Assessment of phosphorus status in a calcareous soil receiving long‐term application of chemical fertilizer and different forms of swine manures
Bibliographic record
Abstract
Abstract The continuous use of organic inputs in crop production calls for an improved understanding of how these inputs might alter soil phosphorus (P). This study assessed how the continuous application of different forms of swine manure influences the mechanism of P transformation and release potential. Soil samples were collected from a clay loam soil receiving no P or 100 kg P ha−1 applied as either chemical fertilizer (CHEM), swine liquid manure (SWL), composted swine manure (SWC), or solid swine manure (SWS) every other year for 16 years in a corn–soybean rotation. Available P increased in soils treated with the chemical and organic fertilizers. The greatest increase was found in the SWC and SWS and was closely related to a 1% increase in the organic C content, and 1.3‐ and 1.2‐unit increase in the soil pH for SWC and SWS treatment, respectively. Nonlabile HCl‐P form was also higher in SWC‐ and SWS‐treated soils. Despite the similarities between SWS and SWC, SWS significantly had a lower maximum P sorption (Qmax) and higher equilibrium P concentration at zero net sorption (EPCO) probably related to the higher organic NaOH‐P. Similarly, higher organic NaOH‐P together with lower cation exchange capacity, aluminum, and calcium explained the lower Qmax in SWL. This suggests that increase in organic NaOH‐P forms limits the soil potential to retain P. Overall, the SWL treatment presents a unique effect on changes in soil property and P chemistry that warrants further investigation.
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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 | 0.000 | 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 | 0.000 | 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".