Agricultural knowledge and information flows within smallholder farming households in Ghana: Intra-household Study
Bibliographic record
Abstract
Legume technologies are widely promoted among smallholder farmers in sub-Saharan Africa, providing opportunities for sustainable agricultural intensification (SAI), and contributing to the nutritional and economic benefits of households growing them.However, legume cultivation is relatively small in most farming systems and on the decline, attributed to low adoption of improved technologies occasioned primarily by the lack of access to actionable information.This study aimed to assess farmers' access to agricultural knowledge and information flows within households in Ghana, in order to guide message design and selection of appropriate information dissemination pathways to reach women, men and youth with legume technologies.An intra-household survey method was used and 300 households and 868 respondents were surveyed.Results show that farmers had access to various information sources, though they mainly relied on neighbours and relatives (52%) and their own experience (49%).Information shared through these sources was mainly on timing of field operations, and good agricultural practices, which may reflect farmers' inherent knowledge and adjustment over time to respond to changing environmental conditions.However, for relatively new practices such as use of Rhizobium inoculants and Purdue Improved Crop Storage (PICS) bags, farmers relied on external sources such as extension officers, radio and demonstration plots.Men and young people exhibited more diverse information sources compared with women and elderly people.Some information sources were the prerogative of men, such as radio and demonstration plots, while women mainly relied on their own experience and family/community members.Results have the following implications: (i) there is still a margin for improving learning of more recently introduced practices, thus it is important to link promotion with targeted information sources; (ii) targeting women and elderly people with channels that are farmbased such as extension visits and on-farm demonstrations may enhance their access to information; (iii) there is a need to focus on the complementary role of legumes in the production of key staples in the region, such as cassava, in efforts to promote SAI; and (iv) given the observed dynamics of intra-household information sharing, targeting information to various gender and age categories provides an opportunity to ensure information can effectively reach different household members.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".