Nitrogen storage in density and size fractions varied with tillage practices and cropping systems under residue return
Bibliographic record
Abstract
Residue return can prevent or restore the degradation of cropland, meanwhile, additional N input from residue return inevitably result in the changes of soil nitrogen (N) pools. Our objectives were to evaluate these changes in a 16-year field experiment. The residue return experiment consisted of no-tillage (NT) and mouldboard plough (MP), combined with continuous maize (Zea mays L.) (MM) and maize-soybean (Glycine max Merr.) rotation (MS) cropping systems, that is, NTMM, NTMS, MPMM, MPMS; conventional tillage (removal of crop residue and deep plough) with continuous maize (CTMM) was included as a control. The soil was separated into density (LF, light fraction) and particle size (sand, silt and clay) fraction. In 0-5 cm and 5-10 cm layers, soil TN content in NT was higher than MP, whereas the opposite trend was observed in 10-20 cm. Thus, the stratification ratio of soil TN was greater under NT. Cropping system affected soil TN as MM > MS. Residue return increased soil N storage by 6.44%-24.85% in the plough layer. Taking CTMM as the baseline, NTMM and MPMM increased the N storage in all physical fractions, while the decrease of silt-N storage was observed in NTMS and MPMS. Under residue return, the distribution of N storage changes in LF and sand fraction was affected by tillage practice, and that in silt and clay fraction was affected by cropping system. In summary, NTMM is effective for soil N accumulation due to its highest N storage and all physical fractions of N storage was enhanced.
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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".