Comparative Assessment of Local Farmers’ Perceptions of Meteorological Events and Adaptations Strategies: Two Case Studies in Niger Republic
Bibliographic record
Abstract
<p>Several studies on farmers’ perceptions on climate variability tend to provide bulked information at either regional or national level. Assessing the disparities of skills and the strategies of adaptations among farmers across locations could be the first step towards solutions in adaption to the climate variability and change. The objective of this paper was to assess and compare local farmers’ perceptions on meteorological events, adaptations and access to agricultural extension services in two agro-ecological zones, Diffa and Aguie, in Niger Republic. The results revealed that climate challenges are well distributed in both areas but, there are significant discrepancies in the perceived climate variabilities compared to meteorological observations. Respondents noted an increase in temperature which is in agreement with climatic data evidence. It was found that majority of respondents adopt crop diversification in the sense of mixed cropping as their major adaptation strategy to climate variability. However, the extent to which farmers perceived crop diversification as a climate change adaptation strategy is not a response to the subjectively perceived changes in weather patterns, but rather a traditional strategy to reduce risk and to adapt to the long-standing inter-annual and intra-annual rainfall variability in the area. The lack of sufficient educational knowledge, external support and access to information are the constraints that hindered farmers to adapt effectively and, this leads to low agricultural productivity. It is recommended to empower farmers with information, technological skills, access to heat resistant crop varieties that enable them to adapt to increasing maximum temperatures.</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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".