Long‐Term Manure Application Effects on Nutrients and Selected Enzymes Involved in Their Cycling
Bibliographic record
Abstract
Core Ideas We assessed long‐term manure rates and application times on soil nutrients and enzymes at two sites. Increasing manure rate enhanced soil fertility but hydrolase enzymes showed a quadratic trend. Manure application prior to period of heavy rainfall (spring and fall) was associated with phosphorus losses. Peroxidase and phenol oxidase activities declined with increasing manure application rates. Oxidative enzyme activities were good indices of long‐term soil organic carbon storage. We investigated how soil nutrients (soil organic carbon [SOC], nitrogen [N], and Mehlich‐3‐extractable phosphorus [P‐M 3 ]) and enzymes involved in their cycling were impacted by 10 yr of beef cattle manure application at two sites in Nova Scotia, Canada: a dikeland site (silty clay loam) and an upland site (sandy loam site), under timothy ( Phleum pratense L.) cover. Manure was applied at four rates (from 0 to 300 kg total N ha −1 ) at different times (spring, summer, early fall, and late fall). Activities of hydrolytic enzymes: β‐glucosidase (BG), cellobiohydrolase (CB), leucine aminopeptidase (LAP), and phosphomonoesterase (PME) and oxidative enzymes: phenol oxidase (PO), peroxidase (PP), were quantified. We hypothesize that manure application will increase soil nutrients and BG, CB, LAP, and PME activities and will decrease PO and PP activities. After 10 yr of manure application, compared to a control, soil pH, SOC, N, and P‐M 3 increased linearly by 41, 45, and 125%, respectively, with increasing manure rate, while BG, CB, PME, and LAP activities showed a quadratic response. Conversely, enzymes degrading less labile components (PP and PO) declined with increasing manure rate. Higher SOC and P‐M 3 contents and higher LAP activity were found at the upland site, whereas higher PME, PP, and PO activities were found at the dikeland site. Inverse relationships between PME activity and P‐M 3 content and between PP activity and SOC or N content were found, whereas a positive correlation between N content and LAP activity was observed. Regular manure application is a good way to sustain soil fertility, with the extent of soil C storage controlled by soil type. The optimal rate and timing of manure application is essential, since application prior to periods of heavy rainfall is associated with phosphorus losses. Our results demonstrated that oxidative enzymes (PP and PO) are good indices of long‐term SOC storage. The lower their activity, the higher is the SOC sequestration.
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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 | 0.000 |
| 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".