Perennial legume phase and annual crop rotation influences on CO2 and N2O fluxes over two years in the Red River Valley, Manitoba, Canada
Bibliographic record
Abstract
Studies have shown that including perennial forages in cropping rotations can increase soil carbon (C) and lower nitrous oxide (N2O) emissions when compared to continuous annual cropping. Research is needed to evaluate the inclusion of a perennial forage in an annual crop rotation on net carbon dioxide (CO2) and N2O fluxes, natural and agronomic drivers of seasonal greenhouse gases (GHGs), and the possibility of using forages as a C sequestration-CO2 mitigation tool. A long-term field experiment site to determine GHG budgets for Red River Valley cropping systems in Manitoba, Canada was used. The site consisted of four plots with the same annual rotation management history. A perennial legume, alfalfa, was grown in 2008 and 2009 on two plots and spring wheat and industrial oilseed-rapeseed grown on the other two plots in 2008 and 2009, respectively. Nitrous oxide and CO2 fluxes were measured continuously using the flux gradient micrometeorological method. For the net study period, the perennial phase sequestered twice the atmospheric CO2 (2070 kg C ha-1) compared to the annual crops. The annual rotation emitted 3.5 times more N2O than the perennial legume phase. When accounting for harvest C removals and considering GHGs in CO2-equivalent (eq.), the perennial legume phase was a net sink of 5440 kg CO2-eq. ha-1 and the annual rotation was a net source of 4500 kg CO2-eq. ha-1 for the two year study period. Information gathered will help bridge missing data gaps in national emission trends and enhance development of Canadian GHG mitigation models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".