Adoption of Improved Soybean Varieties and Differences in Technical Efficiency Between Improved and Local Soybean Varieties in Southern Shan State, Myanmar
Bibliographic record
Abstract
This study determined the factors influencing the adoption of improved soybean varieties and examined the technical efficiencies of improved and local soybean varieties production in Southern Shan State, Myanmar. For this study, data from a sample of 337 respondents were collected by employing a multi-stage random sampling method. Logit model was adopted to determine the factors influencing the adoption of improved soybean varieties. Additionally, a stochastic production frontier was used to examine technical efficiencies of improved and local soybean varieties. Results show that factors that positively and significantly influence the adoption of improved soybean varieties are education, market access, extension access and training access. Examination of technical efficiency reveals that labor, fertilizer, machinery, and use of pesticide and harvester are inputs that significantly contribute to improving production efficiency among the improved variety farmers while seeds, labor, and fertilizer are significant inputs of local soybean production. On average, the estimated yield of the improved soybean varieties is 1.51 t/ha, which is higher than the yield of local soybean varieties grown at 0.88 t/ha. It was also revealed that improved soybean varieties had a relatively higher level of mean technical efficiency (85.04%) than local varieties (70.13%) and significantly different at 1% level. The results show that improved soybean production is more efficient than local soybean production. Therefore, government and non-government organizations should improve and provide market access, efficient and effective extension services and training to encourage farmers to adopt improved soybean varieties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".