Year‐Round Nitrous Oxide Emissions as Affected by Timing and Method of Dairy Manure Application to Corn
Bibliographic record
Abstract
Core Ideas The year × timing interaction affects cumulative N 2 O emissions. Injection of manure produces the highest cumulative N 2 O emissions. Injection of manure produces the highest corn yields. Manure application to agricultural soils enhances N 2 O emissions, but these emissions could be reduced by using improved application methods at the right time. We conducted a 3‐yr study on corn ( Zea mays L.) grown in Elora, ON, Canada, to test the effects of timing and method of liquid dairy manure application on year‐round N 2 O emissions. A randomized complete block design was set up every year evaluating two application times (fall vs. spring) and three methods of manure application (surface broadcasting, incorporation, and injection). Lower cumulative N 2 O emissions for fall than spring application (mean ± standard error = 1.2 ± 0.3 vs. 2.9 ± 0.3 kg N 2 O‐N ha −1 ) were found during the driest year (2012), whereas no differences in emissions occurred between application timing in near‐normal precipitation years (2013 and 2014). Nitrous oxide emissions were not affected by the timing × method of application interaction. Injected manure resulted in cumulative N 2 O emissions not different than surface broadcast manure (3.6 ± 0.5 vs. 3.0 ± 0.5 kg N 2 O‐N ha −1 ) but significantly higher than incorporated manure (2.2 ± 0.3 kg N 2 O‐N ha −1 ). Injection resulted in greater corn yields than the other two methods. Our results suggest that (i) method of application affects N 2 O emissions independently of timing; (ii) including N 2 O emissions for the non‐growing season avoided biased estimates for the fall application timing since 20 to 60% of total emissions occurred during this period; and (iii) incorporating manure is the best practice to mitigate N 2 O emissions, although if N rates are optimized, injection could potentially produce yields with low N 2 O intensity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".