Effects of wheat straw mulching and wet treatment on soil improvement, greenhouse gas emission, nitrogen leaching, and vegetable yield
Bibliographic record
Abstract
Plastic tunnels are a crucial tool used for intensive vegetable production in developing countries, however these tunnels have resulted in significant soil degradation. Another issue that the agriculture industry is facing is that an excess of crop straw is produced every year. This paper aims to combat both of these issues by combining them: to relieve soil degradation and consume crop straw, six treatments of three wheat straw quantities (0, 5 000 and 10 000 kg · hm -2 ) and two soil moisture levels (wet and submergence) were evaluated during two-month high-temperature summers to explore the possibility of applying straw mulching to improve degraded soil in plastic tunnels. Greenhouse gas emission and nitrogen leaching, which are two other significant problems with using intensive vegetable tunnels, were also investigated. Compared to the no straw mulching and wet treatment, the net global warming potential, available nitrogen, nitrogen leaching, and N 2 O emissions from subsequent crop fields decreased by 389.59%, 21.2%, 45.9%, and 41.5%, respectively. The soil-available phosphorus, available potassium, total nitrogen, total phosphorus, total potassium, organic carbon, microbial biomass carbon, microbial biomass nitrogen, activities of urease, sucrase, and acid phosphatase, and yields of cucumber and tomato increased by 2%, 79.6%, 75.3%, 51.4%, 92.5%, 32.8%, 122.1%, 152.5%, 103.9%, 102%, 88.6%, 19% and 13%, respectively, in the 10 000 kg straw and wet treatment. According to the 15 N-site preference value, nitrification was the dominant pathway for N 2 O production in the field, and its contribution was enhanced by straw mulching and weakened by submergence. Considering all factors, no significant advantage was found for submergence compared with wet treatment, while treatment with 10 000 kg of straw showed remarkable improvement over the treatment with 5 000 kg of straw. We conclude that applying 10 000 kg of wheat straw in conjunction with the wet treatment during the summer fallow period has wide application potential to improve degraded soil, alleviate secondary salinization and nitrogen leaching, and consume crop straw without increasing net global warming potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".