Corn residue alters phosphorus sorption and retention dynamics in a leaching-prone soil
Bibliographic record
Abstract
Agricultural phosphorus (P) runoff contributing to water body enrichment is a major environmental issue, particularly from high-P soils with poor retention. Conservation practices involving crop residues can alter P dynamics. This study quantified the impact of corn stalk residue added on a weight-to-weight basis on P sorption and retention in a leaching-risk soil, examining the influence of pH, ionic strength, and dissolved organic carbon (DOC) on P release. Using batch experiments, we assessed P sorption across varying residue rates (0–20.3 % w/w), P dosages (0–71.8 mg L⁻¹), and P release under different pH (5, 7, 8), ionic strength (0–0.05 M), and DOC concentrations (0–500 mg C L⁻¹). Notably, residue additions above 15.3 % substantially decreased P sorption by 30–50 % and reduced P retention capacity from 90 % to 70 %. Higher P dosages also decreased sorption efficiency (from 56 % to 41 %) and retention (from 91 % to 86 %). Furthermore, P release surged at high pH combined with low ionic strength (0 M), while higher ionic strengths (0.01–0.05 M) buffered this effect. Counterintuitively, despite DOC-derived P inputs, increasing DOC concentrations from 100 to 500 mg C L⁻¹ reduced net dissolved inorganic P from 5.8 % above control to only 0.8 % above control, suggesting rapid P transformation or complexation. These results reveal critical interactions between residue management, DOC dynamics, and soil chemistry, necessitating careful integration of P fertilization strategies with residue practices to mitigate leaching risks while preserving conservation benefits. • Corn residue alters phosphorus (P) sorption dynamics in leaching-prone agricultural soil. • Increasing residue rates significantly reduced soil P sorption and retention capacity. • Residue presence diminished P sorption efficiency, particularly at higher P dosages. • High pH and low ionic strength conditions amplified P release from the 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".