Research on the effect of straw mulching on the soil moisture by field experiment in the piedmont plain of the Taihang Mountains
Bibliographic record
Abstract
To reveal the influencing effect of the long-term straw mulching on the soil moisture, this paper employed the field experiment data in 2010 of a typical area of Taihang Mountains plain, observed the soil moisture dynamic regularities under different mulching patterns by virtue of depressimeter and neutron probe, analyzed the characteristics of soil water content and storage in different depths and seasons under the long-term straw mulching. The results showed that the long-term straw mulching can keep the soil moisture conservation of the deep, while decreased the shallow. (1) The long-term straw mulching can changed the type of soil water movement. If no straw mulching, the type is mainly evaporation-infiltration. And with straw mantle the type would change into infiltration. The number of zero flux plane would be reduced or absent. (2) The long-term straw mulching can increase the soil water reserves of the whole soil profile with the depth between 0 cm and 220 cm. But the soil water content of the layer from 30 cm to 80 cm decreased and the soil water content of the layer from 80 cm to 220 cm increased instead., The effect of soil moisture conservation on winter wheat is not obvious; (3) With no straw mulching, the depth of infiltration recharge by rainfall or irrigation is shallower than 80 cm. In a straw mulching, the influence depth is can extend to 120 cm; (4) With no straw mulching, there is a deep layer on the depth of 220 cm between March and June, while this layer will disappear with a long-term straw mulching.
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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 | 0.001 | 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 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".