances transparency and resource optimisation.
Digital ShramSetu Mission
Deploys AI in the informal sector to:
- Enhance service delivery
- Support rural livelihoods
- Align technological deployment with regulatory frameworks
AI in Agriculture
AI operates as a decision-support system at the farm level.
Applications include:
- Weather forecasting
- Pest detection
- Optimisation of sowing and irrigation
Key initiatives:
- Kisan e-Mitra
- National Pest Surveillance System
- Crop Health Monitoring
These systems reduce production risks and enhance farmers’ income security.
AI in Education and Skilling
DIKSHA (NCERT)
- AI-enabled video search
- Read-aloud features
- Supports inclusive learning
YUVAI Programme
- AI education for Classes VIII–XII
- Experiential and socio-technical skill development
These initiatives foster future-ready competencies.
AI for Healthcare: Suman Sakhi
Launched in Madhya Pradesh under the National Health Mission:
- AI-enabled WhatsApp chatbot
- Provides maternal and newborn health information
- Supports ASHAs and ANMs
- Enhances last-mile healthcare access
AI for Language Inclusion
BHASHINI
Launched in July 2022:
- Supports 36+ Indian languages
- Integrated with 23+ government services
- 350 AI language models
- 1 million+ downloads
It promotes voice-first and multilingual governance.
BharatGen (June 2025)
India’s sovereign multilingual and multimodal Large Language Model:
- Supports 22 Indian languages
- Integrates text, speech, and document vision
- Built on India-centric datasets
Adi Vaani
- AI platform for tribal language inclusion
- Digitises endangered languages
- Facilitates governance access in native languages
Conclusion
Artificial Intelligence is steadily evolving into a foundational pillar of rural transformation in India. Through policy frameworks, governance safeguards, digital public infrastructure, multilingual platforms, and sectoral integration, AI is being institutionalised to augment human capacity rather than replace it.
Embedded within principles of fairness, transparency, and linguistic inclusion, AI strengthens last-mile service delivery, enhances participatory governance, and reduces structural inequalities. As India advances toward Viksit Bharat@2047, people-centric AI deployment will remain central to building resilient and equitable rural ecosystems.
UPSC PYQ
Q. With the present state of development, Artificial Intelligence can effectively do which of the following?
- Bring down electricity consumption in industrial units
- Create meaningful short stories and songs
- Disease diagnosis
- Text-to-Speech Conversion
- Wireless transmission of electrical energy
Select the correct answer using the code given below:
A. 1, 2, 3 and 5 only
B. 1, 3 and 4 only
C. 2, 4 and 5 only
D. 1, 2, 3, 4 and 5
Answer: B
CARE MCQ
Q. BHASHINI Sanchalan is primarily aimed at:
A. Promoting satellite-based internet connectivity in rural areas
B. Strengthening multilingual governance through AI-enabled language technologies
C. Establishing new digital universities across India
D. Replacing regional languages with a single national language
Answer: B
Explanation:
BHASHINI Sanchalan is a collaborative initiative of central ministries and the Digital India BHASHINI Division. It aims to strengthen multilingual governance by integrating AI-enabled language technologies into public digital systems.
The initiative incorporates voice-first interfaces and translation capabilities to improve governance processes and service delivery. It supports the development of domain-specific language models, enhances translation accuracy, and standardises terminology through collaborative training.
By promoting linguistic inclusion, especially in rural and underserved regions, it seeks to increase citizen participation and accessibility in digital governance.