Climate change perception and adaptation among farmers in coastal communities of Bayelsa State, Nigeria: a photovoice study
Notice bibliographique
Résumé
Purpose There is an increasing need for greater awareness and understanding of the risks climate change poses to farming communities so as to inform appropriate adaptive responses. The purpose of this study is to investigate farmers’ climate change impacts, awareness, risk perception and current adaptation strategies adopted to deal with the impacts of climate change on their livelihood. Design/methodology/approach This research was undertaken with 67 farmers in Bayelsa State, Nigeria. This study used a combination of focus group discussion and quantitative survey to obtain data. Surveyed farmers were invited to an initial workshop and asked to take photos of climate change impacts on their land and the adaptation strategies being adopted. The photos were analysed and discussed with the farmers in a second workshop. Then, in a third workshop, farmers and other stakeholders came together to rank the most important consequences of climate change and shared knowledge on adaptation strategies. The survey and photovoice data were analysed using descriptive and inferential statistics. Findings The results of this study showed that a majority of the farmers were knowledgeable of climate change, mostly got climate information through media. Floods and high temperatures were perceived as the most occurring climate change-related disaster risks. Majority of the farmers perceived climate change as high risk and have taken up multiple adaptation strategies in response to it, including changing planting times, mulching their land and digging irrigation pits. Farmers’ responses indicated that they want to do more but are restricted by financial resources. Practical implications This study outcomes provide evidence for a need to consider stakeholders’ participation in planning climate change responses to effectively address the challenges posed by climate change, particularly in coastal agricultural communities. Government and relevant agencies as recommended need to support farmers to undertake needed adaptive strategies to adapt with future flooding, high temperature and drought, providing them with necessary facilities to enhance their adaptive capacities. Originality/value To the best of the authors’ knowledge, this was one of the first studies to use photovoice to investigate climate change awareness, impacts and adaptations strategies with majority female farmers in west Africa. This study highlights the importance of participatory approaches to capture grassroots climate adaptation approaches.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».