Perceptions, Knowledge, Adaptation and Socio-Economic Cost of Climate Change in Northern Nigeria
Bibliographic record
Abstract
The objective of this paper is to assess the perceptions and determine the ability of farmers in rural Northern Nigeria to explain climate change, and to ascertain the socio-economic cost of climate change to them. The study also sought to understand how farmers have adapted to climate change and assess barriers to adaptation. The study is based on a survey of farmers in two rural communities in Northern Nigeria. The results reveal that the people generally associate climate change with changes in temperature, precipitation and vegetation. A significant number of farmers believe that temperature is increasing and that precipitation is declining. Those with the greatest experience of farming were more likely to notice climate change and have detailed explanation for its occurrence. The results also showed that there were important differences in the propensity of farmers of different age groups to adapt and there may be institutional impediments to adaptation. Although experienced farmers were more likely to perceive climate change, it is the younger farmers who were more likely to respond by making at least one adaptation, while the older ones often fell on safety nets of their social networks for survival. Although, large numbers of farmers perceive no barriers to adaptation, those that do perceive them tend to cite their age, lack of credit facilities, high level of illiteracy, high incidences of theft, soil erosion, large family sizes, lack of farm inputs and poor access to markets for their produce. About a fifth of the respondent although perceive climate change but are unable/fail to respond. This category may require particular incentives or assistance to do what is ultimately in their own best interests. While it is important to encourage improved farmer education, this alone is not adequate to enhance farmers’ adaptation to climate change. There is room for better adaptation if government intensify activities of extension workers and encourage planting of different varieties of the same crop which the farmers are used to cultivating, enhance weather forecasting potentials and make such information available to farmers to enable them adapt to changing planting dates. There is also need to integrate adaptation strategies to fit the peculiarities of the culture and customs of the societies concerned.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".