Gendered perceptions and adaptations to climate change in Ghana: what factors influence the choice of an adaptation strategy?
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».