FERTILIZATION AND SOIL AERATION EFFECTS ON GRASSLAND PRIMARY PRODUCTIVITY AND SPECIES DIVERSITY IN A MEADOW STEPPE, NORTHERN CHINA
Bibliographic record
Abstract
In the grassland ecosystem, soils are subjected to a range of stresses which may affect their physical and biological properties, as well as the plant community biomass.As biomass is affected by long-term soil properties, we sought to establish a direct link between biomass and resilience to fertilization and soil aeration.We evaluated biomass yield in grasslands managed across a gradient of nitrogen (N), phosphorus (P), and potassium (K) fertilizers at Hulunbuir in Inner Mongolia, China, from 2014 to 2017.Based on the estimates from the simulated optimization and optimal theoretical regression model, we recommend applying N (231.50-238.82kgha -1 ), and P (187.25-218.75 kg ha -1 ), and K (28.28-33.32kg ha -1 ) annually to maximize biomass in the non-aerated grassland.The positive effect of nitrogen and phosphorus on biomass was significantly higher than unfertilized treatment.The effects of aeration on biomass were less explicit.Simultaneously, we compared the Shannon-Wiener Index and Species richness for the suitable fertilizer levels.Shannon-Wiener diversity and Species richness became lower the longer the fertilization treatments lasted.Thus, nutrient resorption is resulting in a decrease in species diversity and richness, while it is an important strategy for increasing plant biomass.
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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".