Mechanical Termination of Diverse Cover Crop Mixtures for Improved Weed Suppression in Organic Cropping Systems
Bibliographic record
Abstract
Cover crops can provide many benefits in agroecosystems, including the opportunity for improved weed control. However, the weed suppressive potential of cover crops may depend on the species (or mixture of species) chosen, and the method of cover crop termination and residue management. The objective of this study was to determine the effects of cover crop mixture and mechanical termination method on weed biomass and density, and relative crop yield in an organic cropping system. A field experiment was conducted from 2009 to 2011 near Mead, NE, where spring-sown mixtures of two, four, six, and eight cover crop species were included in a sunflower–soybean–corn crop rotation. Cover crops were planted in late March, terminated in late May using a field disk or sweep plow undercutter, and main crops were planted within 1 wk of termination. Terminating cover crops with the undercutter consistently reduced early-season grass weed biomass, whereas termination with the field disk typically stimulated grass weed biomass relative to a no cover crop control (NC). The effects of cover crop mixture were not evident in 2009, but the combination of the undercutter and the eight-species mixture reduced early-season weed biomass by 48% relative to the NC treatment in 2010. Cover crops provided less weed control in 2011, where only the combination of the undercutter and the two-species mixture reduced weed biomass (by 31%) relative to the NC treatment. Termination with the undercutter resulted in relative yield increases of 16.6 and 22.7% in corn and soybean, respectively. In contrast, termination with the field disk resulted in a relative yield reduction of 13.6% in soybean. The dominant influence of termination method highlights the importance of appropriate cover crop residue management in maximizing potential agronomic benefits associated with cover crops.
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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 | 0.001 | 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".