Effects of Liming on Soil Physical Attributes: A Review
Bibliographic record
Abstract
The objective of the present study was to gather information on the effects of liming on changes in soil physical attributes. Soil acidity, caused by natural ways, such as rain, weathering and decomposition of organic matter and by human interference, by the use of nitrogen fertilizer mainly ammonia and urea fertilizer contribute to the acidification of the soil. In this context liming is perform to correct soil pH and neutralize the effect of toxic elements. Numerous benefits of liming are known, but their influence on soil physical attributes is poorly studied. Liming directly affects some physical properties of the soil, such as flocculation, aggregates, density and porosity. Flocculation of soil particles initially is smallest, which promotes greater particle dispersion. However, it changes over time, since H+ and Al3+ ions tend to be subsumed by Ca2+ and Mg2+ increasing particle flocculation power which favors its approximation and aggregate formation. For soil aggregates, surface liming in improves soil aggregation by increasing the mean aggregate diameter with positive responses as dose increases. Density and porosity of soil also undergo changes. It is important to point out that liming also has the potential to alter the porous structure of the soil. Porosity liming promotes soil density reduction and increase influenced reducing macroporosity values and increase of total porosity and micropores values. The results of studies carried out present divergent and similar results according to the evaluated physical attributes. This is possibly due to the dynamics between liming under different edaphoclimatic conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".