Determinants of Adoption of Sustainable Land Management (SLM) Practices among Smallholder Farmersâ in Jeldu District, West Shewa Zone, Oromia Region, Ethiopia
Bibliographic record
Abstract
Land degradation in form of soil erosion and fertility loss are ruthless problems in developing countries including Ethiopian Highlands, which have serious implications for food security and livelihoods of local farmers in particular and the nation in general.Low land productivity due to land degradation in form of soil erosion is one of the leading challenges to improving the performance of the smallholder farming system sector in Ethiopia.In this context, the adoption of Sustainable Land Management practices/ technologies is quite crucial to increase agricultural productivity, ensure food security and improve the livelihoods of smallholder farmers.Farmers recommend various SLM practices/technologies for sustainable implementation, but adoption of such agricultural land management practices/ technologies is still very low.There is no clear understanding of the problems encountered by farmers in the adoption of recommended SLM practices/ technologies.Therefore, the main purpose of this study was to assess the socio-economic, institutional, psychological and biophysical determinant factors that influence adoption of SLM practices/technologies among smallholder farmers in Jeldu district in West Shewa zone.Primary data were collected through household questionnaires surveys, focus group discussions, key informants interviews and personal observations while secondary data were collected from relevant local authority reports and records.A total of 224 households were interviewed.Both Descriptive statistics, binary logistic regression model were used to analyze the data.The computed independent T-test for the mean income difference was statistically highly significance between adopters and non-adopters, suggesting that adopters were in better-off position to improve their livelihood.. From the 18 explanatory variables entered into the model, 14 variables were found to be statistically significant at less than 5 to 10% probability levels.These are education level of the household head, farm size, perception of land degradation ,effectiveness of SLM practices, frequency of development agent contact and livestock ownership significantly positively affect adoption o land management practices while distance to market affects it negatively at less 10% probability levels.Planners and policy makers should formulate appropriate policies and programs considering the farmers' interest, capacity, and limitation in promoting improved soil conservation technology for greater acceptance and adoption by the farmer.
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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 | 0.000 | 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) | 0.002 | 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".