TECHNICAL EFFICIENCY OF DAIRY FARMS IN CENTRAL KOSOVO
Bibliographic record
Abstract
Kosovo has the key resources needed for a developed agriculture. However, Kosovo's agriculture consists of very small farms which are featured with the fragmentation of their land, old buildings and equipment, though functional. Ministry of Agriculture (MAFRD) started to support farmers with direct payments in 2009, but only for a few agricultural cultures. Support for dairy cows started in 2012, and support for milk quality started in 2014. In this context, the purpose of this paper is to accurately portray the characteristics, and technical efficiency of dairy farms in Central Kosovo, respectively in the region of Pristina - beneficiaries of direct payments for milk quality. Consequently, through Data Envelopment Analysis (DEA), under Variable Return to Scale (VRS) using output orientation, the efficiency rate of dairy farmers is calculated. Therefore, findings show that not all the farms are fully efficient, or fully utilizing their assets and their inputs. Additionally, the study revealed that the size of the farm, and the feeding system affect the TE. Therefore, large-size farms and farms who used seasonal grazing had overall higher TE. However, the level of education does not have a significant effect on the farm's efficiency.Keywords: Dairy sector, Kosovo, Technical Efficiency, Data envelopment analysisJEL Classifications: D24, Q12, Q1DOI: https://doi.org/10.32479/ijefi.9630
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".