Agricultural community-based impact assessment and farmers’ perception of climate change in selected Ecological Zones in Nigeria
Bibliographic record
Abstract
Abstract Background The impacts of climate change are affecting sustenance and livelihood of many rural farmers in Africa. The majority of these farmers have low adaptive capacity. This study investigates climate change impacts, farmers’ perception, adaptation options and barriers to adaptation in three selected ecological zones in Nigeria using three staple crops. Rainfall and temperature data of over 35 years were analysed using ANOVA, Mann Kendall and Sen’s Slope Analysis. Farmers’ perception of climate change and cropping experiences were assessed with the aid of a well-structured questionnaire, semi-structured interview and focus group discussion. Results The results of the study revealed high variability in the annual and monthly rainfall and temperature during the study period. The highest annual maximum temperature was recorded in Kwara with Tmax > 32 ℃. Though, there appeared to be spatial and temporal variations in rainfall in the study area, the highest was in Ogun with mean annual rainfall = 1586.9 mm and lowest in Kwara with mean annual rainfall = 1222.6 mm. Generally the Mann Kendall and Sen's slope analysis revealed general increase in the minimum and maximum temperature, while rainfall revealed generally downward trend. The study revealed a difference in farmers’ perception but nearly 74% of farmers perceived that climate is changing, which is affecting their farming activities. Nearly 70% claimed that lack of financial capital is the major barrier to climate change adaptation. Conclusions The study concludes that rainfall and temperature variability have significantly impacted cropping and that farmers are aware of long-term changes in temperature and rainfall, but some are unable to identify those changes as climate change. There is a need for affordable and available improved seedlings and variety of crops that can adapt to climate change conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".