Effects of Nitrogen Additions on Soil Respiration in an Asian Tropical Montane Rainforest
Bibliographic record
Abstract
Understanding the impacts of nitrogen (N) addition on soil respiration (RS) and its temperature sensitivity (Q10) in tropical forests is very important for the global carbon cycle in a changing environment. Here, we investigated how RS respond to N addition in a tropical montane rainforest in Southern China. Four levels of N treatments (0, 25, 50, and 100 kg N ha−1 a−1 as control (CK), low N (N25), moderate N (N50), and high N (N100), respectively) were established in September 2010. Based on a static chamber-gas chromatography method, RS was measured from January 2015 to December 2018. RS exhibited significant seasonal variability, with low RS rates appeared in the dry season and high rates appeared in the wet season regardless of treatment. RS was significantly related to the measured soil temperature and moisture. Our results showed that soil RS increased after N additions, the mean annual RS was 7% higher in N25 plots, 8% higher in N50 plots, and 11% higher in N100 plots than that in the CK plots. However, the overall impacts of N additions on RS were statistically insignificant. For the entire study period, the CK, N25, N50, and N100 treatments yielded Q10 values of 2.27, 3.45, 4.11, and 2.94, respectively. N addition increased the temperature sensitivity (Q10) of RS. Our results suggest that increasing atmospheric N deposition may have a large impact on the stimulation of soil CO2 emissions from tropical rainforests in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".