Gendered perceptions and adaptations to climate change in Ghana: what factors influence the choice of an adaptation strategy?
Bibliographic record
Abstract
Climate change poses a significant threat to various sectors, including agriculture, affecting men and women unevenly. Although gender-based perceptions of climate change have been studied, there remains a gap in understanding how these perceptions influence the adoption of adaptation strategies among men and women smallholder farmers in the production of leguminous crops in sub-Saharan Africa. This study investigated the gender differences in the adoption of climate change adaptation strategies among bean and cowpea farmers in Ghana. The findings revealed that socioeconomic and institutional factors significantly influenced the choice of adaptation strategies, with notable differences between men and women. Higher levels of education, farming experience, marital status, access to credit, and education determined the choice of adaptation strategies. On the other hand, women farmers, despite having lower levels of formal education, showed a higher utilization of extension services, possibly due to targeted efforts to reach out to more women farmers. Larger households were less likely to adopt mixed cropping and changing cropping patterns, while married individuals were less likely to use crop rotation. Training and access to credit significantly increased the likelihood of adopting crop rotation, changing cropping patterns, and using improved seeds. The study also found that [f]armers perceptions of the impacts of dry spells and delayed onset of rains influenced the use of climate change adaptation strategies. Furthermore, farmers who participated in climate change planning were more likely to use diverse adaptation strategies, underscoring the importance of a locally focused, inclusive planning process. However, gender differences were observed in the determinants of the use of these strategies. For instance, while access to extension services was found to be more influential for women, men’s decisions were more influenced by their marital status, access to credit, and education. Policy makers and local institutions need to encourage and facilitate farmers’ involvement in climate change planning processes to enable designing of effective, context-relevant, inclusive, and sustainable climate change adaptation strategies. Distinct differences in the factors underlying the use of adaptation strategies by men and women demand creation of and implementation of gender-sensitive programs that effectively reach and benefit both women and women.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".