Economic Analysis of Smallholder Maize Producers: Empirical Evidence From Helmand, Afghanistan
Bibliographic record
Abstract
Since war started at the end of 2001, the economy was severely devasted in Afghanistan, especially for the agriculture sector. Maize is the third most important cereal crop in Afghanistan, but the productivity of maize has a declining trend which may be caused by low efficiency of maize farmers nowadays. This study examines the production efficiency of maize producers and its important factors with the cross-sectional data form a multi-stage sampling survey of 250 maize producers in Helmand province in 2019. With the adoption of stochastic production frontier (SPF) model and production cost function, the paper gets the estimations of the average technical efficiency (0.737), allocative efficiency (0.65) and economic efficiency (0.568). The inputs, including land, labor, seed, fertilizer and pesticide/weedicides, have significant impacts on maize production and most of the farms exhibit an increasing return to scales. In addition, Tobit regression was applied to identify the influential factors of the production efficiencies for maize producers and the results indicate that education, family size, farm size, farming experience, contact to extension services and access to credit have significantly influence on the efficiency level. Finally, the study suggests that government should take some initiatives, such as extending the agricultural extension service, ensuring supply of high quality seeds and sufficient fertilizer with affordable prices and economical provision of mobile internet facility in remote areas, which will enhance the productivity and efficiency of the farmers and ultimately boost up their economic welfare and livelihood.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".