Management scheme influence and nitrogen addition effects on soil CO2, CH4, and N2O fluxes in a Moso bamboo plantation
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Bibliographic record
Abstract
Abstract Background It is still not clear whether the effects of N deposition on soil greenhouse gas (GHG) emissions are influenced by plantation management schemes. A field experiment was conducted to investigate the effects of conventional management (CM) versus intensive management (IM), in combination with simulated N deposition levels of control (ambient N deposition), 30 kg N·ha − 1 ·year − 1 (N30, ambient + 30 kg N·ha − 1 ·year − 1 ), 60 kg N·ha − 1 ·year − 1 (N60, ambient + 60 kg N·ha − 1 ·year − 1 ), or 90 kg N·ha − 1 ·year − 1 (N90, ambient + 90 kg N·ha − 1 ·year − 1 ) on soil CO 2 , CH 4 , and N 2 O fluxes. For this, 24 plots were set up in a Moso bamboo ( Phyllostachys edulis ) plantation from January 2013 to December 2015. Gas samples were collected monthly from January 2015 to December 2015. Results Compared with CM, IM significantly increased soil CO 2 emissions and their temperature sensitivity ( Q 10 ) but had no significant effects on soil CH 4 uptake or N 2 O emissions. In the CM plots, N30 and N60 significantly increased soil CO 2 emissions, while N60 and N90 significantly increased soil N 2 O emissions. In the IM plots, N30 and N60 significantly increased soil CO 2 and N 2 O emissions, while N60 and N90 significantly decreased soil CH 4 uptake. Overall, in both CM and IM plots, N30 and N60 significantly increased global warming potentials, whereas N90 did not significantly affect global warming potential. However, N addition significantly decreased the Q 10 value of soil CO 2 emissions under IM but not under CM. Soil microbial biomass carbon was significantly and positively correlated with soil CO 2 and N 2 O emissions but significantly and negatively correlated with soil CH 4 uptake. Conclusion Our results indicate that management scheme effects should be considered when assessing the effect of atmospheric N deposition on GHG emissions in bamboo plantations.
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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 it