Loss Of Agricultural Land To Urban Expansion In India: Patterns, Drivers And Policy Concerns 1991 To 2022
Bibliographic record
Abstract
India has experienced rapid urban growth since the 1991 economic liberalisation, and this process has led to widespread conversion of agricultural land into built-up areas. Using data drawn from the Directorate of Economics and Statistics (Ministry of Agriculture & Farmers Welfare), Census of India reports, and land-cover classifications produced by the National Remote Sensing Centre up to 2022, this study maps the scale and geography of farmland loss across three decades. At the national level, land placed under non-agricultural uses increased from 18.3 million hectares in 1991-92 to 26.9 million hectares in 2021-22, whereas net sown area shrank by roughly 1.8 million hectares even as population rose sharply. The most severe declines have occurred in Punjab, Haryana, Uttar Pradesh, Maharashtra, Tamil Nadu, and Karnataka, with peri-urban rings around Delhi-NCR, Mumbai, Bengaluru, Hyderabad, and Chennai showing the highest rates of built-up expansion. Economic reforms, real-estate speculation, loosely regulated Special Economic Zones, lax zoning enforcement, and large infrastructure projects have jointly driven this transformation. The resultant effects include slower growth in food-grain output, displacement of marginal farmers and landless labourers, degradation of groundwater recharge zones, and heightened food-security risks. Although several protective laws and policy documents have appeared over the years, implementation has remained weak. The paper argues that, without binding safeguards for high-quality agricultural land and a deliberate shift towards compact and vertical urban development, India will continue to lose its productive soil base irreversibly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".