Long‐term effects of tillage, nutrient application and crop rotation on soil organic matter quality assessed by <scp>NMR</scp> spectroscopy
Bibliographic record
Abstract
Abstract Crop and land management practices affect both the quality and quantity of soil organic matter ( SOM ) and hence are driving forces for soil organic carbon ( SOC ) sequestration. The objective of this study was to assess the long‐term effects of tillage, fertilizer application and crop rotation on SOC in an agricultural area of southern Norway, where a soil fertility and crop rotation experiment was initiated in 1953 and a second experiment on tillage practices was initiated in 1983. The first experiment comprised 6‐yr crop rotations with cereals only and 2‐yr cereal and 4‐yr grass rotations with recommended (base) and more than the recommended (above base) fertilizer application rates; the second experiment dealt with autumn‐ploughed (conventional‐till) plots and direct‐drilled plots (no‐till). Soil samples at 0–10 and 10–30 cm depths were collected in autumn 2009 and analysed for their C and N contents. The quality of SOM in the top layer was determined by 13 C solid‐state NMR spectroscopy. The SOC stock did not differ significantly because of rotation or fertilizer application types, even after 56 yr. However, the no‐till system showed a significantly higher SOC stock than the conventional‐till system at the 0–10 cm depth after the 26 yr of experiment, but it was not significantly different at the 10–30 cm depth. In terms of quality, SOM was found to differ by tillage type, rate of fertilizer application and crop rotation. The no‐till system showed an abundance of O‐alkyl C, while conventional‐till system indicated an apparently indirect enrichment in alkyl C, suggesting a more advanced stage of SOM decomposition. The long‐term quantitative and qualitative effects on SOM suggest that adopting a no‐tillage system and including grass in crop rotation and farmyard manure in fertilizer application may contribute to preserve soil fertility and mitigate climate change.
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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 | 0.001 | 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".