Prospects of Agroforestry as Climate-smart Agricultural Strategy in Cocoa Landscapes: Perspectives of Farmers in Ghana
Bibliographic record
Abstract
Climate-Smart Agriculture (CSA) is increasingly being promoted by the international community to help farmers adapt to climate change and lift them out of poverty. An essential technique that is promoted under the climate- mitigating smart agriculture policy package to reduce forest loss is agroforestry—the planting of woody plants or trees into farming systems. Integrating agroforesty into cocoa landscapes, it is argued, create forest-like habitats which serves as faunal refuges, sequester carbon and therefore contribute to increasing agricultural productivity, resilience (adaptation) and removal of greenhouse gas emissions. This article uses a qualitative data collected from 100 households in seven communities around the Kakum National Park in the Twifo Hemang Lower Denkyira District in Ghana, where a climate-smart agriculture programme is being piloted. The study analysed the extent of willingness of farmers to participate in interventions that promote increased adoption of agroforestry in cocoa landscapes. The result shows that though farmers have favourable perception about the role of agroforestry on cocoa systems, and are willing to adopt the practice, this does not automatically translate into their willingness to participate in agroforestry program that was asking them to extend the number of trees currently maintained on their cocoa landscapes. The study further reveals that size of farms, the age and height of cocoa trees, extension support and the general ecology of the cocoa varieties as some of the reasons influencing whether the agroforestry practices promoted could be adopted or not.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Qualitative study of farmers' willingness to adopt agroforestry in Ghana.
This examines Ghanaian farmers' agroforestry adoption perspectives, not research practice.
Farmer adoption of cocoa agroforestry in Ghana; agricultural development study.
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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".