Climate Variability and Change, Impacts and Adaptation Strategies in Dutsin-Ma Local Government Area of Katsina State, Nigeria
Bibliographic record
Abstract
This paper aimed at examining local peoples’ perceptions on climate variability and change and strategies adopted in combating the impacts of the changes in Dutsin-Ma Local Government Area of Katsina State. A total of 242 questionnaires were administered to households’ heads in the eleven wards of the Local Government Area. Descriptive statistics such as frequency distribution, percentage and mean scores were used in data analysis. The result revealed that majority of the local people have a very good knowledge of climate variability and change in terms of higher temperature, higher rainfall intensity and variability, and the occurrence of extreme weather events such as flood and drought. Findings also revealed that community disobeying God, deforestation, bush burning, combustion of fossil fuel and pollution were the major causes of climate variability and change as perceived by the respondents. The most significant impacts of climate variability and change as perceived by the local people were decline in crop yields, decline in forest resources, water shortages and decrease in soil fertility. These impacts have resulted to rural-urban migration in the area. Sustainable adaptation strategies adopted by the local people are water harvesting, the use of fertilizer/animals dung to improve crop yield, irrigation agriculture, planting of crop varieties and drought resistant crops. It is recommended that strategies for combating impacts of climate variability and change should take into account the traditional and religious beliefs of the people; and there is need to educate the local people to appreciate the scientific basis of climate variability and change.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".