Can Pulse Crops Play a Role in Mitigating Greenhouse Gases from North American Agriculture?
Bibliographic record
Abstract
The atmospheric buildup of greenhouse gases (GHGs) is a serious environmental issue. Globally, agricultural activities are an important source of anthropogenic GHGs, contributing ∼20% of the annual atmospheric increase. Management choices largely determine if agricultural soils will be a source, a sink, or will be neutral with respect to GHG net flux. The proportion of agricultural land that is seeded to pulse crops in the Northern Great Plains (NGP) region of North America has been increasing rapidly over the past decade. Introducing pulses into cereal‐based cropping systems could influence the net GHG balance of those systems because pulse crops are thought to stimulate soil‐emitted N2O, have different pesticide and fertilizer requirements, and the quality and quantity of their residues vary substantially compared with cereal crops. In this paper we briefly review the available literature, and discuss the potential impact of pulse crops on the net flux of CO2, N2O, and CH4 from soils, and the CO2 emissions associated with energy inputs for cropping systems in the NGP. We also calculate net GHG balances for two example sites. Estimating the final GHG outcome of introducing pulses into cereal‐based cropping systems is still uncertain, but current information suggests that replacing a cereal with a pulse crop will likely result in no change or a small but positive net GHG benefit (lower emissions to the atmosphere) for crop rotations in the NGP region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".