Factors Influencing the Adoption of Agricultural Practices in Ghana’s Forest-Fringe Communities
Bibliographic record
Abstract
Two-thirds of rural Ghanaians are farmers, and farming is almost the only income source for Ghana’s forest-fringe communities. Some farmers adopt some agricultural practices to augment their operations while others do not. We examined the factors that influence farmers’ adoption and intensity of adoption of agricultural practices, namely, chemical fertilizers, pesticides, herbicides, improved seeds, animal manure, and crop rotation. We surveyed the agricultural systems and livelihoods of 291 smallholder households in forest-fringe communities and developed a multivariate model (canonical correlation analysis) to test the degree to which social, economic, and institutional factors correlate with adoption and intensity of adoption of the above practices. We found that 35.4% of the farmers do not adopt any of the practices because they perceive them to be expensive, not useful, and difficult to adopt. The rest (64.6%) adopt at least one of the practices to control weeds, pests and diseases, and consequently increase crop yields. Our results indicate that farmers that perceive the aforementioned practices to be more beneficial, cultivate multiple plots, and have access to extension services adopt more of the practices. Farmer age and distance to source of inputs negatively correlate with adoption and intensity of adoption of agricultural practices. Almost two-thirds each of adopters and non-adopters do not have access to agricultural extension services and this could pose threats to the sustainability of the forest reserves within and around which the farmers cultivate. Educating farmers on agricultural practices that are forest-friendly is critical in the forest-fringe communities of Ghana. The correct application of practices could double outputs and minimize threats to forests and biodiversity through land-sparing.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".