Impact of Land Certification on Sustainable Land Resource Management in Dryland Areas of Eastern Amhara Region, Ethiopia
Bibliographic record
Abstract
This study investigates the impact of land certification on sustainable land resource management, long-term investments, and farmers’ perception and confidence on land ownership and land use rights in the dryland areas of Eastern Amhara Region, Ethiopia. Fifteen kebeles from three woredas and 20 households per kebele were selected using stratified random sampling techniques with whom face-to-face interviews were carried out. Analysis of the qualitative and quantitative data showed that, 160 households have on average 0.40 ha of farmland on steep slope area; and about 21.0% and 15% of households have fear land redistribution and the government may take their farm plot at any time, respectively. However, respondents believe that land certification reduced landlessness of women, disable and poor of poor where as it increased youths’ landlessness. The participation of households in land management practices (LMP) has shown a 15.4% increment after land certification. Nonetheless, the mean comparison of major crop yields per household is insignificant except sorghum which decreased significantly at level of p<0.1 level. Generally, land certification improves tenure security; LMP and land use rights of women and marginal groups of societies but did not crop productivity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".