The role of farmers and organizational networks in climate information communication: the case of Ghana
Bibliographic record
Abstract
Purpose The nature of the collaborations that exists among the organizations in the climate change and agriculture sectors can influence the tailoring of climate forecasts into information useable for adapting agricultural practices to the risks posed by climate change. Also, the extent to which farmers are integrated into this organizational collaboration network can influence their access to climate information. This paper aims to examine how organizational collaborations in the process of climate information generation and dissemination acts as either barriers or enablers of farmers’ access to and use of climate information in Ghana. Design/methodology/approach This study used key informant interview and questionnaire survey to interview the organizations in the climate change and agriculture sectors. Using network analysis as an analytical framework, the authors estimated the networks’ core-periphery, density, reciprocity and degree centrality. Findings The authors observed that communication of climate information to farmers is mostly influenced by the collaborations between governmental organizations and nongovernmental organizations. Nevertheless, information flow and exchange through organizational collaboration network is having limited effect on improving farmers’ knowledge about climate risks, impacts and available risk response options. This is mostly because the feedback flow of information from farmers to national level organizations has not been effective in addressing localized climate/agro challenges. Originality/value This paper provides a critical overview of key issues in influencing the relevancy and usefulness of climate information in the Ghanaian agriculture sector. Insights gained and recommendations made are essential for deploying effective climate services in Ghana and can be relevant for many African countries because of similar socioeconomic contexts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".