Expansion of Eucalyptus Woodlots in the Fertile Soils of the Highlands of Ethiopia: Could It Be a Treat on Future Cropland Use?
Bibliographic record
Abstract
A study was conducted to assess the effect of land use change from eucalyptus to cropland on soil physico-chemical properties and perceptions of farmers in Koga irrigation area, Amhara Region. Soil samples were taken from 4 sites of three land uses (eucalyptus woodlots, cropland, and eucalyptus land use changed to cropping) and at 0-20, 20-40 and 40-60 cm depths. The three depths were used for analysis of soil chemical properties, whereas the first two depths for physical properties. Furthermore, randomly selected 15 farmers were interviewed for their perception on the state of soil fertility and crop yield conditions on lands that were recently changed from eucalyptus to cropland. The result showed that except for available P, sampled plots that were changed from eucalyptus to cropland were found better in soil chemical properties (pH, N, CEC) and SOM contents as compared to croplands. As compared to the other two land uses, total N was found larger at eucalyptus woodlots. Regarding soil physical properties (bulk density and texture), little or no difference was recorded among the different land use types. On top of that, farmers perceived that plots that were under eucalyptus have better fertility, require less nitrogen fertilizer and crops perform well compared to plots that are contineously under cropping. Thus, results of this study confirmed that changing land use from eucalyptus to cropland is possible without detrimental effect on soil properties and without affecting productivity of lands to raise crops.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".