Nitrogen transformations and ammonia volatilization losses from <sup>15</sup>N-urea as affected by the co-application of composted pig manure
Bibliographic record
Abstract
Co-application of composted manure (compost) and urea is considered an environment-friendly fertilization practice; however, the high urease activity in compost may stimulate NH3 volatilization and cause N loss from co-applied urea. To test the above hypothesis, we investigated the fate of urea co-applied with compost in a loam-textured soil through two laboratory incubation experiments. Urea (150 mg N kg-1) was co-applied with 0, 4.9, 9.8, and 14.6 g of compost (oven-dry basis) kg-1 of soil, designated as treatments UC0, UC1, UC2, and UC3, respectively. Co-application of compost and urea enhanced urea hydrolysis and increased the 1st order rate constant of urea hydrolysis from 0.047 h-1 in the UC0 to 0.139 h-1 in the UC3 treatments. Soil pH increased from 7.0 for UC0 to 7.6 for UC3, leading to greater NH3 volatilization (up to two times more) in the soils receiving 9.8 g kg-1 or more of compost. Compost co-application also increased the immobilization of urea-derived N, probably because the organic matter added in compost stimulated microbial growth or NH4+ fixation. Between 15 and 17% of urea-N was not recovered at the end of the incubation, but there was no difference in N loss among the treatments resulting from the contrasting effects (NH3 volatilization vs. NH4+ immobilization) of compost on N losses. Our results clearly show that application of compost with high urease activity increases NH3 volatilization loss of N from the co-applied urea, but the total amount of N lost is also affected by immobilization of NH4+ by the organic matter added to the soil through the applied compost. Key words: Ammonia volatilization, nitrogen dynamics, nitrogen-15 recovery, organic carbon, urease activity
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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 | 0.001 |
| 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) | 0.001 | 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".