Determinants of technical efficiency of potato farmers and effects of constraints on potato production in Northern Ethiopia
Bibliographic record
Abstract
Abstract To improve the national average yield of potato in Ethiopia, which is very low as compared to its potential, factors that influence technical efficiency of potato production need to be determined. Therefore, the objective of this study was to investigate the determinants of technical efficiency using a cross-sectional data collected from 368 randomly selected potato producers in Northern Ethiopia using a multi-stage sampling technique. The study employed Cobb–Douglas stochastic frontier model to get farm-level technical efficiency scores. Tobit model and principal component analysis were used to determine the factors that influence technical efficiency of farm households. The results revealed that chemical fertiliser, seed potato, plot size and labour are statistically significant factors that affect potato yield. The average technical efficiency score was estimated to be 75%; and education, experience, off-farm income, household size, membership in a farmers’ association, use of irrigation water, extension contact, use of improved seed, access to product market and weak coordination of stakeholders’ were significant factors influencing technical efficiency. The findings of the study suggest that there is a need for government intervention to create strong market linkage between producers and buyers and to give appropriate training to agricultural extension agents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".