PYQ Relevance
[UPSC 2024] Elucidate the importance of buffer stocks for stabilizing agricultural prices in India. What are the challenges associated with the storage of buffer stock? Discuss.
Linkage: This PYQ is central to GS-III themes of food security, MSP, PDS and price stabilization. It links with the article’s focus on excess stocks and distorted procurement, showing why India’s buffer-stock management is becoming unsustainable. |
Mentor’s Comment
India faces a cereal management crisis marked by procurement distortions, crop diversification failures, import dependence, and systemic leakages. This article unpacks the urgent concerns raised in “Time to sort out India’s cereal mess” and restructures them into an exam-oriented format that aligns with GS II and GS III themes such as food security, agriculture, subsidies, MSP, PDS, and federal coordination.
Introduction
India’s cereal ecosystem, procurement, storage, distribution, and diversification, stands at a difficult juncture. Excessive focus on paddy and rice under MSP, escalating procurement costs, growing import dependence in edible oils and pulses, and logistical inefficiencies have created structural vulnerabilities. The current controversy in Tamil Nadu’s paddy procurement highlights deeper national issues in cereal governance.
Why in the News
Tamil Nadu’s short-term kuruvai paddy procurement turned contentious due to time overruns and corruption charges, exposing systemic weaknesses in the procurement architecture. Despite years of surplus stock, India faces a paradox of simultaneous overproduction of rice and wheat and rising import dependence on pulses and edible oils, with 55% of edible oil demand met by imports. The scale of misalignment, such as rice stocks at 536.14 lakh tonnes in October, five times the requirement, reveals an unsustainable cereal management model requiring urgent correction.
Understanding the Current Procurement Distortions
- Excessive Paddy Procurement: Tamil Nadu’s system led by TNCSC and FCI shows delays, over-coverage, and corruption, with farmers preferring paddy due to assured returns.
- High Central Pool Stocks: Rice stocks reached 536.14 lakh tonnes (Oct 2024) against norms of about 102.5 lakh tonnes, reflecting procurement far beyond requirement.
- Skewed Crop Incentives: Procurement levels for rice and wheat remain consistently higher than norms, reducing incentives for diversification.
Why India’s Cereal Supply is Misaligned
- Surplus in Cereals: India maintains abundant stocks, e.g., rice procurement averaging 322 lakh tonnes over three years, indicating oversupply.
- Deficit in Pulses & Oilseeds: Despite large-scale cultivation, imports form a major share: India meets 55% of edible oil demand through imports.
- Stagnant Diversification: Farmers hesitate to shift due to uncertain support systems, weak price assurance, and inadequate crop guidance.
Rising Import Dependence and Its Consequences
- High Import Bills: Edible oil imports breached 30,000 crore in 2023-24 despite domestic production dips from 157 lakh tonnes to 138 lakh tonnes over a decade.
- Geopolitical Risks: Events like the Russia-Ukraine conflict directly increased global edible oil prices, impacting domestic inflation.
- Oilseed Production Stagnation: Even after 2004 reforms, domestic acreage rose but yields and self-sufficiency remained stagnant.
Structural Issues in India’s Crop Diversification Strategy
- Weak Extension Services: Farmers lack assured technical guidance and support for alternative crops.
- Higher Risk in Non-Paddy Crops: Limited MSP procurement outside cereals increases production risk.
- Fragmented Procurement Framework: Multiple agencies (FCI, State Corporations, NAFED) lead to inconsistent practices across states.
Why Procurement Reforms are Urgent
- Inefficient FPO Integration: FPOs, though expanding, remain nascent and face poor access to credit, logistics, and markets.
- Leakages and Diversions: Instances of paddy moving outside the procurement chain due to better prices in open markets distort the system.
- Need for Commodity-Specific Strategy: Uniform procurement policies for cereals, pulses, and oilseeds fail to reflect regional agro-ecology and market diversity.
Conclusion
India’s cereal management crisis is not of shortage but of imbalance, overproduction of rice and wheat coexisting with deficits in pulses and edible oils. Procurement distortions, poor diversification incentives, and high import reliance underline the need for structural reforms. A shift towards agro-ecology-based diversification, procurement redesign, and FPO strengthening can realign India’s food security architecture.
