Long-Term Land Use Affects Phosphorus Speciation and the Composition of Phosphorus Cycling Genes in Agricultural Soils
Bibliographic record
Abstract
Agriculturally-driven land transformation is increasing globally. Improving phosphorus (P) use efficiency to sustain optimum productivity in diverse ecosystems, based on knowledge of soil P dynamics, is also globally important in light of potential shortages of rock phosphate to manufacture P fertilizer. We investigated P chemical speciation and P cycling with solution 31P nuclear magnetic resonance, P K-edge X-ray absorption near-edge structure spectroscopy, phosphatase activity assays, and shotgun metagenomics in soil samples from long-term agricultural fields containing four different land-use types (native and tame grasslands, annual croplands, and roadside ditches). Across these land use types, native and tame grasslands showed high accumulation of organic P, principally orthophosphate monoesters, and high acid phosphomonoesterase activity but the lowest abundance of P cycling genes. The proportion of inositol hexaphosphates (IHP), especially the neo-IHP stereoisomer that likely originates from microbes rather than plants, was significantly increased in native grasslands than croplands. Annual croplands had the largest variances of soil P composition, and the highest potential capacity for P cycling processes based on the abundance of genes coding for P cycling processes. In contrast, roadside soils had the highest soil Olsen-P concentrations, lowest organic P, and highest tricalcium phosphate concentrations, which were likely facilitated by the neutral pH and high exchangeable Ca of these soils. Redundancy analysis demonstrated that IHP by NMR, potential phosphatase activity, Olsen-P, and pH were important P chemistry predictors of the P cycling bacterial community and functional gene composition. Combining chemical and metagenomics results provides important insights into soil P processes and dynamics in different land-use ecosystems.
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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 | 0.001 |
| 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".