Effects of different surface mulching on soil organic nitrogen accumulation and mineralization in dryland of Northwestern China
Bibliographic record
Abstract
Objective Understanding soil nitrogen transformation and supplying process is the key for optimizing crop cultivation and nitrogen managements. Method In this paper, soils were sampled from a 5-year-long field experiment to study the effects of different soil surface mulching on soil organic nitrogen accumulation and mineralization in dryland of northwestern China. Result The results showed that compared to conventional cultivation, long term surface mulching with straw increased soil nitrogen mineralization potential but decreased mineralization rate. Mulching with plastic-film decreased nitrogen mineralization potential, but increased mineralization rate. The nitrogen mineralization potential for the conventional cultivation, straw mulching and plastic film mulching was 25.0-29.7, 23.2-25.9 and 23.3-26.2 mg.kg-1, respectively. Without N application, straw and plastic-film mulching increased soil organic nitrogen and light fraction organic nitrogen contents. When N was fertilized, straw mulching could increase soil organic nitrogen and light fraction organic nitrogen contents, while plastic-film mulching decreased their contents. At N rate of 240 kg.hm-2, the organic nitrogen content was 1.03, 0.95 and 0.96 g.kg-1, and the light fraction organic nitrogen content was 51, 35 and 37 mg.kg-1 for the conventional cultivation, straw mulching and plastic film mulching, respectively. Conclusion It is concluded that surface mulching with straw causes soil to immobilize more mineral N into mineralisable organic N, while with plastic film is not beneficial to organic nitrogen accumulation. Therefore, in order to increase crop yield, more nitrogen fertilizer is needed or supplemental N fertilizer should be applied at the key nitrogen requirement stage under straw mulching cultivation as it could increase soil N supply capacity. For plastic film mulching, more attention should be paid to application of organic fertilizer.
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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".