An Exploration of the Technical Efficiency of Wheat Production and Its Determinants Among Smallholder Farmers in Kunduz Province, Afghanistan
Bibliographic record
Abstract
This study assesses the technical efficiency of wheat production and its determining factors among 384 smallholder farmers in the Imam Sahib, Ali Abad, and Khan Abad districts of Kunduz Province, Afghanistan. Agriculture in this region is the primary livelihood and essential for food security, yet wheat yields remain suboptimal due to inefficient input use and structural challenges. Using a stochastic frontier analysis (SFA) based on the Cobb-Douglas production function, the study estimates farm-level efficiency while accounting for random variation. A Tobit regression model is employed to identify socio-economic and institutional factors affecting technical efficiency (TE), given that TE scores fall between 0 and 1. The findings reveal an average TE score of 77.89%, suggesting that farmers could boost output by 22.11% using the same inputs more efficiently. Key production inputs, land, labor, improved seeds, fertilizer, and pesticides, significantly influenced output. Additionally, higher education levels, better access to extension services, formal credit, mobile phone use, training participation, and market proximity positively impacted TE. These results point to considerable inefficiencies and highlight the need for targeted policy interventions to improve education, rural infrastructure, and institutional support. Promoting digital tools and effective extension services can also enhance productivity. The study provides practical insights to inform strategies aimed at strengthening agricultural performance and rural livelihoods in resource-constrained, post-conflict regions of northern Afghanistan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".