Nitrogen fertilizer is a key factor affecting the soil chemical and microbial communities in a Mollisol
Bibliographic record
Abstract
Microbial communities drive geochemical cycles in soils. Relatively few studies have assessed the long-term impacts of different types of soil amendments under field conditions in long-term experiments. The response of soil microbial organisms in a Mollisol cultivated with maize for 35 years was examined. Treatments involved the use of N, P, and K fertilizers and two doses of straw residue in isolation or combined. Real-time PCR and Illumina MiSeq sequencing methods were used to characterize the microbial community. The results showed that addition of nitrogen fertilizers decreased soil pH, but this was mitigated when a high dose of straw was also incorporated. Long-term application of inorganic fertilizers was able to alter the abundance of functional soil microbial population. Application of inorganic N fertilizer resulted in distinctive changes on N-cycle microorganisms. Phosphate-solubilizing functional genes abundance was lower in plots with no phosphate fertilizer. Sequencing analysis showed that the presence or absence of N in the fertilizer mix is a key factor affecting bacterial community diversity of agricultural soil, and pH, total organic C, and total N show a high correlation with bacterial community composition. Nitrogen addition increased the N concentration in the soil, which could cause changes in the soil pH and change the soil bacterial community. Our findings proved that interaction of N fertilizer with other fertilizers can affect microbial communities.
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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 | 0.001 | 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".