Field Nitrogen Losses Induced by Application Timing of Digestate from Dairy Manure Biogas Production
Bibliographic record
Abstract
Anaerobic digestion of dairy manure has environmental benefits, but the impact of effluent (i.e., digestate [DG]) application on environmental nitrogen (N) losses from soils has not been well quantified. Our objective was to evaluate how field application of DG affected nitrous oxide (N2O) emissions and nitrate (NO3) leaching compared with raw dairy manure (RM) in spring versus fall applications. We measured N losses year‐round for 2.5 yr in silage corn on tile‐drained clay soil in Alfred, Ontario, Canada. Treatments were: digestate applied in spring (DS) and fall (DF), raw dairy manure applied in spring (RS) and fall (RF), urea applied in spring, and a control. Overall, the source of N had no effect on annual N2O emissions (overall average DG and RM, 4.9 kg N2O–N ha−1 yr−1), but more NO3 leached from DS than RS treatments (8.8 and 4.8 kg NO3–N ha−1 yr−1 on average, respectively). Estimated indirect N2O emissions from leached NO3–N were small (<0.2 kg N2O–N ha−1 yr−1). Timing of application did not affect annual N2O emissions but did shift emissions to the non‐growing season for fall applications (65% on average) and to the growing season for spring applications (60% on average). Overall environmental N losses (N2O–N + NO3–N) from DG were similar to RM when applied at the same time. For the conditions of our study, downstream emissions from anaerobic digestion (i.e., emissions induced by applied digestate) do not present an adverse trade‐off to the environmental benefits incurred during the biogas production phase. Core Ideas Biodigestion has environmental benefits, but N losses from soils are uncertain. Nitrous oxide emissions and NO3 leaching were evaluated. Digested and raw manure had similar annual N losses. Application in spring had lower annual NO3 losses than fall application. Soil emissions were not a trade‐off to environmental benefits of biogas production.
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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 | 0.001 |
| 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".