Effects of mulching with crushed wheat straw padding and plastic film on sunflower emergence, yield, and yield components under different irrigation intensity in the northwest arid regions, China
Bibliographic record
Abstract
Crops in the northwest arid region of China frequently suffer from low emergence and poor yield due to high water deficit. Mulching is an important approach to reduce irrigation amount while increasing productivity but faces the challenge of ecological adaptability in this region. A field experiment was carried out in the three growing seasons from 2011 to 2013 to study the effects of mulching with crushed wheat straw padding and plastic film on sunflower seed emergence and yield under different irrigation intensities. A two-factor (mulching and irrigation intensity) completely randomized block design was applied, resulting in a total of 12 treatments repeated three times. Mulching treatments include zero mulch (N), straw mulching at the beginning of the experiment (S), plastic film mulching when sowing (F; a commonly used mulching by local farmers), and double mulching with plastic film on the crushed wheat straw layer (SF). Irrigation intensity includes high (H = 900 m 3 ·ha −1 ), medium (M = 750 m 3 ·ha −1 ), and low (L = 600 m 3 ·ha −1 ). Results showed that all mulching treatments promoted the early emergence of seedlings compared with N, with SF and F performing better than the rest. SF was the best-performing mulching approach in this study, and it had significantly improved sunflower yield and yield components compared with other treatments. In SF, medium irrigation level had significantly increased sunflower 100-seed weight. Therefore, SF with M irrigation level showed the most positive effect on sunflower production, and it is now the recommended agronomic solution for sunflower production in the northwest arid regions and, potentially, other irrigated areas with similar ecological conditions.
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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 | 0.000 | 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 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".