Effects of Different Plastic Mulching Methods on Soil Water, Temperature and Nitrate Accumulation in a Dryland Winter Wheat Field
Bibliographic record
Abstract
This study has investigated the effects of two different plastic mulching methods on soil water, temperature, and nitrate (NO3-N) accumulation in a dryland winter wheat field after one-year experiment. The drought-resistant wheat (Triticum aestivum) variety Chang-8744 was grown by (i) furrow planting with ridge mulching, (ii) bunch planting with flat mulching, and (iii) conventional flat planting without mulching (or control). Results showed that dryland winter wheat effectively utilized soil water down to 2 m depth, mainly in the first 140 cm. Plastic mulching increased the evapotranspiration during wheat growing season and mostly r flat plastic mulching, by ~18% over the value recorded in the control plots. Soil temperature of the 20-40 cm-layer was higher than the one recorded at 5-10 cm depth during seedling-overwintering stages. Ridge plastic mulching and flat plastic mulching increased soil temperatures at 5 cm, 10 cm, and 40 cm depths during seedling–overwintering stages with reference to the control (no mulching), then lowered them at the same depths during reviving–ripening stages. Residual NO3-N was always detected in the soil after harvesting irrespective of the mulching method. It was mainly concentrated in the first -60 cm accounting for ~50% of soil NO3-N accumulation within the 2-m profile. The highest soil NO3-N accumulation occurred under flat plastic mulching, which represented ~107% of the mean value of the remaining treatments. Finally, flat plastic mulching showed the greatest effects on soil water, temperature, and NO3-N accumulation in dryland wheat field.
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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) | 0.000 | 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".