Soil Organic Matter Compositional Change in Response to Cropping Practices and Environmental Factors in Agricultural and Forest Ecosystems
Bibliographic record
Abstract
Land-use management (i.e., tillage, crop rotation, nitrogen (N) fertilization) and environmental variables (climate, soil texture, litter quality) markedly altered soil carbon cycling however, the underlying mechanisms and soil organic matter (OM) compositional change are not well defined. To fill these gaps, molecular-level soil OM characterization was conducted in agro- and forest ecosystems. Different tillage and N fertilization did not markedly alter soil carbon contents. Compared with conventional tillage, conservation tillage increased the concentrations of long-chain lipids, cyclic lipids and simple sugars. Lignin-derived compounds and aromatic carbon (mainly from lignin) from nuclear magnetic resonance (NMR) analysis increased or decreased with conservation tillage depending on microbial processing. Cutin- and suberin-derived lipids as well as alkyl carbon (mainly from cutin and suberin) did not markedly degrade and were relatively long-lived with conservation tillage. Interestingly, N fertilization either decreased or resulted in similar cutin- and suberin-derived compounds, suggesting that these OM components were not substantively preserved with N addition. Various N fertilization levels (0-260 kg ha-1 yr-1) altered soil OM compound degradation in a rate-dependent manner with the highest degradation observed at the N rate of ~145 kg ha-1 yr-1. Results based on the combination of various practices (i.e., tillage × crop rotation; N fertilization × tillage) showed that the controls of one practice on soil OM dynamics depended on another management. Investigations based on various sites across Canada and New Zealand suggested that the temporal changes in microbial-derived compounds were linked to soil texture; while plant-derived compounds were correlated with climate factors or both climate and soil texture depending on ecosystem properties. Doubling above-ground litter and wood debris in a coniferous forest did not increase soil carbon content, but increased microbial biomass and soil OM decomposition, suggesting soil priming with added litter. Overall, above-ground high quality litter altered soil OM biogeochemistry to a greater extent than other litter types. The work in this thesis shows that soil OM composition is highly sensitive to land-use management and environmental change although soil carbon content exhibits insignificant variations. Molecular-level soil OM composition analysis should be included when assessing soil biogeochemical dynamics in various 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 | 0.001 | 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) | 0.000 | 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".