Cumulative impact of cover crops on soil carbon sequestration and profitability in a temperate humid climate
Bibliographic record
Abstract
Abstract Although soil C sequestration with cover crops (CCs) has been linked with the potential of CCs in climate change mitigation, the long-term usage of CCs on soil C storage and farm-based economics have been widely overlooked. Therefore, in a CC experiment established in 2007 in a temperate humid climate, four CCs and a no-CC control were compared to evaluate their potential to sequester C and provide economic returns. Total amount of plant C added to soil with CCs translated into greater soil organic carbon (SOC) content by 10–20 Mg C ha −1 than the no-CC control across both sites. Greater crop yield and reduced yield variability with CCs suggest the long-term potential of CCs in increasing agroecosystem resiliency. Moreover, greater profit margins with CCs in processing vegetable crops but not grain and oilseed crops indicate CC effects on crop profitability are dependent on the production system. Our study results indicated that the loss in profit margins with CC usage in grain and oilseed crops might be overcome with C pricing (at $50 Mg −1 ) on quantity of C sequestered after 9 years of CCing; thus, providing financial compensation to growers may be a mechanism to encourage CC adoption.
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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".