Plant residues ameliorate pH of agricultural acid soil in a laboratory incubation: A meta‐analysis
Bibliographic record
Abstract
Abstract Background The effect of plant residues on the pH of agricultural acid soil varies markedly across numerous studies, which has been attributed to differences in plant residue characteristics, soil initial properties, and experimental conditions. Aims The aim of this study was to address the need to form a unified framework for the relationship between plant residue incorporation and acid soil pH in agricultural systems. Methods A systematic compilation of data was performed through a keyword search in the Scopus database, which yielded 221 independent pairwise comparisons of soil pH across 23 published articles utilizing laboratory incubations. These data were then used to form a consensus on the effect of plant residue on acid soil pH via a meta‐analysis approach. Results Of the 221 pairwise comparisons, 91.4% of the cases showed a positive effect of plant residue in increasing acid soil pH. Overall, plant residue application significantly improved soil pH of acid soil (p ≤ 0.05) by 12.4% ± 1.0% regardless of the heterogeneity in plant, soil, and experimental attributes between studies. Our analysis also revealed that total alkalinity (≥ 80 cmol kg–1) and N (≥ 10 g kg–1) of plant residue provide the optimal ameliorative effect. This ameliorative effect is more pronounced in extremely acidic soil (pH 3.5–4.4) than at soil with pH > 5.5. Conclusions The insights gained through this meta‐analysis demonstrated the interplay between different plant residue, soil, and experimental attributes, which collectively influence the ameliorative effect of plant residue application to the pH of agricultural acid soil.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.038 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".