Responses of winter wheat yield and water productivity to sowing time and plastic mulching in the Loess Plateau
Bibliographic record
Abstract
On the Loess Plateau, unfavorable late-sowing conditions often arise due to late harvests from the previous season or excessive rainfall during the sowing season, which can delay seed germination, reduce tiller numbers, and decrease winter wheat yields. Few studies have explored whether plastic mulching (PM) can mitigate the adverse effects of late sowing. Consequently, we conducted a 3-year field experiment from 2017 to 2020 on the Loess Plateau combining two mulching conditions [PM and no mulching (NPM)] and three sowing times (normal, 10-day late, and 20-day late sowing). We investigated the combined influence of sowing time and mulching conditions on soil hydrothermal status, crop water productivity (WP), and yield. The results revealed that delayed sowing significantly prolonged emergence times and decreased tiller numbers, leaf area index (LAI), root biomass, and aboveground biomass (AGB). The PM increased soil temperatures, advancing wheat emergence and increasing tiller numbers. Plants under PM had higher LAI, root biomass, and AGB than those under NPM. Moreover, PM reduced ineffective transpiration by accelerating the degradation of ineffective tillers, resulting in higher yields without a corresponding increase in evapotranspiration. The beneficial effects extended to spike numbers, thousand-grain weight, and harvest index. Specifically, PM combined with 10-day late sowing increased yield by 12.8 % compared to NPM combined with normal sowing. Furthermore, under 20-day late sowing, PM mitigated yield losses, reducing them from a 28.7 % decline under NPM to a 12.8 % decline when compared to normal sowing under NPM. We conclude that PM completed compensated for the yield loss under 10-day late sowing and partially alleviated losses under 20-day late sowing. Therefore, combining 10-day late sowing (accumulated air temperature before winter > 430 °C d) with PM was the optimal approach for simultaneously improving yield and WP in winter wheat seasons with unfavorable late-sowing conditions.
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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 | 0.000 | 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".