Finger Millet (Eleusine coracana L. Gaertn.) Production System: Status, Potential, Constraints and Implications for Improving Small Farmer’s Welfare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article aims to investigate the growth in area, production and productivity, mapping of cultivation technologies, economics, potentials and constraints for area and production expansion of finger millet. The exponential growth rates, indicated that though there was deceleration both in area and production there was significant growth in productivity due to introduction of high yielding varieties. The respondents under different production system (rainfed and irrigated situation) were homogeneous with respect to the age and family size except land holding and education level. Finger millet possesses tremendous potential for product diversification and export. Mapping of cultivation technologies indicated that, farmers applied more fertilizers than recommended. Hence, there is a need to strengthen extension/outreach programmes to create awareness among farmers to use the optimum level of nutrients, which helps in reducing the cost incurred by farmers as well as subsidy burden on government. The existing procurement price for finger millet was Rs. 2100/q which failed to cover the cost of production under rainfed situation. In the total land holding, the area under finger millet accounted for major (64%) share in rainfed situation and thus the procurement price must be fixed looking into the cost of production of rainfed (Rs. 2624/q) finger millet, which helps in improving the welfare of finger millet growing small farmers.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 it