Assessment of Household Food Security in the Face of Climate Change and Variability in the Upper Blue-Nile of Ethiopia
Bibliographic record
Abstract
It is widely recognized that climate variability and frequent droughts resulting from El-Nino phenomenon are among the major risk factors affecting agricultural production that might contribute to hunger and food insecurity in East Africa in general and Ethiopia in particular. The objectives of the present study were to examine the food security status and determinants of household food security among 442 randomly selected households in the Muger sub-basin of the Blue-Nile basin using household survey, focus group discussion (FGD) and key informant interview data collection methods. Both descriptive statistics (mean, chi-square test and t-test) and binary logit econometric model were used to analyze the data. The results showed that 57.8% of the households are food secure, while the remaining 42.2% of the households are food insecure. The binary logit regression results revealed that adoption of soil conservation, small-scale irrigation and employing different agronomic practices are important factors influencing household food security. Moreover, land holding and livestock ownership positively and significantly affected household's food security. The results further showed that family size and distance to the nearest market are important factors affecting food security in the inverse direction. The results highlighted careful investments on sustainable land management practices and small-scale irrigation that reduced sensitivity and increased the adaptive capacity of smallholder farmers to the adverse effect of climate change and variability. This study further highlighted the significance of livestock ownership and landholding in attaining food security under changing climate. The findings call for action based on advocacy family planning to curb population growth and invest in creating functional value-chain that help farmers' viable market for their produces.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".