Impact of Tillage and Crop Residue Management on the Weed Community and Wheat Yield in a Wheat–Maize Double Cropping System
Bibliographic record
Abstract
Weeds are often harmful to crop growth due to the competition for space and resources. A field experiment containing four treatments with three replications in a complete randomized design was conducted at Yucheng Comprehensive Experiment Station, Chinese Academy of Sciences since 2008 to assess the impact of shifting from conventional tillage to no-till with crop residue management on weeds and wheat production at the North China Plain. We found that both aboveground weed density and species richness were higher under continuous no-till (NT) than conventional tillage (CT) in the regrowth and stem elongation stage of wheat growth. On the other hand, aboveground weed density in the stage of flowering and filling decreased with crop residue mulching. The density of the soil seed bank in crop residue removal treatments was significantly higher than that of crop residue retention. Besides, either crop residue mulching or incorporating into the soil significantly increased the wheat yield compared with crop residue removal regardless of tillage management. In conclusion, crop residue retention could decrease the weed density and species richness both aboveground and in the soil seed bank and inhibit the growth of broadleaf weeds by the residue layer. Moreover, crop residue retention could improve the wheat yield.
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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".