Climate change perception and adaptation among farmers in coastal communities of Bayelsa State, Nigeria: a photovoice study
Bibliographic record
Abstract
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".