The effect of agroecosystem management on the distribution of C functional groups in soil organic matter: A review
Bibliographic record
Abstract
Abstract To improve soil health and to aid in climate change mitigation, the quantity of soil organic matter (SOM) should be maintained or increased over the long run. In doing so, not only the total quantity of SOC but also the stability of SOC must be considered. Stability of SOC increases as a function of resistance to microbial decomposition or microbial substrate use efficiency through chemical, biological, and physical mechanisms including humification, hydrophobic moieties, molecular diversity, and formation of macroaggregates. One of the mechanisms that enhance stability confers changes in the distribution of C functional groups of SOM. To better understand and quantify how these changes are influenced by agricultural management practices, we collected 670 pairwise data from the body of literature that has evaluated changes in the distribution of C functional groups of SOM measured by solid-state 13 C NMR spectroscopy. The types of agricultural managements discussed herein include (1) fertilization, (2) tillage, (3) crop rotation, (4) grazing, and (5) liming practices. Our meta-analyses show that these practices modify the distribution of C functional groups of SOM. Fertilization practices were associated with increased O-alkyl groups. Tillage resulted in increases in the SOC consisted of aromatic and carbonyl groups. Crop rotations, especially legume-based rotations, were found to increase the proportion of aromatic groups. Although there are fewer publications on tillage and crop rotation than on fertilization practices, the distribution of C functional groups may be more influenced by crop rotation and tillage practices than fertilization management—and should be a focus of future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".