Optimizing Cover Crop Benefits with Diverse Mixtures and an Alternative Termination Method
Bibliographic record
Abstract
Previous studies have demonstrated benefits of individual cover crop species, but the value of diverse cover crop mixtures has received less attention. The objectives of this research were to determine the effects of spring‐sown cover crop mixture diversity and mechanical cover crop termination method on cover crop and/or cash crop productivity, soil moisture and N, and profitability in an organic cropping system. An experiment was conducted between 2009 and 2011 near Mead, NE, where mixtures of two (2CC), four (4CC), six (6CC), and eight (8CC) cover crop species, or a summer annual weed mixture were included in a sunflower–soybean–corn rotation. Cover crops were terminated in late May using a field disk or sweep plow undercutter. Undercutting cover crops increased soil NO3–N (0–20 cm) by 1.0 and 1.8 mg NO3–N kg−1 relative to disk incorporation in 2010 and 2011, respectively. Cover crop mixtures often reduced soil moisture (0–8 cm) before main crop planting, though cover crop termination with the undercutter increased soil moisture content by as much as 0.024 cm3 cm−3 compared to termination with the disk during early main crop growth. Crop yields were not influenced by cover crop mixture, but termination with the undercutter increased corn and soybean yield by as much as 1.40 and 0.88 Mg ha−1, respectively. Despite differences in productivity between spring cover crop mixtures and weed communities, crop yield was not different among these treatments; thus, profitability of the weed mixture–undercutter treatment combination was greatest due to reduced input costs.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".