An economic analysis of area, production of organic products and its export in India
Bibliographic record
Abstract
This paper focus on the economic analysis of area, production of organic products and its export in India. India is endowed with various types of naturally available organic form of nutrients in different parts of the country and it helps for organic cultivation of crops substantially. Organic products are grown under a system of agriculture without the use of chemical fertilizers and pesticides with an environmentally and socially responsible approach. India's total area under organic certification is 5.69 million hectares in 2013-14 and its global rank is 10 th . The growth rate of cultivation of organic area of India is 17.35 per cent; of which wild collection is 10.51 per cent during 2004-2013. Among all the states in India, Uttar Pradesh the has highest area under organic farming followed by Himachal Pradesh, Madhya Pradesh and Maharashtra in 2011-12. The share of export of organic products in terms of volume to USA (42.16 %) was the highest followed by European Union (32.3 %), Canada (21.68 %). The total volume of export of organic products from India was 177765.26 metric tons worth of Rs. 1328.6 crores during the period of 2013-14. Compound growth rate of export quantity of organic products of India is 46.22 per cent and export value is 34.99 per cent during 2002-03 to 2013-14. India exports around 135 organic products; of which, the share of cotton from India was (54.04 %) followed by cereals and millets (19.79 %) basmati rice (11.00 %) in 2013-14.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".