Soil Nitrogen and Greenhouse Gas Dynamics in a Temperate Grassland under Experimental Warming and Defoliation
Bibliographic record
Abstract
Core Ideas Warming but not defoliation increased short‐term N mineralization. The CO 2 efflux initially increased with warming and defoliation but promptly stabilized. Warming and defoliation reduced N 2 O emissions but not when combined. Soil responses to warming were complex and transient in this temperate grassland. Grasslands are important for C storage and supporting livestock production, yet little is understood about how warming interacts with defoliation to alter soil C and N dynamics. We examined how increased growing season temperature and defoliation influenced soil microbial biomass C (MBC) and N (MBN), N mineral dynamics, and greenhouse gas (GHG) flux within the top 15 cm of mineral soil in a northern temperate grassland of Alberta, Canada. We performed a 2‐yr experiment with open‐top chambers to increase soil temperatures, with warming assessed alone and combined with a single intense midsummer defoliation treatment. Soil MBC and MBN did not respond to warming or defoliation ( P > 0.05). Warming increased soil nitrification and net N mineralization in the spring of the second growing season ( P < 0.05) but did not persist beyond May. Although warming and defoliation both increased CO 2 efflux separately during the first growing season ( P < 0.05), the combination of these treatments reduced CO 2 efflux to levels similar to the controls, with no lasting effects into the following growing season. Warming also interacted with defoliation to increase N 2 O flux in the second growing season relative to plots under either warming or defoliation treatment, whereas CH 4 uptake was not influenced by our treatments. These results show the complex but transient impacts of warming and defoliation on soil C and N, particularly GHGs. Further research is needed to understand how these northern temperate grasslands respond to long‐term factorial experiments involving warming, defoliation, and variation in other factors (e.g., precipitation).
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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 | 0.001 | 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 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".