Long‐term nitrogen fertilization decreases bacterial diversity and favors the growth of <i>Actinobacteria</i> and <i>Proteobacteria</i> in agro‐ecosystems across the globe
Bibliographic record
Abstract
Long-term elevated nitrogen (N) input from anthropogenic sources may cause soil acidification and decrease crop yield, yet the response of the belowground microbial community to long-term N input alone or in combination with phosphorus (P) and potassium (K) is poorly understood. We explored the effect of long-term N and NPK fertilization on soil bacterial diversity and community composition using meta-analysis of a global dataset. Nitrogen fertilization decreased soil pH, and increased soil organic carbon (C) and available N contents. Bacterial taxonomic diversity was decreased by N fertilization alone, but was increased by NPK fertilization. The effect of N fertilization on bacterial diversity varied with soil texture and water management, but was independent of crop type or N application rate. Changes in bacterial diversity were positively related to both soil pH and organic C content under N fertilization alone, but only to soil organic C under NPK fertilization. Microbial biomass C decreased with decreasing bacterial diversity under long-term N fertilization. Nitrogen fertilization increased the relative abundance of Proteobacteria and Actinobacteria, but reduced the abundance of Acidobacteria, consistent with the general life history strategy theory for bacteria. The positive correlation between N application rate and the relative abundance of Actinobacteria indicates that increased N availability favored the growth of Actinobacteria. This first global analysis of long-term N and NPK fertilization that differentially affects bacterial diversity and community composition provides a reference for nutrient management strategies for maintaining belowground microbial diversity in agro-ecosystems worldwide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".