A multi-region study reveals high overwinter loss of fall-applied reactive nitrogen in cold and frozen soils
Bibliographic record
Abstract
In cold agricultural regions, animal manure and synthetic fertilizers may be applied in the fall for convenience. However, the fate of applied nitrogen (N) is unclear and may differ depending on N source and interannual and regional variations in winter conditions. A multi-region study using 15N-labelled reactive N (NH4-15N) applied in the fall with pig slurry, dairy cattle slurry, and ammonium sulfate was carried out under a range of climatic conditions. Nitrification and immobilization of applied NH4-N occurred throughout the winter period at all sites. Transformation and losses were slower and less at the sites where significant soil freezing occurred than at the site where soil rarely froze, highlighting the repressive effect of frost. Nevertheless, losses were similar among sites with significant freezing despite marked differences in duration and extent of freezing. This suggests that soil microbes were adapted to prevailing winter conditions at each site and able to use and transform fall-applied N throughout the winter period. Overall, 47%–94% of fall-applied NH4-N was lost from the top 30 cm of soil before seeding in the next spring. Losses were generally greater with synthetic fertilizer than manures, likely because fresh carbon added with manures stimulated immobilization of NH4-N. This multi-region assessment indicates that reactive N applied in the fall has high vulnerability to loss in cold and frozen soils, and strategies for improving N retention over the winter are required even in areas where prolonged freezing occurs.
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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".