Greenhouse gas emissions and economic performance of Canadian cow–calf farms
Bibliographic record
Abstract
The study aimed to quantify financial performance, greenhouse gas (GHG) emissions, and identify relationships between the two in Canadian cow–calf operations. Benchmark farms ( n = 62) were established from 225 cow–calf operations grouped by similar management systems (calving date, weaning date, herd size, winter feedstuff, and winter feeding days). Emissions were estimated using a whole-farm emissions model (Holos), and emission factors for canola meal, protein supplements, and minerals were expressed in kg CO2e per kg liveweight (LW) sold (emission intensity, EI). For economic analysis, the TIPI-CAL model was used to evaluate financial performance. Based on EI, the top and bottom 25% quartiles were designated as the high EI (HEI) and low EI (LEI) farms, respectively. The mean EI was 38.4 kg CO2e/kg LW in HEI and 23.1 kg CO2e/kg LW in LEI. The LEI farms had a higher ( P < 0.01) revenue, related to LW output. Cluster analysis indicated the LEI farms were associated with greater medium-term profit, revenue, cull cow percentages, calf weaning weight, and average daily gain. Some HEI farms were associated with direct and indirect N2O emissions, while others with enteric and manure CH4, and energy CO2. Improving productivity and lowering depreciation cost simultaneously improves GHG and economic efficiency of farms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".