The Role of Agronomic Practices on Soil and Water Conservation in Ethiopia; Implication for Climate Change Adaptation: A Review
Bibliographic record
Abstract
Now, soil erosion, loss of soil fertility, and land degradation due to climate change has been pushing to search for more sustainable systems. Soil and water conservation practice contribute high in the long term agricultural sustainability and sustainable agricultural farming. The objective of this paper is to review the major agronomic practices and their role in soil and water conservation. The review revealed that the major agronomic soil and water conservations practices in Ethiopia are strip cropping, mixed cropping, intercropping, fallowing, mulching, contour ploughing, crop rotation, conservation tillage and agroforestry. The plant canopies, litter and mulching intercept rain by decreasing the amount, intensity and the spatial distribution of the precipitation reaching the soil surface and this protects the soil surface from the direct impact of raindrops which can cause a splash and sheet erosion. In soil and water conservation, this practice is higher than others, because crops and leguminous woody perennials improve and enrich soil conditions by atmospheric nitrogen fixation, an addition of organic matter through litterfall and dead and decaying roots, nutrient cycling, modification of soil porosity and contribution to infiltration rates. It also alleviates and maintains salinity, alkalinity, acidity and waterlogging problems. Hence, the use agronomic practices for soil and water conservation is vital for climate change adaptation and mitigation because it can give both productive role (producing food, fodder, fuel, wood) and protective role (soil conserving functions, windbreaks and shelterbelts).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".