The Effect of Land Use Systems on Soil Properties; A case study from Rwanda
Bibliographic record
Abstract
Land use change has a significant impact on the ecosystem. In this paper the effects of land use change on the physicochemical properties of the soil in Rulindo District, Rwanda have been studied. Three different land use types were selected; forestland, cattle farmland and cultivated land. A randomised complete block research design was used to carry out this research. Nine soil samples were collected and then analysed. The distributed samples were analysed in the Soil Laboratory of University of Rwanda-Busogo campus, while the undisturbed samples were analysed in-situ. Eight physicochemical properties were measured: pH, Organic Matter (OM), available nitrogen, available phosphorus, exchangeable potassium, soil bulk density, moisture content and porosity. The results showed that changing land use from forest or farm to cultivated land reduced the organic matter, available nitrogen, soil moisture and porosity while bulk density and pH were significantly increasing. On the other hand, the exchangeable potassium and exchangeable phosphorus did not change significantly for the both land use changes. Hence, the reduction of forestland and farmland are highly sensible to erosion and will decline soil fertility. The paper proposed few steps and recommendations to be the base for a new sustainable land use management in Rwanda.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".