Long-term fertilization effects on carbon and nitrogen in particle-size fractions of a Chinese Mollisol
Bibliographic record
Abstract
Yan, Y., He, H., Zhang, X., Chen, Y., Xie, H., Bai, Z., Zhu, P., Ren, J. and Wang, L. 2012. Long-term fertilization effects on carbon and nitrogen in particle-size fractions of a Chinese Mollisol. Can. J. Soil Sci. 92: 509–519. The response of soil organic matter (SOM) dynamics to long-term fertilization may be deduced from changes in the accumulation and distribution of different soil organic carbon (SOC) and nitrogen (N) pools. The SOC and N in particle-size fractions were therefore measured to assess the influences of pig manure and synthetic fertilizer application on the characteristics of these pools. A long-term fertilization experiment, established in 1979 in the Mollisol area (Gongzhuling, China) was used for this study. Composite soil samples (0–20cm) were collected in 2005 from 12 treatment plots that had received annual applications of pig manure, synthetic fertilizers or combinations of both. Soils were fractionated into fine clay (<0.2 µm), coarse clay (0.2–2 µm), silt (2–50 µm), fine sand (50–250 µm) and coarse sand (250–2000 µm) and then SOC and N contents in each particle-size fraction were measured. Although most of the SOC and N were associated with clay and silt fractions, the large proportion of silt in the soil mass played a key role in the retention of SOC and N. The application of pig manure alone increased accumulation of SOC and N in each particle-size fraction, but preferential enrichment was found in the coarse sand fraction. This indicates that pig manure is efficient in restoring SOM in the temperate Chinese Mollisol under a tilled maize (Zea mays L.) monocropping system and having a long frozen period in winter. The application of synthetic fertilizers had no clear effect on SOC and N accumulation or their distribution in particle-size fractions. However, the combined application of pig manure and synthetic fertilizers enhanced the accumulation of SOC and N in all particle-size fractions, and led to a shift of SOC and N from fine to coarse particles. We extended the hierarchy model for SOC protection to consider a shift in SOC accumulation from fine to coarse particles, depending on the initial SOC content of the specific soil. The findings reveal a clear positive interaction between pig manure and synthetic fertilizers that may improve the quantity of SOM in the temperate Chinese Mollisol.
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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) | 0.001 | 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".