Analysis of Households Food Insecurity in the Face of Climate Variability: Evidence from North Shewa Zone, Amhara Region, Ethiopia
Bibliographic record
Abstract
Food insecurity is more worrisome now than ever before due to unprecedented climate variability and widespread rural poverty. Research-based and policy relevant empirical evidence is crucial to design strategies to address food insecurity in the face of climate variability. Thus, this study examines the status of food insecurity among households’ and its determinants in North Shewa Zone of Amhara Region using cross-sectional data collected from 382 sample households. Households’ food insecurity status was determined by comparing the total calorie available for consumption per adult equivalent to the minimum level of subsistence requirement per adult equivalent of 2200 kcal. Logistic regression model was used to identify factors that influence food insecurity status of households in the study area. Accordingly, the results of the study show that majority (56.28%) of the sample households in the study area were food insecure. In addition, results revealed that age, literacy, cultivated land size, soil fertility status, number of oxen owned and irrigation water use were the major factors negatively associated with food insecurity. In contrast, sex, household size, distance to the main market and rainfall variability have increased the probability of being food insecure. The findings imply that majority of the households are food insecure where its improvement can be addressed through appropriate policy, institutional and technological options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".