Evaluation of greenhouse gas emissions from hog manure application in a Canadian cow–calf production system using whole-farm models
Bibliographic record
Abstract
The development of beneficial management practices is a key strategy to reduce greenhouse gas (GHG) emissions from animal agriculture. The objective of the present study was to evaluate the impact of time and amount of hog manure application on farm productivity and GHG emissions from a cow–calf production system using two whole-farm models. Detailed model inputs (climate, soil and manure properties, farm operation data) were collected from a 3-year field study that evaluated the following three treatments: no application of hog manure on grassland (baseline); a single application of hog manure on grassland in spring (single); and two applications of hog manure as fall and spring (split). All three treatments were simulated in a representative cow–calf production system at the farm-gate using the following whole-farm models: a Coupled Components Model (CCM) that used existing farm component models and the Integrated Farm System Model (IFSM). Annual GHG intensities for the baseline scenario were 17.7 kg CO2-eq/kg liveweight for CCM and 18.1 kg CO2-eq/kg liveweight for IFSM. Of the total farm GHG emissions, 73–77% were from enteric methane production. The application of hog manure on grassland showed a mean emission increase of 7.8 and 8.4 kg CO2-eq/kg liveweight above the baseline for the single and split scenarios, respectively. For the manured scenarios, farm GHG emissions were mainly from enteric methane (47–54%) and soil nitrous oxide (33–41%). Emission estimates from the different GHG sources in the farm varied between models for the single and split application scenarios. Although farm productivity was 3–4% higher in the split than in single application (0.14 t liveweight/ha), the environmental advantage of applying manure in a single or split application was not consistent between models for farm emission intensity. Further component and whole-farm assessments are required to fully understand the impact of timing and the amount of livestock manure application on GHG emissions from beef production systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".