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Introduction
Agentic AI refers to a new class of artificial intelligence systems capable of executing multistep tasks, adapting to processes, and performing actions independently rather than merely responding to prompts. The term has witnessed a rapid surge in public and industry attention, driven by new academic reports and its promise of automating complex workflows. The development marks a notable shift from conventional chatbots that were largely conversational and instruction-bound.
Why in the News?
It is in the news due to a new report by the Massachusetts Institute of Technology and the Boston Consulting Group describing it as a “new class of systems that can plan, act, and learn on their own.” Google searches for the term have skyrocketed, reflecting a sharp contrast from its obscurity just a year ago.
What Makes Agentic AI Different?
- Autonomous Execution: Moves beyond responding to instructions by executing multistep processes and adapting as they proceed.
- Planning Capability: Breaks high-level goals into sequential steps and performs them independently.
- Human-Like Behaviour: Sounds more natural and expressive, yet retains training-based limitations without genuine understanding.
Why Has the Term Skyrocketed?
- New MIT–BCG Report: Classifies agentic systems as a new AI class with independence in planning and learning.
- Search Spike: Google searches for the term hit a peak earlier this fall.
- Corporate Adoption: Major tech firms such as OpenAI, Google, IBM, Microsoft, and Salesforce are building or integrating agentic systems.
How Does Agentic AI Work in Real-world Tasks?
- Execution of Goal Chains: Systems take inputs like “Here are the great ideas” and “And then complete the task.”
- Application in Online Services: Includes personal finance assistance, bill interpretation, dispute resolution, or travel booking using card data.
- Complex Task Automation: Involves computer access and stepwise execution of guidelines for high-level objectives.
What Is Driving Industry Optimism?
- Workflow Automation Promise: Amazon sees agentic systems as key to automating cloud operations and enterprise-level tasks.
- Operational Transformation: Viewed as one of the biggest AI evolutions since early generative models.
- Security Applications: Potential as “personal shields” against spam, fraud, and phishing by acting on email and digital data.
What Are The Concerns or Limitations?
- Marketing Hype vs Utility: The term is being debated due to its sudden popularity and vague boundaries.
- Lack of True Autonomy: Systems act within training limits despite appearing highly capable.
- Ethical and Trust Issues: The blending of autonomous actions with sensitive tasks (finance/computers) raises oversight concerns.
Conclusion
Agentic AI represents a shift from conversational to autonomous process-executing systems. While the term has rapidly gained traction due to academic endorsement and industry optimism, its real potential depends on responsible deployment, ethical guardrails, and clarity around autonomy and control. Its emergence signals an important moment in the evolution of artificial intelligence with direct implications for governance, security, and digital administration.
Value Addition
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Generative AI
- Definition: AI systems capable of generating new content, text, images, audio, or code, based on patterns learned from training data.
- Core Function: Produces responses to prompts; does not take independent action.
- Examples: ChatGPT, Midjourney, DALL·E.
Large Language Models (LLMs)
- Definition: Models trained on vast datasets to understand and produce human-like language.
- Role: Backbone of generative AI.
- Limitation: No planning ability; follows instructions linearly.
Agentic AI
- Definition: A new class of AI systems that can plan, act, and learn on their own, breaking down goals into steps and executing them without constant user input.
- Core Difference from Generative AI: Moves from responding to acting.
- Example (from article): An agent that interprets medical bills, disputes charges, or handles complex computer tasks.
AI Agents
- Definition: Software entities capable of autonomous actions in an environment to achieve goals.
- Role in Agentic AI: Agents are the functional units that perform the tasks.
Multistep Automation
- Definition: A system that converts a single instruction into multiple executable actions.
- Agentic Relevance: This is the defining capability that transforms chatbots into autonomous systems.
High-level Goal Breakdown
- Definition: Ability of an AI to take an abstract goal (e.g., “organise my travel”) and break it into actionable steps.
- Example: Travel bookings using credit card data.
Autonomy in AI
- Definition: The degree to which an AI system can act without human intervention.
- Agentic Context: Full or partial autonomy is central to its functionality.
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PYQ Relevance
[UPSC 2023] How can Artificial Intelligence (AI) help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?
Linkage: Agentic AI builds on this by not just assisting but autonomously executing tasks such as interpreting bills or acting on sensitive data. The privacy risks highlighted in the PYQ directly connect to concerns over AI agents accessing personal digital information while acting independently.
