Comparing Constant and Variable Rate Applications of Solid Cattle Manure on Greenhouse Gas Emissions From Dark Brown Chernozems
Bibliographic record
Abstract
Although field application of solid cattle manure (SCM) is an alternative, low-cost nitrogen (N) source to conventional synthetic fertilizers, gaseous losses of manure-N, occurring via volatilization and denitrification, are well documented. However, the effect of variable rate application of SCM on gaseous N emissions at a landscape-scale has received less attention. The objective of this study was to compare the nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) fluxes from watershed basins within the same field, with and without the addition of fresh feedlot SCM applied at either constant blanket or variable landscape-adjusted rates. Gas samples were collected in 2019 and 2020 with gas sampling locations further classified according to their catchment area size. The non-manured watershed basins had low cumulative N2O and CO2 emissions, and were strong CH4 sinks compared to manured basins. Additionally, basins receiving the Variable Rate manure application had lower N2O emissions than those receiving the Constant Rate manure application. The low elevation, larger catchment area landscape positions contributed proportionally more to cumulative N2O and CO2 emissions, along with reduced CH4 consumption, compared to the smaller catchment areas higher in the landscape, due to greater soil moisture and organic matter content within those depressional soils.
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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 | 0.001 |
| 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) | 0.001 | 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".