Impact of tenancy on economic efficiency of rice in Andhra Pradesh
Bibliographic record
Abstract
ABSTRACT\n\nThis paper examines the efficiency of tenant farmers and impact of tenancy on efficiency. The present study has been conducted in three districts of rice growing farmers namely Srikakulam, West Godavari and Kurnool districts in Andhra Pradesh. Data Envelopment Analysis (DEA) was employed to carry out efficiency scores followed by regression analysis to find out impact of tenancy along with the other factors on efficiency. Results observed that except in Srikakulam district, technical efficiency was noticed with highest mean values and efficiency ranges. However, majority of the farmers were using minimum quantity of inputs to produce the output. Allocative efficiency and economic efficiency were observed with lower mean and efficiency ranges. It is evident that majority of the farmers had not properly allocated their inputs and finally realized more cost of production. Availability of inputs was sufficient but input allocation was varied. Moreover, results from the econometric model revealed that both tenant and owner cum tenant ownership were influencing negatively to the economic efficiency of rice in Andhra Pradesh. Accordingly, these results suggested that proper allocation of inputs with sufficient output and lower rental values will be reduced the cost of production and changed the efficiency.\n\nKEYWORDS: Andhra Pradesh, Data Envelopment Analysis (DEA), Economic Efficiency, Rice, Tenancy, Tenant farmers
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".