Nitrous Oxide Emissions from a Northern Great Plains Soil as Influenced by Nitrogen Management and Cropping Systems
Bibliographic record
Abstract
Field measurements of N2O emissions from soils are limited for cropping systems in the semiarid northern Great Plains (NGP). The objectives were to develop N2O emission-time profiles for cropping systems in the semiarid NGP, define important periods of loss, determine the impact of best management practices on N2O losses, and estimate direct N fertilizer-induced emissions (FIE). No-till (NT) wheat (Triticum Aestivum L.)-fallow, wheat-wheat, and wheat-pea (Pisum sativum), and conventional till (CT) wheat-fallow, all with three N regimes (200 and 100 kg N ha(-1) available N, unfertilized control); plus a perennial grass-alfalfa (Medicago sativa L.) system were sampled over 2 yr using vented chambers. Cumulative 2-yr N2O emissions were modest in contrast to reports from more humid regions. Greatest N2O flux activity occurred following urea-N fertilization (10-wk) and during freeze-thaw cycles. Together these periods comprised up to 84% of the 2-yr total. Nitrification was probably the dominant process responsible for N2O emissions during the post-N fertilization period, while denitrification was more important during freeze-thaw cycles. Cumulative 2-yr N2O-N losses from fertilized regimes were greater for wheat-wheat (1.31 kg N ha(-1)) than wheat-fallow (CT and NT) (0.48 kg N ha(-1)), and wheat-pea (0.71 kg N ha(-1)) due to an additional N fertilization event. Cumulative losses from unfertilized cropping systems were not different from perennial grass-alfalfa (0.28 kg N ha(-1)). Tillage did not affect N2O losses for the wheat-fallow systems. Mean FIE level was equivalent to 0.26% of applied N, and considerably below the Intergovernmental Panel on Climate Change mean default value (1.25%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".