Assessment of Agricultural Advisory Messages from Farmer-to-Farmer in Making a Case for Scaling Up Production: A Qualitative Study
Bibliographic record
Abstract
Inadequate access to agricultural extension services often results in poor farm practices, affecting yields and subsequently the income and wellbeing of smallholder farmers. Given the high demand for agricultural information and the limited capacity of extension services, a farmer-to-farmer extension approach has been explored by many underserved farmers. In this study, we use a qualitative case study approach explore how cassava farmers who had limited access to agricultural advisory services from public extension agents managed to up-scale their farming business. Our research question was: what lessons can be learned from the lived experience of these farmers to address current challenges of cassava farming? The results of our study revealed diversity in advisory messages from farmer to farmer and agricultural extension agents. Farmers’ messages focused on encouraging farmers’ commitment and motivation towards farming business, availability of needed financial resources for the entire production season, willingness to reinvest profits, and access to farmland for future expansion. In contrast, the traditional messages from agricultural extension agents focused on encouraging group formation to address marketing challenges, diversification of farm operations, and good agricultural practices. These results show the need for pluralistic extension approaches to ensure farmers get access to necessary information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".