Adoption of Hybrid Rice in Bangladesh: Farm Level Experience
Bibliographic record
Abstract
The purpose of the study was to understand the farmers’ response to hybrid rice over the last decade. To achieve this, we used the “diffusion of innovation” model as developed by Rogers. The specific objectives guiding the study were to: i) describe the socio-economic and demographic characteristics of the farmers; ii) survey the varieties of hybrid rice cultivated over the last decade and identify the best performers; iii) assess the extent of adoption of hybrid rice in Bangladesh; iii) investigate the influence of selected characteristics in influencing farmers’ decisions on adopting hybrid rice. The study was conducted in five regions of Bangladesh. A concurrent embedded design using a cross sectional survey was employed. The population of this study consisted of rice growers of the boro season who were responsible for farming decisions. A multistage stratified random sampling design was employed in selecting the sample of 425 farmers. Data were collected through face–to–face interviews using a pre-tested and back translated questionnaire. Data confirmed that the overall extent of adoption of hybrid during the period of 2001-2011 boro seasons was relatively low in the sample areas. Logistic regression results after fitting the full model of eleven selected predictive variables on farmers’ decisions in adopting hybrid rice showed that education, annual family income, communication exposure, and attitude towards hybrid rice made significant contributions to farmers’ decisions in adopting hybrid rice. There is an enormous potential for improving the level of adoption of hybrid rice in Bangladesh.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".