Effect of fall rye cover crop on CO2 and N2O fluxes in the Red River Valley, Manitoba, Canada
Bibliographic record
Abstract
Cover crops can increase carbon (C) sequestration in soils. However, there is limited understanding of how cover crops affect carbon dioxide (CO2) and nitrous oxide (N2O) fluxes from agricultural soils in the Canadian Prairies. Research was conducted at the Trace Gas Manitoba (TGAS-MAN) long-term research site to determine the effect of a fall rye (Secale cereale L.) cover crop on spring-thaw and post-fertilizer N2O emissions, CO2 fluxes, and grain yield. Fluxes were measured over four years (2019-2022) from four 4-ha fields using the flux gradient method. In the fall of 2018 two fields were seeded no-till with fall rye and two were cultivated and left into winter. The cover crop was terminated the following spring with an herbicide application and the cash crops oats (Avena sativa), canola (Brassica napus), and spring wheat (Triticum aestivum L.) were grown in 2019, 2020, 2021, and 2022. 2020 and 2021 CO2 fluxes were removed due to unreliable data caused by flux measurement equipment. In 2019, C assimilation by the cover crop resulted in the system being a C sink of 424 kg C ha-1 after accounting for harvest removals, and the conventional system was a C source of 248 kg C ha-1. In 2022, wet growing conditions resulted in both cropping systems being a C source, with the conventional and cover crop system losing 1,366 kg C ha-1 and 1,558 kg C ha-1, respectively. The cover crop fields saw lower spring-thaw N2O emissions during years of good cover crop establishment. N2O emissions following fertilizer application and cumulative N2O fluxes were lower in cover crop fields in all study years. Combining cumulative CO2 fluxes and N2O emissions in CO2-equivalents (CO2-eq) in 2019 and 2022, the cover crop system was a net greenhouse gas source of 5,665 CO2-eq ha-1 and the conventional system was a source of 7,653 CO2-eq ha-1. The cover crop did not significantly affect crop yields.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| 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".