Resource Use Efficiency of Tea Production in Vietnam: Using Translog SFA Model
Bibliographic record
Abstract
As one of the most important economic activities to small households of Vietnam, tea production is hindered by low productivity, rising of production costs, and bad agriculture practices. To sustain tea production, the near-term strategy is to improve the efficiency of resource utilization. To our knowledge, this article is the first study to evaluate the tea production’s resource use efficiency and to identify the factors affecting it in Vietnam. The data was collected from 243 randomly selected tea farmers in the Northern mountainous region of Vietnam. The study first applied a translog stochastic production frontier model and technical efficiency (TE) technique to estimate resources use efficiency, and then used a Tobit model to identify the factors affecting these efficiencies among tea farms. Based on the mean sum of output elasticity with respect to inputs (0.323), we found that increasing the utilization of resources in the study site was inappropriate. The study also revealed that the average input-oriented TE of tea farms was lower than that of output-oriented TE, 82.21% versus 92.29%, suggesting that the farmers had more ability to reduce resource use than to increase current output level. The results showed that the tea farmers could use resources more efficiently by reducing 17.79% of the current application level without compromising the output. The study also indicates that concerted efforts from government to increase farmers’ accessing extension service, widening soil and water conservation practice, and spreading farmers’ awareness on water scarcity is the key to improve farmers’ resource use efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".