Nutrients and defoliation increase soil carbon inputs in grassland
Bibliographic record
Abstract
Given the regulatory impact of resources and consumers on plant production, decomposition, and soil carbon sequestration, anthropogenic changes to nutrient inputs and grazing have likely transformed how grasslands process atmospheric CO2. The direction and magnitude of these changes, however, remain unclear in this system, whose soils contain -20% of the world's carbon pool. Nutrients stimulate production but can also increase tissue palatability and decomposition. Grazing variously affects tissue quality and quantity, decreasing, standing biomass, but potentially increasing leaf nutrient concentrations, root production, or investment in tissue defenses that slow litter decay. Here, we quantified individual and interactive impacts of nutrient addition and simulated grazing (mowing) on above- and belowground production, tissue quality, and soil carbon inputs in a western North American grassland with globally distributed agronomic species. Given that nutrients and grazing are often connected with increased root production and higher foliar tissue quality, we hypothesized that these treatments would combine to reduce inputs of recalcitrant-rich litter critical for C storage. This hypothesis was unsupported. Nutrients and defoliation combined to significantly increase belowground production but did not affect root tissue quality. There were no significant interactions between nutrients and defoliation for any measured response. Three years of nutrient addition increased root and shoot biomass by 37% and 23%, respectively, and had no impact on decomposition, resulting in a -15% increase in soil organic matter and soil carbon. Defoliation triggered a significant burst of short-lived lignin-rich roots, presumably a compensatory response to foliar loss, which increased root litter inputs by 33%. The majority of root and shoot responses were positively correlated, with aboveground biomass a reasonable proxy for whole plant responses. The exceptions were decomposition, with roots six times more decay resistant, and grazing impacts on tissue chemistry, with shoots undergoing significant alterations, while roots were unaffected. Because neither treatment affected concentrations of decay-resistant compounds in roots, the implied net effect is higher soil C inputs with potentially longer residency times. Areas managed with nutrients and moderate grazing in our study system could thus accumulate significantly more soil C than unmanaged areas, with a greater capacity to serve as sinks for atmospheric CO2.
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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 | 0.000 | 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 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".