Determinants of smallholder farmers’ decision to adopt adaptation options to climate change and variability in the Muger Sub basin of the Upper Blue Nile basin of Ethiopia
Bibliographic record
Abstract
Smallholder farmers’ decisions to adopt adaptation options in response to climate change and variability are influenced by socioeconomic, institutional, and environmental factors, indicating that decision patterns can be very specific to a given locality. The prime objective of this research is to identify factors affecting smallholder farmers’ decisions to adopt adaptation options to climate change and variability in the Muger River sub-basin of the Blue Nile basin of Ethiopia. Both quantitative and qualitative data were collected using a semi-structured questionnaire, focused group discussions, and key informant interviews from 442 sampled households. Frequency, mean, Chi-square test, and one-way ANOVA were used for analysis. Furthermore, a multinomial logit model was employed to analyze the data. Results signified that small-scale irrigation, agronomic practices, livelihood diversification, and soil and water conservation measures are the dominant adaptation options that smallholder farmers used to limit the negative impact of climate change and variability in the study area. The results further revealed that adoption of small-scale irrigation as an adaptation to climate change and variability is significantly and positively influenced by access to credit, social capital, and the educational status of household heads. Greater distance to marketplace and size of farmland negatively affected the use of agronomic practices, whereas crop failure experience and access to early warning systems have a positive influence. The results also point out that adoption of soil and water conservation measures are positively affected by exposure to early warning systems, greater distance to the marketplace, and larger size of cultivated land. It is also noted that livelihood diversification is negatively influenced by socioeconomic factors such as education, the gender of the household head, and livestock ownership. Overall, the results suggested that improved policies aimed at increasing the adoption of adaptation options to offset the impact of climate change and variability should focus on: creating effective microfinance institutions and effective early warning systems, increasing farmer awareness, improving infrastructure, and encouraging farmers’ membership to many social groups. The results further suggested that agroecological and gender-based research should be promoted and increased for a more holistic understanding of farmer adaptation options.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".