THE CHOICE OF CLIMATE CHANGE ADAPTATION STRATEGIES PRACTICED BY CASSAVA-BASED FARMERS IN SOUTHERN NIGERIA
Bibliographic record
Abstract
The study on choice of climate change adaptation strategies practiced by cassavabased farmers was conducted in Southern Nigeria. The following specific objectives were achieved: to ascertain the perceived effects of climate change in the study area and to determine factors influencing the choice of using climate change adaptation strategies by cassava-based farmers in the study area. Data were obtained through the administration of questionnaire to 300 randomly sampled cassava-based farmers in the study area. Data were analyzed using descriptive statistics such as mean, frequencies, percentages and inferential statistics such as Multinomial Logit Regression technique. The result revealed that farmers perceived increase in flood incidence (91.33%), drought (90.67%), high incidence of pests and diseases (55%) and low yield (50%) as the effects of climate change in the study area. Also, from the results, 58% of the farmers chose not to employ the use of climate change adaptation strategies while only 42% decided to choose using climate change adaptation options in the study area. The result also showed that age of the farmer, farming experience, gender, marital status, level of education, household size, access to credit, access to agricultural extension services and membership of association were the factors influencing the choice climate change adaptation strategies used by the farmers. The study concluded that socioeconomic attributes of the farmers affected their choice of climate change adaptation strategies. Policy should be targeted at designing climate change adaptation technology to farmers as well as providing the enabling environment that would encourage them to employ it.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".