Plastic mulch increases dryland wheat yield and water-use productivity, while straw mulch increases soil water storage
Bibliographic record
Abstract
Amplifying drought stress and high precipitation variability impair dryland wheat production. These problems can potentially be minimized by using plastic mulch (PM) or straw mulch (SM). Therefore, wheat grain yield, soil water storage, soil temperature and water-use productivity (WUP) of PM and SM treatments were compared with no mulch (CK) treatment on dryland wheat over a period of eight seasons. Compared to the CK treatment, PM and SM treatments on average significantly increased grain yield by 12.6 and 10.5%, respectively. Compared to the CK treatment, SM treatment significantly decreased soil daily temperature by 0.57, 0.60 and 0.48°C for the whole seasons, growing periods and summer fallow periods, respectively. In contrast, compared to the CK treatment, PM treatment increased soil daily temperature by 0.44, 0.51 and 0.27°C for the whole seasons, growing periods and summer fallow periods, respectively. Lower soil temperature under SM allowed greater soil water storage than under PM. Pre-seeding soil water storage was 17% greater under the SM than under the PM treatment. Soil water storage post-harvest was similar for the PM and SM treatments, but evapotranspiration (ET) was 4.5% higher in the SM than in the PM treatment. Consequently, WUP was 6.6% greater under PM than under the SM treatment. Therefore, PM treatment increased dryland wheat yield and water-use productivity, while straw mulch increased soil water storage.
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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) | 0.002 | 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".