Soil food web controls on nitrogen mineralization are influenced by agricultural practices in humid temperate climates.
Bibliographic record
Abstract
Abstract Agricultural crop production depends upon judicious use of nitrogen (N) fertilizers to sustain yields. Globally, the N recovery rate by crops is about 60%, meaning that the rest of the N applied to agroecosystems is transformed to forms that are not available for crop uptake or are lost to the environment. Considering that part of the soil N supplied to crops comes from biological N 2 fixation and mineralization of soil organic N, quantifying these contributions could reduce our reliance on exogenous N inputs. This review examined how the microbially mediated reactions of N mineralization and nitrification contribute to the soil N supply, and biotic controls on these reactions in the soil food web. Potential N mineralization by heterotrophic bacteria and fungi can exceed 10% of the total soil N per year, and ammonium released by mineralization is rapidly transformed to nitrate through the action of chemoautotrophic ammonia oxidizers (bacteria and archaea) followed by heterotrophic and chemoautotrophic nitriflers (bacteria and fungi). Predation of these micro-organisms, primarily by soil microfauna, accounts for additional release of ammonium, estimated at 32-38% of the annual N mineralization. Soil meso- and macro-fauna also contribute to N mineralization and nitrification by accelerating the decomposition of organic substrates and modifying the soil habitat in ways that favour microbial activity. Tillage, application of organic amendments and improving soil drainage in humid temperate regions should favour N mineralization and nitrification processes in soil food web, whereas agrochemical use is expected to have a negligible effect. In summary, the soil food web contribution to N mineralization needs to be included in the soil N supply concept, which requires the development of field-based measurements and models of the soil N supply.
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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) | 0.001 | 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".