Mineralization of organic nitrogen from farm manure applications
Bibliographic record
Abstract
Abstract This study aimed to quantify the amount of nitrogen (N) mineralized from the organic fraction of farm manures under field conditions. Nine different farm manures were stripped of their ammonium‐N content prior to soil incorporation and establishment of ryegrass at two sites in England. Grass N uptake and nitrate‐N leaching were measured for five consecutive seasons and compared with an untreated control, with the sum of N uptake + leaching (net of the control) used as an estimate of the amount of organic N mineralized from the applied manures. The amount mineralized was related to thermal time (cumulative day degrees above 5 °C – CDD ), with two distinct phases – an initial phase up to 2300 CDD ( c .18 months under UK climatic conditions) where mineralization proceeded at rates ranging between 0.005 and 0.027%mineralized/ CDD and a slower phase at >2300 CDD , where rates were negligible at <0.001%mineralized/ CDD . There was no difference between soil types, both being light‐textured (<20% clay), but there were differences between manure types depending on the manure C: organic N ratios. For pig slurry and layer manure (C:organic N = 9–12:1), up to 70% of the organic N was mineralized, compared to 10–30% mineralization from the cattle slurry and straw‐based farmyard manures‐ FYM s (C:organic N = 10–21:1).The relationships derived provide a useful tool for predicting both the amount and timing of manure N release, with important implications for both crop N uptake and leaching risk.
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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 | 0.001 | 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".