Metafrontier Analysis of Technical Efficiency of Wheat Farms in Sudan
Bibliographic record
Abstract
<p>The major objective of this study is to estimate the technical efficiencies and technological gap of wheat farms in the major wheat producing areas of Sudan, namely Northern, River Nile, Gezira, and Kassala States of Sudan. A total sample of 951 wheat farms was selected and surveyed in the whole country during 2013. Non-parametric Data Envelopment Analysis (DEA) model has been applied to measure the technical efficiency and technological gaps among the regions by means of metafrontier approach. Results show that there is significant inefficiency in wheat farms. The estimated average technical efficiencies with respect to group frontiers for Gezira, Kassala, Northern and River Nile are: 0.52, 0.61, 0.48 and 0.41, respectively. The average technological gap ratios for Gezira, Kassala, Northern and River Nile were 0.82, 0.50, 0.75 and 0.92, respectively. Therefore, the Kassala farms frontier has the most distant to the metafrontier, while the Gezira, Northern and River Nile frontiers have the closest. Our results suggest that farms in the Gezira, Northern, and River Nile regions could improve their productivity through more efficient use of inputs using the existing technologies such as sowing, fertilizer application, irrigation water scheduling, and harvesting at the right time. In contrast, improved technologies generation and dissemination such as integrated pest management in the Kassala region are required to improve wheat productivity.</p>
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".