Technical Efficiency of Dairy Farms: A Stochastic Frontier Application on Dairy Farms in Jordan
Bibliographic record
Abstract
The present study was conducted to determine the level of technical efficiency of dairy producing farms in Jordan by applying the stochastic production frontier (SPF) methodology. Technical efficiency estimates were generated for 100 dairy farms in Jordan. The results of the study indicated that technical efficiency of milk production by most of dairy farms in Jordan is low. The mean technical efficiency was estimated to be only 39.5% for the sampled dairy farms. This means that an average farm in the sample could in principle increase its level of milk production by 60.5% using the current input quantities. The results also implies that the dairy farms in Jordan are producing milk to only about 40% of the potential frontier production levels of this industry, implying that the production is about 60% below the frontier due to technical inefficiency. To enhance farm efficiency there is a need to improve farmers’ access to extension services. The need to involve farmers more in the extension process itself should be encouraged.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".