Long-term cropping system effects on soil organic matter fractions and enzyme activities in a cool oceanic climate
Bibliographic record
Abstract
Information about soil organic matter fraction and enzyme activity responses to agricultural management may help guide decisions that sustain crop productivity and soil health. We measured carbon and nitrogen in bulk soil, mineral-associated organic matter, particulate organic matter, water-extractable organic matter, and the potential activity of β-glucosidase, N-acetyl-β-d-glucosaminadase, acid phosphomonoesterase and arylsulfatase in three cropping systems after: (1) 21 years of conventional- or no-tillage silage corn monoculture (0–20 cm); and (2) 6 years of nitrogen fertilization with or without nitrification inhibitors, and (3) 9 years of 100% or 200% the recommended broadcast or fertigation nitrogen rate in two distinct mature highbush blueberry systems (0–15 cm). Soil organic carbon, particulate organic carbon, particulate organic nitrogen, water-extractable nitrogen, and arylsulfatase activity were 17%, 38%, 50%, 25%, and 68% greater, respectively, with no-tillage than conventional tillage. Particulate organic carbon accumulated with two decades of no-tillage, which increased soil organic carbon without altering mineral-associated organic carbon. Nitrification inhibitors did not impact any soil organic matter fraction or enzyme activity after 6 years in a mature highbush blueberry system. Broadcasted nitrogen led to higher soil organic carbon than fertigation, but excessive application (200% vs 100% rate) depleted soil organic carbon, accumulated reactive nitrogen and reduced potential activity of N-acetyl-β-d-glucosaminadase, acid phosphomonoesterase and arylsulfatase after 9 years in a mature highbush blueberry system. Excessive nitrogen may deplete organic carbon and cause reactive nitrogen accumulation in soil of mature highbush blueberry systems. Intensive agricultural management has tradeoffs for carbon and nitrogen cycling that should be balanced with sustainable crop production.
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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) | 0.001 | 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".