How to Increase Productivity in Kosovo Agriculture: A Story of Size and Technical Efficiency
Bibliographic record
Abstract
Kosovo is struggling with low productivity in agriculture and an overwhelming majority of small farms. This paper analyses changes in the factor mix that can bring the highest increase in marginal productivity, employing a non-parametric quantile regression based on Farm Accountancy Data Network (FADN) data. Two different quantile regressions are estimated, for the median 0.5th quantile, describing the nature of the input-output relationship for a ‘typical’ farm and for the 0.8th conditional quantile, characterising a reasonably ‘efficient’ farm. The results show that optimal marginal productivity can be achieved by ‘typical’ farms by increase in farm size but it requires drastic changes in factors which are currently hardly feasible in Kosovo (e.g. 3-4 FTEs family labour, 0.5 to 1.8 hired). For an efficient farm, the optimal marginal productivity is achieved at lower values of inputs. This suggests that productivity enhancements can be obtained by a careful balance of both efficiency and scale augmentation measures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".