Dynamics of lentil (Lens culinaris) production and trade: Global scenario and Indian interdependence
Bibliographic record
Abstract
Increasing pulse production is one of the national priorities for food and nutritional security of India. In this context, incremental changes in pulse production can play a pivotal role. During 2003–2022, for example, the area under lentil (Lens culinaris L.) registered a decline (2.08%), however; productivity improvement (41.26%) led to the enhancement in its production (by 37%) in the country. This study portrays the temporal trend and patterns of lentil production growth during the period 2003–2022. A substantial growth in imports of lentil from 63.97 thousand tonnes in Triennium estimate (TE) 2003 to 814.20 thousand tonnes in TE 2022 was registered in India. Moreover, the share of lentil imports to total lentil production increased from 6.94% in TE 2002 to 63.24% in TE 2022. Lentil imports exhibited a high annual growth rate (15.83%) and high instability during the overall study period (2003–2022), which was higher than the annual growth rate of imports of overall pulses during the same period. Approximately 0.65 million tonnes of lentils were imported in the year 2022, to meet domestic consumption demands. Canada and Australia accounted for 61% and 36% of the total lentil imports to India in year 2022, while Bangladesh (49.61%) followed by UAE (21.74%) and Nepal (18.33%) were the major export destinations of the total lentil export (2022). The gap between production and consumption, coupled with the changing trade regulations and consumer preferences, contributed to the observed instability in lentil trade in India over the past two decades. Concerted efforts in intensifying the technology transfer, capacity building and convergence of resources among the stakeholders can impact the productivity of lentils, thereby developing strategies for optimizing its import and export dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".