Culturable autotrophic ammonia-oxidizing bacteria population and nitrification potential in a sheep grazing intensity gradient in a grassland on the Loess Plateau of Northwest China
Bibliographic record
Abstract
Liu, T., Nan, Z. and Hou, F. 2011. Culturable autotrophic ammonia-oxidizing bacteria population and nitrification potential in a sheep grazing intensity gradient in a grassland on the Loess Plateau of Northwest China. Can. J. Soil Sci. 91: 925–934. Grazing is known to enhance the activity of soil microbial communities in many types of grasslands; however, the potential impacts of rotational grazing activity on soil microbial functional groups remain poorly understood. We investigated the effects of 9 yr of rotational grazing by livestock on culturable autotrophic ammonia-oxidizing bacteria (AOB) population size, nitrification potential and soil properties in a semi-arid grassland of the Loess Plateau in Northwest China. Three stocking rate treatments of 2.7, 5.3 and 8.7 wether lambs ha−1were evaluated in geographically separated paddocks. Grazing increased nitrification potential and culturable AOB populations compared with ungrazed treatments. Ammonia-oxidizing bacteria populations increased from 155 bacteria g−1dry soil with 0 sheep ha−1to 16 218 bacteria g−1dry soil with 8.7 sheep ha−1. Grazing led to an increase in population of AOB at 0–10 cm soil depth, but had no effect on AOB at 10–20 cm soil depth. Nitrification potential increased from 1.21 mg NO3-N kg−1soil d−1in ungrazed treatments to 2.86 mg NO3-N kg−1soil d−1at the highest stocking rate. Soil ammonium and nitrate concentrations increased; however, total soil nitrogen and soil moisture content decreased with increased stocking rate for both sampling depths (0–10 cm and 10–20 cm). Soil organic matter was not affected by grazing treatments. Soil nitrification potential and the size of culturable AOB populations were dependent on grazing intensity, soil depth and season. This information is potentially important for the optimal selection of stocking rate for grazed ecosystems.
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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 | 0.001 | 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".