Mineralizable Nitrogen Responds Differently to Manure Type in Contrasting Soil Textures
Bibliographic record
Abstract
Manuring soil alters mineralizable N pools and organic matter fractions, but the magnitude is manure-type and soil-texture specific, complicating prediction of N mineralization. Our objective was to determine the responses of residual soil mineralizable N parameters to manure-type and evaluate their relationships to initial organic C and N fractions, C/N ratios, and mineral N concentrations in sandy loam and silty clay soils after three annual spring applications of manure. Manure types were liquid swine manure (LSM), liquid dairy cattle manure (LCM), or solid poultry manure (SPM), all applied at 90 kg available N ha-1 yr-1. Mineral fertilizer (NPK) and a zero-N control (CTL) were also included. Composite soil samples collected (0- to 20-cm depth) before manure application were aerobically incubated at 25°C for 48 wk. Both soils mineralized N linearly over 48 wk (r2 = 0.96–0.99) and the silty clay soil did not converge to nonlinear, first-order kinetics. Pool I (N mineralized in first 2 wk) was the only mineralizable N pool affected by manure-type, which was depleted by SPM in the sandy loam and increased by LCM in the silty clay. Salt extractable organic N was significantly correlated to Pool I in both soil textures. Only Pool I was significantly correlated with N mineralized over 48 wk in the sandy loam and silty clay soils (r = 0.92 and 0.64, respectively). Overall, readily mineralizable N (Pool I) was the most sensitive and robust indicator of mineralizable N after three annual manure applications to agricultural soils from a humid temperate region.
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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".