A Study Of Factors Affecting Rice Yield in The Valley Districts Of Manipur
Bibliographic record
Abstract
In a quality effort to improve rice production in the valley areas of Manipur, this research discusses the key factors that determine yield under rain-fed management conditions with elements of modernisation and partial mechanisation. Using cross-sectional data combined with multiple linear regression analysis on the sample of 191 farmers, the research restricted its analysis to the 2020–21 Kharif season. The research establishes several factors that have a direct bearing on rice yield. Of these, the kind of plantation, the level of mechanisation, the availability of irrigation, the cost of fertiliser, and the literacy level of farmers were identified as major factors that influenced yield risk. These observations point to the fact that improving farming techniques as well as infrastructure is a very effective strategy towards enhancing productivity. On the other hand, the influence of other variables like the size of the farm, cost of family labour and bullock labour were established to be relatively insignificant here. The study incorporates the need to increase irrigation facilities, utilise high-yield varieties (HYVs), apply fertiliser correctly, and use the right machinery to improve yields in rice. Thus, if these factors are considered, there is a high possibility of improving rice yield in the valley regions of Manipur. The study also imposes the generalisability of these suggestions, meaning that similar measures could be useful in other areas of India, especially the Northeast, where agricultural improvement and food security are critical.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".