Soil Organic Carbon Composition in a Northern Mixed‐Grass Prairie
Bibliographic record
Abstract
Growing interest in the potential for soils to provide a sink for atmospheric C has prompted studies of effects of management on the amount and nature of soil organic C (SOC). In this study, we evaluated effects of different grazing management regimes (light grazing [LG], heavy grazing [HG], and non‐grazed exclosures [EX]) on amount and composition of SOC at the USDA–ARS High Plains Grasslands Research Station (HPGRS), Cheyenne, WY. Soils (0–5 cm) from each treatment were analyzed for total C and N contents and lignin composition. Soil organic C and N contents were significantly greater in LG (SOC–13.8 Mg ha −1 ; total N–1.22 Mg ha −1 ) than HG (SOC–10.9 Mg ha −1 ; total N–0.94 Mg ha −1 ) or EX (SOC–10.8 Mg ha −1 ; total N–0.94 Mg ha −1 ). From CuO oxidation studies, significantly greater ( P < 0.05) total lignin (Vanillyl [V] + Syringyl [S] + Cinnamyl [C] compounds) contents were noted in EX (21 g kg −1 SOC) than LG (12 g kg −1 SOC) and HG (15 g kg −1 SOC) soils. The lignin composition of humic (HA) and fulvic (FA) acids indicated that HA under LG contained significantly greater V and S than HG or EX. Fulvic acids contained S‐depleted lignin compared with HAs and FAs from HG, which contained significantly greater V and C than FAs extracted from LG and EX. Nuclear magnetic resonance (NMR) spectra of HA and FA, however, did not vary significantly among the three grazing treatments. Results from CuO oxidation and NMR spectroscopy emphasized the familiar problem that determining the nature of soil organic matter (SOM) is a difficult task and sometimes different analytical techniques provide different information about the nature of SOM. Nonetheless, results of this study indicate that LG is the most sustainable grazing management system for northern mixed‐grass prairies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".