Bibliographic record
Abstract
This paper follows a narrow definition of agri-products that include products of agriculture, horticulture, floriculture, animal husbandry, and poultry. Like most other states in India, Gujarat has also prepared several reports and policy papers assessing the potential for agro-processing, identifying constraints in the development and exports of agri-products, suggesting or announcing several important policy measures for removing physical and financial infrastructural bottlenecks, and promoting R&D activities in the sector. However, these exercises lack realistic assessment of the potential, important features of agri-exports from the state, and Gujarat's comparative advantage over the rest of the country in specific product categories. This paper addresses these aspects. A recent survey of exports originating from Gujarat conducted by the Gujarat Industrial Technical Consultancy Organization (GITCO) estimated that, during the year 2000–01, Gujarat contributed Rs 495 billion (or 20.8%) out of the total national exports of Rs 2,385 billion. However, excluding gems and jewellery and petroleum products, Gujarat's share in the national exports is only 9.2 per cent. Compared to this overall proportion, Gujarat's share in national exports in commodities like groundnut, oil-meals, castor oil, poultry, dairy products, spices, sesame and niger seeds, and processed food, fruits, and vegetables is much higher indicating Gujarat's revealed comparative advantage in these product categories. Some important features of the exports activity in Gujarat are: Only 20 per cent are pure traders in the export business. Only a quarter of the units have ‘export house’ or upward status for special benefits. More than 40 per cent of the exporting units have come up after 1991–92. Two-thirds of the exporters belong to small and medium enterprises. Export intensity of Gujarat's agricultural sector is about 12 per cent. Agri-exports represent excess supply and hence highly volatile and fluctuating activity over time. Agri-exports are price elastic. Agri-exports would be highly responsive to exchange rate depreciation. In recent years, Gujarat's agriculture shows considerable dynamic characteristics in contrast to the gloomy official income estimates in the sector. Nineteen out of 30 crops show significant positive time trend in area while five crops show significant negative trend. The cropping pattern in Gujarat has been shifting away from the low value traditional crops to high value commercial crops with business and export potential. A detailed consideration of yield rates of different crops in the state and other states over the past three decades indicates a realistic potential of 5 per cent per annum growth rate for agriculture in Gujarat over the next eight to ten years. In order to ensure exclusive and regular supply to the export market, quality standards have to be according to the foreign destination and not the domestic market. This calls for large-scale production, assured input supplies, good logistics, infrastructural facilities, R&D activities, and technological upgradation. This involves giving priority to investments in several infrastructural facilities and agricultural R&D besides perfecting agricultural land market and encouraging contract farming in the state.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".