Animal manure application promotes nitrogen and organic carbon accumulation in soil organic matter fractions: A global meta-analysis
Bibliographic record
Abstract
The United Nations identified nitrogen (N) management in agriculture as one of the most important environmental challenges of the 21st century. Animal manure applications provide nutrients to crops and enhance soil organic matter (SOM) content and fertility. However, few comprehensive studies have assessed the effect of animal manure applications on N and OC in physical SOM fractions, thus limiting our ability to provide a global perspective of manure-derived N management. A meta-analysis of 57 experimental sites was performed to evaluate how animal manure applications modify the concentrations of N and OC in physical SOM fractions. Overall, N and OC concentrations are greater following animal manure applications than mineral N fertilization, and the difference is even greater when compared to an unfertilized control. The relative response is particularly pronounced for particulate organic matter (POM), light particulate organic matter (L-POM), and heavy particulate organic matter (H-POM) compared to mineral-associated organic matter (MAOM) and the whole soil. A positive linear relationship is observed between manure application rate and the response of N and OC in whole soil and all fractions. Moreover, the response of POM N and OC is greater with solid than liquid manures, in alkaline than in acidic soils, and in dry than in moist climates. This study highlights the significance of POM, and more specifically the H-POM fraction, in the retention of animal manure-induced C in agricultural soils, and possibly as pools involved in the N supply capacity of manured soils, thereby providing insights for improving N management.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.033 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".