INDIA’S SOYABEAN EXPORT: TRENDS, DIRECTIONS AND WAY FORWARD
Bibliographic record
Abstract
The key objective of this paper is to analyze the trends of India’s soyabean, exports and its directions and way forward. This research work highlighted the post-independence performance of soyabean productivity and changing pattern of crops that pave the way for promoting soyabean cultivation. The present study revealed that India is seventh largest country by area, the second most populous country after China. This work investigates the trend in area production and yield of soyabean in India from 1980-1981 to 2019-2020. The results indicate that expansion of area was continued during whole study period. However, the expansion is higher in soyabean area than that of yield of soyabean. The finding exhibits that the exports of soyabean was highly volatile but increased during given time period. The composition changed from traditional tropical to horticulture and sea foods. The present study has been focused on analyzing demand, supply and export of soyabean in India. This study revealed that United States of America turned out to be India’s biggest soyabean buyers followed by Canada, Belgium. According to data China termed as top importer of soyabean but India’s exports were nominal to China. The analysis also covered the comparative price difference in Indian export verses imports of China during 2020. This paper contributes by providing significant suggestions for improving the market structure for acquiring higher market share. In addition to that this study also provides suggestion regarding, how to create significant opportunities to increase the exports which strengthen the balance of payments position.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".