Economic Analysis of Smallholder Maize Producers: Empirical Evidence From Helmand, Afghanistan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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 it