Sustainability, institutional arrangement and challenges of community based climate smart practices in northwest Ethiopia
Bibliographic record
Abstract
Crop productivity in the highlands of Ethiopia is critically challenged by land degradation and climate change and variability. Massive plantations and several kilometers of soil and water conservation technologies have been practiced in response to those challenges. Yet land degradation and the level of sustainability of the technologies have continued to be critical challenges. Thus, the objective of this research was to examine the sustainability, challenges and future prospect of climate smart community-based soil and water conservation practices. Data were collected using household survey, key informant interview and focus group discussion. Analytic hierarchy process for multi-criteria decision making was used to analyze the sustainability of community-based climate smart practices. The finding revealed that the overall score of the sustainability dimensions was about 67.5%, which lies in the zone of "sustained but at risk." The institutional arrangement has limitations in that farmers were involved in implementation phase while there is little room in planning, monitoring and evaluation phases. The major challenges of the soil and water conservation practices were: destruction of the communal forests and structures for personal benefits, overlapping work calendar with irrigation and off-farm works and structures wasted farmland. The success of the structures from the perspective of officials is expressed in terms of the numbers of kilometers constructed and community participation. However, it overlooks how it reduces the problem of land degradation and challenge of climate change and variability. Thus, in order to sustain the structures, direct participation of farmers at all stages of the work is encouraged. The sustainability of structures can also be partly ensured if it generates benefit to the local community.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".