Cover Crop Mulch and Weed Management Influence Arthropod Communities in Strip-Tilled Cabbage
Bibliographic record
Abstract
Cover crop mulch and weeds create habitat complexity in agricultural fields that may influence arthropods. Under strip-tillage systems, planting rows are tilled and preestablished cover crops can remain between rows. In field experiments conducted in Michigan in 2010 and 2011, a preestablished oat (Avena sativa L.) cover crop was allowed to grow between rows of strip-tilled cabbage and killed at 0, 9-14, or 21-27 d after transplanting (DAT). The effects of herbicide intensity and oat kill date on arthropods, weeds, and crop yield were examined. Two levels of herbicide intensity (low or high) were used to manipulate habitat vegetational complexity, with low weed management intensity resulting in more weeds, particularly in 2010. Oat kill date manipulated the amount of cover crop mulch on the soil surface. Later oat kill dates were associated with higher natural enemy abundance. Reduced herbicide intensity was associated with (1) lower abundance of several key cabbage (Brassica oleraceae L.) pests, and (2) greater abundance of important natural enemy species. Habitats with both later oat kill dates and reduced herbicide intensity contained (1) fewer herbivores with chewing feeding guilds and more specialized diet breadths, and (2) greater abundance of active hunting natural enemies. Oats reduced cabbage yield when oat kill was delayed past 9-14 DAT. Yields were reduced under low herbicide intensity treatments in 2010 when weed pressure was greatest. We suspect that increased habitat complexity associated with oat mulches and reduced herbicide intensity enhances biological control in cabbage, although caution should be taken to avoid reducing yields or enhancing hyperparasitism.
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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) | 0.001 | 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".