Variable rate precision application of feedlot cattle manure mitigates soil greenhouse gas emissions
Bibliographic record
Abstract
Solid cattle manure amendments provide a low-cost alternative nutrient source to inorganic fertilizers, while providing a carbon input to the soil. The augmented soil organic carbon levels, however, may be largely offset by manure-related greenhouse gas (GHG) emissions. Soil nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2) emissions were measured at the landscape-scale in a Canadian prairie agricultural field supporting silage barley (Hordeum vulgare L.) production. Manure was applied to meet barley P requirements, while total N rate was supplemented using anhydrous ammonia. A non-manured control (NMC) also was included, to calculate N2O emission factors. The NMC zone consisted of an annual application of anhydrous ammonia at 80 kg N ha−1. In addition to solid cattle manure at a constant (CRM; 45 Mg ha−1) or variable (VRM; 0–72 Mg ha−1) rate, the manured treatment zones also received 80 kg N ha−1 of anhydrous ammonia. The VRM treatment included set-backs from the watershed basin centers in ephemeral wetlands that did not receive solid cattle manure. Gas samples were collected using chamber-based methodology, with chambers installed at 130 locations across six watershed basins (n = 2 per zone) during 2019–2021. Cumulative N2O emissions were 76 % (CRM) and 62 % (VRM) higher following manure addition. The normalized N2O emissions for CRM were 24 % greater than VRM and NMC, with CRM having 31 % larger manure-induced N2O emissions than VRM. Though all soils were net CH4 sinks, manure application reduced CH4 consumption by 33 % (CRM) and 25 % (VRM) compared with the NMC. Manure addition did not impact cumulative CO2 emissions. Although VRM application mitigated manure-related GHG emissions, enhanced GHG intensity following manure addition highlights the importance of ensuring balanced soil fertility, to support optimal crop growth and maximize yield-scaled GHG performance metrics in manured landscapes.
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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) | 0.001 | 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".