EFFECTS OF NITROGEN ADDITIONS ON A LEYMUS CHINENSIS POPULATION IN TYPICAL STEPPE OF INNER MONGOLIA
Bibliographic record
Abstract
Leymus chinensis, a rhizomatous graminoid, is a dominant species in the grasslands of northern China. The characteristics of L. chinensis populations have been well documented in many research papers. Because of overgrazing, grasslands of northern China have become degraded since the 1980s. As a result, the density and biomass of L. chinensis populations have decreased significantly. Fertilization is a common technique for management of pastures in many countries; however, it is not widely used in the grasslands of China. Nitrogen is an important driver of community succession in grassland ecosystems, but the response of L. chinensis populations to nitrogen additions in typical steppe, a semiarid area of northern China, remains unclear. We conducted a sequential nitrogen addition experiment in a lightly degraded grassland plot that was fenced in 1999. Nitrogen (NH 4NO 3) was applied on July 5 for two years at application rates of: 0, 1.75, 5.25, 10.5, 17.5, and 28 g N·m -2 ,respectively. There were 9 replicate 5 m×5 m plots of each of the six treatments with each plot spaced 1 m apart. A completely randomized design was used for this experiment. Before the experiment, soil samples were collected and dry bulk density, pH, soil nitrogen and soil carbon were analyzed. After two years of nitrogen fertilization, we measured the density, height, aboveground biomass and belowground biomass of L. chinensis in each plot. The results showed that L. chinensis population characteristics were highly responsive to nitrogen additions. With an increase in nitrogen application rates, the density, height, aboveground biomass, belowground biomass and total biomass of L. chinensis increased significantly whereas the ratio of aboveground biomass/belowground biomass decreased. The allocation of biomass among plant parts was significantly affected by nitrogen additions: the proportion of biomass allocated to rhizomes decreased remarkably with increasing nitrogen rates whereas that allocated to leaves and roots increased significantly. The relative biomass and relative density of L. chinensis also increased with increasing nitrogen additions. In summary, adding nitrogen to lightly degraded grassland not only increased the density and biomass of L. chinensis population but changed the resource partitioning among plant parts as well.
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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 | 0.000 | 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 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".