ANAEROBICALLY DIGESTED DAIRY MANURE AS AN ALTERNATIVE NITROGEN SOURCE TO MITIGATE NITROUS OXIDE EMISSIONS IN FALL-FERTILIZED CORN
Bibliographic record
Abstract
Anaerobically digested dairy manure (AD) has been proposed as an alternative nitrogen source to reduce soil nitrous oxide (N2O) emissions compared with raw dairy manure (RM). The aim of this research was to compare soil N2O emissions associated with AD and RM according to three application methods: surface broadcasting (SB), incorporation (SBI), and injection (INJ). The field experiment was conducted on a loam soil at Elora, ON, from November 2012 to November 2014, using a randomized block design with four replications. Manure was applied in mid-November (fall), and corn (Zea mays) was planted in late-May of each year. Nitrous oxide flux was measured using nonsteady state chambers sampled weekly or bi-weekly. Cumulative N2O emissions were significantly affected by the interaction between source and method (F = 3.99, P < 0.01), with the highest value for surface broadcast AD (6.4 kg N2O-N ha−1) and the lowest value for injected AD (2.6 kg N2O-N ha−1). Manure source affected cumulative N2O emissions (F = 4.67, P < 0.1), with the largest emissions for AD (4.8 kg N2O-N ha−1). Anaerobically digested manure was proven to reduce cumulative N2O emissions when it was fall injected to corn in cold climates; however, if AD is broadcasted or broadcasted and incorporated, it may result in greater N2O emissions than those produced by RM.
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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".