Distribution of nitrogen storage in density and size fractions varied with tillage practices and cropping systems with residue return in black soil of Northeast China
Bibliographic record
Abstract
Abstract Crop residue return can prevent the degradation of cropland caused by conventional tillage practice in Northeast China. Meanwhile, additional nitrogen (N) input from crop residue inevitably changes soil N pools. Our objectives were to evaluate soil N storage changes in soil physical fractions. The residue return treatments consisted of no‐tillage (NT) and moldboard plow (MP), combined with continuous maize ( Zea mays L.) (MM) and maize–soybean [ Glycine max (L.) Merr.] rotation (MS) cropping systems, that is, NTMM, NTMS, MPMM, MPMS; conventional tillage (removal of crop residue and deep plow) with continuous maize (CTMM) was included as a control. The concentration of total N (TN) in bulk soil and physical fractions (light fraction [LF], sand, silt, and clay) was measured. In 0‐to‐5‐cm layer, TN content was higher in NT than MP, whereas the result was opposite in 10‐to‐20‐cm layer. Thus, the stratification ratio (SR) of soil TN was greater under NT. The TN content in MM was greater than MS under both tillage practices with residue return. Residue return treatments increased soil N storage by 6.44–24.85% in 0–20 cm compared with CTMM. Continuous maize increased the N storage in all physical fractions, whereas the decrease of silt‐N storage was observed in MS. Overall, it was concluded that residue return could enhance soil N storage, whereas the distribution of N storage changes in LF and sand size fractions was influenced by tillage practice, and the distribution of N storage changes in silt size and clay size fractions was influenced by cropping system.
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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 | 0.001 |
| 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".