Consumer demand, export and entrepreneurial opportunities of millets: An overview
Bibliographic record
Abstract
Millets are grown in India suiting to different agro-climatic conditions, including Sorghum, Pearl millet, Finger millet and small millets like Barnyard millet, Porso millet, Kodo millet, Little millet (Kutki) and Foxtail millet. Millets were produced and consumed extensively in the country since ancient times and had almost equal area coverage to rice and wheat. However, the post- green revolution period witnessed a drastic decline in the area under cultivation of nutri-cereals by 41.65 percent between 1950–51 and 2018–19. Jowar, Bajra and Ragi are the most popular millets across India. They constitute nearly 90% of total millet production and around 60% of the millets produced in India is Bajra. Millets are highly adaptive to different ecological conditions and bloom well in rain-fed and arid climate. Millets have superior micronutrient profile and bioactive flavonoids with low Glycaemic Index as compared to cereal crops like wheat and rice. Millets are consumed in India as food for human as well as feed for livestock and as raw materials in industries for ethanol blending in distilleries etc. Due to extensive campaigning and initiatives taken up by government, the consumer demand and Start UPS of millets are expected to increase by 2030. India has nearly 40% share of global millet production but it exported 1% of its millet production in 2021-22, earning $64.28 million (over $59.75 million in 2020-21), according to APEDA. On the other hand, Canada, Russia, Ukraine and the US are importing millets and exporting value-added products. Thus, wider prospect lies in millet export and millet entrepreneurship. This paper will review the trends of consumer demand, export scenario and entrepreneurial opportunities centric to millets. India may increase exporting millets and value added products of millets. Besides, millets have good nutritive value. There is a need to create awareness amongst people about the benefits of millets. Appropriate processing technologies of millets are need of hour which may increase entrepreneurial opportunities of millets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".