Technical Efficiency of Wheat Producers in North Shewa Zone of Amhara Region, Central Ethiopia
Bibliographic record
Abstract
The aim of this study is to analyze technical efficiency of wheat producers and identifying the sources of inefficiency in North Shewa Zone of Amhara National Region State, Central Ethiopia. A multi-stage sampling technique was used to select sample respondent households. In the first phase, three potential wheat grower districts namely, Bassona Worrana, Siyadebrna Wayu, and Moretna Jiru were selected purposively. In the second stage, six potential kebeles (lowest administrative level), two from each of the three districts were selected. In the third phase, proportionally with population size, 374 sample households were selected using simple random sampling techniques from each of the selected kebeles. Cobb-Douglas Stochastic Frontier Production (SFP) function and inefficiency function were used to estimate the technical inefficiency level of wheat producers using a one-step maximum likelihood estimation procedure. The average productivity of wheat was found to be 32.60 quintals per hectare. Land size, fertilizer, seed, labor, and oxen power were important factors of production positively and significantly affecting wheat output. Age and education level of a farmer, off-farm income, extension contact, and credit services were found negatively and significantly affecting the technical inefficiency of wheat producing farmers. The inefficiency components contributed about 70.62% of the total deviation from the best possible production output. The average technical efficiency (TE) level of wheat producers was 72%. Overall, the provision of fertilizers, credit, and extension services would make wheat producers more productive and technically efficient in the study area.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".