Can Organic Agriculture Feed the Smallholders? Experience from Rural Bangladesh
Bibliographic record
Abstract
This study empirically tests the benefit of the smallholders from organic farming in Bangladesh through their improved food security which is realised from their increased productivity and farm income. The respondent smallholders were picked up from two districts of Bangladesh namely Mymensingh and Bogra. The respondents (80) were the beneficiaries of the organic agriculture promotion project of Bangladesh Agricultural University. Three years data were collected by the project staff and were crosschecked with the base line. Findings of the study explored that before joining with the project extreme majority (93%) of the small farmers were involved in rice mono-culture and more than half (67%) of them were food deficit. The study also revealed that at the initial year of joining organic agriculture project their farm productivity was 10–12% lesser and it increased continually in the successive years. In some cases, it crossed the yield compared to conventional farming. The findings of the study showed that 100% of the farmers have followed crop diversification with high value vegetables and spices along with rice. Due to adoption of organic practices, the cost of production of the smallholders has declined from 27% to 36% and additionally they enjoy 10% to 15% premium prices which have enhanced their farm income significantly. The study shows that 62.5% of the smallholder farmers had attained household food security due to adoption of organic agriculture. Thus, adoption of organic agriculture effectively increased smallholders’ access to surplus safe food. However, the study also explored that farm size, extension media contact, access to assured market and access to institutional support are the most important factors in improving smallholders’ household food security through participation in organic agriculture programme. Thus, it can be concluded that organic agriculture can feed the smallholders in a better way.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".