Net ecosystem carbon budget and net greenhouse gas emissions under diverse crop rotation using cover crops compared to a conventional crop rotation
Bibliographic record
Abstract
Introducing cover crops and diversifying annual crop rotations can provide additional carbon (C) input to soils and are practices that have been promoted as climate change mitigation measures. However, year-round field studies are needed to evaluate the net effect on carbon dioxide (CO 2 ) uptake, including consideration of carbon removal from the field during harvest depending on the crop grown to diversify the rotation. In addition, evaluation of net greenhouse gas (GHG) emissions requires accounting for changes in soil nitrous oxide (N 2 O) and CO 2 emissions from production of farm inputs and field operations in the altered crop rotation. In this study, our objectives were to evaluate the annual dynamics of CO 2 exchange and to estimate net GHG emissions for a diverse (DIV) corn-soybean-winter wheat crop rotation that included cover crops and a conventional (CONV) corn-soybean-soybean crop rotation. The year-round net ecosystem exchange (NEE) of CO 2 on two side-by-side 8-ha fields (one under DIV and one under CONV management) was measured using the eddy covariance method over three years (2018–2021) in Ontario, Canada. Results showed that DIV rotation significantly increased cumulative NEE, gross primary production (GPP), and ecosystem respiration (Re) –578, –3750 and 3170 g C m −2 , respectively, compared to the CONV (–501, –3361, 2859 g C m −2 ) over three years. Overall, the DIV rotation resulted in a 15 % increase in NEE compared to CONV. Accounting for the grain C removed during harvest, on an annual basis, DIV and CONV corn and soybean fields were C sources. However, net ecosystem carbon budget (NECB) was a sink (–44 g C m −2 ) for DIV winter wheat + cover crops if only grain was removed and reversed to a C source (197 g C m −2 ) with straw + grain removal. The summed NECB across the three study years revealed that both fields were C sources (NECB > 0). Similarly, the DIV rotation was a net GHG source compared to CONV for straw + grain removal but had similar net GHG emission to CONV when only wheat grain was removed. From the present study, DIV did not reduce the net GHG emissions in the short term (3 years) as expected, but long-term measurements are needed to confirm this trend. Further multi-year eddy covariance and soil carbon stock measurements should be done to design effective diversified crop rotations.
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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) | 0.000 | 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 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".