Farmers’ preference for soil and water conservation practices in central highlands of Ethiopia.
Bibliographic record
Abstract
Land degradation is a major socio-economic and environmental concern in the Ethiopian highlands where the phenomenon has rendered vast areas of fertile land unproductive. To reverse this trend, the adoption of soil and water conservation (SWC) practices is crucial. However, failure by research and development organisations to take into consideration farmers preference for SWC practices have resulted into low adoption of these technologies. This paper presents the findings of a study that evaluated farmers’ preferences of SWC practices, including the economic perspective; as a basis for enhancing adoption of the technologies in the central highlands of Ethiopia. Four soil and water conservation (SWC) practices; (i) soil bunds alone (SB), (ii) soil bunds with vetiver grass (SB+Vg), (iii) soil bunds with Susbania susban (SB+Ss) (iv) and soil bunds with elephant grass (SB+Eg), were evaluated in the Borodo Watershed in the central highlands of Ethiopia. These are the only SWC measures introduced and implemented in Borodo watershed. Data on these SWC practices were collected from farmers using focus group discussion. A multi-criteria analysis (MCA) approach was used to analyses the data. The criteria were weighted using pair-wise ranking and SWC practices were scored with a scale of 1(not good) to 5 (best) based on each criterion. The overall weighted scores were obtained using the Simple Additive Weighting Model. Farmers assigned highest relative weights to criteria related to economic criteria (0.58) than technical (0.29) and stability criteria (0.13). Based on the overall weighted scores obtained using MCA approach, farmers prefer different SWC practices in an order of SB+Eg> SB+Ss> SB+Vg> SB. In general, this paper argues that farmers’ economic concerns should be accounted for or more seriously taken into account by research and development institutions. Therefore, there is a need to develop SWC practices which are technically effective and economically efficient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".