Dynamics of Microbial Biomass, Nitrogen Mineralization and Crop Uptake in Response to Placement of Maize Residue Returned to Chinese Mollisols over the Maize Growing Season
Bibliographic record
Abstract
Returning residue to soils is not only an effective nutrient management method, but also can reduce the air pollution caused by residue burning, which has become an important factor in global warming. However, it is not clear whether returning residue to the soil can affect the nitrogen mineralization and the nitrogen cycle process, and the environmental impact caused by the nitrogen loss in gaseous forms. Therefore, a pot experiment was conducted to study the effects of residue placement on the nitrogen turnover process, including microbial biomass N (MBN) and C (MBC), inorganic N, crop N uptake, and the contribution of residue-derived N to maize at different maize growth stages. Three treatments were assessed: no residue addition (T0), residue addition to the soil surface (T1), and residue incorporation into the 0–10 cm soil layer (T2). Soil samples were taken at the 0–5 and 5–10 cm layers for all residue treatments. Residue retention (T1 and T2) significantly affected the MBC and MBN contents and decreased MBC/MBN ratio at different maize growth stages. MBC/MBN markedly increased at the R1 stage compared to other growth stages. The differences in total inorganic nitrogen (TIN) were attributed to the balance in net N immobilization and net mineralization in the different maize growth stages. In addition, T2 significantly increased the residue-derived N source for maize by 11.3% compared to T0 in the R3 growth stage. Overall, relative to T1, T2 is a better agriculture management measure to promote N transformation and supply, and enhance residue-derived N release and uptake in maize.
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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".