Impact of Climate Change Adaptation Strategies on Food Security of Farm Households in Rural Dire Dawa Administration, Ethiopia
Bibliographic record
Abstract
Background: The impact of climate change on smallholder farmers in underdeveloped countries—specifically, Ethiopia is widely recognized. Farm households employ a range of geographically and temporally varying adaptation strategies to cope with the adverse consequences of climate change. Therefore, it is critical to examine the few empirical studies that examine how rural Ethiopian farm households confronting drought have responded to climate change to ensure food security. Primary data were collected from 385 randomly selected farm households using a semi-structured survey form. Data analysis was performed using endogenous switching regression models and descriptive statistics. Result: Results show that the majority of the sample households (76.7%) adopted climate change adaptation strategies (livelihood diversification, soil and water conservation, and chemical fertilizers separately or in combination) while the remaining 23.3% are non-adopters. Climate change knowledge is validated as an instrumental variable. Model results revealed that adopter farmers would have significantly lower (11.6%) daily calorie intake if they had not adopted them, and non-adopter farmers would have gained significantly higher (12.8%) daily calorie intake if they had adopted them. Sex, marital status, land fragmentation, education, family size, farm size, credit access, extension contacts, and livestock ownership are significantly associated with the likelihood of adoption. Results also show systematic differences where the sex of the head variable is inversely related to the food security of adopters and vice versa for non-adopters. Conclusion: The majority of farm households in the study area know the implications of climate change (73.5%) and suffer from food insecurity (59%). Farmers' knowledge about climate change and variability varies, affecting social, economic, biophysical, and institutional issues. This was shown using descriptive statistics and OLM data. Farm households led by young, male farmers who are married, frequently interact with extension agents, have access to loans and information about climate change, and have non-fragmented plots possess greater knowledge about climate change than other households. Adaptation interventions should consider the above factors and heterogeneities to increase adoption and improve the food security of farm households in the study area.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".