Factors Determining TFP Increase in Small-Scale Lowland Rice Farming
Bibliographic record
Abstract
Rice is a strategic commodity, so the Government of Indonesia puts forward the standard of building a globally competitive rice farming model by increasing the Total Factor Productivity (TFP).However, until now, farm managers have had a relatively shallow understanding of the TFP concept.This study, focusing on lowland rice farming in Indonesia, identifies the factors that determine the development of the TFP.The main questions in this research are, what are the impacts of farming scale, technical efficiency, allocative efficiency, and the efficiency scale?Has lowland rice farming adopted technology to reduce wasting resources due to an inefficient use of inputs?This study used 329 cross-sectional pieces of data on small-scale rice farming.The research results indicate that lowland rice farming is in a decreasing return condition and that there is technical inefficiency.TFP tends to increase when the farm scale increases.Technical efficiency, allocative efficiency, and scale of efficiency are the main determining factors in developing TFP at the level of lowland rice farmers; of these, technical efficiency is the most important factor.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".