Stability and stabilisation of biochar and green manure in soil with different organic carbon contents
Bibliographic record
Abstract
Due to its recalcitrance against microbial degradation, biochar is very stable in soil compared to other organic matter additions, making its application to soils a suitable approach for the build-up of soil organic carbon (SOC). The net effects of such biochar addition also depend on its interactions with existing organic matter in soils. A study was established to investigate how the status of pre-existing soil organic matter influences biochar stabilisation in soil in comparison to labile organic additions. Carbon loss was greater in the C-rich sites (C content 58.0 g C/kg) than C-poor soils (C content 21.0–24.0 g C/kg), regardless of the quality of the applied organic resource. Biochar-applied, C-rich soil showed greater C losses, by >0.5 kg/m2.year, than biochar-applied C-poor soil, whereas the difference was only 0.1 kg/m2.year with Tithonia diversifolia green manure. Biochar application reduced the rate of CO2-C loss by 27%, and T. diversifolia increased CO2-C losses by 22% in the C-poor soils. With biochar application, a greater proportion of C (6.8 times) was found in the intra-aggregate fraction per unit C respired than with green manure, indicating a more efficient stabilisation in addition to the chemical recalcitrance of biochar. In SOC-poor soils, biochar application enriched aromatic-C, carboxyl-C, and traces of ketones and esters mainly in unprotected organic matter and within aggregates, as determined by Fourier-transform infrared spectroscopy. In contrast, additions of T. diversifolia biomass enriched conjugated carbonyl-C such as ketones and quinones, as well as CH deformations of aliphatic-C mainly in the intra-aggregate fraction. The data indicate that not only the stability but also the stabilisation of biochar exceeds that of a labile organic matter addition such as green manure.
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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".