Enhancing farmers’ capacity for botanical pesticide innovation through video-mediated learning in Bangladesh
Bibliographic record
Abstract
Despite the general success of farmer-capacity-building methods such as Farmer Field School in promoting pest management innovations, only those farmers directly involved benefit. How can agricultural extension enable farmer-to-farmer learning about botanical pesticides beyond such schools? We wanted to know how different learning methods, such as video shows and workshops, change farmers’ knowledge, attitudes and practices about botanical pesticides. This paper explains how video engages men and women farmers in spreading botanical pesticides across 12 villages in Bogra District, north-western Bangladesh. We conducted ex ante and ex post surveys among farmers from November 2009 to September 2010. For data analysis, we used t-test and McNemer and Wilcoxon sign rank tests. Our findings suggest that video improves the ability of both male and female farmers to communicate about pest management among themselves and with other stakeholders, as ‘intricate ethno-agricultural practices’. Video-mediated learning sessions are more effective than conventional workshop training in enhancing farmers’ knowledge about botanical pesticides, changing their attitude and finally taking a decision to adopt these methods. In other words, video is capable of communicating complex issues such as the biological and physical processes that underlie pest management innovations. From our case, we conclude that agricultural extension is more effective with the use of facilitated video learning and that this process clarifies complex agro-ecological principles, bias and normative perceptions of the learners. Also, video-mediated learning is not only transferable across villages, but also works well in combination with other media, such as radio, television and mobile phones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".