Evaluating Greenhouse Tomato and Pepper Input Efficiency Use in Kosovo
Bibliographic record
Abstract
Determinants of vegetable production input efficiency affect a Kosovar farmer’s decision to contribute to the agricultural sector. This study evaluates the input efficiency of greenhouse tomato and pepper farms in Kosovo. Using data collected from farm surveys, we conducted an input-oriented data envelopment analysis (DEA) to empirically assess input efficiency. Second, linear regression analysis was used to investigate what farm variables predict greenhouse tomato and pepper technical efficiency (TE). The DEA results indicated that, among the seven regions in Kosovo, Prizren emerged as the most efficient greenhouse tomato-producing region with a mean efficiency of 0.83 (on a scale of 0 to 1.00). Prishtina followed with a mean efficiency of 0.80. In the production of greenhouse peppers, Prishtina was the most efficient region with a mean efficiency of 0.99. Ferizaj followed with a mean efficiency of 0.93. Conclusions about farm characteristics that explain differences in efficiency were sensitive to model specification. Nevertheless, depending on the structural and operational characteristics of the greenhouse tomato and pepper farms, there is an opportunity for the technically inefficient farms and regions to improve their use of inputs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".