Characterization of manure management, nutrient composition, and greenhouse gas emissions from cow-calf operations in Manitoba
Bibliographic record
Abstract
This research was conducted to characterize management practices on Manitoba cow-calf operations, as well as nutrient composition and greenhouse gas emissions from solid beef cow manure during summer storage. A survey of management practices, including animal and manure management, was conducted on 10 cow-calf operations across Manitoba, which were known to generate stored solid manure from beef cows. Physical characteristics and nutrient composition of manure were measured on 2-3 dates, and manure greenhouse gas emissions (CO2, CH4, N2O, NO2, and NH3) were measured approximately biweekly over four months using a steady-state, flow-through hood with an in-line FTIR multi-gas analyzer. Farm calving season and manure disturbance (piling and/or mixing) were management practices which influenced the quantity and timing of GHG emissions, in that manure stored in piles had higher CH4, N2O, and reactive N gas, NO2 emissions, than manure in bedding packs, on a per area of manure basis (CO2-e m-2 ). Average cumulative CO2 flux for manure stored in piles ranged from 4,319.0-25,276.1 g m-2 per month, as compared to 375.3-1,628.3 g m-2 for manure stored as a bedding pack. Across all farms, CH4 was responsible for the largest proportion of emissions, with a mean whole-period cumulative flux of 771.9 g m-2 or 19,298.0 g CO2-e m-2 across all farms. Understanding the influence of management practices on manure composition, degradation, and GHG emissions will help position the cow-calf sector towards sustainability through the development of GHG emission models, improved emissions estimates, and the development of best management practices for producers. Not all manure storage types were captured (e.g., true composting of manure) and therefore additional research in this area will allow for improved understanding of chemical processes/mechanisms responsible for observed GHG emissions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".