Effects of liming on soil biota and related processes in agroecosystems: a review
Bibliographic record
Abstract
Soil acidity is associated with nutrient deficiencies and phytotoxicity, and is detrimental to crop productivity due to its effects on plant roots and soil biota. Liming is an effective strategy for managing acidity in agricultural fields. Liming increases soil pH, changing soil chemistry, leading to shifts in community composition and activity of soil biota. However, it is not fully understood how liming impacts different groups of soil biota, their interactions and soil biological health. This review summarizes liming effects on soil biota i.e., plant roots, bacteria, archaea, fungi, nematodes, and earthworms. Reviewed literature indicates that liming increases abundance, diversity and activity of most bacterial species. Liming often increases the abundance of nematodes and earthworms, but some studies showed no liming effects on these communities. Application of lime improves mycorrhizal colonization of plant roots. Liming mostly reduces abundance, biomass and activity of most fungi which tend to favor acidic soils. However, liming effects on fungi were inconsistent; in some studies, liming reduced fungal abundance and the fungal to bacteria ratio, while in other studies, liming showed no impact on both abundance and the ratio. Some variations in liming effects on soil biota were due to differences in duration of liming, liming material used and site-specific environmental characteristics. Overall, this review highlights the complex and variable impacts liming has on soil biota, emphasizing the importance of considering species-specific responses, soil type and environmental conditions when implementing liming strategies. Understanding these dynamics is crucial for optimization of soil health and crop productivity in acidic soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.002 | 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".