Corn and Wheat Residue Management Effects on Greenhouse Gas Emissions in the Mid-Atlantic USA
Why this work is in the frame
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Bibliographic record
Abstract
Greenhouse gas (GHG) emissions from crop residue management have been studied extensively, yet the effects of harvesting more than one crop residue in a rotation have not been reported. Here, we measured the short-term changes in methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions in response to residue removal from continuous corn (Zea mays L.) (CC) and corn–wheat (Triticum aestivum L.)–soybean (Glycine max L. Merr.) (CWS) rotations in the Mid-Atlantic USA. A first experiment retained five corn stover rates (0, 3.33, 6.66, 10, and 20 Mg ha−1) in a continuous corn (CC) in Blacksburg, VA, in 2016 and 2017. Two other experiments, initiated during the wheat and corn phases of the CWS rotation in New Kent, VA, utilized a factorial combination of retained corn (0, 3.33, 6.66, and 10.0 Mg ha−1) and wheat residue (0, 1, 2, and 3 Mg ha−1). Soybean residue was not varied. Different crop retention rates did not affect CO2 fluxes in any of the field studies. In Blacksburg, retaining 5 Mg ha−1 stover or more increased CH4 and N2O emissions by ~25%. Maximum CH4 and N2O fluxes (4.16 and 5.94 mg m−2 day−1) occurred with 200% (20 Mg ha−1) retention. Two cycles of stover management in Blacksburg, and one cycle of corn or wheat residue management in New Kent did not affect GHG fluxes. This study is the first to investigate the effects of crop residue on GHG emissions in a multi-crop system in humid temperate zones. Longer-term studies are warranted to understand crop residue management effects on GHG emissions in these systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it