Effects of surface straw mulching and buried straw layer on soil water content and salinity dynamics in saline soils
Bibliographic record
Abstract
Field experiments were conducted to evaluate the effects of wheat straw return methods, which included the use of surface straw mulch and a buried straw layer, on soil water content, electrical conductivity (EC), and sodium adsorption ratio (SAR) of saline sodic soils in an effort to identify useful ways for reducing soil salt accumulation and enhancing soil water content. The results showed that the straw return treatments were effective for inhibiting salt accumulation and soil water loss, resulting in a reduction of EC and SAR but an enhancement of soil water content. After a year-long experiment, compared with the treatment with no straw return, the straw burial and straw mulching treatments decreased the EC by 10.5% and 3.5%, reduced the SAR by 7.4% and 21.5%, and increased the soil water by 0.9% and 4.4%, respectively. Furthermore, the combined application of straw layer burial and surface straw return had a more significant effect than the individual treatments; the positive effect of straw return occurred mainly focused in the topsoil (0–40 cm) and decreased with increasing soil depth. Our results allowed us to conclude that burial of the straw layer was necessary to enhance the effects of surface mulch, and the combination of surface mulch (3.0 t ha−1 of wheat straw) and straw layer burial (6.0 t ha−1 of wheat straw) proved to be a better straw return method than the others.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".