Maize Farmers’ Perceptions of Climate Change and Determinants of Adaptation Decisions in Northern Ethiopia
Bibliographic record
Abstract
Rain-based agriculture is highly vulnerable to climate variability and change. Farmers’ decisions about how to adapt to climate change are influenced by socioeconomic setups and local institutions. The objectives of this study were to evaluate farmers' perceptions of climate change, identify the local adaptation techniques they used, and pinpoint the major socio-economic challenges they faced when putting those strategies into practice. 250 maize farmers were used as samples for the collection of primary data. Descriptive statistics were used to evaluate the data on socioeconomic characteristics, and the multinomial logistic model was used to identify the factors influencing farmers' decisions to adapt. The majority of households (91.2%) believed that climate change is occurring, and its main symptoms include unpredictable rainfall (88.4%), warming temperatures (83.2%), and more frequent droughts (79.2%). The findings show that farmers' perceptions of rising temperatures and weather data matched; however, there was a discrepancy between perception and rainfall records. Reduced maize yields (78%) and declining soil fertility (83%) were the two biggest effects of climate change perceived by the farmers. Accordingly, 92.8% of farmers have developed their best adaptation, primarily through the combination of crops and livestock (24%) and the adoption of enhanced maize varieties (20.8%). The econometric model's findings showed that the primary variables influencing farmers' decisions were age, gender, education, farm size, animal ownership, and poverty. The study recommends supporting the indigenous adaptation techniques of maize farmers from a variety of institutional, policy, and technological angles, both at the farmer and farm levels.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".