Impacts of Soil and Water Conservation on Land Suitability to Crops: The Case of Anjeni Watershed, Northwest Ethiopia
Bibliographic record
Abstract
Soil loss in Ethiopia due to water erosion is a serious economic and environmental problem. Soil and water conservation (SWC) practices provide multiple onsite and offsite benefits. Thus, the present study was carried out to examine the long-term impacts of SWC measures in improving ecosystem services in general and land suitability to crop production in particular. Land suitability classes (LSC) were accounted using the multi-criteria analysis (MCA) on bio-physical variables of the environment. LSC were sorted by combining the FAO framework of land evaluation with GIS tools. Thus, LSC for teff (Eragrostis teff), maize (Zea mays L.), barley (Hordeum vulgaris L.), and wheat (Triticum aestivum L.) were found S2 and S3 in 1984 and 1997 whereas in 2010, some areas were transformed to S1 classes for wheat and teff. Suitable land allocation for these crops was made and 50% of the watershed is found to be S1 class for wheat while about 40% is in S2 class for all crops. In 1997 barley and teff covered 29.2% and 28.9%, respectively. While in 2010, 19% of the area was covered by teff, 18.9% by maize, 16.9% by barley and 15.6% by wheat. Wheat and maize showed significant spatial expansions that are best indicator crops for the betterment of the land quality or soil improvement.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".