Climate Change Adaptation Options in Farming Communities of Selected Nigerian Ecological Zones
Bibliographic record
Abstract
Abstract This chapter examines the impacts of climate change on three tropical crops and assesses the climate change adaptation options adopted by rural farmers in the region. The study was conducted among farming communities settled in three major ecological zones in Nigeria. Over 37 years of data on rainfall and temperature were analyzed to examine climate change impacts on three major crops: rice, maize, and cassava. Farmers’ adaptive capacity was assessed with a survey. Climatic data, crop yields, and survey data were analyzed using both descriptive and inferential statistics. The relation between rainfall/temperature and crop yields was examined using the Pearson correlation coefficient. Results show a high variation in the annual rainfall and temperature during the study period. The major findings from this research is that crops in different ecological zones respond differently to climate variation. The result revealed that there is a very strong relationship between precipitation and the yield of rice and cassava at p <0.05 level of significance. The results further showed low level of adaption among the rural farmers. The study concludes that rainfall and temperature variability has a significant impact on crop yield in the study area, but that the adaptive capacity of most farmers to these impacts is low. There is a need for enhancing the adaptation options available to farmers in the region, which should be the focus of government policies.
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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) | 0.003 | 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".