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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".