Knowledge, Perception and Adaptation Strategies to Climate Change Among Farmers of Central State Nigeria
Bibliographic record
Abstract
This study was designed to assess the knowledge, perception and adaptation strategies to climate change among farmers of central Nigeria. Multi-Stage sampling technique was used to source respondents for the study. Three out of the five local government areas (LGAs) were randomly selected in the study area. Five village communities were randomly selected from each of the five LGAs to give fifteen villages, while 10 farmers were also randomly selected from each village to give 150 respondents. Data collection was through an interview schedule. Simple descriptive statistics such as frequency counts, percentage and mean scores were used to achieve all the objectives of the study. Most of the respondents relied on radio as their major source of information on climate change. The perceived indicators of climate change by the respondents were excessive high temperatures, low and irregular rainfall pattern as well as low crop yields. Adaptation strategies used in the area included agroforestry practices, crop diversification, early maturing and disease/drought resistant varieties. The Major constraints to adaptation by the respondents were inadequate finance, poor infrastructures, unfavourable government/trade policies and poor technology. Extension agents in the study area should incorporate information on climate change in their extension messages.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".