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Introduction
India recorded a historic goods trade deficit in October ($41.68 billion), following a sharp rise from September’s $32.15 billion deficit. The decline in exports, driven largely by the U.S.’s steep tariffs, coincides with an abnormal spike in gold and silver imports, rupee depreciation, and heavy portfolio outflows. The article highlights how India’s dependence on the U.S. market has exposed it to both economic and diplomatic vulnerabilities, raising questions about whether the shift in trade patterns is structural or a temporary response to external shocks.
Why in the News
India’s record October trade deficit of $41.68 billion, the sharpest ever, signals a significant disruption in its external trade landscape. Exports plunged due to the U.S.’s sudden 50% tariffs, critical because the U.S. is India’s largest export market, while gold imports tripled and silver inflows rose fivefold, creating an unprecedented import spike.
A Rising Trade Deficit and What It Reveals
- Record Deficit ($41.68 bn): Reflects a sequential deterioration from September’s $32.15 bn deficit, signalling a disturbing shift.
- Export Fall (-11.8% YoY): Goods exports dropped to $34.38 bn (from $38.98 bn in 2024), driven primarily by U.S. tariffs.
- Heavy Import Surge: Driven by a dramatic rise in bullion inflows and the use of cheaper imported intermediates.
Why the U.S. Tariffs Hit India Hard
- 50% Tariff Shock: Imposed in August, directly affecting sectors for which the U.S. has been India’s major market since 2018-19.
- Large Market Dependence: The U.S. remains the biggest buyer of India’s textiles, yarn, readymade garments, and engineering goods.
- Export Decline (-9% YoY): Overall exports to the U.S. contracted sharply in October.
What Is Driving the Surge in Gold and Silver Imports?
- Gold Imports Tripled: Rising from $4.92 bn (last October) due to economic uncertainty.
- Silver Imports Up Fivefold: Indicates hedging behaviour rather than seasonal demand.
- Rupee Weakening (â‚ą85.6 to â‚ą88.4): Encouraged investors to seek bullion as a safe asset.
Sector-Wise Export Stress
- Cotton Yarn & Handlooms (-13.31%): Major labour-intensive sector hit due to tariff-led slowdown.
- Man-Made Yarn (-11.75%): Reflects weakening competitiveness.
- Readymade Garments (-12.88%): Particularly vulnerable to U.S. demand contraction.
- Engineering Goods (-16.71%): Hit despite being a major export strength area.
Is the Import Surge a Structural Pattern?
- Cheaper Intermediate Goods: Firms increasingly rely on imported inputs to maintain export competitiveness.
- Depreciating Rupee: Makes imports costlier but also signals reduced domestic sourcing.
- Need for HS-Chapter Analysis: A breakdown by commodity and source country will clarify which imports are rising structurally.
Government Measures and Their Limitations
- Export Promotion (â‚ą25,060 crore over 6 years): Centre has stepped in to cushion exporters.
- RBI Relief Measures: Target tariff-affected exporters.
- Too Early to Call It Structural: Realignment of supply chains and market diversification could take years.
Geopolitical Shifts and Bilateral Trade Dynamic
- India-U.S. Bilateral Trade Agreement: If concluded soon, October’s deficit spike may be temporary.
- Russian Imports Down (-27.73%): Sharp drop indicates effort to reduce crude dependence.
- U.S. Imports Up (13.89%): Suggests attempt to ease American concerns over trade imbalance.
Conclusion
India’s record trade deficit underscores the risks of concentrated export dependence and volatile imports driven by economic uncertainty. While the current shift may be partly reactionary, persistent decline in labour-intensive exports and rising reliance on imported intermediates signal deeper structural weaknesses. Managing this transition will require sustained policy intervention, diversification of markets, and a recalibration of India’s trade portfolio to mitigate vulnerability.
PYQ Relevance
[UPSC 2018] How would the recent phenomena of protectionism and currency manipulations in world trade affect macroeconomic stability of India?
Linkage: The U.S. tariff shock and rupee weakening in the article directly mirror the PYQ’s theme, showing how protectionism and currency swings widen India’s trade deficit. Together, they illustrate the resulting stress on India’s macroeconomic stability.
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