Global status of lentil production with special reference to India
Bibliographic record
Abstract
India needs to increase it pulse production to meet the growing demand. Lentil is one of the important pulse crop grown in winter season in tropical and subtropical regions of world. The study analysed the trends in production and trade scenario in India and globally from 2000–2018 and came out with strategies for increasing production. The state-wise analysis was carried out for major producing states like Madhya Pradesh, Uttar Pradesh, Bihar, West Bengal and Rajasthan from 2000–2019. The lentil production in the world reached around 6.33 million tonnes in 2018 with an annual growth rate of 4.4% since last 20 years. Asia alone contributed more than half of total global lentil production. The share of American and Oceania regions in global lentil production increased in recent years. There was increase in lentil yield in recent years in Africa, Asia and American regions due to adoption of improvedproduction technologies. India and Canada together contributed more than 50% of total world lentil production. Lentil yield was highest in Canada (1425 kg/ha), lowest in India (744 kg/ha) in 2018. The lentil production in India touched 1.47 million tonnes with larger share contributed by Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh and West Bengal in 2018. India is the leading importing nation sharing more than one-fourth of total world import during last five years. Adoption of drought, heat, biotic stresses tolerant high yielding cultivars are required to meet estimated demand of 2.00 million tonnes by 2030.Keywords: Agricultural production, Economics, Legumes, Lentil, Policy Interventions, Trade
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".