Perception and Resilience Strategies of Livestock Farmers and Agro-Pastoralists Affected by Climate Change: Case of the urban commune of Tera, Niger
Bibliographic record
Abstract
This study aimed to identify and strengthen the resilience of livestock and agro-pastoralists in the face of changing climatic conditions. The study was conducted in the urban commune of Tera. The methodological approach consisted of desk research and data collection. In order to find the number of households to be surveyed in the selected camps, the method of taking a sample (8%) of the target households is adopted. In total, forty-eight (48) herders and agro-pastoralists are selected. The analysis of the perception of the herders and agro-pastoralists on the climate trend showed a decrease in the amount of rainfall (94% of respondents), increasingly high temperatures (92%) and an increase in strong and sandy winds in all seasons (96%). The disappearance of plant cover was the main cause of climate change according to 79.2% of respondents. The impacts of climate change are numerous. Pastoral resources (water and fodder) have been greatly reduced. The health of the animals has been affected, as has their production. Strategies have been developed by farmers and agro-pastoralists to reduce or anticipate the negative effects of climate change. According to some respondents, the strategies have not fully met expectations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".