Soil Phospholipid Fatty Acid Biomarkers and β‐Glucosidase Activities after Long‐Term Manure and Fertilizer N Applications
Bibliographic record
Abstract
Core Ideas Thirty‐five annual manure applications increased soil microbial biomass and activity. Responses occurred mostly in the top 15 cm of soil, with some in the 15‐ to 30‐cm depth The three times recommended application rate had some microbial responses down to 60 cm of soil. Twenty annual fertilizer N applications did not affect soil microbial properties. The microbial properties were positively related to soil organic C. Manure applications to soil usually increase soil microbial properties, but their vertical distribution is rarely evaluated. Soil phospholipid fatty acid (PLFA) biomarkers and β‐glucosidase activity were measured down to the 120‐ cm depth in a trial that had received annual manure or N fertilizer applications for 37 or 20 yr, respectively. The manure application rates were 0, 60, 120, and 180 Mg ha –1 , representing 0×, 1×, 2×, and 3× the recommended rates. The effect of N fertilizer at 100 kg N ha –1 was compared with that of manure at 60 Mg ha –1 . Total PLFAs at 60 Mg ha –1 manure rate were 3.2× those of the control, and β‐glucosidase activity 1.9× to 2.4×, but fertilizer N had no effects on soil microbial PLFAs or enzyme activity. These manure effects occurred mostly in the 0‐ to 15‐cm soil depth, with smaller effects in the 15‐ to 30‐cm depth. Increasing manure rate to 180 Mg ha –1 also increased total PLFAs linearly, but quadratic increases were observed for β‐glucosidase activity. The vertical distribution of the responses at 120 Mg ha –1 was similar to that at 60 Mg ha –1 ; however the 180 Mg ha –1 rate shifted some responses down to the 30‐ to 60‐cm soil depth. These PLFA contents and enzyme activities were positively correlated with soil organic C, total N, and available P. Therefore, manure improved the biological quality of mostly the top 15 cm of soil, but down to 30 or 60 cm for the high manure rates, by increasing microbial biomass and activity, but N fertilizer had no effects.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".