Climate Change Mitigation Activities and Determinants in the Rural Guinea Savannah of Nigeria
Bibliographic record
Abstract
<p class="StandardTextkrperSAR">Nigeria loses about $750 million annually to the depletion of its 350 000 hectares of land by direct human activities and climate change. Consequently, the Sahara Desert has been moving southwards by 600 metres annually. 10 000 farming families have already been forced to move off the degraded land that has become barren. In the light of this, this study examined climate mitigation activities and determinants in Nigeria. Specifically, the study examined climate change knowledge/perception, cost implications and mitigation practices of rural households as well as factors responsible for the level of mitigation activities. 120 household respondents were selected across 8 communities in the Guinea Savannah of Kwara State, Nigeria. Study analytical tools used were descriptive statistics, principal component and Tobit analysis. Results showed that households perceived the effects of increased temperatures, reduced rainfall, desertification, flooding and increased crop pest and disease infestations. Crop harvest losses due to changing climate were large and worrisome. Principal component analysis PCA results implied that prevalent practices undertaken to combat climate change were crude and non-radical. These activities were inorganic and organic fertilizers use, mulching, bush fallow and crude agro-forestry practices. Factors determining the extent of mitigation activities were found to include educational status, type of farming activities and farm size. The study therefore calls for radical efforts at educating the rural masses on climate change devastations and the need for mitigating climate change, use of early maturing crop varieties. Green zone/forest should be developed while tree planting and afforestation should be encouraged and possibly enforced.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".