Oilseed brassica in India: Demand, supply, policy perspective and future potential
Bibliographic record
Abstract
India is the largest agrarian subcontinent supporting 26% world’s agricultural population on 12% arable land. India is also the fifth largest vegetable oil economy accounting 7.4% oilseeds, 5.8% oils and 6.1% oil meal production, and 9.3% of edible oil consumption in the world. Oilseeds are the second most important agricultural economy in India next to cereals growing at a pace of 4.1% per annum in the last three decades. Oilseed brassica shares 23.5% area and 24.2% production of total oilseeds in the country. Despite being the third largest producer (11.3%) of oilseed brassica after Canada and China in the world, India meets 57% of the domestic edible oil requirements through imports and ranked 7th largest importer of edible oils in the world. Oilseed brassica achieved significant growth in India in the past, however, the productivity levels are still low owing to large cultivation under rainfed situation, biotic and abiotic stresses, and resources crunch. It is also facing the challenges of low genotypic potential, climate change and price fluctuation. Though, it embraces the immense scope to increase the production in traditional and non-traditional areas in India with proper inputs, technological interventions, and suitable policy framework. This needs to develop strategies in a well-planned, targeted manner with multi-scientific inputs, policy interface and stable price systems to bring the desired growth in oilseeds brassica production, and to reduce the import of edible oils in the country.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".