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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 it