Lower Nitrous Oxide Emissions from Anhydrous Ammonia Application Prior to Soil Freezing in Late Fall Than Spring Pre‐Plant Application
Bibliographic record
Abstract
Fall application of anhydrous ammonia in Manitoba is common but its impact on nitrous oxide (N 2 O) emissions is not well known. A 2‐yr study compared application before freeze‐up in late fall to spring pre‐plant application of anhydrous ammonia on nitrous oxide (N 2 O) emissions from a clay soil in the Red River Valley, Manitoba. Spring wheat ( Triticum aestivum L.) and corn ( Zea mays L.) were grown on two 4‐ha fields in 2011 and 2012, respectively. Field‐scale flux of N 2 O was measured using a flux‐gradient micrometeorological approach. Late fall treatment did not induce N 2 O emissions soon after application or in winter likely because soil was frozen. Application time did alter the temporal pattern of emissions with late fall and spring pre‐plant applications significantly increasing median daily N 2 O flux at spring thaw and early crop growing season, respectively. The majority of emissions occurred in early growing season resulting in cumulative emissions for the crop year being numerically 33% less for late fall than spring pre‐plant application. Poor yield in the first year with late fall treatment occurred because of weed and volunteer growth with delayed planting. Results show late fall application of anhydrous ammonia before freeze‐up increased N 2 O emissions at thaw and decreased emissions for the early growing season compared to spring pre‐plant application. However, improved nitrogen availability of late fall application to crops the following year is required when planting is delayed because of excessive moisture in spring. Core Ideas Late‐fall ammonia application prior to freeze‐up did not induce N 2 O emissions over winter. Late‐fall application did increase N 2 O emissions during thaw the following year. Despite lower area‐based emission in the first study year, poor yield increased yield‐scaled emissions. For the second study year, yield‐scaled emissions were similar for application treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".