Climate change impacts on agriculture and barriers to adaptation technologies among rural farmers in Southwestern Nigeria
Bibliographic record
Abstract
This chapter examines climate change impacts and local adaptation options among rural farmers in southwestern Nigeria. Satellite climate datasets for rainfall and temperature from the 1980s to 2020 and a dataset including responses to a survey and focus group discussions were used. A case study of the impacts of climate change on cassava yields using correlation and multiple regressions is presented. The results show a relative increase in temperature, while rainfall showed large seasonal variations. Rainfall trends appear to be relatively upwards from the 1980s – early 1990s but below the normal trend from the period from 1997 to 2020. The results from the survey show that nearly 80% of the rural farmers perceived general changes in temperature and rainfall in recent years, while nearly 97% of them adopted changes in the planting date of some crops, as an adaptation option. The results further show a very strong relationship between cassava yields and rainfall in the growing seasons. The study concludes that there is a need for governments at all levels to encourage rain-fed agriculture and more agricultural research to improve crop yields as climate changes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".