Local Climate Trends and Farmers’ Perceptions in Southern Tigray, Northern Ethiopia
Bibliographic record
Abstract
This study aimed to investigate changes in local climate, farmers’ perception to the change and factors affecting perception of farmers to climate change. For trend analysis, we gathered station based rainfall records for the period 1978-2012, while for perception analysis survey was carried out. 600 farming households were randomly selected from four districts using a multi-stage sampling method. Nonparametric analyses were used for analyzing trends and testing significance. Farming households were asked their observation about changes in local climate using structured questionnaires. We also utilized logistics regression to identify factors that influenced perceptions of farming households on climate change. Results indicate that while annual rainfall showed no change across the region, Kiremt and Belg rainfalls exhibited significant increasing and decreasing trends in the last three decades respectively. The study confirmed that the change in rainfall trend varies by agro-ecology. Kiremt rainfall in the lowlands increased by about 106mm/decade; yet, highlands got non-significant change. Besides, when the highlands lost significant amount of Belg rainfall (35mm/d), lowlands didn’t show any significant reduction. As to perception, about 87% and 50% of respondents perceived Belg and Kiremt rainfall decreasing respectively where their observation was more or less consistent with statistical findings. This study learned that gender, education, farm experience, extension, climate information, economic status, drought experience and local agro-ecology positively influenced farmers’ perception. Yet, irrigation negatively affected farmers’ perception. Results suggest further works in the areas of information dissemination, inclusion of local knowledge in adaptation programs and irrigation developments to reduce impacts. Key words : seasonal rainfall , climate change, farmers’ perceptions, perception determinants, Northern Ethiopia
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".