Policies and Socio-economics influencing on Agricultural Production: A Case Study on Maize Production in Bokeo Province, Laos.
Bibliographic record
Abstract
<p>Since 2005s, agricultural land in northern Laos has become to be dominated by maize mono-cropping. The rapid expansion of this commercial crop has the resulted of policy implementations and demand of maize from the neighboring countries. The purpose of this study was aim to analyze the impact of commercial agricultural policy and socio-economic factors influencing on maize production in Houyxai Distirct, Bokeo Province of Laos. A survey of 98 maize farmers by face to face interviews was conducted in September 2010. Ordinary Least Square regression model was applied in order to explain how these policies and socio-economic factors effect to farmers and contribute to maize production. The results revealed that 94% of the variation in maize production (ton) is explained by the selected explanatory variables. Seven variables have a positive significant and one variable has a negative effect on maize production volume. The results also demonstrated that the most important effect to maize production is a “policy push” mainly variables of farmland, farmer organization, support market and credit access and a “market pull” by private sectors with providing input factors namely seeds, land preparation and techniques. Therefore, the government should look at the way of credit providing system that may effect in high production cost, at the same time the expansion of farm size is needed to take into account.</p>
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".